Bridging the gap between data and money

This article was co-authored by Elizabeth Press and Peter Schroeter

Data is a top priority on almost every C-suite agenda, and for good reason.

When data is properly sourced, compiled, and understood, it has the potential to add tremendous value to a company’s profitability and competitive positioning.

However, without proper business acumen, data organizations often fail to control their costs, and are far less likely to realize return on their data investments.

How can organizations effectively generate financial return from their data? And what are some common mistakes that companies make when trying to do so?

I recently co-hosted a Harnham LIVE LinkedIn event with Harnham’s Peter Schroeter to answer these questions and more. Here’s a summary of our chat below.

Setting Clear Data Objectives is Key

When building data teams, many companies underestimate the effort that’s involved in garnering valuable insights from data.

There seems to be a common misconception that, if a company invests in the right tools and hires a bunch of smart people who understand the “modern data stack,” then that company will realize a positive ROI on its investments in data.

What executives often forget is that, if a business doesn’t have clear objectives on how its data can help make money, investing in data can end up having a negative impact on profit.

Therefore, the path to positive ROI in data starts with identifying how data supports the business strategy and how it can accelerate monetization.

To make sure their data teams are set up for success, companies must plan for enough positive cashflow impact to justify their investment in data.

Requisite Skills Go Beyond Tech

Once clear objectives have been set, companies need to ensure their data teams and data products stay on track.

This requires a few things. One of the most important ways to ensure revenue and data goals are aligned is to build a well-rounded team. That means building a team that has a mix of technical knowledge and business acumen.

Of course, it can be challenging to find data professionals that are also business experts. Luckily, it’s more important to have a team that’s diverse rather individual employees that “have it all.”

When building data teams, a common mistake companies make is hiring a technical person who says they know business, rather than hiring a person with a solid business background.

Data is not a purely technical domain. Thus, in-depth knowledge in areas beyond code and tools is important. Skills in business, strategy, project management, and communication, as well as process expertise are critical to manage a data organization successfully. Security, compliance, and privacy are also becoming non-negotiables.

Keep It Lean

An efficient data ecosystem is also important if companies want their data to generate a positive ROI.

Lean processes with a focus on governance, data quality, and observability will create a more robust data ecosystem that has a lower chance of producing incomplete, incorrect, or unfocused data, especially if a company is just starting out in the data space.

Focus on Break-Even Points and Payback Periods

Initial investments in cloud technology and other critical steps are expensive. Think in terms of break-even points and time horizons when projecting the investment payback period.

Being realistic about a payback period and decisive about an effective monetization strategy are critical for realizing return on long-term investment in data.

Over time, data has the power to make valuable change in an organization—if it’s harnessed in the right way.

For more information about the connection between data and money, you can watch a recording of our entire live discussion on Harnham’s LinkedIn.

This article was also published by Harnham

Relevant D3M Labs articles

Data only has financial value if it can be monetized – An interview with Michael Guthammar

What I look for when I hire a data professional

Lehren aus New York: Es gibt nie „nur Business“

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