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The most essential facet of being a manager, be it in any walk of life, is for sure the one skill that differentiates him/her from anybody else and that is: Decision-making. It is most definitely the power, rather the ability of a person to take prudent, relevant and effective decisions that set him/her a notch ahead in the race that the corporate run comprises. Thus, we not only need to know what or why, but how to make decisions, how to make the choices that we make, to be able to foresee what lies in store having made those choices and to be positive that the alternatives have an opportunity cost that is less likely to exceed the benefits from the plan A that we choose. The mind always works on matter, matter that is assimilated, accumulated, observed, absorbed and created. This matter more often than not comprises facts and figures that we come across over a period of time and it is this that formulates our opinions on issues and circumstances and should guide us in choosing the way forward. One’s experiences are a key to learning and avoid mistakes, but life is all about new challenges and it isn’t necessary that one’s challenge will be the other’s or that one’s solution will work out for the other, problems, solutions and hence decisions, vary, with time and tide, to each his own. Decision-making is a crucial skill for treading on the path to success. It is imperative thus that one take informed decisions, rather than impulsive or intuitive ones. This itself can be illustrated by the use of numerical figures in terms of probability of a decision being a thumbs-up or a downer based on whether it is based on gut-feeling or not, where a gutsy decision has a fair and square half chance of hitting the bulls-eye, an informed, data driven decision based on historical data can warrant a chance much greater than that of being a winner. Data analytics, a field of research, an active component of all industrial data processing and reporting, has become the buzz-word in the world of business. It is owing to the stress on hard core historical data projection and information related to past trends, their relevance, their cyclical patterns, any relational change between parameters and many other such ways of engineering data to deduce important facts and identify occurrences, areas of improvement, effect of factors and even have a decent foresight by extrapolating trends, assuming at times ceteris paribus. It is the mere fact that this subject is being undertaken as a specialization in some of the newer and premiere business schools in the country that relays its importance, its applicability and the extent to which it can play a role in making key decisions easier for every managerial level all courtesy the heavy data dependency, real data building up virtual images only for a clearer picture albeit a surer one. While the success of data driven decision making can itself be proved through data, its wonder lies in the fact that one can also prove the risk related to intuition based choices in a data driven manner. There is a reason the rational mind which processes such data is called analytical. It overcomes the biases of the emotional mind and loosens the shackles of a narrow-minded attitude towards things or the safe zone preference based on conservative beliefs, prevalent practices and recency effect of happenings. Strong and concrete data has the power to shake people out of the herd mentality and of Utopian expectations based on rosy optimism rather than hard-hitting realism. Mostly intuitive decisions tend to land people in false situations, because they are taken either in haste or due to ignorance. Either way, it makes waste due to the hurry in which it is taken without any deliberation or any heed to the consequence, totally reliant on luck for the outcome, well if good, expected if bad. In case of ignorance, the risk is even higher as the stakeholder in question very conveniently underestimates the potential risk and can in fact land in a whole lot of trouble, based on the decision ending up seeming gullible, irrational and more often than not irresponsible and unreliable. Thus we can say that data plays a key role, be it that of the number of germs that a disinfectant can kill or that of the amount of disposable income in the economy that drives consumption, be it the household consumer or the government bodies, be it the decision of keeping yourself healthy or the nation, it is data alone that can be relied upon for a less blurred vision of the future through the spectacles of the past. If such decisions were to be made on the flip of a coin, only probability data related to the outcomes of a coin flip can tell us which side we would be losing, the health of the nation or the its people, either of which states the vulnerability of intuition as a tool for making choices against rationality which is but obvious the only choice to make when deciding, anything, big or small, for the better and not remotely for the worse. Note - Visitors shall not be able to comment on this article until they are logged in.

In the FMCG sector, our company is rated among the top 5 organisations both by the consumers and industry. Much of the success is attributed to the increased consumer confidence and round-the-clock shelf SKU availability of our product line. The role of our Purchase Division has always been at the core for achieving this mark. Over the years our team under your guidance has been able to keep the inventory at optimum levels thereby helping the Production Unit to make delivery on time. It is due to the careful execution of our fundamental function - 'Material Management' which ensures that raw material is procured from the suppliers at the right time and in right quantities. This is the key to success in our sector and thus any minuscule improvement towards this can play an important role in raising our organization's bar. Our well maintained database of daily Material Management activities can help us in this direction for making more accurate estimations. An approach-'Data Driven Decision Making' can be applied in which assessment data and background information can be used to take decisions related to planning activities. According to a study by MIT Center for Digital Business, organizations driven most by data-based decision making had 4% higher productivity rates and 6% higher profits. With data driven decision making we can deploy Just-In-Time as approach and Material Resource Planning as the method for waste minimization in the purchase cycle. To demonstrate the strategic, operational and financial advantages of DDDM approach, we can consider the following conventional process being carried out at our unit. Under usual circumstances, we have to keep a safety stock of inventory items so as to counter the market and supply chain uncertainties. These may include logistics delay, plant failures, supply side variability, demand side variability and others. This leads to estimations made with the help of brainstorming methods within the team and experiential knowledge that cumulatively determine the amount of inventory to be ordered. With the help of estimations, performance is delivered in terms of fulfilling various conditions like on time delivery, safety stock, safety time related to inventory items. But there could occur estimation errors during the process due to which stores or warehouses can have lower or excessive amounts of inventory. This can affect the overall process efficiency both in monetary and operational terms. To improve this scenario, Data driven decision making mode can be deployed. Considering the situation that we faced last month. The production unit had given a demand forecast of 100 packs for one of our products and keeping safety stock levels in mind, we had ordered 120 packs. But we received only 115 packs from the supplier due to production based variability at his end which had not been a factor of consideration in our process. Also, the actual demand turned out to be 118 packs which was closer to our safety stock levels. So, overall we faced a shortage of 3 packs. From this we can identify that there is a chance of around 4% variability in delivery from supplier's end. This data if incorporated in taking decisions before ordering again from the same supplier can help us in achieving more accurate outcomes. This was an example of countering lower inventory received from the supplier where DDDM could have helped otherwise. Considering another situation that occurred 6 months back where we had ordered around 200 packs where as the production had ordered for 120 packs only but finally picked up around 150 packs from the warehouse. We had a rough idea of up-scaling from demand side as well as in the costs from the supplier's end due to which we increased the order. Though a more accurate estimation using DDDM would have given an additional gain by reducing the wastage of the remaining underutilized 50 packs as well. Using DDDM as one of our modus operandi would be convenient and beneficial too as its output depends on the quality of data gathered and a well managed database already in place can help us in reaping maximum benefits out of this investment. Further its effectiveness depends on defining the questions to be considered before analyzing the data and with your experience level in this area, we can easily frame pertinent questions to get relevant results from the data. Data driven decision making would give us added advantages of faster processing, refocusing our resources to increase the yield, relevant data backing to explain our rationale behind purchase decisions to the management, foreseeing the opportunities and threats in the market and overall supply chain. Over a period of time, this approach can also lead to building a reliable group of suppliers giving us a competitive advantage gained by adopting a data backed strategic purchasing model. An early adoption of Data driven decision making would bring maturity to our supply chain infrastructure and resilience towards unforeseen circumstances so that we can quickly respond to them without compromising on financial and operational aspects. Note - Visitors shall not be able to comment on this article until they are logged in.

