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Data: The World’s Most Underused Valuable Resource

The cover story of the May 6th, 2017 issue of The Economist proclaimed data as “the world’s most valuable resource,” which is a new reality that has taken shape over the years.

Technology used to be all about products, but the 1990s brought about the rise of products and services. In the early-to-mid-2000s, our attention jumped to social media services that were all about your relationships with other people. Now the technology industry is focused on data and the insights that can be extracted from it, but this supposed obsession with data is two-faced.

Many may agree that data is in fact the world’s most valuable resource, but the numbers show that data is actually the world’s most underused valuable resource. The truth is that only 1-5% of all data is actually used. It might be easy for companies to pat themselves on the backs about the valuable data that they’re collecting, but if it’s not being used, then what’s the point? Based on the numbers, companies could make decisions that are 95-99% better if they just used the data they already have.

Part of the challenge with making use of all of that information is that 80% of data is unstructured in content like documents, messages, social media posts, pictures, videos, and audio. Unstructured data is notoriously difficult to make sense of because it’s not necessarily organized in a way that can be easily processed. Simply said, it’s difficult to compute.

Thankfully, the combination of artificial intelligence and machine learning technologies with cheaper computing power has made it possible to analyze unstructured data and make it usable. This means that companies can start to extract the valuable insights that have been buried and fossilized in all of that data for months, years, or even decades.

Even more than that, the promise—and reality—of AI is that the right data insights can be delivered to the right person at the right moment without requiring them to think of or search for anything. Instead of being standalone systems, these AI components can be purpose-built and plugged in to existing enterprise systems such as Salesforce and ITSM applications so that companies can manage their data and insights in a single location that has the correct context.

All companies—large and small—need to investigate how AI and machine learning can help them make sense of the mountains of valuable data that they have access to regarding their business operations, customers, competitors, and industry trends. With each passing day, there is a rapid increase in the amount of fascinating unstructured data that is being produced and collected, so if you’re behind today, just imagine how many new valuable insights you’ll continue to miss out on as time goes on if you don’t make data analysis a priority. Eventually, a dismissive attitude about data analysis is what will kill your company.

You might think that the point of this article is to encourage you to use all of your data at once, but the best place to start is to try to analyze and act on even just a few more percentage points of data than you’re currently using. Start with a step-by-step approach: Make relevant data highly accessible at the right time to the right person, and then provide actionable recommendations that are based on that data. Insight-driven businesses take every opportunity to use the power of data to differentiate products and customer experiences.

The overall goal is to make progress and start treating data like the valuable resource that it is. Unlike other valuable resources, there’s always more data that can be consumed.

About the Author

Dr. Dorian Selz is co-founder and CEO of Squirro, an advanced context intelligence and insights solution provider. Before that he founded the Swiss search platform local.ch and made it the market leader in four years. Previously he was a partner and COO at Namics, the largest e-business consultancy in Switzerland & Germany. He holds a PhD from the University of St. Gallen and a Master in Economics from the University of Geneva.

 

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Comments

  1. Dr.S.Shyam Sunder Rao says:

    I am interested to write an article for the International conference on Big Data applications in University Libraries.

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