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    DevsenaMishra
    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.
    Keval

    By Keval, in Process Excellence,

    SMART Goals
    Once you have planned your project, turn your attention to developing several goals that will enable you to be successful. Goals should be SMART - specific, measurable, agreed upon, realistic and time-based.
    A goal might be to hold a weekly project meeting with the key members of your team or to organise and run a continuous test programme throughout the project.
    The acronym SMART has a number of slightly different variations, which can be used to provide a more comprehensive definition for goal setting:
    S - specific, significant, stretching
    M - measurable, meaningful, motivational
    A - attainable, agreed upon, achievable, acceptable, action-oriented
    R - relevant, realistic, reasonable, rewarding, results-oriented
    T - time-bound, time-based, timely, tangible, trackable
    This provides a broader definition that will help you to be successful in both your business and personal life.
    When you next run a project take a moment to consider whether your goals are SMART goals.
    To quote renowned American philanthropist Elbert Hubbard:
    SMART Goals Specific
    Well defined. Clear to anyone that has a basic knowledge of the project.  
    Measurable
    Know if the goal is obtainable and how far away completion is. Know when it has been achieved.  
    Agreed Upon
    Agreement with all the stakeholders what the goals should be.  
    Realistic
    Within the availability of resources, knowledge and time.  
    Time Based
    Enough time to achieve the goal. Not too much time, which can affect project performance.  
     
     

    rahulbillapati
    "Whatever a manager does, he does through decision-making", said by none other than the man who invented management theory Peter Drucker. Decision-making is an indispensable component of the management process. It is a well-balanced judgment and a commitment to action. What a manager needs to do is rational decision making to achieve his objectives effectively.
    If you have been successful in taking decisions with your intuition, which means that you have acquired the skill through experience. In the fast moving world where change is only constant, decision making requires the scientific approach .The time has come for you to acquire an additional skill if you want to be a competitive and an updated manager.
     
    A decision should be rational because it may facilitate expansion of business and give more profit, goodwill and fame to a business unit. Decision making is continuous activity, mental/intellectual activity, based on reliable information, time consuming activity, and a responsible job. Good decisions are always taken by proper analysis of reliable information. The decision making process at all levels of the Organisation can be improved with the support of an effective management information system (MIS). This facilitates the manager to take a bold and confident decision.
     
    Reasons to use data based decision making
    Storing of data is becoming more cheaper and easier. The data is flowing with a characteristic of 3V's (Velocity, Variety and Volume). The velocity of data incoming to companies is very high. This is through various mediums like sensors, transaction systems, social media web sites, forums, research reports etc. This data is coming in variety of formats like text, numbers, audio, video and images. The volume of the data is a similar to tsunami. As of 2012, data that is created in a day is 2.5 Exabyte’s (2.5×1018). Every two years, the amount of data available on internet is doubled.
    Companies boast of their growth in terms of
    Customers grow at x% CAGR grow at y%  
    So it is time to realize that data also grows at an even faster pace. Ignoring this vast amount of data is nothing but losing a wonderful opportunity to dive in and taking better decisions. Decision-making is an intellectual process which involves selection of one course of action out of many alternatives. The effectiveness of management depends on the quality of decision-making.
     
    Here are some of the Loop holes in decision making through gut and intuition
    · Decision depends on the mood of the manager
    - The decision on same situation can be contradicting when a manager is in different moods if he is not using data.
    · Especially in Uncertain environment, it is the most difficult task to take decisions. So without the use of data, you are in great risk.
    · A situation may be encountered wherein a manager have too many choices. Moreover more than 1 seems to be right. Then the manager faces the dilemma of which choice to be selected.
    · Personal element in decision-making is a common phenomenon in the process. Similarly, every decision-maker will have his own personal background in the form of personal beliefs, attributes, preferences, likes and dislikes and so on.
     
    Need for data driven decision making
    a) The higher u climb up the corporate ladder, the more data is available just before you. Remember the more power you get, the more responsible you become. Consider a situation wherein you were managing hundred people. You may know each and every name of them and how they work. You may be very comfortable to take decision on promoting a certain employee. But, when you are given a responsibility of a thousand or more employees under you. Then the importance of data comes into picture.
    b ) A person at management position can be posted in any division of the company. The manager at different division who took decisions based on his experience may no longer be able to do the same.
    c) Data driven decision making is always backed by logic by proper analysis of relevant data.
    d) This power of analytics is realized and now, it is extensively used by top fortune companies of the world.
     
