Data Governance: imperative for Digital Business

Data Governance and Digital Business

Data Governance and Digital Business

We have been talking about digital business and digital transformation for some time now, considering them decisive for the future of businesses. Key factors both in the success of digital native companies and in the survival of incumbents that are experiencing the unexpected competition capable of operating with innovative business models.

They are entirely based on information technologies, able to erode significant shares of that market until now exclusively prerogative.

A lot has been talked and written about how a digital business transition involves changes not only in technology, but in processes, skills and business culture. It is undeniable however,  that there is no digital business without data.

What is the role of data in the design and execution of a digital strategy?

Let's look at some examples. Digital interaction with customers through an app entails the exchange of data. A complex process of managing the digital customer journey is conceived, designed, created, executed, governed, thanks to data collected, enriched, aggregated, acquired, transformed, generated in several phases of its life cycle, from conception to operation.

 

Let's look at these phases more in detail. 

Execution

We need data to manage customer relations , which in turn generates data: in a digital intensive model this interaction is automated, partly because part of its value lies on execution speed, not interrupted by human tasks; human intervention is limited to supervision, decision-making role, for example, in the case of non-automatable choices (perhaps for now ...) among multiple options.

This delegation of operational management of the process  alone implies not only trust in its ability to carry it out successfully, but in the reliability of data it deals with: it must be complete, accurate and timely. In other words, we are talking about satisfying data quality criteria that must be guaranteed beforehand, proactively.

Design

Data is also needed to support design and implementation phases of technological solutions supporting the process: both for solution testing and machine learning model training.

You must identify data that configures representative use cases, guaranteeing personal data protection constraints such as: pseudonymizing test data or training sets; but this requires a set of metadata (ownership, semantics, masking rules) that enables qualification in an "identity card" data used for this purpose.

Research and development

Even preparatory phases for solution implementation (such as the search for digitalization opportunities, sustainability/feasibility of a business idea) require appropriately qualified data, equipped with metadata capable of determining in detail:

  • The location in the IT systems or
  • The availability in accessible company sources t
  • Reliability
  • Transformation paths to which they are subjected to (discovery rules, quality, lineage)

 

Is a data strategy useful? 

Above all we need a data strategy aligned with a digital strategy. Without a strategic vision to pilot and program digital transformation, the success or the future of a company hang by a thread, which is certainly not a guarantee of results. In the same way it is necessary to establish a strategy to govern key business asset: information asset. You need to verify that you have everything needed for data to be recognizable, assessable, available in all phases of expression of digital business, from strategy to execution: metadata, processes, organizational models, technologies, skills. You must identify gaps and have an adjustment plan in line with requirements and digital strategy timing . It means taking data governance to a degree of enabling a strategic vision for the future of the company.

Many CDOs I have met are aware of this need, but what about the many CEOs C-levels? Almost all Italian banks and insurance companies, for example, have defined and activated their own data governance standards and have set up specific organizational units to oversee these standards, but they have done so above all in response to regulatory pressure (Basel, Solvency, BCBS 239, etc ...). On Compliance to regulations was an opportunity for growth only for a few of them, a starting point of a path aimed at a better qualification of information assets for a more strategic use.

For others, data governance system remained a task, a formal compliance obligation at the lowest possible cost. In other sectors, less characterized by regulatory constraints, companies are undertaking programs to qualify and enhance information assets in a more aware manner of the key role of data in corporate strategies. For them it was immediately clear data is the key component of digital business and that only a data governance capable of guaranteeing constant monitoring enables full potential advantage.