Integrating AI starts with robust data foundations. Here are 3 strategies executives employ

Business leaders recognize that strong foundations are essential for any company exploiting artificial intelligence (AI). Your business could jeopardize the whole project if it doesn’t sort its data strategy before explorations begin. In short, if you put garbage in, you’ll get garbage out.

Also: How to level up your job in the emerging AI economy

So how can professionals create the foundations to help their organization use AI safely and successfully? Three business leaders detail their top tips for building an effective strategy for exploiting emerging technology.

1. Put your people first

Claire Thompson, group chief data and analytics officer at insurance giant L&G, said a strategic approach to information is crucial for any company that wants to innovate: “I always say data foundations are important for whatever you do next.”

She told ZDNET that strong foundational elements link rules and regulations to dollars and cents.

“Make it clear how the data strategy will drive tangible value — why is it important, for example, that your email addresses are up to date and accurate so that you can do targeted digital communications?”

Thompson recognized that many people don’t want to get bogged down in a long-term strategic plan that defines the technology, processes, people, and rules required to manage information assets. However, she said the planning stage is critical to reaping the benefits of technologies like AI.

“I can understand why people might say governance is boring,” she said. “But in today’s digital organizations, where people want to do straight-through processing, it becomes even more critical that your data is good quality. So, all roads are leading to governance.”

Also: How your business can best exploit AI: Tell your board these 4 things

One key element of Thompson’s strategy at L&G is a close working relationship between her data team and the IT department. Effective collaboration relies on clarity about the skills each party brings to the relationship.

“You need a hand-in-glove partnership. Technology is hugely important to what we do in the data space, and we can’t do our work without the cloud environments, the data warehousing, and the tooling. Data is held in all the applications that the IT team maintains,” she said.

Also: 3 ways to help your staff use generative AI confidently and productively

“We’re trying to ensure we do data quality by design. That’s about ensuring we embed the design philosophy into our core systems. The more you can do that work, the more it stops the ripple effect of poor data quality further down the line and prevents any remediation effort.”

Thompson said the data they collect will push customer experiences in new directions: “How do we start to build personalization into our mobile applications? How do we start to build that into our asset management? How can you automate trades and use AI to support that process?”

2. Master your transactional data

Jon Grainger, CTO at the legal firm DWF, said there’s no time like the present when it comes to creating a data strategy. Smart business leaders focus on the foundational elements for data use long before they think about how to exploit AI and machine learning.

“I always say the best time for a data strategy is four years ago,” he said. “It’s a supertanker piece of work. Ultimately, there aren’t many shortcuts. There is a view that says, ‘Well, if it’s going to take that long, why bother?’ And I think that’s why many folks haven’t been able to get to grips with their data.”

Grainger told ZDNET he wants his firm to build a reputation for delivering great experiences through a digital transformation — and a data strategy is a crucial component of that approach.

Also: What is digital transformation? Everything you need to know 

He joined DWF in late 2022 and enacted a new strategy based on cloud-based software-as-a-service (SaaS) products and open application programming (API) interfaces.

Data at the firm covers a range of entities, such as cases, partners, clients, and internal business processes, including billing and financials.

“The data strategy is all about ensuring transactional data — the source of truth — is mastered in those sections.”

Also: 98% of small firms are using AI tools to ‘punch above their weight’

The aim is to help the organization move quickly but not to the detriment of quality or cost.

“Each SaaS product has a clear identity on the enterprise map,” Grainger said, explaining the fine points of his data strategy. “That identity is driven by the data you master in each area.”

He said the “absolute minimum requirement” to get onto the firm’s target architecture is well-developed APIs that DWF can access and use.

Grainger said SnapLogic technology ensures a solid and reliable connection between services, API, and users.

Also: Agile development can unlock the power of generative AI – here’s how

“Invariably, you’ll get 15 different spellings of a particular address, and the technology can see that pattern and correct it,” he said.

“It can also do something called enrichment. So it might take someone’s reference, go off to an API, come back, and say, ‘This is the right information.'”

Grainger said the data strategy also focuses on the models DWF creates to answer its key business questions.

In combination with the firm’s concentration on SaaS products and APIs, the business has solid foundations to explore emerging technology.

“It turns out you’re setting yourself up pretty well for generative AI if you’ve got all those elements in your data strategy.”

3. Work with your industry peers

Nic Granger, director of corporate and CFO at North Sea Transition Authority (NSTA), said a great data strategy goes beyond internal working practices and spans organizational boundaries.

NSTA collects data from the oil and gas sector. Granger’s team has created digital platforms that allow industry, government, academia, or other interested parties to access data openly.

As part of that work, she chairs the Offshore Energy Digital Strategy Group (DSG), a specialist body formed in late 2022 to create a collaborative effort across UK public bodies that deal with data collection in oil, gas, and renewables.

Also: The future of computing must be more sustainable, even as AI demand fuels energy use

“It was recognized that we needed a cohesive digital data strategy across the offshore energy sector,” she told ZDNET.

“There were good pockets of excellence across the industry in data management and digital technologies, but they weren’t necessarily talking together. So that was a big priority for us.”

In addition to UK government departments, the DSG is supported by other contributors, including the Open Data Institute and Technology Leadership Board.

Also: The fall of Intel: How gen AI helped dethrone a giant and transform computing as we know it

Granger said this collaborative approach has paid dividends: “We’ve got the data strategy now, and it’s about working on three key streams of work.”

The first stream focuses on data, standards, and principles: “Making sure the underlying quality of the data is good because we’re all working on the same basis.” The second stream looks to create common data toolkits and interoperability, said Granger.

“It shouldn’t matter if you’re working in an offshore energy company or on a project in an oil and gas company, you should have data that’s useable across the platforms. That work is all about, ‘How do you get that data from A to B without duplication?'”

Also: The best AI for coding in 2024

The third workstream focuses on cross-sector digitalization: “That’s about ensuring the data and digital skills are there across the industry, and ensuring the sector complies with cybersecurity best practice.”

With these data foundations in place, it’s much easier to start thinking about how to make the most of emerging technologies.

“Our focus is on ensuring we’re making the data accessible and in the right formats for others to use AI and machine learning,” said Granger.