    Ways of data based decision making
    Big data is the emerging trend in the world now. Companies are investing heavily on the use of big data to get the powerful insights from data. This data may be of various formats as mentioned before. Many statistical tools like SPSS, SAS are available for analysis of data. These tools facilitate the manager to get important insights from the data. Open source tools like R and weka are gaining popularity these days.  
    Finally, Nothing but Change is constant in this world. So the manager should be embracing change
     
     
     
     
    It is in your moments of decision that your destiny is shaped.
     
     
     
    Do it wise...
     
     
     
     
     
     
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    Kongkan
    Decision
     
    Decision is something that every organization has to deal with every day. From the smallest scale, like labor distribution, to the largest, as in targeting new markets, it is often the major reason for success or failure at that particular level and beyond. In modern times the managers and executives, who control the future and destination of the companies, are facing more and more complicated situations where they need to make decisions considering numerous criterion within limited time period. At the same time the business competitors are coming up with more and more innovative ways to deal with the situation.
    DDDM
     
    The information explosion from increasingly sophisticated computer tracking of shipments, sales, suppliers and customers, as well as e-mail, web traffic and social-network is a huge challenge for some and for others it is a great opportunity. The later base their decisions on the analysis of data rather than purely on intuition, this practice is called Data-driven decision making.
     
    The benefits of data-driven decision making have been demonstrated in a study conducted by the economist Erik Brynjolfsson and his colleagues from MIT and Penn's Wharton School. They developed a measure of DDDM that rates firms as to how strongly they use data to make decisions across the company. They show statistically that the more data-driven a firm is, the more productive it is—even controlling for a wide range of possible confounding factors. And the differences are significant: 4–6% increase in productivity. DDDM also is correlated with higher return on assets, return on equity, asset utilization, and market value, and the relationship seems to be causal.
     
    Drawbacks of Purely Intuitive Decision Making
     
    Complex situations. Pure intuition may be fine for small decisions where your mind processes part or all of the information automatically, quickly, and without awareness of any details. But as the complexity increases it becomes impossible for human mind to process all the scenarios individually.
     
    Time to build intuition. Good intuition takes a long time to build. Chess players, for example, need 10 years of dedicated study and competition to assemble a sufficient mental repertoire of board patterns.
     
    Specific to environments. Some environments provide accurate indications about what’s going to happen next. They exist in poker and firefighting, but not in, say, stock markets. Feedback from the environment is information about what worked and what didn’t. Intuition doesn’t work well in environments where the results of decisions are vague and takes time to materialize.
     
    Inconsistency in application. A person following intuitive decision making often makes largely different decisions for the relatively similar situations and if one decision works on a particular situation there is no guarantee that it will work the same in others.
     
    Bad judgments. Intuition can easily be biased by overreaction to available information. Psychologists call this availability bias, which although the most prevalent is not the only form of bias that might exist, for eg. there is confirmation bias when people have a hard time believing and remembering evidences that contradict their beliefs. The decision makers could be a victim to many such biases.
     
    Origin of idea. There’s no to know if a spontaneous idea is the result of legitimate expertise or of a pernicious bias.
     
    The Way Forward
     
    Intuition and DDDM are not necessarily at odds. There are many times when decision-makers don't have very good data because something is new, the situation has changed or because what might seem like intuition is actually well-rehearsed behavior that comes from years of experience at something, so even though people can't articulate the pattern they recognize, they still are acting on a huge body of experience and knowledge. And on the very other side of experience there are virtues to the gut reaction of naive people, as those who are not properly brainwashed may see things and come up with ideas that expertise drives out of their brains.
     