As a Computer Illiterate growing up in the new millennium, I had tremendous difficulty making sense of how to do most of my stuff. Most of my decisions were snap, on-the-moment and intuitive ones. By the time I was in my teens, I had learnt my way around the big computer problem. My experiences and the Great Indian “Jugaad†mind-set had provided me with a sufficient enough method wherein I only worked on the computer when absolutely necessary. If there was any other way to do the task, any escape route, I gladly followed it. By now you must have realised that I was and am sceptic and data-averse. Data to me was always those unreadable files that eat a hell lot of my storage space. I was gaining proficiency in getting around this data problem and along came Big Data. Everyone from Google to Barrack Obama was using it.[1] As a student at one of the most prestigious B-Schools in the knowledge market, not only was I supposed to know what it was, but was expected to be able to tune in and utilise it to make a difference. Life has strange ways of getting back at us, mere mortals, and here was my customised gut wrenching sucker punch. The fighter in me knew I had to do this, but I did not find a way I could. A 3 month long research in the field led me to Data Driven Decision Making or D3M. Simply put, it is the pleasure of sipping a coffee in your office chair while you watch your computer work up some algorithms and provide you invaluable decision making tips to face some of your most frequently encountered problems. Immediately after, I experimented with the different sorts of decisions that can be made with D3M. The results were a true eye-opener. If we think of decision making as a broad spectrum from operational decisions at one end to strategic decisions at the other. Operational decisions can be characterized as highly structured, routine, short-term oriented and increasingly embodied in sophisticated software applications. On the other hand strategic decisions are taken by the top management and serve to set the long-term directions, policies and procedures of an organization. They tend to be complex and unstructured because of the uncertainty and risks that generally accompany longer term decisions. In between these two extremes, we can have varied decisions including non-routine ones in response to new or unforeseen circumstances beyond the scope of operational processes, and tactical decisions dealing with the necessary adjustments required to implement longer term strategies. [2] Half a decade ago, D3M could have only helped you with the more structured forms of decision making but no more. With the advent of Big Data, machines know much more about humans and human behaviour than humans themselves. Sample this, personal analytics can actually allowed me to analyse my Whatsapp chat history and find why most of the girls I chatted with refused me a date. The word cloud usage showed me that my texting skills were uninspiring to say the least with the most common words being “ok†and “yaâ€. I realised I needed to be more creative and engage better. What did not help was that most girls chatted in the window of 8-12pm whereas I had the habit of taking a short nap at the time. Also it helped me identify my most productive work hours and helped me plan out my work better. Thus far, all seemed well with D3M but on further research most early moving corporates into the field were still undecided on its benefits. A closer look exposed a distinctly similar pattern in them. Most early moving managers thought that D3M will help them save money or time or both. Research shows that they are misguided to say the least. D3M does neither, atleast not in the short term. What D3M does allow though is discovering solutions you never knew existed. What it will do though is finding needles in the haystack consistently. Another interesting insight was that D3M depends a lot on the data collection. Great collection leads to great results. All we are required to do is ensure data cleanliness, variety and velocity. In his book, Data Driven: Profiting from Your Most Important Business Asset, Prof. Thomas Redman summarizes the whole decision making via data as “Good decision makers follow at least three Bayesian principles. First, they bring as much of their prior experience as possible to bear in formulating their initial decision spaces and determining the sorts of data they will consider in making the decision. Second, for big, important decisions, they adopt decision criteria that minimize the maximum risk. Third, they constantly evaluate new data to determine how well a decision is working out, and they do not hesitate to modify the decision as needed.†After months of thorough investigation and experimentation, I have arrived at this conclusion, “To stay a step ahead in this ever competitive world, using Data Driven Decision making is a must. So let D3M take care of all the external data that you need to work with and let your mind focus on understanding “the data from insideâ€. That will surely lead you to not just success but contentment. References 1. http://swampland.time.com/2012/11/07/inside-the-secret-world-of-quants-and-data-crunchers-who-helped-obama-win/ 2. http://blogs.wsj.com/cio/2013/09/27/data-driven-decision-making-promises-and-limits/ Note - Visitors shall not be able to comment on this article until they are logged in.

Data driven decision making (DDDM) As it is rightly said by Plato, “Necessity is the mother of inventionâ€; it comfortably fits in the life and business perspective. In today competitive world, we cannot even think of succeeding over others if we are not better than them. It is applicable in both day to day lives as well as in Businesses. In day to day life, we have numerous examples of us competing with our colleagues for ranks, posts or various other things. But it is the candidate which has better skills and qualities will always succeed. This thing is also applicable to a larger extent to businesses where every day lakhs and crores of transactions are performed. Any organization should use all its data available judiciously and all the decisions should be based on these data instead of personal beliefs. This process of making decisions based on data is called Data-Driven Decision Making. In a recently published article, “Data Science and its Relationship to Big Data and Data-Driven Decision Making,†Foster Provost and Tom Fawcett define Data-Driven Decision Making as “the practice of basing decisions on the analysis of data rather than purely on intuition.†Equally succinctly, they view data science “as the connective tissue between data-processing technologies (including those for big data) and data-driven decision making.†This DDDM is being viewed as a tool to help people make smarter, more effective decisions. Also according to Electronic Learning Assessment Resources (ELAR), a DDDM focus uses student assessment data and relevant background information, to inform decisions related to planning and implementing instructional strategies at the district, school, classroom, and individual student levels. Even the concept of Data literacy meaning “a person possesses a basic understanding of how data can be used to inform instruction†is closely inter-weaved with DDDM. We can say Data Literacy as an underlying technique of use of DDDM. Considering the reference from the research paper “Strength in Numbers: How Does Data-Driven Decision making Affect Firm Performance?†by Erik Brynjolfsson, MIT & NBER Lorin Hitt, University of Pennsylvania and Heekyung Kim, MIT. A detailed survey data on the business practices and information technology investments of 179 large publicly traded firms, it was found that firms that adopt DDD have output and productivity that is 5-6% higher than what would be expected given their other investments and information technology usage. Such surveys and studies have every now and then showed the importance of data in taking important managerial decisions. Even the share-market is not luck or belief based. It is based on various complex logics which have to interpreted using different other factors. So it is the need of the hour to work on the data analysis for having better forecasts, demands and market scenarios. If we see all the prospering companies of the world, they go by numbers. It is the challenge on the part of the management to lead the organization towards data-driven decision making. This DDDM is important because of the following reasons: 1. Commodity priced computing 2. Massive file system storage and retrieval technology 3. Bandwidth 4. Smart devices: Records are everywhere Keeping in mind these important factors, it become necessary for any company to take decisions very precisely as each and every decision has very long term effects on the company and its revenues. Recently, so many technologies have evolved including Big Data which have made the analysis of data far easier as it was earlier. Now even small information which is sort out of crap data is very useful for the organizations in taking future decisions. In last few year many new organizations had come up which provide services in the field of data analysis which indirectly helps the companies hiring them. Lots of social networking sites provide some data which are used by these analysts to provide related advertisements to the people. This explosion of decision making from personal instincts to data driven can be largely attached to Big Data. With the advent of Big Data, this has come out even more drastically and most of the companies shifting towards it. Lot of money is being invested in getting meaningful data out of bulk of data available in the companies. It’s not surprising that data-driven decision making is one of the most promising applications in the emerging discipline of data science. It has an explosive growth. There are large numbers of characteristics of Data which have to study before taking certain decision. These include variety, volume, velocity, veracity, variables and sources. In finding meaningful information from the raw data the following steps are to be followed: 1) Collect all data from various sources 2) Create a file of raw data and arrange properly 3) According to predefined index, interpret the data to make a data file 4) Analyze this data file generated This complete procedure is a basic process of DDDM. It has to be followed if the accurate analysis is required. In a nutshell, we can conclude that this data driven decision making is the need of the hour and each and every company should move towards it as soon as possible. It may be looking a tedious and unnecessary at the present moment but its long term effects are very soothing and beneficial for the entire company. References: · http://blogs.wsj.com/cio/2013/09/27/data-driven-decision-making-promises-and-limits/ · http://online.liebertpub.com/doi/full/10.1089/big.2013.1508 · www.a51.nl/storage/pdf/SSRN_id1819486.pdf‎ · https://www.950.ibm.com/events/wwe/grp/grp017.nsf/vLookupPDFs/Michael%20Kowolenko%20Presentation/$file/Michael%20Kowolenko%20Presentation.pdf Note - Visitors shall not be able to comment on this article until they are logged in.

