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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.

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.

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.

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.