Governing the Use of Data Analytics

Author: Don Springer, Guest Contributor

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Companies continue to embrace data analytics and data science as an opportunity to learn more about their performance, their customers, and the context for strategic decisions. It is becoming a capability in a greater number of companies and, in larger companies, it has become a distinct function within the organization. With the quantity of data growing, the value creation opportunities expanding, and the demand for data-driven decisions increasing, board of directors are now forced to consider how they are governing the use of data analytics.

Most companies today are looking for ways that technology can be used to improve their operational efficiency, customer experience, and business models. Boards must foster the move forward, advocating for change by asking key catalytic questions. Those companies and boards that do so will most likely see better financial results than those that don’t.¹

I previously wrote about digital transformation having both an internal focus of digitizing the company for operational efficiency as well as an external focus of digitizing value that enhances customer offerings. Of course, both of these necessitate a board with digital smarts, so independent directors and augmented board agendas were suggested to facilitate that. In a second article, I presented the formation and use of a technology committee to further build the competencies within a board of directors that can address digital opportunities. If we focus on opportunities surfaced from data analytics, we realize that another board competency is required, i.e., that of governing data-driven problem definitions and decisions.

What are data-driven decisions? How are data analytics being used to create value? How are they used for customer experience and strategic insights?

More specific for this article, how does the board govern this digital process? How does the board gain competence in using a digital approach to value creation and strategy?

Data-Driven Decision Making

In recent years, the basis for operational decisions has increasingly shifted from historically oriented reports to real-time data. Empirical based decisions are enabled with data connected devices, machine learning algorithms, abundant data, and decision relevant experiments. Additionally, more strategic and marketing decisions now demand informed decision making supported by timely, empirical data. Those companies with digital capability and knowledgeable leadership are taking advantage of these trends by integrating operational and strategic decision-making in new and powerful ways.²

Data analytics is being used in multiple areas of the business, such as customer experience, employee experience, business model enhancements, and corporate strategy, to name just a few. To set the stage for presenting an approach to governing data analytics, we will briefly explore just one of the many applications, namely, customer experience.

Value Creation with Data Analytics

In addition to operational efficiencies, data analytics is progressively being used to create value propositions and one area of that use is enhancing customer experience. Viewing a business from the customer’s perspective has always been important, but data analytics has parsed that demand into more valuable and focused competencies. It is a question of not only collecting customer data, but designing customer experience and creating emotional engagement with customers.³

Collecting Customer Data

Most data analytic oriented companies are understanding their customer behavior and distributing that knowledge across company departments and functions. That has resulted in integrating operational responses across those typical silos. However, with machine learning available, real-time data can be collected and analyzed, enabling personalized interactions. These “recommendation engines” now allow the company to proactively offer advice to specific customers on “their next” product and service.⁴

Designing Customer Experience

While customer experience has become the primary competitive theater for companies and their brands, it is usually easy to recognize, but difficult to design and create. Designing customer experience requires customer ethnography, i.e., empathetic design based upon seeing, recording, and analyzing the customer’s full range of behaviors and interactions, not just capturing the customer’s thoughts and opinions. That ethnography can be enhanced through a specialized use of data analytics because customer behavior is now digital more than ever. Subrah Iyar, CEO and cofounder of Moxtra and former CEO, chairman and cofounder of WebEx stated that “Consumers’ expectations have gone digital, and there’s no turning back. Regardless of a shift back towards pre-pandemic life, the convenience and flexibility of a digital customer experience is here to stay.”⁵

Creating Emotional Engagement

Finally, customer experience requires emotional engagement. This is being addressed by data analytics through the use of ratings, forums, and gamification that solicits customer participation across the entire customer value chain. The Braze 2021 Global Customer Engagement Review found companies that have formal customer engagement initiatives outperform those that don’t. Customers who are messaged (versus those that aren’t) are 7.2 times more likely to make purchases. These customers are also retained three times longer than customers who aren’t receiving messages.⁶

Collecting and analyzing data from various perspectives enhances the ability to create value through customer experience initiatives. With this now in mind as an example data analytics application, how does a board govern the use of data analytics?

Governing Data Analytics Strategically

Boards of directors can govern data analytics initiatives more strategically by using four frameworks for their exploration, guidance, and oversight. These frameworks are as follows:

Data Analytics Process,
System Dynamics,
Iterative Learning,
Risk Mitigation.

