The Value of Data

In my last post, I introduced the notion of “data decay”: how data degrades almost from its creation.?

Data decay costs businesses in three ways:?

1. Capital spent to acquire low-value third-party data;?
2. Time spent on wasted effort trying to act on bad data;
3. Lost revenue due to missed opportunities.

In this post, I will discuss the value of data. There’s no overstating the vital role of data in today’s marketplace. Fortune Business Insights values the global big data analytics market at about $272B in 2022, with the North American share around $101B. And it’s estimated to grow to more than $745B by 2023.?

Data has been called the new oil, the new gold, and most recently – and, I think, most accurately – the new water: from a business perspective, we can’t live without it. Like water, data has to be accessible.? But it also has to be treated to address specific goals to be helpful. We can use water for drinking, irrigation, aquariums, industry… the list is endless. The destination determines the journey water will take and the steps we use to modify it. Likewise with data: each application needs a different treatment.? And if data doesn’t directly apply to your goals, it’s useless and possibly harmful.

Accessibility isn’t a problem if you have the budget for it. There are vast quantities of data available from big-name data providers, often with a binding contract attached.

Getting data that caters to your specific requirements can be a challenge. Unless you have the resources to hire a dedicated team to refine and focus your data, it is unlikely that you will get what you need on time. Resources and costs are significant limitations of the current data paradigm. Subpar data costs companies over $3 trillion annually in the United States alone.

To get a sense of the scope of the problem, consider that in just one hour:?

• 521 businesses will change their corporate addresses;?
• 872 telephone numbers will change or disconnect;
• 1,504 URLs are created, modified, or altered.??

Now multiply that impact by the time since the data was collected.

Data decay can make or break a business. I know this firsthand from my experience with the one-size-fits-all approach of data providers or, more accurately, one-size-fits-none.? - and I’m far from alone.?

We hear a lot of talk about the “personalization” of data from the companies that sell it. I’m not impressed. Like many buzzwords, I don’t think “personalization” has much value. Even with personalization, I do not see the kind of data refinement that delivers quality, actionable results. Instead, you may see communications with just the first few words “personalized” by AI. Or datasets that are segmented but not truly personalized: they still suffer the same data decay problems weakening the whole data industry.

Companies today have little choice but to invest in data, whatever its quality, and hope it delivers a positive ROI. Companies need data that is accessible, leads to conversions, empowers the sales team, and informs marketing. In short, they need data that can deliver ROI many times what it costs, or they may not survive.??

For the past decade, big data has had a significant impact on businesses, with data decay being an ongoing issue. However, the adoption of Web3, spatial computing, ML and AI, AR, blockchain, and IoT technologies will amplify the effects of data decay, causing a ripple effect throughout the information ecosystem. As such, it is imperative to have a scalable, adaptable, and easily accessible solution.

Sources:

Cited within article