Determining the return on investment (ROI) and analytics is a multi-faceted, complex endeavor. Given executives want to see detailed projections before handing over a blank check for metrics analytics tech and training on energy related projects. That’s because many are hesitant to invest money in anything they can’t quantify ahead of time.

However, many leaders underestimate the cost of not investing in analytics. Yes, there’ll be lower initial spend by sticking with legacy tech or providing analytics capabilities only to power users rather than to the entire workforce. However, the value of these tools will also be much lower as they severely limit outcomes.

The ROI on Advanced Energy Data Analytics

According to a 2018 study by the research firm Vanson Bourne, organizations were only using about half of the data they create and store — and data was driving slightly less than half of decisions throughout the org.

The same report found companies that were planning on investing $1.7 million on average into their data strategy over the next five years could expect to see their revenue increase by $5.2 million as a result. That’s a potential return of over 500 percent.

The takeaway? It’s cheaper up front to avoid investing in advanced analytics with artificial intelligence capabilities in terms of IT and also then the energy budget for hosting the IT. But it also hamstrings how much value a firm can derive from data-driven decision-making.

Important in these ROI calculations are the price tags of specific pain points analytics can help solve — like monthly customer churn, percentage of machine downtime, inventory dead stock, etc. The cost of various inefficiencies can help businesses understand how much they have to lose through inaction. 

Digital Transformation: Is Your Energy Company and Data Center Leading or Lagging?

According to research from Harvard Business School, companies are becoming increasingly divided into digital leaders (firms ranking in the top quarter of businesses evaluated) and digital laggards (firms ranking in the bottom quarter in terms of digital transformation).

The report revealed:- Leaders saw a higher gross margin (55 percent vs. 37 percent)- Leaders beat out laggards in terms of average earnings (16 percent vs. 11 percent)- Leaders reported better average net income (11 percent to 7 percent)

Before you attribute these differences to budget, keep in mind there was no substantial difference between leaders and laggards in that department. The difference came from the culture and practices surrounding technology — including data-driven decision-making based upon a solidly executed data analytics strategy.

The ability to harness current, relevant data insights is a game-changer capable of affecting profitability, among other key areas of performance. This study demonstrated that leaders are much more likely than laggards to capitalize on real-time data analytics to improve the customer experience and minimize churn.

Realizing the Full Cost of Failing to Compete on Analytics

Another basic risk of falling behind on data analytics is that your competitors, well, won’t. Almost nine in 10 companies are feeling “greater urgency” to invest in AI and analytics, and 92 percent actually are accelerating the pace of their investments in this arena — according to research from Harvard Business Review.

In other words, it’s safe to assume your competitors are doing so.

Yes, harnessing advanced analytics does require an investment, but there’s also a cost to falling behind — especially in highly competitive sectors full of companies trying to gain an edge. There are a few ways to consider this cost: in terms of missed revenue and in terms of missed opportunities to address costly pain points.

There’s still lots of opportunity as companies figure out which technology to deploy and how, as well as foster a genuinely data-driven culture around these investments. But one thing is clear: Falling behind limits the business value companies can ever derive from their data analytics strategy in terms of data-driven decision-making.

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