Data Driven Decision Making - injecting rationality in your gut feeling Sector: Banking Sector in India The data centricity of banking industry is the universal truth. Traditionally banking has been the one sector which handled the maximum data about any person that are particularly critical and dear to those persons as they deposit their trust with the banks in the form of their finances. In the recent times this has been further reinforced with the RBI implementing the “Know your Customer†mandate that aims to compulsorily maintain the customer data which should be relevant, concurrent & authentic. Despite the proliferation of such data, effective analytics and data mining techniques has been at its elusive best. The information industry has grown leaps and bounds and the remarkable advances in analytics software and its processing power aided by the cloud computing systems is just the tip of huge iceberg of potential that such data is capable of achieving. As the industry tries to grow out of the recent financial crisis towards the shady future of uncertainty, banking and retail banking in particular must inculcate the power of analytics in them to be able to improve decision making, indulge in constant innovations which ought to become the bread and butter for survival & be more compliant with the stringent financial regulatory environment that the RBI is supposed to impose for greater control. The siloed approach to banking should give way to enterprise wide resource planning (data being the most critical resource) for fostering greater transparency, efficiency & effectiveness through integration and unified image of the entire sector. This will also help in garnering greater customer trust & rejuvenate customer relationship which is the single most critical factor for survival in times of uncertainty, mistrust & risk. The recessionary trends have forced the clientele of banks to a more frugal approach to managing their funds. Careless consumption has been replaced by need based one and “ROI†has suddenly become the buzzword which never had such a great reputation except amongst the business houses. However it is interesting to note that despite reduced spending the world has not stopped itself from the adoption of latest technologies. Be it smart phones or social media presence the huge numbers are truly defiant of the existing economic conditions and its implications. Such behaviour re-confirms the value of innovations in today’s society besides such channels could provide source of huge data tapping which can help retail bankers to provide a more rewarding experience to their customers enhancing their brand loyalty. The usage of “Big Data†as the new window to the world of increased productivity, innovation & competition is important to be considered here. The rapid adoption of analytical tools would help banks process the information they have into market knowledge which would enable them to differentiate themselves through service excellence. It may sound contradictory that previous paragraphs talked of unified image and integration and now differentiation is promoted. Well, competition has been and will always be the root for future growth without which the need for existence of mankind comes under the radar. Rather we should look at a new dimension of competition – “Competition through Cooperation†where competitors would be on the same page with respect to technology and new inventions yet they would have to constantly evolve themselves to be relevant. Advanced analytics provides the banks with a new path of continuing business by overcoming the obstacles of risk and uncertainty, the prime growth drivers being stricter regulation, better risk management, effective strategising and stronger CRM. The various ways to achieve data salvation is revealed as below: The analytics software would speed up the financial and risk reporting services as required by the new norms as and when implemented by the government ensuring service delivery with no or minimal cost. The usage of Enterprise wide data architecture would provide a single version of banking creating transparency and restoring customer confidence. Data crunching would enforce better risk management by identifying malicious transactions and preventing its recurrence. Usage of technology to combine past and current data can help in predicting future scenarios with greater accuracy and provides an opportunity to face the shady future in a planned manner with confidence. Besides the data analytics tools may be used to boost revenues as well like, Customer data analytics – enhance service and bring more clients Investment analytics – improve lending process Process analytics – find process inefficiencies and take corrective measures thereby reducing costs, to name a few. [*]Data collection from various sources like KYC, social media websites etc... and it analysis using Big Data and relevant technologies can help in providing customized banking solutions, new financial products to suit customer needs & gather feedback on marketing campaigns launched. This would lead to greater customer satisfaction and tighter relationship. Mobile banking is the new brainchild of the banking sector that allows customers carry out transactions on the move. This means greater volume of transactions to be handled and the usage of analytic software to integrate data across channels become essential. Also multichannel banking is constantly evolving with the endeavour of providing cross channel banking across websites. So far the discussions lead us to the conclusion that usage of Data Driven Decision Making through Data Analytics & ERP is imperative to the future competitiveness in banking industry. But there are major speed breakers in the path to this rediscovery, which are as follows: Modifying existing IT infrastructures and the corresponding data migration might incur substantial initial costs Using analytics at the strategic level would require identification of relevant data and standardization of processes and data structures Resolving frequent data issues & inconsistencies that exists in the customer data in the baking domain Required expertise in analysing data points, process expertise & technical expertise is important Support & Initiative of key stakeholders Finally the embracing of analytics as a service depends on the internal culture and dynamics of the organization. Hence to successfully implement the same nurturing of the employees to convince them of the power of data driven decision making is very crucial. However the conviction in employees can be developed only if the leaders & top management of the enterprise believe in the vision of “Analytics as the future of bankingâ€. Hence the purpose of this article would be to inspire the top management, so that they can realize the importance of using data in their organizational decision making and inject rationality in their decision making. Note - Visitors shall not be able to comment on this article until they are logged in.

I observed that the scope of six sigma has increased it is now applicable in many industrial sectors. When Bill Smith created this methodology his main focus was defect reduction and improvement which brings excellence and process improvement, later on Quality formed the core of other methodology/framework too. I have seen that the approach of employees toward quality management has not yet changed, they still consider this as the job of quality unit, as for them quality means compliance, reporting, QMS manual and most importantly Audits. I found that organizations are spending good amount of money in quality related training/workshops/paperwork etc. but still are not successful in changing the approach of individuals. At high level, every organization’s policy talks about quality and its importance but at ground level due to fixed processes, departmentalized structure it become a task of small number of people, like managers and specifically of those who belongs to quality department, since it’s their job. Induction program- By definition an induction program is an important process for bringing staff into an organization. It provides an introduction to the working environment and the set-up of the employee within the organization. An induction program is part of an organizations knowledge management process and is intended to enable the new starter to become a useful, integrated member of the team. But I believe now organizations have a need to adopt a new approach of induction program: Organizations should provide Process Excellence/Quality training to each and every employee at the time of induction, this training they can design as per the requirements of their LOB (Line of Business). Also when induction of an employee gets conducted at that time we can tell employee that they are suppose to share their views (positive or negative) after six months from now about quality of process/operation to which they belongs (preferably through a presentation) so that this training of quality not become just a training and they feel involved with the things they have learned. This kind of treatment at induction will make employee responsible for his own quality as well as quality of his team and organization. Why this should be done at induction program because when we start with induction: 1. The new employee will feel himself linked with the quality department. 2. This will make new employee feel that he is responsible for the quality of operation to which he belongs. 3. This will convey a message to employee that organization has a serious approach towards quality. This approach will bring mutual benefits and this also links with the high level objective of each and every organization that is “alignment of individual objectives with organization’s objectiveâ€. Now the question comes since induction is for new employee what will happen to those who are already working? For this we need not to do anything but to ask them to attend this “Quality session†which we are conducting for new employees and they can join this from there and then. I believe that each and every individual should feel himself responsible for quality and there is a need to make quality a routine habit rather than an event.

