Editor's Pick (1 - 4 of 8)
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Utility Game-Changers: Solar, Wind, Hydro and Fintech
Level of Resources versus Urgency of Problem
The Business of Service Management
Reinventing Electric Power Value Chain
Joseph Santamaria, CIO, PSEG
Will the Smart Meter Deliver on its Promise?
John Burke, CIO, Ambit Energy
IT Governance Built to Last: The Wisconsin Enterprise Model
David Cagigal, CIO, State of Wisconsin
The Role of CIO in the Cloud-First World
Yvonne Wassenaar, CIO, New Relic, Inc
Analytics: What Has Changed and What Has Not?
By Zhongcai Zhang, Chief Analytics Officer, New York Community Bancorp (NYCB)
Analytics is still in the same business. Analytics technologies have changed. However, analytics is still in the same business: make sense of data. Regardless what we call it, insight or foresight generation, the descriptive, predictive, and prescriptive dimensions of analytics remain unchanged albeit the means of arriving at them, the speed of producing them, and their relative values to the business. Across the analytics maturity spectrum, organizations are found to be all over, from those who have established analytical capabilities to harness data to those who may be still struggling with getting the single version of the truth. Often, organizations are more prone to drown in the ocean of data and information than thriving on insights, to be precise, business relevant insights that are actionable, measurable, and profitable. Organizations that may be called analytical are reaping troves of treasures in their data repositories and have largely moved beyond merely the operational use of analytics, as strategic-insight focused analytics are unleashing significantly more value-added (as illustrated in the diagram below).
Analytics still sits on the same foundation: data. Data and analytics go hand in hand at any organization. Analytics can rarely flourish on any data foundation that is fraught with quality and integration issues, letting alone data being accumulated at the variety, volume, and velocity unprecedentedly. Processes ensuring a high level of data quality are of paramount importance, and data integrity is a non-negotiable pre-requisite for any analytics endeavors. Data cleansing is now-a-days a much different endeavor than decades ago, thanks to the technological advancements. It is, nonetheless, still a sizable undertaking that needs to be dealt with skill and domain expertise.
It is in this contextual perspective that an observed tepid demand for analytics at a given organization is often rooted in the problems
Furthermore, a unified or 360-degree view (revealed in the data, to be precise) of a customer remains a challenge for some organizations as it was for others decade plus earlier on. Having this foundation properly forged with the flexibility and scalability for optimal fine-tuning spells half of the success for enterprise-wide analytics.
Analytics is still flowing the same blood: rigor. Analytics sometimes is misused. While inadvertent misuse is innocent albeit still harmful, cognizant one is sinful and poisonous. Analytics within any given organization thrives and sustains on rigor. Rigor refers to the objectivity of the analytical process, the thoroughness of the employed methodology, the reasonableness, and testability of associated assumptions, and the soundness of output interpretation. In the meantime, analytics is sometimes overused. While analytics can empower and inform decision makers, it is not a panacea. It is largely true that, for every business problem, there is certain analytics that can be performed to shed light on the problem at hand. It is unequivocally false that, for every business problem, there is an analytical solution. It is unwise not to use analytics because of certain known or unknown limitations. Rigorous measurement oriented test-and-learn is the way to effectively progress on the optimality scale in business decision making. On the other hand, it is also dangerous to put full (blind) faith in any analytic model that at best captures the approximation of a limited number of factors, variables, imprecisely observed relationships, and data points. History does produce black-swan events. Many times, “big events don’t have big parents” (The Black Swan by Nassim Nicholas Taleb). A grain of salt is often a necessary and healthy dose of due diligence.
Analytics still requires the same core ingredient in the process: business acumen. Decades ago, automated data mining algorithms were promised to ease up the demand for statisticians and likely-trained talents through analytics democratization only to find a steady increase of analytics professionals, more and more colleges offering graduate programs in business analytics, and the current disequilibrium between the ever-increasing demand for and the tight supply of analytics resources, especially when it comes to the ones possessing savvy business acumen. Seasoned business analytics professionals are like medical doctors who are helped, not replaced, by technological advances in diagnostic tools such as CT’s. In a relatively matured analytics environment, one tends to observe that business acumen is not only helping refine analytical products and project deliverables, making them better and easier to be consumed by decision makers, but also shaping the pioneering, conceptualization, and development of new analytical capabilities or solutions to emerging business challenges. Analytics remains largely an add-on susceptible to the relegation to an after-thought until it is organically immersed with the business, becoming an integral part of the corporate decision-making process.
Analytics issues are still largely the same challenges with the supply side. Analytics can be looked at from both the demand and supply sides. When analytics is done right, you observe a healthy equilibrium in the supply of and demand for analytics. In such an environment, the supply of quality analytic insights often nurtures a steady flow of demand, and fresh business challenges, in turn, constantly help generate the demand for timely and actionable analytics. The interplay of the supply and demand thus pushes this dynamic equilibrium to the next analytic frontier. It is in this contextual perspective that an observed tepid demand for analytics at a given organization is often rooted in the problems with the supply side, and likely elevating the organizational stature of analytics is an essential step in this arduous but rewarding journey of using analytics to better decision making and optimize business processes. Indeed, analytics issue within a given organization is neither a budget issue nor lack of executive sponsorship issue, which may be the common rationale for bottlenecked analytics across institutions. It is, ultimately, a leadership issue. With the right leadership, one may have the needed executive buy-in and the budget. Without, there would be likely neither. “Leadership is the ability to translate vision into reality” (Warren Bennis). For analytics to root and thrive in an organization, it needs a visionary leader with strong translation skills.
Analytics is a journey. It is not a destination albeit destination-oriented. It is about empowering business decision making in an ever-evolving and increasingly competitive world. Learning to walk while facing the urge (from within) and temptation (from without) to run takes measured discipline and wisdom. Along the journey, one needs to constantly stay focused on the changing business landscape; one needs to precisely understand where all major pieces of the puzzle stand; and one needs to navigate through available paths to determine the viability and optimality of each (cost, speed, and bottom-line impact). It takes a visionary leader to guide along the way if one aims to avoid costly and painful detours and pitfalls.