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Beyond Data and Analysis


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Beyond Data and Analysis, illustrative photo

Credit: Vitaliy Novitsky

Wherever business executives turn these days, someone expounds the merits of business analytics, or some derivative of business analytics like supply chain analytics or marketing analytics or human resources analytics, or even predictive or Web or visual or data or streaming analytics, or any number of others.5,8 The academic community is also promoting this emerging view of analytics.6 There has recently been a call for a new professional role, that of the data scientist, to implement and diffuse analytics methodologies into and across organizations.3 There is even concern there will not be enough of these new professionals to meet the growing demand for this analytics specialty even in the immediate future. So, what does all of this really mean?

Today, businesses are awash in data.10 In wave after disruptive wave of technological and organizational change, business leaders face a host of powerful forces. For example, information processing has become increasingly more powerful and flexible, with faster and higher-capacity storage and networks. Simultaneously, globalization and other competitive factors have exerted strong pressures to improve efficiencies and effectiveness, and to strengthen business and customer relationships. Each successive stage of this competition requires more data and more analysis to support strategic, managerial, and operational decision-making. This competition, therefore, is driving a quest for more and better analytics technology; and this technology, in turn, helps to make competition even more intense. This cycle results in a confluence of competitive imperatives and technological advancements that interact dramatically. More effective analytics enables a higher level of competition; and higher competition creates further imperatives to make the analytics more effective. Advancing technology creates more competition, which creates more technology, which creates more competition, and so it goes.

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Real Change or More of the Same?

Still, as a practical matter, it is not difficult to be skeptical about business analytics.1,7 This is primarily because what is being touted as 'new' is a set of well-established and already widely used analytical approaches and methodologies which, aside from a few refinements, are not new at alland have historically on occasion been unreliable or impractical. These new analytics employ essentially the same multivariate inferential and descriptive statistical methods and mathematical modeling techniques that have long been used by businesses for analyzing data to support instances of complex decision making. For example, correlations, cluster analysis, filtering, decision trees, Bayesian analysis, neural network analysis, regression analysis, textual analysis, and so forth are all in the analytics arsenal, and none of this is particularly new.2 Furthermore, even with today's most modern techniques and tools, this kind of analytics still has practical limitations. For example, software and data complexities can impede effective analysis, and interpreting the results of complex analyses accurately can be potentially perilously misleading. It is, therefore, difficult to appreciate this latest emphasis on business analytics is anything unusual or different in comparison to the analytical processes that have been routinely employed by a host of serious-minded business decision makers in the past. One might well argue this change appears to be essentially incremental and does not embody any fundamental paradigm shifts.


What dependence upon analytics implies about the ways businesses must now compete is what is truly profound.


Another key aspect of business analytics is often called big data.9 This is characterized by vast collections of variously structured and even unstructured data that, when appropriately rationalized, can provide understanding and insight into various issues that reside embedded within that data.4 But this is also something that executives have been addressing for a long time. Remember the term "information overload" that was so popular a decade ago? The essential tools and techniques for dealing with big data include database management, especially data warehousing, data mining, dashboards, and associated technologies. These are hardly new constructs in the realm of managing data. Neither are the concepts, for example, of data ambiguity, data filtering, data context, data interpretation, data conversion, or data redundancy. Again, aside from a few progressive data management refinements, it can hardly be argued that any of this is particularly new either.

In theory, big data is different because of the three V'svolume, velocity, and variety.10 That is, big data consists of expansive collections of data (large volumes) that are updated quickly and frequently (high velocity) and that exhibit a huge range of different formats and content (wide variety). These factors force organizations to pursue increasingly innovative and cost-effective approaches to organizing, processing, and delivering timely information. It is argued that big data represents a real departure from the past. Fair enough; still, this argument is essentially evolutionary. Companies have been experiencing incremental expansions of volumes, velocities, and varieties of data for decades, more recently accelerated by the growth of the Web. Except perhaps for its sheer volume, this does not appear to be anything particularly unusual or unexpected. Nonetheless, cumulative change is only one aspect of this question. The real thrust here involves how this accumulation interacts with and impacts organizational strategies, operations, and controls beyond the technology.

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Basic Drivers for Change

So, why is business analytics suddenly drawing so much attention? One point is becoming clear. This is not likely to be just another buzzword that is hyped for a while and then recedes from the limelight quietly. Companies are really concerned about this issue, and there is traction for real change here. Why?

Businesses are experiencing ever-expanding cycles of change caused by the interaction of competitive forces and the harnessing of analytics and big data technologies as essential competitive weaponry. Technologies for collecting, manipulating, transmitting, and analyzing data have been improving for a long time. What is new and different is that these technologies have reached, and are surpassing, a capacity threshold for processing and storing data that is swamping conventional levels of organizational ability to cope with the volumes of data being generated. Business analytics technologies enable organizations to better cope with these new processing realities. This is important because the cumulative reach and scope of the underlying technologies and methods in use today reflect a level of impact on organizations that makes large-scale analytics critical for both sustaining business competitiveness and enhancing day-to-day decision making. It is the reach and scope of these technologies and methods that actually matter here, not whether the analytics and data tools are newly invented or not. This revolution is real and it is permanent.

