A Never-Ending Race of Change: What Organizations Should Focus on to Gain Competitive Advantage

January 10, 2021

Organizations, today, are in a state of constant evolution. Changing customer demands and interactions, and an increasingly competitive business environment have put most organizations on an accelerated path towards digitalization. The digital transformation wave, which was already well underway before 2020, has now picked up pace as organizations adapt to the recent shift to primarily virtual operations and make better use of data and analytics.

The investment in data management and analytics solutions has seen a considerable increase over the last few years, with organizations viewing them as a pathway to create value, extract new insights, and identify previously hidden opportunities for growth. For some organizations, however, digital transformation has yet to deliver the competitive advantage they expected, and this has led to tensions within the organizational cultures with respect to its promise and value.

To a vast degree, this challenge can be explained by the pace and range of changes in both the external and internal environment. Before we evaluate potential resolutions, let us consider some of the key changes faced by organizations.

Key Changes Being Seen by Organizations

Changing customer interactions: Customers are sprinting towards digital channels of engagement from the earlier physical or in-person interactions, and this has resulted in a change in both the type and volume of data for most organizations. Every interaction a customer has with an organization now leaves a footprint, be it a purchase, an ad click, or just a visit to the website. The quantum and detail of data organizations have at their disposal in the present day is unmatched to any period in the past.

Emerging technology: As new technologies surface and new ways of engagement become the norm, the data these generate also change, forcing organizations to adapt. Technologies like virtual reality and AI-driven solutions like chatbots, and automated contact centres are only the tip of the iceberg. Moreover, advanced analytics and machine learning capabilities have also raised the bar on how organizations can process, transform, and analyze their data.

Organizational expectations: Until a few years ago, most organizations did not know the true value or potential of data. Much has changed since then, and leaders now rely on data-driven insights to make business decisions and predictions about the future. Customer data, for instance, not only conveys information about sales and profitability, but also about changing customer expectations, preferences and can, consequently, offer valuable insights for future product mix decisions. Having tasted initial success with their data and analytics tools, business leaders and management are now becoming increasingly demanding, and even impatient with their expectations of value creation.

Use case articulation: While organizations have become better at articulating use cases, the rapidly changing business dynamics and pressure for quick returns sometimes lead to a misdirected focus on narrow use cases. A short-sighted view of digital transformation through narrow Use Case articulation, while suitable for quickly delivering a Minimum Viable Proposition, usually leaves the organization shortchanged with its expectations of return as they are unable to extract the full value of digital transformation at an organizational scale.

What Do These Changes Have to Do with Value?

Keeping in mind the organizational dogma of maximizing the value extracted from their assets, many companies are now confronted with two contradictory challenges:

• The need to leverage their data to create new value, quickly
• The perception that value has not been realized through historical data and analytics projects

There are two possible explanations to this. One, that historically organizations were more patient and that the present cultural context has made them more anxious to get immediate value from their initiatives. This can be resolved, to a degree, by strengthening communication and collaboration between the leadership and data management teams.

The other explanation is that with the rapid changes in data and technology, the models that were used to measure returns have lost robustness. The historical models were designed with the assumption that value realization would not be instantaneous, but instead, would happen over time during the initiative’s lifespan. The valuation curve (shown below) has changed with changing inputs, and this causes a mismatch between expectations and reality. Organizations are having a hard time understanding the value of their new initiatives because historical models of valuation do not account for changing data volumes and types.

How Do We Solve for This?

We have determined that historical models are ill-equipped to measure the value of today’s solutions, especially with data being in a constant state of flux. This leaves organizations in the precarious position of not knowing if their solutions are working well, and occasionally, altogether questioning the prudence of digital transformation. Moreover, the same models are often used to prioritize initiatives or business priorities, and an inaccurate assessment of value can derail the organization from its growth path.

Broadly, there are two ways in which this conundrum can be resolved. Either the valuation models and/or the solutions themselves need to evolve with changing data. But how does one go about doing that?

Firstly, organizations need to change their frame of reference when it comes to the lifecycle of initiatives and impact measurement. Instead of looking to historical references, set fresh benchmarks based on current experiences.

Next, new valuation models will need to be developed to take into consideration changing data types. Measurement remains key for all initiatives, and the results from new initiatives will undoubtedly provide valuable insights for the development of more appropriate models.

Finally, a broader perspective needs to be taken when it comes to use cases. With data changing rapidly, Minimum Value Propositions based on narrow use cases and data sets tend to be difficult to scale and become obsolete faster. While organizations can still take a bottom up approach (starting with something small and building up), they need to apply a holistic lens to fundamental elements to ensure that the value of digital transformation can be perceived at an enterprise level. Where speed is of essence, instead of compromising on initiatives, organizations can consider adopting DevOps and AI Ops approaches to automate processes and reduce cycle time.

Conclusion

A holistic approach to data management will increase the resilience and value of data over the longer run. With increasing data volumes, elements like data governance and analytics governance become even more important, and issues in data quality can drastically affect the results derived from data processing and analytics.

Investment in newer architecture and modern data estates will provide continuous return, but it is imperative that organizations adjust their valuation models with changing data types, volumes, and other evolving factors. A flawed valuation model can force organizations to invest in short-sighted, sub-optimal solutions that may not stand the test of time, spend too much time on initiatives based on an inaccurate expectation of value, or discard their initiatives too early before their value is fully realized.

Most importantly, organizations cannot continue to look in the rear-view mirror and expect the road ahead to be the same. The rapidly changing data and analytics landscape holds the promise of unprecedented value but also necessitates continuous education and maturity building on part of organizations. By constantly looking over their shoulder instead of working on building and strengthening newer capabilities, organizations are likely to miss opportunities and could find themselves being left behind.