Being ‘data-driven’ has become a bit of a buzzword, but for many organisations, a data strategy is still in development. Data innovation is often also stifled by budget limitations or by organisations not knowing how to get the most out of the tools and technology available to them.
It can therefore be difficult for leaders to understand the return on investment for data platforms. They may know there’s immense benefit in gleaning insights into their business and customers, but getting to that point and quantifying that value is more challenging.
Investment in data isn’t slowing down, with data leaders expecting to invest 63% more in existing data and AI platforms and 69% more in new platforms by 2025, according to the Databricks and MIT CIO Vision 2025 report. So how can they justify the costs and enable the shift to a culture that recognises data’s potential for profit?
How should organisations approach costs?
One metric used to understand the overall value of an investment in a service or platform is the total cost of ownership (TCO), a financial estimate of the total cost of a product across its whole lifecycle.
Sebastian Wedeniwski, chief technology officer at Deutsche Börse since April 2023, suggests that TCO for data platforms should be categorised into three primary areas. The first area is infrastructure costs, including the initial investment, operational costs, energy consumption and labour costs. The second area is the cost of downtime, which is difficult to measure but includes factors such as reputational damage. The third area is hidden costs related to the efficiency and value of the data.
“How much of the data is copied?” he asks. If there is duplication and redundancy, “you’ll have a higher infrastructure cost, higher maintenance costs and higher energy consumption costs.”
Kate Harrison, senior manager, business value consulting at Databricks agrees about the importance of expanding the scope of TCO beyond infrastructure costs: “If we’re taking a point of view that data is an asset, it’s maybe a bit more than just the infrastructure costs that are tied to the total cost of owning and using that data.”
Understanding the business value of data
Yann Lepant, Europe data and AI delivery lead at Accenture, says one of the biggest issues organisations face is looking at how to go from data being a by-product of IT to it becoming a business asset.
“The hard bit we see with a lot of our clients is learning how to build the right strategy on how you’re going to exploit data and measure the value,” says Lepant. “With a number of clients, we work with CFOs to turn that data into something you can put in a balance sheet – suddenly, you’re building business cases you can actually report on.”
At the core of making the most of, and quantifying, the value of data is a robust data strategy. “You need to have the right data strategy, or rather a data and AI strategy, because AI is increasingly at the centre of what operations are trying to scale up,” says Lepant.
You need to have the right data strategy, or rather a data and AI strategy, because AI is increasingly at the centre of what operations are trying to scale up
Harrison notes the contradictory objectives data teams face when they have to justify costs and do more with less, as well as drive the most value from data. “Data needs across the business are growing, and the outcomes data is meant to be driving are increasing. But, all of a sudden, you’re meant to do everything just so much cheaper than you are currently. And there’s a tension there.” She stresses that cutting corners is not the way to go. There’s no point investing in cheap data that’s barely going to make a difference to business outcomes.
The case for simplification and consolidation
Another challenge for organisations is complexity. Harrison says: “What we’ve seen over the past 10 years is this explosion of tools and partners. All these elements have ended up creating really complex ecosystems for making data available in the business and then being able to move it around.”
She continues: “One of the things that I think is key, and a big shift we’re going to start to see in the industry, is around consolidation and simplification.”
For large, complex organisations, simplification will mean reducing the tech stack and moving towards more efficient, self-service architectures. Enabling agility with adaptable and scalable tools is also crucial, says Harrison. “You might not be doing data science now and being predictive, but you know that’s going to come down the line. So how are you creating an environment that will give you the agility and speed to be able to upskill quickly when those capabilities become relevant to your business?”
Expanding the data ecosystem
While some organisations are still refining their basic data strategy, others are starting to look to developments on the horizon around data decentralisation and emerging technology. As a leader in a highly-regulated industry, Wedeniwski sees great potential in data and artificial intelligence being used to simplify and automate time-consuming and complex work. For example, it could allow architects to stop spending too much time on governance issues and focus more on creative and strategic tasks.
Wedeniwski believes it is important to move beyond silos, both internally and across the whole business ecosystem, to find opportunities for data sharing. However, he notes that there are questions to be answered around data ownership and who is ultimately responsible for how data is being shared and used.
Harrison agrees that there can be a mutual benefit in data exchange between partners and customers. This works best in non-competitive relationships. For example, sharing information may be useful in manufacturing to manage the uptime of equipment, but less so in retail, where information about consumers is a competitive advantage.
Lepant concludes: “I think the next step could be interesting to see, where data is itself the product and is being augmented across the ecosystem to be enriched at the end, not just by one party, but as a chain.”
It’s all new and exciting territory. And it’s clear that, despite a gloomy economic outlook, the sheer value of data as a currency makes it a key area to invest in. It’s an important time for organisations to get a handle on their data and foster a culture of innovation.
To find out more, visit databricks.com
Being ‘data-driven’ has become a bit of a buzzword, but for many organisations, a data strategy is still in development. Data innovation is often also stifled by budget limitations or by organisations not knowing how to get the most out of the tools and technology available to them.
It can therefore be difficult for leaders to understand the return on investment for data platforms. They may know there’s immense benefit in gleaning insights into their business and customers, but getting to that point and quantifying that value is more challenging.
Investment in data isn't slowing down, with data leaders expecting to invest 63% more in existing data and AI platforms and 69% more in new platforms by 2025, according to the Databricks and MIT CIO Vision 2025 report. So how can they justify the costs and enable the shift to a culture that recognises data's potential for profit?
