Data Demystified: Asset Management
In my last article I laid out four foundational principles of my consulting business. The first and foremost is that the data function is a business function not under the control of your technology organization.
The reason I've come to this conclusion is decades of observing behaviors that expose a mindset contrary to the value proposition of data. Behaviors like:
- Focus on cost containment and risk mitigation.
- Dismissive attitudes towards users requesting data capabilities to do their jobs.
- Blaming "the business" for not knowing how to speak data or articulate their "requirements".
- Insistence on imposing outdated, project-managed, SDLC methodologies to satisfy the simplest of requests.
- Outright refusal to quantify the financial benefits of data team activities.
- Excel extermination initiatives.
On the other hand, a business perspective sees that data management is asset management and acts accordingly.
We've all heard the mantras around data being an asset and the value of data products, but I've yet to see the exercise of discipline that accompanies managing assets in support of business execution when it comes to data. That's not to say it doesn't happen. I've just never seen it personally.
One of the greatest ironies I've observed is that the obsessive focus on containing costs and mitigating risk in the IT-centric approach to implementing a data function actually produces an astounding amount of waste and lost opportunities to leverage the data.
- Millions of dollars poured into technology platforms promising competitive benefits that never materialize.
- Bloated processes that consume endless hours of high-priced resource time in meetings about nothing more than administrivia and redundant status updates to an equally bloated chain-of-command.
- Year-end death marches to meet objectives promised by middle managers to executive stakeholders in Q1 but impeded by said process bloat producing outcomes of questionable quality and unconfirmed value.
Worst of all, incalculable losses incurred while imposing unnecessary delays between business value conception and delivery of a "product" the usually falls short. Opportunities unseen because data wasn't made available. Revenue lost because all the focus was on "big wins" with executive stakeholders rather than effective management of the data supply chain to bring data to point of business in time to act.
The focus needs to move away from big events like project delivery and towards continuous, effective management of turning raw data assets into usable products appropriate for the moment of opportunity.
If your business is packaged food products, you don't wait until the end of the year to procure, store, prepare and process your ingredients into a demand forecast someone made ten months ago. Rather, you'd continuously respond to shifting markets, commodity prices and other economic indicators to ensure that you don't buy more ingredients or make more product than you can sell profitably and within the freshness window.
And you certainly wouldn't wait until the end of the year to produce your entire product inventory.
Why would you not follow the same rigor when managing your data assets?