“A point of view can be a dangerous luxury when substituted for insight and understanding. – Marshall Mcluhan †We all have come a long way from industrial revolution where mass production and consumption was the order of the day. Over the years, the once not so knowledgeable consumer has transformed into a living storehouse of information. What has changed over the years? Why did the companies who were the industry leaders at one point of time failed miserably later on? What changed? The answer to all this lies in data. In our thirst to find newer markets for products and differentiate them from our competitors, we pushed the consumer towards seeking knowledge and then when he demanded, we failed to live up to standards. We pushed the customer to give more importance to value i.e. value today has become emergent as compared to the old times when it was measurable. Now the new age customer wants more and he/she will switch, the moment he/she finds more value from another product or another competitor. How do we live up to this? Given the rapid changes in technology and the rapid adoption of management information systems by almost all industries, we need not look further. We have the solutions but we are too thick skinned to look at it. Today’s customer is like a walking hard drive with copious amounts of data. What we need to do is analyse it, find patterns where none exist, combine it with the experience of mankind and predict the needs and demands of future backed by strong data analysis. If we stop and look back into the history what will be distinctly visible to us is the fact that companies that were present in the first list of Standard & Poor’s 500 only 74 remain in operation today. Almost half of the 25 companies that passed the rigorous test for inclusion in Tom Peters and Robert Waterman’s 1982 book “In Search of Excellenceâ€, have gone bankrupt or have shut down due to poor performance. The reason being that when organisations are thriving and successful, it creates an illusion that whatever gave them success in the past will work in the present and still remain valid in the future. They forget the fact that the world is constantly changing, constantly evolving and to survive they need to evolve with it too. Today there is no dearth of examples of companies that failed after achieving success on a grand scale, to quote a few, Wang Labs, Digital Equipment, Borders and Blockbusters and many more; of which many either fell after achieving industry leadership position or shrunk in size. The reason is not that they failed to act or didn’t know what was happening but the fact that they had all the data but didn’t analyse it. The patterns were there, had they analysed the data they already possessed they would have seen it. They believed that experience can upstage data but they failed to comprehend that with today’s rapidly changing global economy, it’s impossible for the human mind to absorb all factors that go into making sound decisions. Statistical analysis often uncovers correlations that no one could have predicted on the basis of their past experiences. We won’t be wrong in accepting the fact that data and statistical analysis isn’t perfect but who hasn’t made poor decisions on pure gut instinct!! It is said that sustainability is based on transforming data into analyzable information for insights and decision-making. This is where data driven decision making comes in and plays its part. (Cokins) Take for example the case of Wal-Mart as reported in New-York Times. This occurred when Hurricane Frances was moving across the Caribbean, threatening to directly hit on Florida’s Atlantic coast. Residents were making a rush for the higher ground, but at the same time, far away, in Bentonville, Ark., executives presiding over the Wal-Mart Stores decided that the situation opened a great window of opportunity for their newest data-driven weapons, Predictive technology. A week ahead of storms predicted landfall date, Linda M. Dillman Wal-Mart’s CIO at that point of time asked her staff to come up with forecasts based on what happened when Hurricane Charley struck several weeks earlier. Their experts mined for data and found that the stores would need certain products more than the usual batteries, flashlights, and bottles of water. All this brought out some very interesting facts about the pre-storm behaviour of consumers. Strawberry pop tarts and beer were the top-selling items. By predicting what was going to happen rather than waiting for it to happen, Wal-mart was able to deal with this unexpected situation in a holistic manner. It is said that if we can improve our ability to estimate for a given customer, we stand to gain by applying the same logic to millions of customers. This applies to many other areas where intense application of data science and data mining is seen like direct marketing, credit scoring, financial trading, help-desk management, fraud detection, search ranking, product recommendation and many more. An example for this would be the instant recommendations of Amazon.com and Netflix which are a result of split-second advertising decisions based on the items a user has previously seen or is viewing right now, which has resulted in increased revenues for both of them. The increasing amount of data generated by humans while doing their daily chores has lead us into a world where the purchases and habits of a consumer give us an insight into the behaviour of the consumer, how he reacts to various offers , what are his preferences, Is the customer a sort of person who will pay back loans. The algorithms have been designed and implemented which predict accurately how likely a person is to get diabetes. All this has a potential to give rise to platform of customization for every consumer i.e. mass customization. A recent study by MIT professor Erik Brynjolfsson has brought out the fact that most revolutions in science begin with better methods of measurement. When we can see new things, we are driven to seek answers and thus build new ways of thinking and operating. The main underlying part of his findings was the fact that organisations that relied on data driven decision making, enhanced their performance by 4 – 6 % viz–a-viz their peers in the same market. This figure has remained robust despite taking into account the contributions for labour, capital, purchased services and traditional IT investment. (Lohr) Another research conducted by Mr. Erik along with Mr. Lynn Wu, an assistant professor from Wharton Business School used publicly available web search data to predict housing-price-changes in metropolitan areas across the United States. They didn’t possess any special knowledge of the housing market when they began their study, but they reasoned that virtually real time search data would enable good near term forecasts about the housing market- and they were right. In fact their predictions proved to be more accurate than the official ones generated by the National Association of Realtors which had far more complex model but relied on slow changing historical data. (Brynjolfsson) Now if we analyze the business that exist today and have been running on intuition, tradition and convenience, we observe that they have scattered staff development programs, the budgetary decisions are based on prior practice and programs, Staff assignments are based on interest and availability and goal setting is based on votes , favourite initiatives or fads. In contrast, the companies that have adopted data driven decision making for the staff development programs are more focussed as an improvement strategy to address the problems identified by the analysis of data. Staff assignments are based on skills required for future as well as the present requirement. Goal settings are done on the basis of problems identified by data analysis and their possible explanations. One factor that we must consider is that with the ever increasing rapidly changing pace of technology, companies today have multiple points of contact with the customer i.e. blogs, call centres , customer reviews or simple comments on social media platforms which are all a valuable source about client segments , insights and behaviour. Today it can be observed that increasing competition, decreasing margins and availability of information has left many companies struggling for survival. The only way out seems to stem from the fact that it is very important today to analyse the vast amount of data being generated daily and convert it into actionable knowledge so that client needs are addressed adequately. (Mike Lynch) An example worth mentioning here is that of researchers at John Hopkins School of Medicine, who provide a classic example of how simple models and big data win over more elaborate analytic approaches. They used the data from Google Flu Trends, which is publicly available, to forecast surges in flu related emergency visits a week before similar warning were received from the Centres of Disease Control. Similarly, twitter updates provided accurate records similar to the official reports that were tracking the spread of cholera in Haiti after the 2010 earthquake. (Brynjolfsson) From the numerous examples quoted above, it can be substantiated that data analytics unlock huge amounts of useful information, which can be made more transparent in nature by use of Big Data. It is often seen that we search for data in forms other than electronic and then transfer it to electronic form. This effort represents a huge inefficiency; if we could the information directly from other forms to electronic form without human intervention it could save us a lot of resources and time. Our online financial transactions or any transaction for that matter, if and when collected over a period of time can bring out interesting facts about our choices and behaviour. Now if we apply the same to a huge organization, what we have is copious amount of data that can provide us with very accurate and detailed information about various aspects of the organization. This further helps the middle level and upper level management make meaningful and informed decisions regarding day-today decisions to designing long term plans for the organizations along with requisite data to support it. Data collected from consumer transactions can help us predict the behaviour of the customers and help the organisations in maintaining requisite inventory levels. They can also tailor and customize the products and services according to the needs of an individual customer without the customer making an additional effect in telling the provider about it. It also leads to narrower segmentation of customers. Data obtained when combined with power analytics techniques can be used to find hidden patterns which otherwise would remain hidden and help in predicting future needs , improvise after-sale services and meet the expectations of the consumers in a holistic manner. In short the transfer of information between consumer and manufacturer can be defined as the art of communicating; without actually communicating; as no formal/informal communication takes place between the parties’ involved; just observation, which leads to predicting the needs and aspirations of consumers. We need to understand that the data is not the only tool for the decision making. If applied in isolation without experience, the end result is a Frankenstein’s monster of business process and best practices woven together. As value increases its dependency on unique knowledge and human experience, objective data sources must be supplemented with perspectives from organizational cultures, communities of interest and other relationships. Only then can we take holistic and sound decisions. Bibliography (Debates, 2011) Bonabeau, E. (n.d.). Harvard Business Review . Retrieved 11 21, 2013, from hbr.org : http://hbr.org/2003/05/dont-trust-your-gut/ar/5 Bono, E. D. (n.d.). Search Quotes. Retrieved 11 21, 2013, from searchquotes.com: http://www.searchquotes.com/quotes/author/Edward_De_Bono/3/ Brynjolfsson, A. M. (n.d.). Harvard Business School . Retrieved 11 21, 2013, from Harvard Business School Website : http://hbr.org/2012/10/big-data-the-management-revolution Christensen, C. (n.d.). Design Intelligence. Retrieved from di.net: http://www.di.net/articles/the-innovators-dilemma-when-new-technologies-cause-great-firms-to-fail/ Cokins, G. (n.d.). analytics-magazine. Retrieved 11 21, 2013, from analytics-magazine.org : http://www.analytics-magazine.org/may-june-2012/577-corporate-decision-making-why-do-large-once-successful-companies-fail Debates, D. (2011). When it comes to important business decisions , should you trust your gut or follow the numbers . Deloitte Development LLC. Lohr, S. (n.d.). New York Times . Retrieved 11 21, 2013, from New York Times Website : http://www.nytimes.com/2011/04/24/business/24unboxed.html?_r=0 Mike Lynch, c. e. (n.d.). Financial Times . Retrieved 11 21, 2013, from Financial Times Website : http://www.ft.com/cms/s/0/62f37a4a-931c-11de-b146-00144feabdc0.html#axzz2lITs0aA5 Plisch, B. G. (2004, 02 04). WTN News . Retrieved 11 21, 2013, from WTN News Website : http://wtnnews.com/articles/557/ Tim McGuire, J. M. (n.d.). Ivey Business Journal . Retrieved 11 21, 2013, from iveybusinessjournal.com: http://iveybusinessjournal.com/topics/strategy/why-big-data-is-the-new-competitive-advantage#.Uo47PsXlbrN Wladawsky-Berger, I. (n.d.). Irving Wladawsky-Berger. Retrieved 11 21, 2013, from Irving Wladawsky-Berger blog: http://blog.irvingwb.com/blog/2013/09/data-driven-decision-making-promises-and-limitations.html Zwilling, M. (n.d.). Forbes . Retrieved 11 21, 2013, from Forbes.com: http://www.forbes.com/sites/martinzwilling/2012/03/25/too-many-leaders-still-rely-on-their-golden-gut/ Note - Visitors shall not be able to comment on this article until they are logged in.

