“What's measured improves.” ― Peter Drucker
No, I am not going to preach about the benefits of measurability. I am exploring what needs to be the focus of improvement for success in today’s digital world. And, if there is one metric that can act as the “north star” for data-driven innovators, it is “Return on Data.”
We have all come across articles, reports, researches and customers that shaped our thinking that data is the business-enabling capital of the digital era. So, we know that data is important. And, rightfully so.
The value of data grows over time unlike traditional assets. And, this creates a powerful source of management insight and competitive advantage. Data is the new sun. It is a relevant analogy to describe digitally-transformed business models, as the ‘sun’ can power an infinite number of uses at zero marginal cost, never wears out (or depreciates) and never depletes.
There is a lot of data available today, and it is growing substantially. By 2020, for every person on earth, 1.7MB of data will be created every second. That amount of data can fill a 512GB cell phone in 3.5 days.
But, today’s data is locked away in more organizational silos, stove-piped systems and air-gapped environments than companies can handle.
Did you ever use your TV to browse through your family’s RAW format photographs taken by your mobile phone/digital camera and stored in your house’s network attached storage? If you think that doesn’t work intuitively, imagine it at an enterprise scale.
As this McKinsey report describes, while data itself will become increasingly commoditized, value is likely to accrue to the owners of scarce data, to players that aggregate data in unique ways, and especially to providers of valuable analytics.
Ok, so if we aggregate data, how can you find out if it’s worth it? How can you measure the value? How can you get “Return on Data”? And, how, if possible, can you improve it?
Return on Data
The simplest way to summarize Return on Data (ROD) is to get the maximum data under management at the lowest cost and deliver it to as many data innovators as possible.
Theoretically, ROD is a performance measure that can evaluate the efficiency of data. It measures the gain or loss generated on data relative to the cost of data. Squirro postulates the following ROD formula here,
ROD = (Gain from Data – Cost of Data) / Cost of Data
For example, a company is investing $500,000 into its data analytics initiative providing the sales and commercial team actionable customer insights.
In return, the company gets within the first 12 months, 12 additional deals of $200,000, each with net contributing margins of $50,000 each.
The ROD on this investment would be the profit (12*$50,000 – $500,000 = $100,000) divided by the investment cost ($500,000) for a ROD of $100,000/$500,000 equals 0.2 or in percentage terms 20%.
So, we all want the maximum ROD. And, this sounds simple. But, is it?
Let us understand the notable trends shaping your ability to maximize ROD.
Trends Shaping Your Ability to Maximize ROD
Yes, there are numerous trends out there. But, there are three significant data-related trends impacting ROD:
1. Distributed Digital Infrastructure
Data is no longer under the complete control of a central data center. Now, data is created and analyzed everywhere: On-premises, hybrid cloud, public cloud, multi-cloud and at the edge. Modern enterprise infrastructure is more distributed than ever before and continuing to be. To maximize ROD, you need to manage an exponentially growing number of distributed data sources.
2. Rise of Artificial Intelligence (AI) and Machine Learning (ML)
Insights are useful if they are diversely informed, adaptable, and split-second relevant. The value that impactful business data analytics can have is amplified by constantly evolving and learning digital automation, but so are the demands this speed and churn have on data operations. This is the additive ROD that is realized when diverse, disparate data sources are working independently to constantly factor in change as a variable.
3. Corporate Data Responsibility
Regulatory requirements impact how data is protected, retained and shared. Managing data compliance in an ever-evolving regulatory landscape is critical to data security. But more importantly, it is a responsibility towards our customers to manage data responsibly. Increasing ROD means strategically governing those disparate data sources, augmenting dark data with context, simplifying your compliance view, and managing responsibly.
Path to Innovation
Though there is no silver bullet, below are some tips you can use to maximize ROD and march onward on your path to innovation.
- Using ROD metric: This business metric measures the ability to use your data to catalyze growth and to reduce risk. Measuring it will give you an upper hand in driving innovation, transforming customer experiences and unlocking new revenue streams - all with the certainty that your business will grow safely and securely.
- Speed to business impact: Make ROD your watchword when selecting use cases and measuring their successes. Substantial impact can be made faster by being clear on which high-impact wins to prioritize.
- Start small, scale fast: Use an innovation framework as a building model. One that can be used step by step, brick by brick. Entire data platforms don’t have to be dropped in one big bang effort. Start small and scale fast by building on what is already there (your data, your current platforms, etc.).
- Do not focus on data, focus on value: The whole point of data and analytics is to provide value for your customers and your company. Focusing on value rather than data prioritizes critical business outcomes and ensures that the step-by-step approach yields wins earlier and keeps momentum going longer.
- Power to the people: Regardless of how potent your data potential may be, it will not reach its true impact if it is locked in silos. Democratizing data and making it available to stakeholders creates the most fertile landscape for data insight.
Do you have additional pointers on return on data? What am I missing here? Share in the comments below.