Is data governance the secret ingredient to data quality?


Artificial Intelligence (AI), Blockchain, the Internet of Things (IoT), Security and Automation are just some of the tech trends that have been raised for 2019. And what’s the one thing they all have in common? Data of course!

As businesses continue to invest in technologies poised to generate a competitive advantage and future growth (with aspects like AI and Machine Learning set to lead the pack in my view), naturally, a more structured focus is being placed on data and the management thereof.

While I certainly advocate this, along with the fact that a business needs to ensure the quality of the data outputs being driven into such technologies for viable results, sustainable growth is also dependent on an identified trend that cannot be overlook among all the hype: data governance. In fact, data governance processes should be steering the direction of any tech trend investment, otherwise a business is simply wasting money and effort.

You see, data quality is not just about the source of the data and its scale of reliability. It’s much deeper than that. An industry article I came across recently sums this up beautifully. So, here’s my take on it.

A well thought out data strategy that places governance alongside quality can make AI and Machine Learning outputs very powerful competitive tools. For a start, governance almost always ensures data quality, as its direct method supports the identification of data errors and can filter this data out.

Further to this, strict governance processes requires the data source information to be readily available and transparent, meaning that the business has an understanding upfront of where and how the data was sourced. All of which results in the business not needing to spend valuable resources trying to ascertain the source of the data required for AI and Machine Learning to take place. These two streams, together, result in governance promoting data authenticity – precisely the aim of data quality.

However, as with any good come the ‘bad’. Although in this case, the bad is just a few aspects that need to be considered as part of the governance process or model, to ensure governance does not become a hindrance for moving forward. Of course, governance can become a challenge if it starts to draw attention away from the business’s main objective. And, getting caught up in excessive governance can cause a business to forget the purpose of aligning governance and data quality, with too much time being spent on analysing data marked with errors, to understand why. Over and above this, an organisation must also be cognisant of governance imposing too many limitations on data handling or restrictions on how the data can be accessed to the benefit of organisational processes. Lastly, businesses must ensure their governance strategies and models are not too rigorous and impact the ability for AI and Machine Learning to utilise the data.

While these slight challenges do exist, they can be overcome with proper identification and planning. The reality is that any current or future technology trend that relies on data to provide benefit, will need to be governed accordingly. Getting around governance is not an option. Embracing it now, however, may just mean your business is one step ahead of the rest.

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