Once when asked what he does, if the data does not support his decision, John Maynard Keynes replied – “I change my opinion. What do you do?†In a haystack of information today, that one thing which helps organizations take sound decisions is the ‘analysis of data’. Often, companies find themselves in situations where from a variety of choices, they need to pick one. In such cases, data-driven decision making enables following a systematic procedure. A successful completion of any process begins from a decision well made. It forms the first step of any execution process, and is thereafter followed by modification, as and when changes in information arise. The question that one would ask then is – ‘If it is data that is needed, then exactly how much of it?’. Authors like James Taylor and Stephen Covey, in their writings explain keeping ‘the end in mind' before undertaking a course of action. They say that the goal is never to build on the data; rather it is to use the facts to make work easier. Thus, the perfect quantity would be one which helps an organization make ‘timely’ as well as ‘correct’ choices. But even a manager’s power to predict can do this job, isn’t it? On digging deeper we realize that the ‘intuition' we are referring to is nothing but the gut feeling that arises based on a manager’s experiences of the past, and thus its own roots lie in data as well. What people actually follow is ‘informed intuition' – that uses previous occurrences as its basis. This is justified, since not only is complete information necessary, but also alongside is corporate alignment and clarity. These days, a term that managers often hear is ‘big data’ – which refers not only to the volume of data available, but also to the variety of it and the rapid pace at which it alters. Big data brings with itself the complexities of processing and interpretation, causing confusion and delays. It is here that just-the-right-kind of filtration is needed, to separate what is relevant and what is not.Infact sometimes, even lesser amounts of data can lead to better decisions being taken. As they say, the ‘first impression’ can indeed be beneficial if taken as the ‘last impression’. This is exactly where the use of instinct comes into picture. Whether it be studying a consumer insight or predicting the future, it is a blend of analysis and a manager’s intuition that leads to the apt solution. In a post in Forbes, Robert Carraway, a professor at the Darden School of Business said that big data and the increasing use of frameworks require not less, but a higher amount of managerial insight to accompany them. There have been faults based on judgment (remember Google claiming to overtake Firefox when launching Chrome?) ; and so exist popular crisis due to over-reliance on data. The idea hence is to strike a balance between these two seemingly different ways that managers use to reach a conclusion. The more an association can accommodate diversity in terms of style, emotions and experiences, the higher is the probability of improved performance. In a nutshell, if a corporation can make sure that it has as members both left-brain and right-brain thinkers – it can strike the nail on its head! (*source for the cartoon : Google images) Note - Visitors shall not be able to comment on this article until they are logged in.

The story revolves around the growing of square watermelons in Japan. Japanese grocery stores had a problem. They are much smaller than their US counterparts and therefore don’t have room to waste. Watermelons, big and round, wasted a lot of space. Most people would simply tell the grocery stores that watermelons grow round and there is nothing that can be done about it. But some Japanese farmers took a different approach. ”If the supermarkets want a space efficient watermelon,” they asked themselves, “How can we provide one?” It wasn’t long before they invented the square watermelon. The solution to the problem of round watermelons wasn’t nearly as difficult to solve for those who didn’t assume the problem was impossible to begin with and simply asked how it could be done. It turns out that all you need to do is place them into a square box when they are growing and the watermelon will take on the shape of the box. This made the grocery stores happy and had the added benefit that it was much easier and cost effective to ship the watermelons. Consumers also loved them because they took less space in their refrigerators which are much smaller than those in the US - which resulted in the growers being able to charge a premium price for them. What does this have do with anything besides square watermelons? There are a five lessons that you can take away from this story which will help you in all parts of your life. Here are a few of them: 1.) Don’t Assume: The major problem was that most people had always seen round watermelons so they automatically assumed that square watermelons were impossible before even thinking about the question. Things that you have been doing a certain way your entire life have taken on the aura of the round watermelon and you likely don’t even take the time to consider if there is another way to do it. Breaking yourself from assuming this way can greatly improve your overall life as you are constantly looking for new and better ways to do things. 2.) Question Habits: The best way to tackle these assumptions is to question your habits. If you can make an effort to question the way you do things on a consistent basis, you will find that you can continually improve the way that you live your life. Forming habits when they have been well thought out is usually a positive thing, but most of us have adopted our habits from various people and places without even thinking about them. 3.) Be Creative: When faced with a problem, be creative in looking for a solution. This often requires thinking outside the box. Most people who viewed this question likely thought they were being asked how they could genetically alter water melons to grow square which would be a much more difficult process to accomplish. By looking at the question from an alternative perspective, however, the solution was quite simple. Being creative and looking at things in different ways in all portions of your live will help you find solutions to many problems where others can’t see them. 4.) Look for a Better Way: The square watermelon question was simply seeking a better and more convenient way to do something. The stores had flagged a problem they were having and asked if a solution was possible. It’s impossible to find a better way if you are never asking the question in the first place . Always ask if there is a better way of doing the things that you do and constantly write down the things you wish you could do (but currently can’t) since these are usually hints about steps you need to change. Get into the habit of asking yourself, “Is there a better way I could be doing this?” and you will find there often is. 5.) Impossibilities Often Aren’t: If you begin with the notion that something is impossible, then it obviously will be for you. If, on the other hand, you decide to see if something is possible or not, you will find out through trial and error.

I have compiled quotes for Six Sigma beginners and my comments are in green. Feel free to change the green text and use wherever you like. However, the black text is protected by the wise men who developed them. Here you go.. "It's better to be prepared for an opportunity and not have one, than to have an opportunity and not be prepared" - Whitney Young Jr *There is no better way to prepare than to learn the world best techniques in decision making "It does not matter where you are coming from. All that matters is where you are going" - Brian Tracy *Chances are - you may not be going anywhere with the old ways. "The right place and the right time is here and now" - Michelle Ustaszeski *Absolutely! "The more you seek security, the less of it you have, but the more you seek opportunity, the more likely it is that you will achieve the security you desire" - Brian Tracy *Taking this further - when you succeed with one opportunity, you get loads of them. "It is not necessary to change. Survival is not mandatory" - W. Edwards Deming *Bow. "The greater the difficulty the more glory in surmounting it. Skilful pilots gain their reputation from storms and tempests." - Epictetus *The BHAG is what you need. The big hairy audacious goals. Do not set unless you are prepared well to tackle them. "Big jobs go to the men who prove their ability to outgrow small ones." - Ralph Waldo Emerson *Undoubtedly "Your life changes the moment you make a new, congruent, and committed decision." - Robbins, Anthony *Make it now "Without a sense of urgency, desire loses its value." - Jim Rohn *Do it now "If you are not willing to risk the unusual, you will have to settle for the ordinary"- Jim Rohn "You must take personal responsibility. You cannot change the circumstances, the seasons, or the wind, but you can change yourself. That is something you have charge of." - Jim Rohn *Rohn says same thing in so many ways "We can't solve problems by using the same kind of thinking we used when we created them." - Albert Einstein *Change the thought process. Welcome to the Six Sigma world. "The larger the island of knowledge, the longer the shoreline of wonder." - Ralph W. Sockman *After so many years with these techniques the wonders have not ended for me yet "Intellectuals solve problems; geniuses prevent them." - Albert Einstein *I can only take a bow on this. "First they ignore you, then they laugh at you, then they fight you, then you win." - Mahatma Gandhi *You need to ensure that the first three stages do not get extended too much "One that desires to excel should endeavor in those things that are in themselves most excellent." - Epictetus *Get all the ammunition that is excellent in itself. Even if you miss a few goals, it will not matter. As the jackpots you hit shall reap great rewards for you.

