Why gaining flexibility through data starts with people

Almost any company will tell you, and for good reason, that its people are its most important asset. A service provider without people is just an empty office, a manufacturer just a stationary piece of heavy machinery, and a healthcare system just a collection of empty beds.

This truth will not change, but in recent years the perception of the value of an organization beyond its people has fundamentally changed. Where different industries once made very different statements about where their value lies, today data is the second largest asset in most areas.

More digitalized tools, infrastructure and processes that are better connected have delivered an explosion of data that delivers deeper customer insights, more efficient operations, higher quality innovation, faster decision-making and much more. In short, data is promised to enable all the flexibility and responsiveness that companies need to drive growth.

The promise of artificial intelligence

Of course, as any CTO or CIO charged with leveraging data to gain a competitive advantage knows, raw data itself is only a promised, hypothetical advantage. The exponential growth of data does not arrive in an easily usable form precisely because it comes from everywhere. Sources such as connected devices and physical sensors, financial transactions and website user journeys, social media chatter and market trends all collectively transmit a vast, chaotic flow of information for companies to manage.

This chaos leaves technology leaders juggling two major pressures. First, there are rising storage costs. Knowing that there may be value in all this data drives the need to keep as much of it as possible, which – especially for heavier formats like video and audio – can mean storage bills can run into the millions per year.

The increasing weight of IT costs is exacerbating the second major pressure: rising expectations around finding value in the noise of business data. In particular, companies are increasingly trying to respond to the overwhelming nature of modern business data by leveraging AI, with the goal of finding workflows that (unlike human-driven processes) can scale seamlessly with data volume by adding additional computing resources to add.

However, this leads to something of a paradox: while emerging AI tools certainly have the ability to handle data at scale and make it valuable, those tools are generally only as good as the data they’re fed. Bad, irrelevant or incorrect data, stored in useless or contradictory formats or locations, will not deliver the AI-driven value that companies expect. The AI ​​answer to enterprise data challenges depends on first solving some of the challenges companies experience in organizing and managing their data.

Understanding what is important

The idea that the quality of an AI solution’s output is limited by the quality of the data you feed into it has been a common statement since the last boom in AI technology took off a few years ago. ‘Garbage in, garbage out’ has been a saying in the IT industry for a long time, and it still holds true today.

However, that leaves an open question about what “good” data actually looks like for companies looking to use AI to find the growth-enhancing benefits it promises. One way to look at this is to go back to the fundamental fact that the most valuable thing a company has is its people, so the data has to work for the people who need it.

From experience working with large organizations to transform their data strategies, we know that typically half of all recorded data is noise. This could be duplicate information, outdated information or information that never needed to be stored in the first place. About another quarter of an organization’s information will be needed for paper journeys rather than for actual application use: knowing the journey a piece of information has taken is important for an audit, but not for the end user.

And then not all remaining data that is relevant and necessary has the same status. For example, details of a customer’s unfulfilled order should be readily available, but invoices from ten years ago could likely be placed in slower-access cold rooms to save costs and energy.

We’ve probably all been in situations where getting the information we need at work means sorting through layers of useless or unnecessary files and records. If a company wants to deploy an AI solution like a chatbot to enable employees or customers to access that data more effectively, that underlying challenge doesn’t go away: whether it’s working on a traditional database or on more of an unstructured media data , the AI ​​solutions only need access to what really matters.

Step one, therefore, is to ensure that the data is relevant and well managed from a human perspective.

The useful data a company generates may be in the minority, but it is certainly not small – and it will continue to grow at a rapid pace. Scalable AI solutions will be critical to extracting value from that data, but the process starts with a transformational approach to the organization’s data strategy that lays the foundation for success.

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