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

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.

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