There’s Gold In Them Thar Hills
Do you want to realize an incremental 1-2 ppt in revenue growth? The hoopla around Generative AI has company boards and executives asking how they can extract value from AI.
But many mid-market companies need to build fundamental data & analytic capabilities first. Those capabilities fall into 5 components:
Alignment on use cases & value potential
Master data environments
Data governance & quality
Analytic solutions & capabilities
A data & analytics organization
Each component is necessary but not sufficient. Only when you build these assets and capabilities collectively, in an iterative manner, can you capture meaningful value from your data, and move beyond the hype of Gen AI to tangible revenue and customer growth.
Alignment on use cases & value potential
If you were designing and building a house, a set of prioritized use cases, or problems to be solved, would be the blueprints. Everything starts with them. How do we identify and secure our most valuable customers? What are the drivers of customer engagement that most influence likelihood to upgrade or transact again, or drivers of disengagement that predict likelihood to churn?
These examples often rise to the top, but the priorities in your company depend on your unique business drivers.
Alignment on use cases drives the iterative development of the next 4 components.
I have seen this virtuous cycle firsthand after incubating and growing these assets and capabilities at multiple companies. In my experience, companies realize an incremental 1-2 ppt in comparable year-over-year revenue growth from the first iteration of data & analytic capabilities, well before jumping into the Gen AI hype.
Master data environments
Turning on AI in your company without robust master data environments would be like building the top floor of your luxury house on a foundation of toothpicks.
Almost every company has an abundance of data across multiple source systems. But if you’re like many, you have yet to invest in master data environments that join upstream, source system data in an automated, secure, and timely process and enable efficient processing of that data for downstream applications (e.g., dashboards, metrics, analyses, AI algorithms).
Master data environments serve as the foundation for accelerating revenue growth, enabling you to run your business with full visibility and alignment, and for scaling as you grow.
Data governance & quality
Building master data environments without data governance & quality would be like building the foundation to your home with porous concrete.
You may be able to live in the house for a time, until rainy weather (in data terms, changes to upstream systems) make it unusable.
If you are like many companies, you have ad hoc data governance practices within your source systems, such as a proliferation of billing codes and obsolescent SKUs, that complicate data aggregation and jeopardize downstream data quality and integrity.
You need a data governance process, structure and ownership, along with tools, logic and process to prevent data quality issues and, when they inevitably occur, quickly identify, triage and fix them.
Data governance & quality will enable you to focus discussions on implications and actions, rather than on data reconciliation, and will give you confidence that those actions are informed by the best data & insights available.
In short, data governance & quality are necessary to manage and scale the master data environments.
Analytic solutions & capabilities
Once you have the foundation in place with master data assets, and you’ve poured that foundation with non-porous concrete in the form of data governance & quality, you can leverage analytic solutions & capabilities - the kitchen, bedrooms, and bathrooms in my (rather weak) house metaphor.
Analytic solutions & capabilities transform the potential value from high-quality, well-governed data into tangible impact - accelerated revenue and customer growth.
If your company is like many others, you have access to an incredible number of reports. And you and the rest of the leadership team know your business and market incredibly well.
To complement these strengths, you need easily digestible and timely insights (“What are these reports telling me?”), and rigorous, objective analyses that help inform critical decisions. Specifically, your near-term analytic needs likely extend beyond reports and dashboards to test design and measurement, segmentation, and value & risk driver analyses, to name a few analytic examples.
These capabilities inform pricing, packaging/bundling, upsell, retention, and other critical customer decisions. They are necessary to extract value from your investment in the master data environments and data governance & quality.
A data & analytics organization
Now that you have a solid, non-porous foundation (through Master Data Environments and Data Governance & Quality) and kitchen/bedrooms/bathrooms (Analytic Solutions & Capabilities), you need to fill your house with people - your Data & Analytics Organization.
If your company is like many others, you have employees working on data, reporting and analytics scattered throughout your organization today.
But to deliver, maintain, and scale your master data environments, data governance & quality, and analytic solutions & capabilities, you need to design and establish a data & analytics organization with the right skills, structure and resources.
This need will become even more apparent as you begin realizing the revenue growth and other opportunities from your initial investments, ask more questions, identify more opportunities, and capture more value in a virtuous cycle.
I have seen firsthand this virtuous cycle deliver 1-2 ppt acceleration in revenue growth, after incubating and growing these capabilities at multiple companies. The best organizational model depends on your data & analytic maturity, use cases, roadmap, company culture, and growth plans, and evolves over time as you learn and grow.
Contact us to learn more about how to transform your data into revenue and customer growth.