Creating an Analytic Culture through Data Interaction

Creating an Analytic Culture through Data Interaction

Creating an Analytic Culture through Data Interaction
Becoming a knowledge-based organization takes a heavy investment in people, process, and technology. In the current analytic movement, there is much focus on amassing data and hiring the data scientist and analytic talent needed to control the effective use of information across the organization.

Yet such a supply-side investment may be a bit lopsided. Concentrating too heavily on the tools, data, and technical staff needs can neglect bringing the functional side of the organization along for the ride. The scale of such a concern is well established. McKinsey estimated that over the next few years U.S. industries face an analytic talent shortage of 140,000 to 190,000 people with the data skills needed. However, the same report points to the even larger talent gap of estimated 1.5 million managers and analysts with the know-how to actually use the data to make effective decisions.

A few key efforts lead to the changing environment for decision makers in organizations. First is the move from aggregate, point-in-time data to working with more real-time, transactional data. Such a shift requires decision makers to deal with increased velocity of data, anomalies and patterns arise in different ways that may be smoothed out by analysts when reports are monitored weekly or aggregated differently. But data-rich tools and methods bring more information into decision making at a granular level for which many managers are not ready. Visualization has become a strategic way to deal with the complexity of the data. However, solely focusing on data artistry and infographics to replace statistical rigor can quite simply be replacing information overload with visual overload, requiring decision makers to understand and interpret various visualization techniques and choices from their analysts.

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The complexity of information environments is challenging most organizations. Not all decision makers are ready to become more analytic, and as we focus on visualization, data mining, and predictive analytics, decision makers will become even more reliant on analysts and may not perceive they have the abilities to keep up with the quants.

With new analytic tools and increased and unfettered access to more raw data, we are faced with great opportunities to change the roles and tasks in organizations, but such change is not happening as quickly as it can. Analysts can deliver data right into the decision-making process and let decision makers form their own conclusions and monitor activity on a more frequent basis. Yet managers are busy and may not have time or talent to constantly do such work. They may assume rather that the analysts are the ones who are knee-deep or shoulder-deep in data, know it best, and should be recommending what to do. Traditional roles and training become big barriers to becoming a more analytic culture: at heart, analysts want to analyze, decision makers want to make decisions. Introduction self-service analytics challenges these default roles and the years of training that have gone into their work.

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Organizations that have successfully moved toward self-service analytics are experiencing promising results. In such cultures, where users across the organization have access to data and tools with which to interact with it more independently, they experiencing a paradigm shift. Decision makers in these visual analytic contexts no longer act on the findings of analysts and analysts are not just presenting findings, but are creating systems where interaction invites curiosity and empowers others.

So what are the effects of self-service analytics and data interaction during decision-making?

This emerging trend toward interactive visualization is significant. Interaction is what gives control of information flow to a user. Analytics create a meaningful starting point for decision makers to explore a data set and rather than being and “end user,” their interaction is an extension of the analysis process.

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