Be sure to get aligned with projects that are delivering ROI to the business - increasing revenue or reducing expenses. When you're positioned as a vertical function spread out over the organization, you don't get counted in ROI. That route may be OK for some, and may have been OK for your program, but now it needs to change. You can still do those enterprise functions - and really DG is most effective when unshackled like that - but your project work (data catalog, data quality, metadata, etc.) needs to more than make up for that work, until it is recognized as integral.
This is the inevitable path of Data Governance in organizations that are struggling or otherwise tightening the belt and for which data governance has not proven itself as essential.
A lot of C level groups expect return within the first
year of kick off of an initiative. Are there any other ways
to measure ROI when you are first starting out with a data
strategy? Especially if you can't use a project to show
ROI by definition is about cash flow so unless there's an application being supported providing the cash flow, you'll have to create the project/ROI yourself. Perhaps there are some applications that could be measurably improved with better data quality or perhaps the application efforts towards data curation would be centralized and done more efficiently with a data governance program done by real data experts, as opposed to application by application. You'll have to think this way.
Those data governance organizations that are thriving define and execute on a charter that delivers to the organization both in support of projects and as a horizontal organizational function.
Ray Diaz, CBIP, CDP, CSM, ICP-ATF
I would even question using ROI to justify a Data Governance program.
The ROI performance measure is totally inappropriate to guide decision making on investments in the 21st century, especially for innovations, technologies, and programs such as AI, Analytics, and Data Governance.
ROI is an industrial age measure that works great for manufacturing, so you could decide if to invest 100K on new machine that makes 50% more widgets in a time duration. The new technologies and their architectures provide knowledge and automation that require the major investments up front, before starting to obtain knowledge after performing the set up, data provisioning and analysis, modeling... and much more.
You don't know what return you will obtain on these investments until work the process, obtain knowledge, and then make good decisions, which is even harder to connect.
For example, Data Governance can help increase data security and data privacy compliance, by reducing the risk of data breaches. How do you calculate the investment of this Risk Management and not experiencing Reputational Harm from breaches because of your governance program.
Jose Mari Taleno
Thanks for sharing your insights Ray! Is this the thinking of the C Level nowadays when it comes to ROI on their Data Investments?
Ray Diaz, CBIP, CDP, CSM, ICP-ATF
Jose, for some this is a controversial topic, but vigorous discussion is good so here we go.
- Knowledge to increase Data Security that protects from having data leaks and breaches.
- Not experiencing Reputational Harm and the tarnish of customer and public perception from data breaches.
- Not paying huge penalties for not adhering to compliance of Regulatory Statutes and results of Audits
- Increased efficiency from enhanced Impact Analysis and Project Discovery.
- Increased knowledge sharing across the enterprise instead of information and knowledge silos.
Cost savings from increased Data Quality to make better informed decisions and added trust of information shared internally, to partners, and customers.
Cost savings of reduced systems and data complexities and support effort reduction.
- Cost savings of Self-Service information consumption.
- The reduction of the current cost of dealing with Dark Data and Content (lost or unknown data).