Let us assume the talent pool available in the market is same for company A and company B. Company A decides to to use Six Sigma for improving their results from recruitment process while company B does not do this. Now we would like to understand how company A can get better results with Six Sigma. Let us assume that Company A goes through DMAIC in the following way. Define - Using appropriate Six Sigma tools, it defines the problem as inability to meet SLA for recruitment. This problem is converted into a charter document and discussed in a senior management meeting. The objective of the discussion in the meeting is to finalize whether this improvement project meets the criteria for approval as a Six Sigma project. As senior management gets convinced about the opportunity for improvement and sees the possibility of good return on investment through this project, the project gets approved with a leader assigned. A team is identified to work on the project with clear objective and a time frame of 4 months is provided to the team. These 4 months shall be used to select the right Six Sigma techniques on the issue, with right people working on the project and senior management support assured with resources (time, meeting places, communication support). Company B continues in a haphazard manner fire fighting as always. You might have noticed that well begun is half done for company A. Measure phase - Team at company A prepares a detailed process map of the current process, finalises the CTQ with an operational definition (bringing absolute clarity about when do we consider SLA as met and when is it considered as not met). It also checks and fixes the measurement system and brings visibility and accuracy (Company B does not know at a point in time how many people are on rolls, how many recruitment requests are pending and for what reasons). Company A now measures and documents its current process performance to be at sigma level = 2.3 and fixes a target of improvement at 3.0 Analyze phase - Using appropriate six sigma techniques, team at company A now identifies that various factors that influence the recruitment success. These are listed as - i. Understanding of hiring needs from departments properly. ii. Identifying the right approach for recruitment drive iii. Selecting the message (including introduction to company and career prospects) iv. Selecting the right media for recruitment. v. The short-listing mechanism. vi. Making the offer with terms and conditions vii. On-boarding and induction process. Analyze phase part II - Team at company A now identifies the factors that are critical to recruitment success. For this it carries out a survey on good (selected people or people who got an offer) people who did not join in a recent drive. Team uses certain techniques like FMEA, graphical techniques like box plot, hypothesis tests like the t-test to finalize on validate few critical factors that influence selection success. Company B meanwhile knows that there are several factors that influence the efficiency of recruitment process, but has no idea where to focus for improvement. Improve phase - Team at company A identifies solution alternatives using techniques like creative thinking, benchmarking. The team then selects the best solution using the appropriate technique out of pugh matrix, multi-voting, delphi technique etc. Before implementing the solution, the team finds that they can carry out a DOE to optimize the solution further. The new alternate process (improved process map) is documented, piloted, validated and implemented. The team records the benefits with the next recruitment drive. Company B continues as before. It has no mechanism to learn from its own mistakes or to learn from mistakes of others (and no data driven approach to reach conclusions, no management support to approve good recommendations when they arrive) Control phase - Team at company A puts in the right controls in the improved process so that the benefits can be sustained over a period of time. It carries out mistake proofing at few places, and creates a control plan with audit check-lists to ensure that desired behaviour changes does actually happen. Finally with a better process in place and removal of gaps, delays, errors, and ineffective communication (at the right places), Company A starts attracting the best talent more effectively. To put in in brief Six Sigma philosophy is about using a systematic, scientific, data driven approach that leads to the best possible process (the best does not remain best for long so there is a need for continual improvement and more improvement projects periodically) Six Sigma methodology provides us with world best tried and tested techniques and we select and use the appropriate ones for a specific improvement project. Benchmark Six Sigma Green Belt and Black Belt training gears you up with not just the right techniques but also provides the wisdom about when to select which one. Do let me know if you have more questions.

The inductive inference consists in arriving at a decision to accept or reject a null hypothesis (H0) after inspecting only a sample from it. As such, an element of risk - the risk of taking wrong decisions is involved. In any test procedure the four possible mutually disjoint and exhaustive decisions are: Reject Null Hypothesis when actually it is not true, i.e. when Null is false Accept Null when it is true Reject Null when it is true Accept Null when it is false The decision in (I) & (ii) are correct decisions while the decisions (iii) & (iv) are wrong decisions. These decisions may be expressed in the following dichotomous table: Thus, in testing the hypothesis we are likely to commit two types of errors. The error of rejecting Null when Null is true is known as a Type I error and the error of accepting Null when Null is false (i.e. Alternate is true) is known as Type II error Remark: Type I & Type II error: We make type I error by rejecting a true null hypothesis We make type II error by accepting a wrong null hypothesis If we make P (Reject Null when it is true) = P (Type I Error) = Alpha (a) P (Accept Null when it is wrong) = P (Type II Error) = Beta (b.) Then a & b are also called the sizes of Type I error & Type II error respectively In the terminology of Industrial Quality Control while inspecting the quality of a manufactured lot, the Type I error amounts to rejecting a good lot and Type II error amounts to accepting a bad lot. Accordingly, a = P (Rejecting a good lot) b= P (Accepting a bad lot) The sizes of Type I & Type II errors are also known as Producer's risk & Consumer's risk respectively. An ideal test procedure would be one which is so planned as to safeguard against both these errors. But, practically, in any given problem, it is not possible to minimize both these errors simultaneously. An attempt to decrease a results in an increase in b & vice versa. In practice, in most of decision-making problems in business and social sciences, it is more risky to accept a wrong hypothesis than to reject a correct one, i.e., consequences of type II error are likely to be more serious than the consequences of type I error. Since for a given sample, both the errors cannot be reduced simultaneously, a compromise is made by minimizing more serious errors after fixing up the less serious error. Thus, we fix a, the size of type I error and then try to obtain a criterion which minimizes b, the size of type II error. Obviously, when Null is true, it ought to be accepted. Hence, minimizing b amounts to maximizing (1-b.), this is called the power of the test. Hence, the usual practice in testing of hypothesis is to fix a the size of type I error and then try to obtain a criterion which minimizes b, the size of type II error or maximizes (1-b.), the power of the test.

Decision making is the inevitable truth of the life of every practicing manager. The best of the corporate leaders constantly emphasize on the fact that the litmus test of a manager is to take correct decisions in the difficult times. Management styles of the managers have varied widely and there has been always a contest between the merit of data driven decision making and the intuitive method of decision making. So any attempt to reach a conclusion about the greater efficacy of one over the other or the chance of success of one over the other lies in accessing the arguments made in support of each of them. The intuitive managers would argue that this style of decision making is justified because it is not a fluke. Rather it is the gut feeling of the manager which he has acquired out of experience. Further such decision making is quick and less rigorous to reach conclusively for the decision maker. Also there are examples which prove that this method has produced unexpectedly great results. But does this certify the method beyond doubt? Even when claim of intuitive manager regarding generation of unprecedented, unexpected great result is accepted yet this argument is incomplete. This is because the frequency of the success rate in the above stated intuitive method can always be questioned. The decisions taken intuitively are equally likely to produce highly good and highly bad outcomes. On the other hand the decisions taken based on data are likely to produce outcomes with lesser variance. In their case the outcomes are expected to be far more standard. An analogy can be drawn with the individuals trained in the discipline of management and individuals who do the business based on their experience and not on the basis of tools, techniques or methods taught to them. While the latter might produce very high outcomes, the former can be expected to produce standard set of expected outcomes on all instances. The risk involved in the case of the former is bracketed and is well within bounds. This helps us further argue the case that the data driven decision making helps organization to create a standard process for carrying out decisions making. This makes the company less dependent on skillsets of a given manager rather it makes the skills ubiquitous through the designed processes. We have examples of successes in decision where the manager is guided by gut feeling and intuition. Hitler in the battlefield, a manager at war or Indian cricket captain M.S. Dhoni, a manager in the stadium or Steve Jobs, a manager spinning the invention wheel without any empirical evidence of demand are all such examples. But there are also examples that did just the contrary like Napoleon on the battlefield, Nate Silver in the baseball match and modern India’s entrepreneur like Snapdeal founder Kunal Bahl. It should be concluded here that data driven decision making helps create a framework which allows validation of a standard method by repetition. Data driven decision making has produced remarkable results in several domains and has helped to solve historically unsolved problems. In the field of education, the top notch management colleges have expressed concern on not being able to create the best of peer groups that could maximize peer learning. The problem was essentially a consequence of intuitively formed groups with seldom any recognition of the educational background, past academic performance; data based analysis of individual’s strength and weaknesses etc. Modern research has shown that application of decision based system has resulted in remarkable increase in the peer learning. This study was based on the assumption of keeping all the externalities equal and the examination results were taken as the proxy for the improvement in performance. In the field of crime mitigation and managing police administration the USA has shared reports that could startle any intuitive manager. The manager in this case is Anne Milgram, former Attorney General, State of New Jersey who was trying to put a check on the amount of crime rates. For all the time prior to Anne the decision was based on intuitive skills of the officers concerned. Anne revolutionized the entire methodology. She instructed to collect data in greater detail and based on the profiling of the people and the locality she redesigned the entire schedule for the police petrol and beet police. The impact was a sizable decrement in the crime rates. Another example that validates the claim is the research of Prof. David Pall of Wharton School. In relation to e-commerce the professor says that attempt to push sales by an online enterprise should be based on some empirical findings. He says that the information about the social capital and the buying behavior of the customers are positively correlated with a correlation of 0.4. This correlation might appear to be small but even this much insight into the buying behavior of the customers has far reaching consequences on the company’s strategy. This gives the sales team a sizable knowledge that where their efforts in selling goods are more likely to fructify. This example becomes clearer when we see it in the light of the failure of an e-commerce company that worked without any regard to data based decision making. This company, boo.com based in U.K. had established offices in the elitist of the locations for which it spent more money than Amazon and eBay and yet failed. The reason primarily was making decisions that weren’t corroborated by data and facts. They launched products unmindful of regulations in respective countries, did absolutely no market research on the expectations of target population in terms of getting the touch and feel of the products that they offered. The final nail in the coffin came when they expected very high volume of customer orders on the Christmas when they actually received just eight orders. This clearly shows that data based decision making has greater significance and are likely to have a strong impact on business. Note - Visitors shall not be able to comment on this article until they are logged in.