    The tools and techniques provided by data science to process data can contribute a great deal by the following ways:
    Extracting useful knowledge from data to solve business problems can be treated systematically by following a process with reasonably well-defined stages. Evaluating data results requires careful consideration of the context in which they will be used. The relationship between the business problem and the data analytics solution often can be decomposed into tractable subproblems via the framework of analyzing expected value. Information technology can be used to find informative data items from within a large body of data. Entities that are similar with respect to known features or attributes often are similar with respect to unknown features or attributes. Provides close attention to the presence of confounding factors, possibly unseen ones.  
    Finding a balance between intuition and data DDDM is important not just for the top mangers and executives but throughout the organization. For eg. using data analysis in marketing would allow the business to provide the most desirable products to its prospective customers.
    Many data focused companies employ data-science teams to bring advanced technologies to increase revenue and to decrease costs. Companies like Facebook and Twitter, along with many other “Digital 100†companies,5 have high valuations primarily due to data assets they are committed to capturing or creating.
     
    Conclusion
     
    Using data analytics for business processes is important, but we must think beyond the algorithms, techniques, and tools in common use. We must think about the core principles and concepts that foster success in data-driven decision making. Success in today's data-oriented business environment requires being able to think about how data analytics can be applied to particular business problems. Understanding this process and its stages helps structure problem solving, makes it more systematic, and thus less prone to error.
     
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    P JAYANT
    Have you ever attended school? Well, I did and it’s laden with bitter-sweet memories. Sweet because it was that one time where I had this amazing thing called “Work-Life†Balance. Back then,, I had never heard about such a word although I had it in my life. And by the time I came to know about it, it was nowhere to be found in my life. What an irony!
    Bitter were those memories where there used to be a peculiar event called “Parent-Teacher Meetingâ€. The good kids were appreciated, the bad ones were reprimanded. And what was the key metric that they used for this? Marks! “Did you see your son’s marks? How pathetic. He is not at all attentive in class and his marks shows. Your daughter is amazing. You don’t need to worry about her. Her marks are excellent. She will make you a proud parent indeed.â€
    The point I am trying to impress upon you is the fact that data-driven decision making isn’t something of a recent origin. It has been there since ages. Why did Copernicus come up with the theory that earth revolved round the sun? How did America get discovered by the Columbus? Because these people relied on numbers and calculations, based on hard facts and not on what popular opinion lay at that time. Why did Vasco-Da-Gama travel all the way to India? Because he believed in data which showed that India was a spice rich country. Why were more and more spies sent behind enemy lines during World-Wars? Data, data, data.
     
    Today’s world is no less competitive either. Business leaders are in a continuous battle trying to improve their topline, bottomline and also keep employees satisfied. Data holds the key unlock all of these questions. Data analytics can give an insight into patters which may or may not be easily visible. For instance, a firm that grows at 5% as compared to last year in a market that’s expanding at 10% is losing market share. Although a casual observation might show that topline and bottomline has gained, only a rigorous analysis done on the industry reports can give the whole picture. Similarly, a yearlong research and analysis by Harvard Business Review on the industries that did well during recession brought forth some key insights into the focus areas which could help the leaders of companies perform better in the future.
     
    Such data driven analytics helps in better understanding of the situations and allows for more informed decision making. Another recent study on the internet usage in India showed that access to internet via mobile phones was at 62.5% as compared to 37.5% for desktop. Such data analytics helped Google in understanding where its future lay. And the recent ad on Indo-Pak friendship clearly depicted how google search on mobile could take care of the requirements of the users.
     
    It’s not just Technology companies that such analytics. In fact, recently at a company event in San Francisco, Jeff Immelt, CEO of GE talked about how analytics is the “next holy grailâ€. He also talked about how such analytics was helping in cutting down “unplanned downtime†for its customers. Such data driven decision making not only helps you in making better informed decisions, it also helps in making those decisions faster.
     
    In today’s fast paced world, waiting for the quarterly/annual financial reports and then taking decisions is not the correct way of running a business anymore. What we need today is real-time decision making that‘s dependent on strong analytical foundations. These changing trends could also give you a headstart in future businesses. The changing trends that brought about the fall of Kodak, Nokia could have been averted had they seen the changing trends. The next big trends are in E-cigarettes, LED lightning, 3-D printing which are all gaining momentum. And being able to tap into these and many such areas at the right time, at the right place allows a firm to outgrow its competitors. Such understanding and insights of the future trends could only be gained and developed by data driven analytics.
     
    To sum it up, Data Analytics is very important tool that’s available with the decision makers who can tap into the vast amounts of data that's on the internet and analyse them, ask the right questions to understand where they can improve on their existing operations and where to invest their future into.
     
