DaaS: A Data Company Maturity Test
As a follow up in my series on Thinking Like a Data Company, a litmus test to determine where you are in your data management thinking.
As a follow up in my series on Thinking Like a Data Company, a litmus test to determine where you are in your data management thinking.
In 2016, Gartner published an “Enterprise Information Management” model to help executives assess where their companies’ cross-functional, strategic “information management”.
I’ve adapted that model here slightly to focus on data and data management. Importantly, much of this does not focus on data commercialization. This is more of a Step 1 rubric — a diagnostic to understand where you are in general data management maturity- before the Step 2 capabilities of productizing and commercializing your data assets.
For the below questions, rank yourself on a scale of 1 (strongly disagree) to 5 (strongly agree). This is golf scoring- the lower your score, the more mature your data management model.
Data Vision
- Data is managed in silos
- Data fiefdoms and data ownership arguments exist
- People spend time arguing about whose data is correct
- A general feeling that data management (or lack thereof) is a serious problem
- Data is not perceived as a competitive differentiator
Strategic Management
- We lack executive support, understanding, or leadership around our data
- There is uncertainty on what data exists and where
- Data may be used in planning, but we rarely plan for data or data capabilities
- No clear budget or objectives for data or data capabilities
- Data is not treated or managed as an investment/asset
Business Utility
- Metrics, measures, and objectives feel highly subjective across the business
- Non-financial metrics are mostly incompatible or siloed
- Qualitative metrics don’t link to business KPIs
- Data and data management are separate from business operations
- Data quality, value and cost are unknown or inconsistently measured
Governance and People
- Data is easily copied / reproduced, leading to “data sprawl” & inconsistency
- A lack of data definitions results in low data trust and usage
- Data “owners” are assumed or unknown
- Data “users” have to queue up behind IT backlogs or circumvent IT
- Data is a sub-activity, rather than a core responsibility (e.g. a CDO or Data Product team)
Systems and Processes
- Tendency toward local efficiencies rather than total data lifecycle management
- Commonly hear that “data isn’t available” or “is hard to get to”
- Lack of understanding of the data lifecycle / flows across the organization
- Data is commonly deleted / inhibited by lack of infrastructure of capacity
- Data and BI tooling feel redundant and/or ineffective
For more of my ongoing thinking on commercializing data products for “non-data companies”, read on below for some expanded thoughts or follow along the series for deep-dives into each topic.
Series Contents