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Ten Signs Your Data Centric Program Will Fail, Even If You Seem To Be Making Progress

Every CxO knows that their enterprise must be data-driven to be successful in the digital age. There was a time, I remember CxOs used to ask, “why bother about data, when the business is doing just fine on it’s own?”

Well, today is a different world.

Every forward looking leader knows the cost of not investing adequately into data capabilities for their business. In-spite of this realisation, data-centric programs are tough nuts to crack.

Not every business is comfortable with successfully executing their data-centric programs. In this context, by data-centric program I mean all those investment programs that are initiated with an aspiration to gain massive business benefit through better utilisation of enterprise data, in the chosen business model. These could include, but not limited to cloud adoption, data analytics, data governance, master data management and the like.

The only measure for success is a positive impact to the bottom line. And nothing else!

Here are ten signs your data centric program will fail, importantly, even if you seem to be making “good” progress. The only measure for success is a positive impact to the bottom line. And nothing else!

  1. The executive team doesn’t have a clear vision for its entire data portfolio
    • With the best of intentions many executive teams are just not clear about their vision. With the endless possibilities that numerous technologies  and vendors offer today, it takes hyper-focus to zero-in on exactly what’s required to manoeuvre the business, over a fifteen to twenty year time horizon. Even if some of them are clear, their ability to communicate that vision consistently and coherently to mindsets seeded in a legacy culture is a challenge
  2. No one has determined the economic value that the initial use cases can deliver in the first year
    • In most cases business cases are developed “procedurally and abstractly” as opposed to clearly “defining done”. When “done” is defined, it forces accountability and responsibility. And not every one is comfortable in dealing with ambiguity.
    • Also, forcing definite outcomes demonstrating business benefit in the first year helps build sponsor confidence, with out compromising the morale of the delivering teams 
  3. There’s no comprehensive data strategy beyond a few use cases meeting immediate needs and perceptions
    • The language and the everyday tools used in an enterprise reveals the existence of a data strategy or not. It is crucial that a real data strategy exists and that it is measurable. More importantly, the key participants in the organisation should be clear about what the strategy is and what it is not. Often, strategy is assumed to be mere desk-ware. Of course, the data landscape of such organisations will reflect this reality, anyway. 
  4. Data centric roles—present and future—are poorly defined
    • Most mid-level and senior managers, tend to struggle with this shifting demands of new competencies in the workplace. As such, may poor decisions are made in defining roles accurately. Resulting in mis-management of talent.
  5. The enterprise lacks “datapreneurs”
    • Datapreneurs are experts at “Getting to Yes, Now!” with all available technologies and data. When programs fail at an alarming rate in an organisation or even it they are abandoned mid-way, it is certain that the organisations do not have the needed competencies
  6. Data capabilities are isolated from the business, resulting in an ineffective data organization structure
    • When a business model fails to recognise the role of data in creating value, data is under-utilised and not leverage to create business benefit. As a consequence the organisation set up is weakened, resulting in inefficiencies and ineffectiveness
  7. Expensive data-provisioning and uneconomic data integration efforts are initiated without adequate data management and governance foundations
    • Any data program is doomed, if data-nomics is not understood in the digital age
  8. Data platforms aren’t built to purpose
    • When data platforms are not purpose built, it is difficult for enterprises to justify the quantum of capital investment required to build and maintain one, that delivers true economic value to the business
  9. Nobody knows the economic value added by data centric initiatives
    • It is indeed very rare for the leaders in a data centric initiative to actually take the time to pen the true economic value of a data centric initiative. This is probably because historically, value propositions have been typically evaluated by finance functions. Whereas, the demands of today’s technology operating models require cross-functional collaborations, and not every business is able to pull this off, successfully
  10. No one is focused on embracing a data-centric mindset that embraces the ethical, social, and regulatory implications of data initiatives
    • Whilst recognising the critical role that data plays in an enterprise is one thing, realising that data has wider and external consequence in a business model context, is quite another thing. As such, addressing the ethical, social and regulatory implication of data is no more optional in the digital age. However, not every organisation is equipped with the right competencies to address this

For more information on how to organise and enterprise data for creating economic value, get your copy of The Billion Dollar Byte.