Creating an Analytic Culture through Data Interaction

by   |   November 30, 2016 5:30 am   |   0 Comments

Brian Williams

Brian Williams

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.

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.

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.

Enabling interaction

Interaction transforms decision making from an event into a process. When interaction is presented, decision making is not about just merely acting on the findings of others. This happens because data interaction allows the decision maker to point, click, drill into the data and keep exploring the data. Analysis and decision making become intertwined for a user, and the can analyze and discover insights not uncovered by the analyst. Enabling interaction causes two key implications in the work of analysts.

Treating visual as starting point rather than an end point

When analysts design visuals without user interaction in mind, their charts and infographics simply are a static sharing of their own analysis. But as information visualization continues to become more common, the visual presentation is becoming less important in business settings. Design needs to be sufficient enough to be a starting point for a decision maker that invites interaction. A malleable visual presentation is best thought of as a starting point of the analytic process of the decision maker.

Unclean data

Quality is latent within data. When users can interact with data, organizations may be placing too much focus on refining and cleaning data. Visualizing data presents patterns and easily shows anomalies and such data revelations lead to data discovery (George, Haas, & Pentland, 2014). Getting less refined data into the hands of decision makers may reap better decision impact than when data is aggregated and simplified.


Interaction with data is a process of discover. But for organizations striving to become thrive in a knowledge economy, the inherent question then is whose job is it to be interacting with data? If we stay in siloed roles where analysts analyze, and decision makers decide, we fail to leverage the analytic insight that can be brought to a problem or task by anyone in an organization. Self-service analytics empowers users across an organization but it takes leadership and shifting of traditional roles to shift to an analytic culture.

Perhaps the most important implication to data interaction and working with real-time data is that leaders need to learn humility. When decisions are made based on the analysis provided, there is a reliance on analysts to provide the most relevant information to the task at hand and decisions are made on the combination of that data and their experience and intuition. With a shift to data-informed decision making, that raw data can tell many stories. The more users interact with data the more it will shed light on a problem. It takes humility to share data widely, to be in a culture where one iteration of a chart or a refresh of the data can change the decision making process.  A decision makers desire to make decisions and act rather than explore, ask, and share creates a large barrier to adoption and organizational success.


In Spring 2016, Brian G. Williams earned his doctoral studies as a Non-profit Fellow in the Weatherhead School of Management at Case Western Reserve University in Cleveland, Ohio. His area of research is focused on how a visual analytics culture affect decision-making roles in organizations. Mr. Williams currently serves as Vice President for Enrollment and Institutional Analytics at John Carroll University in University Heights, Ohio. Under the leadership of the Provost and President of the university, his work supports the effective institutional use of data. Mr. Williams presents and writes nationally on data visualization practices and data-informed strategies for higher education; most recently presenting at AACRAO Strategic Enrollment Management and helping design and present at the Academic Impressions conference on Taking a More Data-Informed Approach. Prior to working at John Carroll, Mr. Williams worked most recently as Dean of Enrollment Services at Providence College in Rhode Island from 1998-2006. He holds a B.A. in English from University of New Hampshire and a M.A. in Higher Education Administration from Boston College.


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