A write-up to influence my manager – a gut-feel decision maker with a decade of experience – who is a non-MBA yet has a good skill-set, towards adopting DDDM. Key decisions are made by unstructured brain-storms and personal belief, and DDDM is vehemently opposed. Over the last ten years, you have been doing a wonderful job as a manager. This is especially remarkable, considering your advocacy of instinct-based decisions. The bulk of today’s managers wouldn’t be as lucky though, to be endowed with such a definitive gut for decision-making. Nevertheless, there are several of them – with much lesser experience and exposure - who have apparently climbed the ladder faster and have stayed steady in their lofty roles. And there is only one factor that seems to have been the difference – devotion to evidence over instinct. I totally understand your apprehensions about data-driven decisions, and with due respect, I request you to hear out my explanation. You might be an adversary of data-driven decision making, but any dignitary of your capacity will certainly be a strong adherent of reason and evidence. All of what I say is based on just pure and plain reason, more than anything else, and you will agree with me that data-driven decision-making is nothing but corporate pragmatism in its most palatable form. The primary problem with data, much to the annoyance of managers, is the hefty costs incurred in collection and analysis of evidence. However, the paybacks and benefits offered by data analysis hugely offset the initial costs of analysis and databasing. It can be safely asserted that all multi-national companies have adopted data-analysis practices right from the operational level up to the strategic level. This has been the cornerstone of their survival stories along varied cultures, business models and tactical transformations across geographies. What might be a sunk cost on the balance sheet might actually be an asset to the fortunes of the company and its long-run sustainability. There is a very valid argument about how today several companies fail in spite of using data-driven decision-making – I’m sure it has been a discerning insight in your mind too. It is one thing to gather and store data, and a completely different thing to analyze the collected database. Poor analysis of data is akin to searching for something in the wrong place. Carefully devoted time and effort on data-analysis has fueled the recovery and resurgence of several Fortune 500 companies from the brink of bankruptcy. On closer observation, I find that the root of aversion to data-driven decisions has been more of an inexpressible psychological factor than anything else. The technicalities involved and directness expressed in evidence-analysis has fueled a feeling of insecurity among traditional decision-makers, who are staunch believers in their strong management foundations drawn by rich experience. In reality, adoption of data-based decision making initiatives is not a means of discounting, but a strong facilitation of core management practices. Any gut decision backed up by appropriate data makes for a solid case since it adds a measure and credibility to the result. Data does not define the direction of action; it merely indicates the consequences of all possible paths taken. It is not a traffic signal, but a map. Also, data-driven decisions are not necessarily defensive. This is another common misconception; all that data-analysis does is to help take calculated risks. You will appreciate that today’s businesses are steered by forces that are completely different from those a decade ago. Competition is getting tighter and survival tougher as newer and more agile enterprises are mushrooming in a bid to cash in on hitherto unseen growth-rates. Flexible operations and non-conventional marketing tactics are enabling these companies to gradually eat into the market-share of the incumbents. The only way to survive, therefore, for an established firm like ours, is to constantly top our performance by maintaining our competitive advantage. And the first step towards identifying and nurturing our competitive advantage is data-analysis on our core competencies. We have to face the fact that whatever be the domain of profession, analytical models work better than human calculations and perceptions. Any sort of development or application of models begins with collection and analysis of data. With the advent of affordable technology, devices and programs have become increasingly more compatible to design models and programs. This has led to an unprecedented level of information processing, leading to a knowledge race, rather than a technology race, amongst competitors. As a result, firms that are too rigid to evolve from their legacies are under severe threat – invariably, these threats percolate to key stakeholders in each organization. Going global will demand us to learn and understand the tone of modern business. And all of today’s businesses across the globe speak one language: data. Have you ever wondered why a bulk of our review meetings and internal conferences tend towards stalemates and end up with you having to take a call on the stakes? Any viewpoint without a firm backing of data is deemed as baseless and subjective. Even a surmountable, innovative solution will cease to be appreciated simply because people’s perceptions are different. An evidence-based approach, on the other hand, makes a strong pitch as it directly presents the consequences in plain, direct numbers – in black and white. Arguments and alternatives are better received, and every conference will lead to a consensus, not just a conclusion. Disillusion and skepticism at our board meetings will be replaced with confidence and clarity. After all, what is an organization but a set of reasonable men working towards a common goal? It is only natural that our reasoning be shaped by rationality and pragmatism, in the best interests of our stakeholders. Decisions made based on data-analysis exude reason and rationale. This is the bare truth. Thank you for hearing me out! I’m assured that if you have thoroughly considered my statement, you have already begun to appreciate the imperative to take a data-based approach to decisions. It is in the cause of our sustainability: as individuals, and as an organization. Note - Visitors shall not be able to comment on this article until they are logged in.

