By John Walubengo
In previous articles, we talked about the importance of digitizing government services and said we would continue the discussion to the next logical topic – Data Governance.
Many data experts agree that digitizing without a ‘Data governance framework’ is not very effective. It is like trying to grow maize in very poor soil.
The long-term output or yield of the automation exercise is likely to be stunted or quite limited.
The greatest value of automation or digitizing manual processes goes beyond just the cost savings gained from streamlined business processes; it also creates opportunities for new data-driven revenue streams.
Data governance can therefore be considered a set of rules, procedures, and practices that leverage data as an asset in the same way that corporations would exploit labour, capital, or land to create wealth.
In other words, how can we deliberately collect, store, and structure data-related processes in such a way that they create value for the organisation?
That is the main objective and perhaps the larger part of the definition of data governance: using data as a raw material for wealth creation.
Using data to create wealth has been exemplified by the so-called big-tech companies over the last two decades.
Private Sector Data Governance
Google, for example, allows us to search for items online by typing in some search phrase, and it returns to us a list of possible answers, without charging us a cent.
However, Google is also able to leverage and monetize our search phrases by storing, analysing and grouping the search phrases into meaningful categories of users.
For example, out of the billions of searches per day that hit Google servers, those looking for a holiday, those looking for mobile phones, and those searching for a college education, amongst others, Google knows them all.
Using these insights, Google is then able to better target or match various advertisements from different Hotels to very targeted categories of Google search users.
A target advertisement has a greater chance of closing the deal, compared to if the Hotel marketed itself in traditional or print media.
Similarly, they can better match customers to specifically targeted users with a greater probability of closing any other sale, be it educational, or mobile phones, among others.
In essence, Google provides a service for free to one segment of the market and monetizes the data and insights learned for another segment of the market.
Data-driven companies like Google must have a very strong data governance framework that informs them what data they should collect, how they should store it, process it, and mine it for insights.
The data governance model would include which technical tools they would use, which range of data employees they need, and which business model to adopt to drive and execute their data strategy.
Public Sector Data Governance
In the public sector, similar questions have started to emerge, with governments asking how they can leverage the ever-increasing amounts of data under their custody.
How can this data be mined to create wealth and better public policy for citizens?
Largely driven by the idea of Open-Data Initiatives’, many governments are realizing that the data collected and stored in silos across different ministries could perhaps generate better insights and public policy – only if it were to be shared within and beyond government circles.
For example, the social challenge of drug and alcohol abuse is often handled in isolation by different government agencies.
The police would focus on regular raids against drug or alcohol dens and arresting suspects, hospitals would struggle with the rehabilitation burdens, and parents would remain wondering why their youth of ‘nowadays’ behave the way they do.
There are no formal structures where all three affected stakeholders (police, hospitals, and parents) can meet and exchange data insights to get a holistic picture of the challenge. This leads to several uncoordinated responses to the social challenge, where no single stakeholder benefits from the insights of others.
An effective Data Governance framework would provide incentives that would align processes and procedures for each stakeholder to collect and curate data in a standardized way, to enable data-sharing opportunities that take place for the benefit of all stakeholders and effective public policy interventions,
The data-driven public sector is the next level, a level that goes beyond simply automating government processes and begins to explore patterns and insights from data, to provide effective and smart public policy interventions.
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John Walubengo is an ICT Lecturer and Consultant. @jwalu.
