Governance from Within

Dr. Ian Tho, Executive Director, Head of Advanced Analytics & Data Science, RSM Australia

Dr. Ian Tho, Executive Director, Head of Advanced Analytics & Data Science, RSM Australia

There is a need to mirror corporate governance with the use of information, to ensure integrity and trust; but more importantly, to deliver a distinct advantage personal information is used.

We know the evidence from data is simply more credible than any other source of insight. In addition to revealing the most intimate behavior and preferences of every individual who interacts with the business, data cannot be manufactured but exists because of some activity. It is no wonder, the most obviously potent tool is found in advanced analytics & data science. But, integral to the powerful use of data, is also its Achilles heel, where the biggest weakness or vulnerability lies as a detailed understanding of personal preferences and profiles come at a price.

The challenge with the ownership of personal data, however, is its use. Whilst most use it to deliver better products and services, there is always the possibility of malicious or criminal use; demonstrated by many recent public cases including Facebook, the Australian Government census and Singapore’s healthcare records. And, many would agree that it would be significantly more effective rather than be governed, enterprises should govern from within.

Governance takes many forms, driven by many different motivations and with many different results.

With governance of data, it needs to apply to what already exists, but will also have to accommodate with what will likely exist

With the governance of data, it needs to apply to what already exists, but will also have to accommodate with what will likely exist. For example, whilst there is scant data on where a household typically shops; it can be inferred from apparently irrelevant information like a car driven and amount of petrol purchased. A type of vehicle provides the mileage, and when combined with the volume of fuel suggests typical driving distances. And in turn, suggest most likely shopping destination within a radius from the home. In another example, DNA profiles of individuals suggest a propensity for disease, but when combined with the content of food consumed (from information on food content) and activity (from digital exercise trackers); can be used to mitigate or reduce the same propensities. None of this data was available to begin, but delivered when multiple sources of almost irrelevant sources are combined. Both these support the need to governance from within, as generalized frameworks will never be as effective.

In addition, governance needs to effectively account for the constantly changing states of data. The constantly changing dynamics of data as described in terms of often referred to as the Vs of big data i.e. its volume, variety, velocity, veracity, and value; however, means that governance of the content in the data is also ever changing.

The focus of governance needs to be on understanding the content, not the containers of data. Driven in frenzy, common approaches many executives apply include a solution-based approach enabled by artificial intelligence, the cloud, and mobile technologies. Few take sufficient effort or time to really understand data. Simple names can infer gender, ethnicity, age and often also language capabilities depending on how this information is linked and associated. In this example, keeping names may be a breach of personal information, but if the additional elements could be inferred before the names are removed significantly enhance its utility. So, governance needs to cater to the timing of what is stored, as well as how and when it is used.

Whatever form of governance is adopted and used, it is obvious no one size fits all especially in the midst of exploding availability of public, commercial and freely available or Big.