In a Gartner survey, 87.5% of respondents had low data and analytics maturity, falling into “basic” or “opportunistic” categories. How does one give such category?
Gartner’s Maturity Model for Data and Analytics have 5 levels as described below, where almost 90% of organizations were on the bottom Level 1 & 2.
Level 1 (Basic) maturity organization are those using spreadsheet-based analyses and individual effort extracting data from core system.
Level 2 (Opportunistic) maturity organization have individual business line stand-alone data analytics project with no enterprise-wide structure and coordination. As noted, the projects are business line focused with no re-use value for other areas of the organization.
Organizations in the early stages of data and analytics maturity often do not have the ability to exploit advanced analytics. They struggle to deal with poor data quality, inconsistent processes and poor coordination across the enterprise. Due to the limited scope within the confines of a business unit, budget are limited and without corporate wide support- data sources are also limited due to limited support from other business units.
To move to Level 3 (Systemic), Top Management team whom oversees beyond business units have to:-
- craft an enterprise-wide vision and strategy to achieve it
- impose a corporate structure, process and compliance for data & analytics to maintain consistencies
- Build and prioritize key project pipelines and build structures and cultures that support business units limited projects. Many large organization have grand projects but failed to build capabilities to support smaller and shorter projects needed for each business units.
Once the entire organization is working in tandem for analytics projects, it’s time to optimize the process, which will lead to Level 4 (Differentiation) where the organization summarizes all the lessons learn and improve the process and build best practices. Structures are build for continuous feedback loop so the organization continuously review and improve the structure, process and best practices. Metrics are made to track the activities outcomes.
Finally, Level 5 (Transformational) is achieved when the organization decides that Data and Analytics becomes a central capability of the organization and major investment are made to improve it (since it has already realized that Analytics is confidently able to push revenue and profit). Another telling for an organization that’s on Level 5 is that CDO (Chief Data Officer) sits on the board where the biggest organization decision are made. Lower level organization may assign Data related responsibilities to an existing board member BUT a full fledge Data only person comes to the board- that is when you know Data is key for that organization.
How organization moves beyond Level 1 and then beyond it?
Business unit head must formalize the data and analytics effort for his/her business unit. They can take que from the corporate pillar, if there are however, if there’s no top-down pressure to do so- it probably means there aren’t any formalization corporate wide.
In order for business unit to impose structure and consistencies, leaders must:-
- Manage the analytics team as a team effort within the business unit. There shouldn’t be any argument over whose data is correct (within the confines of the business unit).
- Business unit to determine the kind of analytics that is important and continuously tracked. Business unit leaders and one-down members are aware of what kind of analytics are being provided so there are no duplication of effort and data are shared among sub-teams with clear data ownership (called data producers) and data quality standards applied to all data producers.
Level 2 maturity are analytics at best on a business line level, with limited budget (compared to corporate level) and data sources are limited within the confined of the business line.
Level 3 would required stakeholders from multiple business lines and a corporate level leader that impose structure and process to all business units and pushes for cross business unit efforts to enrich the data and share knowledge. A corporate level team to be created to support each business units.
Level 3 organization is in the process of building the capability of enterprise wide analytic capabilities. It will level up once this process is completed, when all business units are on-board with analytics and data are shared seamlessly and data quality validation are in place.
Level 4 is when the organization as a whole continuously learn and establish a continuously improving best practices. To level from here is to establish causality between revenue/profit with analytics.
Finally, the top Level 5 maturity is achieved when a CDO is a member of the board, the highest level of decision making quorum. There’s no more levelling up anymore. The goal of analytics have been achieved.
What are the tools required to level up?
While you can see clear differentiation between levels when you look from the perspective of business processes, the tools are there for support only, so there’s no tools specific to certain level.
Tools that will be necessary to achieve corporate wide maturity may look unnecessary when you’re in Level 1 or 2. That is because higher maturity requires:-
- multi-user access controls for security and audit
- historical versioning controls of any codes, configuration and metadata
- automated testing of data quality and standardized metric reporting for tracking.
- orchestration of task related to analytics
- central repository that’s scalable and accessible for the right users
- template for standardized reporting
These enterprise wide analytics tools need to ensure collaboration, establish control mechanism, track historical versions, scalable to manage growing data size and automate task to ensure consistencies. That said, can your Excel spreadsheet does all this?
