January 15, 2014
When it comes to data investment, data management is still asking the wrong questions and positioning the wrong value. The mantra of – It's About the Business – is still a hard lesson to learn. It translates into what I see as the 7 Deadly Sins of Data Management. Here are the are – not in any particular order – and an example:
- Hubris: "Business value? Yeah, I know. Tell me something I don't know."
- Blindness: "We do align to business needs. See, we are building a customer master for a 360 degree view of the customer."
- Vanity: "How can I optimize cost and efficiency to manage and develop data solutions?"
- Gluttony: "If I build this cool solutions the business is gonna love it!"
- Alien: "We need to develop an in-memory system to virtualize data and insight that materializes through business services with our application systems…[blah, blah, blah]"
- Begger: "If only we were able to implement a business glossary, all our consistency issues are solved!"
- Educator: "If only the business understood! I need to better educate them!."
It's not to say that when we look at improving or modernizing our data architecture that we shouldn't seriously consider how it improves life for us as business technologists. Or, that we are completely disconnected from the business need. I mean, seriously, why do something that is often times a lot of pain and suffering only to get nothing out of it for us in terms of hitting departmental goals and making our lives easier?
Oh, wait, isn't that what the business is complaining about?
Over the past year it is becoming increasingly clear that we have to stop thinking as data managers and start thinking as data designers. Client conversations are showing that data management organizations that think like designers deliver higher value to strategic business objectives than those focused on risk management, performance reporting, and rationalizing data across applications. You need to get intimate with how data connects to customer experience, impacts manufacturing, optimizes supply chains, and potentially contributes to an understanding of data valuation. What matters is what data drives for the business first and then design a data system around that. We need to educate ourselves on what the business does with the data.
Think about proposals for investment as 90% determined by what the business outlines as the value and success factors and 10% of what you show needs to be acquired and implemented to meet this expectation.
So, what is your next move?
- Do create the single slide that communicates what success looks like. Do this with your business stakeholder. This becomes your beacon.
- Do review current and past data AND business process requests to look for patterns of need.
- Do design a conceptual model that illustrates a non-techical view of the experience of interacting with data.
- Do listen and review line of business plans to attain goals, objectives and strategies.
- Don't start by analyzing your data.
- Don't architect the system based on vendor sales advice and market hype.
- Don't ignore business KPIs and buisness plans.
- Don't eductate the business on data management.
This is not just an annual exercise when planning for the year occurs. Investment efforts should continually be incorporated in the way you engage and support the business. Make it part of your data management and governance efforts.
You can get more information by reading How To Make A Business Case For Data Investment.