Why is this happening now? Beyond technological innovations that make it possible to accumulate and process massive amounts of data ever more cost-effectively, the other key concept here is a competitive mandate that businesses continuously improve their decision-making capabilities in order to survive.6 The consistent, systematic analysis of complex data for decision making enables a company to operate more intelligently at all levels. In particular, the emphasis upon strategic business analytics in recent years has elevated executive expectations and helped to transform the business analytics ideal into a significant competitive force. The application of business analytics methods leads to improvement in an organization's overall decision-making capacity, which enhances its ability to conduct its business intelligently. So, the desire (and accelerating need) to achieve a higher level of organizational intelligence is a prime driver for implementing business analytics.

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An Executive Perspective

These arguments resonate well, but there may be a broader explanation for this phenomenon. Since the advent of computing (and networking) as a profession, computing professionals have shared a common visionthat this technology over time is destined to eventually become wholly integrated into every operating and managerial function in every part of every organization. Today's business analytics is a manifestation of that vision. In a very real sense, the usage threshold that has been reached for computing technologies in modern organizations is that of utter dependence upon timely information for basic competitive viability. This business analytics ideal implies a mandate for data collection and manipulation on a grand scalejust to be able to compete in the modern global marketplace. This dependency has evolved from (and is driven by) the relentless march of progress in computing technologies, undoubtedly; but what dependence upon analytics implies about the ways businesses must now compete is what is truly profound. Business analytics and big data are not just marketing hypeor more of the same old statistical analysis and data manipulation methods that have always been around. This is the future. The concept of analytics, as it is understood today, really is new. It is a term that embodies the realization of a historic vision of how computing will challenge and change the world of business, forever and irrevocably, a vision that is now coming to pass under the guise of business analytics.

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References

1. Barton, D. and Court, D. Making advanced analytics work for you. Harvard Business Review 90, 10 (Oct. 2012), 7883.

2. Davenport, T.H. and Harris, J.G. The prediction lover's handbook. MIT Sloan Management Review 50, 2 (Feb. 2009), 3235.

3. Davenport, T.H. and Patil, D.J. Data scientist: The sexiest job of the 21st century. Harvard Business Review 90, 10 (Oct. 2012), 7076.

4. Davenport, T.H., Barth, P., and Bean, R. How "big data" is different. MIT Sloan Management Review 54, 1, 4346.

5. Hopkins, M.S., Lavalle, S., and Balboni, F. 10 insights: A first look at the new intelligent enterprise survey. MIT Sloan Management Review 52, 1 (Jan. 2010), 2226.

6. Hsinchun Chen, H. Chiang, R.H.L., and Storey, V.C. Business intelligence and analytics: From big data to big impact. MIS Quarterly 36, 4 (Apr. 2012), 11651188.

7. Jacobs, A. The pathologies of big data. Commun. ACM 52, 8 (Aug. 2009), 3644.

8. Kiron, D. and Shockley, R. Creating business value with analytics. MIT Sloan Management Review 53, 1 (Jan. 2011), 5763.

9. LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., and Kruschwitz, N. Big data, analytics and the path from insights to value. MIT Sloan Management Review 52, 2 (Feb. 2011), 2122.

10. McAfee, A. and Brynjolfsson, E. Big data: The management revolution. Harvard Business Review 90, 10 (Oct. 2012), 7883.

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Author

Charles K. Davis (ckdavis@post.harvard.edu) is the Cameron Endowed Chair and Professor of Information Management in the Cameron School of Business at the University of St. Thomas in Houston, TX. He was recently a Visiting Scholar at the Center for Transportation and Logistics at the Massachusetts Institute of Technology, which supported this research.


Copyright held by Author.

The Digital Library is published by the Association for Computing Machinery. Copyright © 2014 ACM, Inc.


Comments


peter wright

Information Technology is constantly changing. Organizations or firms have to keep up with the ever changing markets. Data analysis as observed in this article above goes way beyond the ordinary regressions. Data is needed in almost every facet of the todays fast pace workplace and or organizations.
Globalization has strengthen the way people perceived or retrieved information. Take for example, the pregnant mother or expected mother. Because of Innovation and Technology she is able to know the type of baby she's expecting.
Same can be said, on the political scope, one can predict the outcome of an election base soley on data that is computed and the areas by which the candidates are leadking. Data harvesting is also another for of gathering information. Then there's Facebook, one of the World's largest Social Media that links one person to as many as possible.
Advancement in technology creates much more competition - for example NASA and sending astronauts into space - verses now, sending the first car into space, without any human being.


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