This blog series counteracts the blind adoption of analytics with a solid framework for business value that is generated by analytics within enterprise IT systems. The question is…Why should companies invest resources in analytics as part of their IT infrastructure?

Previous blogs in this series have covered:

1. The Journey sets the stage by introducing the economic value principle. To have value, analytics must enable the organization to execute improved actions that, in aggregate, changes its behavior and improves its expected payoffs. Hence, the focus shifts to how analytics transforms data into actions, not just insights.

2. Our BI Legacy explores our seven-decade legacy using various IT technologies to support Business Intelligence (BI). Each decade had a unique value generator, for which we noted six trends in flow patterns from one decade to the next: Cumulative, Pervasiveness, Disruption, Actionable, Generalizing, and Effectiveness.

3. Next-Gen futurizes about the value generators for the 2020-decade, based on six trends observed over the past decades of IT systems evolution. The next decade is challenging with the emergence of neural networks analytics, AnalyticOps to manage analytic systems, continuous learning systems, and intelligent effectiveness customizing each transaction.

4. Action Distance examines the ‘distance’ (or coupling) of data to actions in the Analytic Value Chain. How faithfully does analytics respond to data, both in timeliness and substance?

5. Value Drivers are seven drivers for realizing value from analytics, based on interviews with four organizations having successful analytic projects. It concludes with the implications for managers from each of these value drivers.

This sixth blog investigates the Value Drivers in terms of the challenges for technology change versus social change. As organizations invest heavily in analytics, they should have realistic expectations of the cost, effort, and duration of the required changes, both socially and technically.

Changing Your Culture and Technology

The following analytic Value Drivers were critical to achieving value from analytics by the four organizations interviewed: (details in the previous blog)

1. Data-Driven Emanates from The Top
2. Pervasive Data Literacy
3. Searching for The Signal
4. Effortless Customer Interactions
5. Inclusive Data Ingestion
6. Social Issues
7. Complete Data Lineage

What are the requirements to enable these drivers to be effective, in terms of changes to the organizational culture and the technology infrastructure? The figure positions the Value Drivers based on the cost and effort required for cultural changes and technological changes respectively.

The following highlights how the various Value Drivers require varying changes in culture versus technology.

V1 Data-Driven Emanates from The Top: This requires top management attention throughout an analytic project, especially in the early stages of setting business objectives and expectations and in the latter stages in monitoring outcomes and realizing business value. Little changes needed for technology.

V2 Pervasive Data Literacy: This value driver is like the oil on every one of the above AVS steps. All hands on deck! Analytics must be part of the organizational vision statement and the expected skill of most employees. Everyone involved must speak the same language and motivate in the same direction. To be a data-driven organization, everyone needs to be data literal. Technology must support and facilitate this literacy. Interactions must be easy to use and tutorial in nature. Collaborative BI techniques (such as sharing and commenting on analyses) are also essential.

V3 Searching for The Signal: This value driver is probably the largest value contributor. True signals are elusive. Guesses, hunches, and even seasoned instincts are biased in unexpected ways.  The passion behind the data detective mentality is a significant cultural boost. The challenge is that close coordination among both management and technical groups is essential for large scale success. A bunch of techies in the back room may have great data insights. However, the value comes from business insights based on an intimate understanding of business processes and customer behavior.

V4 Effortless Customer Interactions: The obvious requirement is to understand thy customer. It is not just knowing what bugs them now about using your systems, but creating new interactions with your systems that make their experiences seem effortless and even fun. Lots of cultural savvy mixed generously with deep technical creativity is required.

V5 Inclusive Data Ingestion: This is a technical issue initially. It deals with new cloud-based services within an open flexible infrastructure, which should simplify the data pipeline and reduce IT costs. Hence, if your organization is not there technically, that is a problem. Latter, this value driver assists with the curation of new and often messy data, which requires a large-scale system for soliciting and refining. It is Data Lakes prevented.

V6 Social Issues: This value driver deals with growing restrictions (and opportunities) on data usage amid a complex global economy. Dealing with the social issues related to analytics throughout the entire analytic lifecycle is probably the toughest challenge with which all organizations must now resolve. Lots of coordination among all management levels plus continual interactions with technical groups as technology improves. Unfortunately, new technology often causes new social issues to resolve, like face recognition in the case C1.

V7 Complete Data Lineage: This value driver contributes to resolving data problems impacting customers, like tracking packages in the case C4. The problem may be anywhere along the data lineage, especially when analytic modules are sprinkled throughout. Legal issues of liability are increasingly risky and expensive when explaining why your system performed an action that resulted in harm or loss to a customer. The explainability of analytic results is getting better but still opaque to most, especially to a jury in a courtroom setting. Data lineage is essential for analytic explainability. 

The implication for executives is to identify your unique Value Drivers and thoughtfully assess the required cost and time required. Several aspects seem to appear from this figure.

First, note that there are roughly three clusters of Value Drivers: ones that are primarily cultural changes, ones that are primarily technology changes, and then ones that are mixtures. The cultural ones will be the focus of top management to set visions and policies. The technology ones will be the focus of technical groups to design and implement the proper technology infrastructures. The last cluster is interesting because it requires closer coordination among all levels and groups. Hence, these value drivers will be the most difficult ones to plan and execute.

Take-Away: To make a Value Driver successful, is your organization able to accomplish the required changes? Will this achieve the desire business objectives in a pragmatic financial manner? This summarized the analytic strategy challenge behind the AVS concept.

Second, note that culture changes require more cost and time than technology changes, as indicated by the arrow. The implication is that culture changes should have the immediate attention of top management early and pervasive throughout the change process.

Take-Away: To make a Value Driver successful, is your top management willing and able to accomplish the required changes? Will this achieve the desire business objectives in a pragmatic financial manner? This summarized the analytic strategy challenge behind the AVS concept.

In conclusion, analytics is forcing organizations to reassess the accepted thinking and policies about their business systems. Executive attention is increasingly required to manage the strategy by which business value is derived from analytics, along with necessary changes to organizational culture and technology infrastructure. If an analytic value strategy is well-executed, an organization will likely realize increased economic value from their use of analytics. And, perhaps this will initiate the revitalization of their organization and even the redefinition of their industry.

In memory of Clayton Christensen, remember that innovation (especially as driven by analytics) can be highly self-disruptive. Understand the dilemma of the analytic innovator role. Learn from the (sad) experiences of many great companies like Xerox. Understand the principles behind Disruptive Innovation.