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The Do’s and Don’ts of Big Data Analytics
Session Time and Date
Thursday, May 25th from 1pm – 2pm CDT
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Steps to Follow
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The only constant with Big Data is change. The data itself is not necessarily new but the fundamental paradigm shift is increased access to data. Advancements in technology vis a vis apps alongside the continued need for advanced programming skills have provided the tools that have allowed for this increased access. But increased access to data has resulted in exponential growth and the need for analytics both from an advanced and non-advanced perspective. Questions abound that with new Big Data technologies, the analytics discipline, itself, needs to undergo transformational changes. But does it?
In this session, we explore the four step process of analytics and how it can be applied to all analytical exercises and projects. Within the Big Data paradigm, we identify what is unique and new to this discipline versus what has remained constant over the many decades of applied analytics within such sectors as credit card risk and direct marketing.
The explosive growth of analytics is best manifested by the development of data science and business analysis departments within many organizations. Yet, unlike other disciplines such as marketing and accounting, which have strong academic foundations, analytics and in particular big data, analytics still relies on its practitioners in the formulation of knowledge as a foundation of learning for its practitioners. The key do’s and don’ts of analytics are discussed in the context of how they can be applied to both big and small data and their ultimate impact to businesses. Case studies and examples both reinforce and provide much richer detail in such key areas as the ability to define the right business problem. Alongside the business problem, we also highlight how the lack of due diligence and detail to the data leads to ineffective solutions even when the business problem has been correctly identified. Given all the discussion on Big Data, the challenge today is to determine what is the appropriate level of information. Gone are the days when practitioners used to extract everything.
Use of the right tools and techniques is also discussed but with emphasis on what is simple and understandable as it is these solutions that are more easily embraced by the marketing and/or business area. Simple is indeed better for most marketing solutions and the rationale for this is discussed in this session. At the same time, Big Data technology has altered expectations in the speed and delivery of solutions. Increasing reliance on automation, which include advanced techniques in machine learning and ultimately artificial intelligence, certainly can deliver on the goal of speed but at what cost. As businesses operationalize and automate more of their processes and methodologies, organizations are relying more strongly on black box solutions. Yet, despite the advantages of enhanced solution delivery, organizations are not always considering the negative impact of “black box” type solutions on being able to effectively analyze their decisions. The key to success is identifying what is the “optimal” compromise which balances the need for speed of the solution versus the ability to understand the details of the solution for effective measurement of the solution.