Every business, regardless of size and sector, is subjected to continual change. Business models are constantly evolving and adapting as consumers react to new technologies, laws and trends.
Take a look at Amazon as an example. It has adapted its business model in an agile fashion by offering streaming of films and popular TV shows. This is a huge business model shift for any company, but entirely in line with the platform strategy that many large organisations are adopting (read fellow Roundtabler Phil Simon’s book for an in-depth view of Amazon’s platform strategy: The Age of the Platform).
The problem comes when the rate of business model change surpasses the flexibility of the data model.
Many systems lack the ability to cope with agile changes to the business model. Telecom companies in particular have been crippled by their ability to adapt legacy data models to new business models they could never have envisioned ten years ago.
The banking sector is still going through a major upheaval, and certainly in the UK acquisitions and mergers are a common theme. Speaking to our banking members on Data Migration Pro this week it is clear that the constant round of business model changes are playing havoc with their outdated system architectures.
It is easy to see poor data quality as something to be fixed; it's an unavoidable cost of error-prone humans in the information food chain.
The reality is that you need to look far deeper and broader into your data quality results. Look beyond those dimensions of completeness, currency and accuracy. What is the real root cause?
In many, many cases I’ve found the root cause of bad data stems from a separation of data model and business model. Gaps emerge because the business is typically far more agile and forward-thinking than the IT department.
What is the answer?
Articulate these gaps and become part of the continual evolution of data model and system design. Work with these teams to feed into the design process. Help them build systems that will not only serve the business models of the future, but also enable defect prevention at the source.
Have you spotted data model and business model gaps emerging in your data quality assessments? Please share your experiences below.