Data is one of the most valuable commercial assets, but for too many, its full potential has yet to be realised. Companies find far too late that they do not have the right data foundations to meet their needs

At this point, businesses tend to acknowledge they have neglected their information management and that the quality and structure of their data, their core asset, is lacking.

How can companies ensure that their data is being put to best use, and what can they do to assuage consumer fears over the collection of personal information?

The UK Government’s recent Data Reform Bill proposed a number of significant changes to the rules around how companies collect consumers’ personal information. Part of this includes changes to cookie consent, suggesting a one-stop data-privacy setting that will allow consumers to opt out of tracking.

What the reforms miss, however, is that cookies and tracking are an outdated approach to begin with. Cookies track viewer behaviour moving from one site to the next.

Without understanding the full context – i.e. what the viewer thought or felt about what they saw online, what led them to view a particular piece of content etc. – uses for the data generated will be relatively limited.

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Although the Government has arguably made a good first step in developing its proposals for the Data Reform Bill, it’s clear that more work needs to be done to further encourage the industry in the right direction – i.e. away from spammy, bothersome tracking.

This is a golden opportunity for us not to simply fix what’s already in use, but to start afresh and encourage a best practice approach that works for everyone.

Below I outline the best practices for successful data management and the business basics for the future of data-driven companies.

Moving on from cookie tracking

The first change is in the way we are thinking. A “tracking” approach that is not contextually aware is always going to lack a) connected context, and b) situational awareness. We must instead go in entirely the other direction: we must base our route to relevance on a subject-led, contextual principle.

This is already underway with forward-thinking brands. Rather than hoping a user is still wanting to buy a product they viewed online recently and showing them an advert for that product on an unrelated experience, businesses are increasingly surfacing the product or promotion only where it is contextually relevant to what the viewer is enjoying.

Having relinquished control of discovery and customer insights to Google search and cookie-powered advertising for so many years, businesses are realising that they must take their information management more seriously in order to give a more relevant, subject-led experience to their customers and consumers.

Good information management

If an organisation has not prioritised the creation of structured information and information management practices that provide context and meaning for their data they will create data silos. This siloing – or ‘data disconnections’ – makes it extremely difficult to leverage data and information for new business use cases.

In this scenario, businesses will not only have issues building their AI training corpora (the datasets required to train machine learning models with the desired behaviour), but they will also hit issues with explainability, feedback loops, performance optimisation, and the efficient ongoing support of a live production system.

The physical separation of data is not a problem – in fact, distributed data solutions have numerous potential benefits. However, regardless of where the 0s and 1s are stored, they must live together conceptually: this is the importance of data governance and coordinated management.

Only well-structured and well-described data can carry context and meaning across an enterprise. The future of data requires ‘data portability’, breaking down existing information silos and preventing the creation of new ones.

Knowledge Graphs

Organisations with disconnected and context-less information silos will find it expensive, painful and slow to deliver new products and features and to make things worse, they will tend to create expensive new legacy support overheads with every new feature they ship.

Many organisations are now considering the role of a Knowledge Graph – a backbone of information adhering to the structure described in a “domain model” – as a foundation for their information management strategy.

A Knowledge Graph allows the gluing together of business information with context and meaning, enabling both machines and humans to understand and efficiently use the data in any number of contexts.

This approach will allow businesses to fully move away from spammy tracking and frustrating, interruptive ad experiences, towards contextually relevant and high-utility product experiences that build loyalty and advocacy.

To maximise chances of success on this approach, organisations must use and access data in a way that is contextual and meaningful. This facilitates the creation of an information management strategy that covers how data is created, organised, described, and harnessed.

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