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    onam.priyadarshi13h
    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.
     
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    deepakgupta312
    “A point of view can be a dangerous luxury when substituted for insight and understanding. – Marshall Mcluhan â€
    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.
     
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    Plisch, B. G. (2004, 02 04). WTN News . Retrieved 11 21, 2013, from WTN News Website : http://wtnnews.com/articles/557/
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    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/
     
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    Aseem Shandilya
    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. Other references mentioned inline
     
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    Hari Shankar Shashvat
    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.
     
     
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    prateekchaturvedi
    I tried to keep the topic neutral because neither did I want one to decide against reading the article based on gut feeling nor based on previous data analysis of topics. I myself am a firm believer of the two approaches; however, I believe people tend to take either of the sides without realizing that the two approaches are not at odds.
    A situation that is frequently cited by proponents of use of intuition for management is when the decision-makers do not have access to extensive data. This may be the case when the environment has changed significantly (a simple scenario that may be relevant at an individual’s level can be a decision to invest money). The decision in such situations may not seem to be driven by data due to non-existence of structured data. However, what often seems like intuition is well-rehearsed behavior that comes from the years of experience. Not being able to present a pattern or inability to articulate a recognized pattern (in cognition) does not imply that it is a gut-based decision. The cognitive pattern is built upon the extensive data of experience and knowledge accumulated over the years by the decision-maker. However, data-driven decisions may not always be the way out of a situation. People who are not brain-washed or are not entirely influenced by the data can see things in an entirely different perspective and can generate ideas that expertise may drive out of one’s brain. This may be seen in situations of business crisis – where to turnaround a business the top management is changed and new people from within the company or outside is brought to bring in an entirely fresh perspective.
     
    The problem with gut feeling is that we all already know of a lot of cognitive biases and related flaws which may impact the decision making. There is a lot of research that would yield “gut-feeling†approach to be highly susceptible to these flaws:
    confirmation bias – one’s difficulty in believing and remembering evidence that contradicts one’s beliefs
    fallacy of centrality – this is very important for the decision-makers to understand where they believe that they will know about anything and everything that is important.
     
    Although I believe in gut-feeling, the biases make me apprehensive of using the same for decision making – I fear my unknown biases creep in the decisions. Thus, intuition, according to me, works only for the people who act on their beliefs, but always doubting what they know. Such decision-makers would always be on a look-out for some contradictory evidence. This would in turn create an environment where others around them would also challenge their beliefs and result in constant updating of the “basis†of intuition. Few decision-makers that may fit this particular mindset could be Brad Bird from Pixar and Andy Grove - CEO of Intel. However, there are other interesting cases which, unlike the popular belief, have had seen errors in their judgment or in decisions based on gut-feeling. Steve Jobs made this mistake. Google believed that they were going to crush Firefox with Chrome, but it has not happened. The market share of Chrome still remains modest. This is not to bring anything negative about these two decision-makers or others. All of them have extremely impressive track records, but needless to say their experiences also include some failures which are usual for a human as well as a human-run organization. We need to understand that there are conditions where judgment may fail, and it holds true for the best of the leaders and managers. As is the case for intuition, similar arguments would hold true for data-driven decisions where decisions solely based on data may fail in absence of intuition.
     
    Finally, in my view, gut-feeling and data-driven analyses do not oppose each other. These may be looked at as tag team partners to achieve the best decisions. Hunches may be necessary to generate the initial hypotheses or even alternate decisions, but evaluating the same based on hard evidence (both quantitative and qualitative data) would help to achieve better results vis-à-vis decisions solely based on hunches and ingrained behaviors. Following gut-feeling mindlessly may result in believing that the process is impervious even after observing clear signs of failing. The gut-feeling may be captured in the qualitative data analyses, but in a much more structured and streamlined manner which also helps in easy identification of a pattern and repeated usage of the same, if required.
     
    To be driven by gut-feeling does not imply foregoing the advantage that structured data can provide. A move towards data-driven decision making while not shifting from the gut-feeling process is important in this highly dynamic environment.
     
    References: http://bobsutton.typepad.com/my_weblog/2009/11/intuition-vs-datadriven-decisionmaking-some-rough-ideas.html
     
     
     
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