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  1. 9 points
    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.
  2. 4 points
    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.
  3. 3 points
    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.
  4. 2 points
    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.
  5. 2 points
    “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.
  6. 2 points
    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.
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