You have been making decisions on the basis of your gut; it has never led you astray. Undoubtedly, you have been hearing this buzz about using data to make decisions around you. Can Data Driven Decision Making (D3M) be trusted? Is it worth all the hoopla surrounding it? Let us find out. As the name suggests, D3M is literally making decisions based on the analysis of data or information. Primarily, the information that must be analysed has to be relevant and there has to be a sufficient amount to help you make your decision. We shall discuss more about the nitty-gritties later as we dissect this term further. The concept of D3M was born out of necessity. Today, any action by a human causes data to be generated. According to an article on TechnologyReview, in the year 2012, 2.8 zettabytes of data was generated. That is almost equal to 3 billion hard disks of 1 terabyte each. A one terabyte hard disk can store 250,000 songs. Mind boggling, is it not? Even if you were to deal with the smallest fraction of the human race, there is still too much data for a mere mortal to handle. Enter the concepts of Big Data and D3M; these concepts have perhaps single-handedly revolutionized the way we make decisions. Surprisingly, the concept of D3M has been around for a really long time; organisations started using it as early as the 1980s. It is just the scale and corresponding technology that has evolved. It is best stated in an article by Pamela Wheaton Shorr back in 2003 where she states that making a decision without data is similar to flying a plane in the dark without any radar. She also states that using D3M makes sure that everyone is held accountable; in her words-“ there is nowhere to hide†because the analysis of collected data can find out the most minute of discrepancies ensuring you do not lose any money to such irregularities. Contrary to what many people believe, D3M is not that different from the methods that we have been using to make decisions. For starters, it is used all across the organization in different capacities as stated by Irving Wladawsky-Berger in this article on the Wall Street Journal. Henceforth, I aim to make it clear that D3M is an extension to the current intuition based decision making and not a replacement. As mentioned by several experts, every decision is made two times- a.) Intuition: We make a decision on the basis of our intuition and past knowledge b.) Data driven: Once data is available, we decide to continue with or forego the decision altogether. The benefit of D3M is that you can combine these steps into one and get better value for the time spent in making that decision. Even though it is not apparent, what we call intuition and belief are often a sum total of data. Our belief is born out of our past experiences; that too is data evaluated by our brain. So in essence when we talk about D3M, we have brought out the workings of our brain into the real world. Just like our brain, the D3M too has points which if not watched out for may cause incorrect decisions to be made. i.) Time consistency : Any decision that we make is of use to us only within a limited time frame. Similarly, for any decision made through the D3M system, every decision is good as long as it is made on time. What causes a delay in decision making? The answer is rather simple when you think about it. Just like in life, here too, you cannot have the perfect answer. It is important to draw the line somewhere. Before making any decision using the D3M, you have to ask yourself- “Whether you want the correct answers – quickly if possible or do you want the answers quickly – correct if possible?†Something we must do in life as well. ii.) How much data is enough data ? : While making decisions conventionally, it is quite possible for us to overthink things and in trying to account for everything, we fail to make an apt decision in time. The equivalent of this in terms of D3M is the above mentioned question. Here, experts advise us to use our instinct and decide the bare minimum amount of data that we need to make our decision. Most experts give a fairly simple mantra to highlight the importance of this concept- “ Garbage in, garbage out â€; this phrase highlights that the better the quality of data you put in for analysis, the better the results. It is here that your formidable intuitive skills will be useful the most. iii.) Confirmation : More often than not, we tend to pre-formulate an opinion or decision in our minds and instead of arriving at that decision through analysis; we use that analysis to verify our decision. Most of the times, we tend to ignore any discrepancies between the suggested decision and our pre-decided decision. This causes us to make the wrong decisions. An easy way out is to be ready to make changes to our initial assumptions. This is similar to the way we streamline our decisions in real life based upon feedback. In the discussion so far, I have tried to cover all the important details about D3M. The purpose of presenting even the possible issues above is to show you that this system is just an extension of what you have been doing till now. The benefit of D3M is that it gives you the ability to quantify your decision and you have absolute control over the decision making process. You do not have to take my word for it; just refer to the links provided within the article and make sure for yourself. References: 1. Decision-Driven Data Management: A Strategy for Better Decisions with Better Data (SAS White paper) 2. 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There is nothing which compares to the effectiveness of Six Sigma when it comes to improving a company/organization’s operational efficiency, raising its productivity, and lowering its costs. Six Sigma improves design processes, gets products to market faster with fewer defects, and builds customer loyalty. Perhaps the biggest but most unheralded benefit of Six Sigma is its capacity to develop a cadre of great leaders. We need to understand the fact that Six Sigma is one of the great management innovation happened during the last 30 years and an extremely powerful way to boost a company’s competitiveness. Yet, Six Sigma causes a lot of anxiety and confusion. If it is done right, it is energizing and incredibly rewarding. It can even be a fun, because a lot of creativity is also involved. You just have to understand—what Six Sigma really is. Six Sigma is about a lot of statistics, but—it is very much more. We need to know what Six Sigma is all about and why it matters so much. “Six Sigma is a quality program that, when all is said and done, improves your customer’s experience, lowers your costs, and builds better leaders”—Jack Welch Six Sigma establishes that by reducing waste and inefficiency and by developing a company’s products and internal processes so that customers get what they want, when they want it, and when you promised it. We all know that making your customers to stick with your company, you need to meet or exceed their expectations, which is exactly what Six Sigma helps you do. One thing that is sure to kill stickiness is inconsistency in service or products. Consider the following hypothetical example. Suppose you are a manufacturer of spare-parts and you promise your customer of 10 days delivery. You start with making a record of say, three deliveries and you find that your customers receive their parts on day 5, 10, and 15. Your average delivery time is 10 days. Then you record the next 3 deliveries to your customers and your find that they receive their parts on day 2, 7, and 12. This time your average delivery time is 7 days. You would state that there is a big improvement in the customer experience. This is not the reality—you might have done some internal process or cost improvement. What would your customers experience? Nothing, but inconsistency! If you use Six Sigma, your customers would receive all three deliveries on day 10, or in the worst case, on day 9, and day 11. In other words, Six Sigma is not about averages. It is about ‘variation’ and removing it from your customer’s interface with you. To remove variation, Six Sigma requires companies to unpick their entire supply and distribution chains and the design of their products. The objective is to wash out anything that might cause waste, inefficiency, or a customer to get annoyed with your unpredictability. So, that’s Six Sigma—the elimination of unpleasant surprises and broken promises. From 30,000-feets, Six Sigma has two primary applications. First, it can be used to remove the variation in routine, relatively simple, repetitive tasks—activities that happen over and over again. And second, it can be used to make sure large, complex projects go right the very first time. Examples of the first kind of application are a large number of Call Centers in India or other parts of the world. They use Six Sigma to make sure the phone is answered after the same number of rings for each incoming inquiry. Credit Card processing facilities use it to make sure people receive accurate bills on the same day every month. The second application of Six Sigma is the territory of engineers and scientists involved in multipart endeavors that sometimes take years to complete. If you are spending hundreds of millions of dollars on a new jet-engine or a gas turbine, you cannot afford to figure out process or design inconsistencies late in the game. Six Sigma is incredibly effective in discovering them on the drawing board, i.e., the computer screen. Obviously, the amount of Six Sigma training and education required depends upon where and how you intend to apply it. Yet, Remember: Six Sigma is not for every corner of a company. Jamming it into creative processes, such as Ad-copy writing, new marketing initiatives, or one-off transactions like investment banking, make little sense with Six Sigma. When deploying Six Sigma, it’s important not to stifle creativity for the sake of operational efficiencies. For example, successful Research and development (R&D) involves a good deal of original creative thinking. Research may actually suffer from too much rigor and focus on error prevention. Cutting-edge research is necessarily trial and error and requires a high tolerance for failure. The chaos of exploring new ideas is not something to be managed out of the system; it is expected and encouraged. To the extent that it involves process design and product testing, including the concept of manufacturability, Six Sigma will certainly make a contribution to the development part of R&D. The objective is to selectively apply Six Sigma to those areas where it provides benefit. Six Sigma is meant for and has its most meaningful impact on repetitive internal processes and complex new product designs. You better not look for statisticians or experts for marching your company on the path of Six Sigma. They might be great, but for relatively straight-forward projects, you just need everyone in your company to understand Six Sigma. You don’t see Six Sigma as the purview of experts, you see Six Sigma in the blood of your company. Thanks. (The article is originally published at my blog at wordpress.com)

One of my friends is a Sales Wiz with wonderful selling skills with little Six Sigma knowledge. In his previous job, he was leading Sales in an IT company. His job profile rotated mainly around closing deals initiated by junior Sales Executives. He spent a lot of time in resolving quotation related issues with clients. Issues arose almost everyday because Clients used to call different Sales persons in the company and used to get different pricing or discount offers. These different quotes for the same models to clients were within the discount authority available to Salespersons. Even after lot of counselling sessions, these issues continued and were accepted as a way of life. My friend as a Sales Head, had a challenging job of reaching each disilllusioned client (some of them feigned it to get an advantage) >>>explaining why the difference in quotations arose>>>>doing some numbers jugglary and >>>finally getting the Order. He used to get most Orders as he was too good with his Sales pitch and relation building abilities and the product was quite good. This sequence of tasks also provided him a feeling that he was doing a really worthwhile job resolving some big issues and an achievement feeling almost every day. He was made to feel special by juniors who honestly respected and admired him for his special abilities that succeeded in bringing them out of troubles so many times. He recently joined a process driven (Six Sigma implementing) company where some good Six Sigma projects had been done. One Six Sigma project had ensured that there was absolutely no possibility of different quotes landing up with a clients for the same model, even if different Sales persons are reached by the client. He found the new job boring as the entire fascination in his previous job was due to troubleshooting and firefighting which was a daily affair. Here the scope was little as he found the working to be quite streamlined. There were no quotation disputes, no erroneous invoices, no delayed invoicing, lesser delays in shipments, rare mismatches between order and delivery to name some of the areas. (All this was achieved through persistent Six Sigma afforts project by project) After some time with this new company, this person realized that the only way he could use his creative energies was by working on some futuristic tasks. This led him to work and succeed with some big deals which were reached with collaborative efforts with associate companies. He had to spend very little time here with juniors. And whatever time he spent went into discussions about strategic clients and deals. He realized that this was impossible in his previous job as his creativity was being utilized in firefighting issues (those issues did need a lot of creativity as each case had to be managed in a unique fashion). This brings us to some important questions - Are their ways by which we can spend more of our time in futuristic tasks that can bring success and laurels for self/ company/ nation? Are we using our creative energies in furturistic tasks (like worthwhile projects and improvement activities)? or are we losing ourselves in repetitive work? Coming back to my friend's example, it is not difficult to visualize the benefits he and his company could have realized if they had utilized the power behind Six Sigma early? (To share with you, the company he left still continues to provide the same job profile to Sales leaders and they derive the same kind of daily appreciation from juniors for doing great troubleshooting. Sales folks at that place still think that there is no better way of working.)