Insights

Measuring Your Analytics Maturity

Analytics & Insights

Throughout my career, I have had countless conversations with business leaders struggling to define the “value” from analytics.  All too often these discussions spiral into an assessment of value based on the complexity of technologies involved or techniques employed. Unfortunately, this thinking confounds “maturity” and “complexity”; it implies that the value to the business is driven by the increasing difficulty of the analytic approach. A quick internet search on ‘analytics maturity model’ seems to show this misinterpretation is quite common and not in isolation.  Should we be measuring maturity using the degree of difficulty in the underlying math?

Complexity ≠ Value

Let us explore the dangers of the “complexity drives value” paradigm to an organization. For starters, some of the most impactful analytics ‘work’ – which I define as an activity with an impact to the business – involves nothing more than simple counts, percentages, and ratios. These critical business enablers fall pretty far down the complexity scale.  The value to business should not be based on how complicated the technique is in driving insights.

Recently, I have sat in discussions with business leaders insisting on things like, “we must have AI to be competitive” or “we need MORE data”.  Unfortunately, these conversations overemphasize a specific technology and tend to be limited to an isolated portion of the analytics value chain.  All the while ignoring any consideration of people and processes. Most organizations in this state are susceptible to marketplace hype and the ‘me too’ strategy for innovation.  Too often, this leads to thinking that bolting on a new complicated technology will add value to the business, while in reality, it will likely do the opposite.

This perspective is not limited to business leadership. I have witnessed it throughout organizations. For example, an IT department decided they needed to build a new enterprise data warehouse. During the vetting process, a few of the demo presentations showcased  ‘new and advanced’ analytics features – none of which the IT department really understood, however, they sounded good. Buzzwords like ‘predictive modeling’ and ‘machine learning’ were hot topics in these sessions. The decision process failed to address how these great new features would be leveraged – specifically intended for analytic savvy teams – or whether those features even warrant a change in the existing landscape.

Often, the lack of collaboration results in chaos and dysfunction as one department pushes for wide adoption of technology to other departments that weren’t consulted on their needs.  In this situation, the focus on acquiring more features in technology (aka complexity) did not align with how the business would use the platform to drive value.

Maturity = Organizational Execution

By pivoting to measuring outcomes to the business over the complexity of the analytic techniques, we overcome the analytics maturity confusion.  The nature of specialized analytic processes requires a deeper conversation beyond functional checkboxes.  Collaboration and understanding of the marriage between business processes, people, and technology is essential.  This enables organizations to focus on coordinating the appropriate data, people, processes, and technology to drive value to the business.

It is not easy for every company to pivot.  I have participated in several organizational initiatives to the promise land – that is, becoming competitive by using analytics as a primary driver of performance and value.  As I reflect on those experiences, these are the common themes that drove success:

  • Data and Analytics Strategy: Constructed a data and analytics strategy to enable the effective coordination of people, process, and technology to maximize business value and alignment with strategic objectives
  • Collaboration – Encouraged cross-functional collaboration across the organization, establishing both formal and informal relationships
  • Different Types of Decision Making – Differentiated analytics that are relevant to strategic business decisions vs those needed for making everyday business decisions
  • On-going Confidence in Data – Ensured confidence, trust, and access to the data on an ongoing basis, constantly re-assessing and expanding the scope of data under management

There is a strong correlation between the integration of analytics and the success of the business. Companies that empower their people using the right processes, organization, and technology to drive outcomes stay ahead of their peers. Reach out if you’d like to talk about how you can realize more value from analytics in your organization.

 

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