Data Analytics Process

To provide actionable insights, a board must question the processes inherent in data analytics, especially the phase of problem formulation. While mining available data will surface unforeseen patterns, many times data science initiatives become the proverbial “hammer looking for the nail.” A board should ask, “What questions are we trying to solve?” Whether those questions are generated from automated pattern recognition or strategic questions directed from company leadership, clarity of purpose will define the needed data and data sources, while also enhancing the value of the analysis.

Once clear about the problem, questions that follow the process of data collection, analysis, and resultant alternatives for action, come to the foreground. Those questions can focus on the requirements and availability of the right data, models, people, competences, resources, partnerships, and culture to achieve the desired outcomes all along the way.

Systems Dynamics

The board also needs to take a systems view of the environment of data analytics. While everyone is aware of the volatility and uncertainty of today’s world, complexity is another aspect critical to governing the use of data analytics. An awareness of system dynamics has relevance for data analytics.

For example, companies tend to think that defining customer experience or developing a strategy is a sequential process, i.e., define the objective, gather the required resources, execute the plan, analyze results, and make decisions. Yet, every decision and execution changes the “state of the system” or market, in this case, creating feedback loops. If that were not complex enough, a company’s decisions have unanticipated “side effects” that also change the market with another feedback loop. Let us also not forget other “agents”, or companies, in the marketplace who are also making decisions and taking actions that, in turn, change the market. So defining customer experience or a strategy is not a single, sequential project. The market continually changes from all of those agent actions and feedback loops, as well as geo-political shifts and natural disasters.⁷

With respect to data analytics, a board must ask questions about the sources of data and their timing. It must also consider how the system and multiple agents are modeled. And of course, the board must explore how optional paths of action are determined and what the expected outcomes might be. The qualities of the questions derived from a systems view brings us to the third framework, namely, iterative learning.

Iterative Learning

Data analytics within the systems environment above is another form of a “continual learning process” for companies and their boards. In this case, questions yield answers that, in turn, yield further questions and expectations. Traditionally, companies set directions and look for technology for implementation. However, in a dynamic environment, data analytics is more of an adaptive, agile process and boards must adapt by continually exploring and advocating for iterative experiments and insights that continually alter the models and data.

For example, a digitally savvy and agile oriented board will ask, “How can we quickly test whether customers really like this new offer?”, assess what has been learned, then ask “What are the options to adjust to our newly acquired knowledge?” They can also ask, “When we launch this new offering, how do we track the usage, impact, and resultant bottom-line?” These kinds of adaptive questions comply with a changing environment while continuing to exhibit the guidance, oversight, and advocacy that is a board’s contribution.

Risk Mitigation

Of course, there is a risk side to all of this. Boards must explore the risks of privacy and cyber during the collection and usage of data analytics, particularly in the example of customer experience we have used in this article. How both privacy and cyber are covered in data preparation, collection, and usage, as well as event preparedness, response, and recovery, are paramount concerns for a board. Asking the hard questions about the risks inherent in a data analytics initiative is as necessary as asking those about value creation.

By using these four frameworks, boards and management essentially take a test-and-learn approach to data analytics together, experimenting to see what works and then scale the successes for value while also monitoring the activities for risks.

Summary

The promise of data analytics for value creation in customer experience, business model transformations, and corporate strategy are matching and exceeding the results from their application to operational efficiencies.

Boards today must become digitally savvy enough to guide these strategic initiatives in order to fully realize the promised contribution of data analytics to competitiveness, performance, and value creation for all stakeholders.

References:

P. Weill, T. Apel, S. Woerner, and J. Banner, It Pays to Have a Digitally Savvy Board, MIT Sloan Management Review, Spring 2019
D. Bonnet and G. Westerman, The New Elements of Digital Transformation, MIT Sloan Management Review, Special Collection on How to Embrace Digital Transformation, Spring 2021.
Ibid
Ibid
A. Swinscoe, 11 Customer-Experience-Related Predictions For 2021, Forbes, December 2020.
https://www.braze.com/resources/reports-and-guides/2021-global-customer-engagement-review
J. Sterman, Business Dynamics: Systems Thinking and Modeling for a Complex World, McGraw-Hill Education, February 2000.