Addressing the legacy: Modernising creaky cloud infrastructures for data benefits

Data skills shortages

Kyndryl’s survey also found technology outpacing training, with about 40% of surveyed leaders reporting skills gaps hindering modernisation.

“If you’re not ready, you say modernisation can solve this, but if you don’t have the future skills, it doesn’t matter what you do,” says Goude.

Seth Ravin, CEO, president and chair at enterprise software and support services provider Rimini Street, adds that a lack of enterprise architects, data scientists or integrators rather than programmers can also prove restrictive.

“It’s tough to structure data in big data sets without understanding how that data is connected and structured, really understanding how to get the most out of data,” Ravin says. “We need people who can tie programs together using integration tool sets.”

When people see layoffs, only about half are typically about cost-cutting – the other half is rotation for needed skills, moving people out and bringing people in with new skill sets, he adds.

The data needed to achieve good outcomes

Once an enterprise has agreed, defined and described relevant business outcomes, then ask what data will be needed to achieve that, and how to collect, manage and control it. This way it is possible to minimise what would otherwise potentially result in an overwhelming or expensive volume of data to store, analyse and maintain.

“Data modernisation for data modernisation’s sake can have you in one of those hype cycles,” Henninger adds. 

Often, it’s about acquiring a 360-degree view of the customer, yet organisations may fail to examine this data problem end-to-end. Instead, many simply add ERP, CRM or other IT solutions.

For example, you might find you cannot answer a seemingly simple question about current employee numbers because when you talk to different functional stakeholders, the concept of “employee” varies. 

“The number of employees for payroll purposes can be different from the number of employees for legal reasons, or the number when it comes to holiday pay,” Henninger adds.

Enterprises do not want to be in a position where they are trying to answer six different questions, and trying to fudge an average answer among them. That means ending up with a data set and a complicated, expensive data infrastructure built for everybody and useful for no one. “That happens over and over again,” Henninger says.

Trust and data security

Modernising data infrastructure is crucial partly because of the role that trust and security now play around data use in general.

“Partly, the artificial intelligence challenge makes it quite a lot easier to access and interrogate data sets, including potentially people you don’t want,” Henninger says. “But on some level, the degree to which data was disorganised and trapped in documents was a natural form of security in the past.”

Previously, even if someone got into the network, they would still have to read the documents – but this is much easier for everyone with AI, including malicious actors.

The Kyndryl polling also reported that 65% of executives worry about cyber attacks, but just 30% say they feel ready to manage that risk. 

Organisations must be able to use their data confidently and measure the value of doing so, including identifying and setting appropriate metrics. Then when you can measure it properly, you can quantify progress or triage further intervention successfully, adds Henninger. Once an enterprise knows what data they need, who controls it and how it is maintained over time, they can start to work out the infrastructure needed for necessary analyses.

Goude prescribes thinking about it as “the right workload in the right platform”. Revisit each application and decide what they want to do: speed up, reduce cost, or whatever. Some might not even need to be maintained.

A heavily transactional system in a bank, for instance, might “skyrocket costs” without adding value. In that case, it can make sense to decouple the data from the transaction, perhaps moving the data elsewhere. That might in turn offer different capabilities for cloud analytics or AI.

“You might enhance applications without completely redoing them. Or you might reinvent business processes,” Goude says. “If you do one approach on everything, you likely won’t optimise.”

Henninger says that beyond a vanishingly small number of compute-intense analytical problems,  technology questions for the infrastructure side of data modernisation have largely become software questions.

It’s more about business intelligence (BI) than AI and advanced analytics – management reporting and the resources needed – and it is less about how data is stored or queried, streamed or in tables, but creating the right controls and incentives to actively manage the datasets.

Problems arise despite “getting the data right”, because there is drift, or it degrades or the person managing it leaves. “Then data is unreliable and the whole system breaks down, and the organisation goes back into silos,” says Henninger.

“Modern data infrastructure resembles lots of other things: likely cloud-based,” he says. “98% of what compute is needed for decision-making is in the realm of reasonably available commodity hardware.”

Cost concerns versus budget for innovation

Ravin says it is also important to retain some budget for innovation – and not spend it all upgrading multiple software packages.

On this point, it is important to consider all the software and its “true useful life”. Then start making decision on investment in automations and productivity versus upgrades or migrations.

“Software vendors may say the ERP has to be changed up every three to five years, but that’s a work project for everybody,” Ravin says. “Individual usage analysis might reveal it’s good for much longer.”

A rule of thumb is to spend no more than 60-70% of annual budget on operations, and leave 30-40% for innovation. “Otherwise, you’re dead,” Ravin adds. “Costs are up. You can’t sell for more because of competition, and the place that gets squeezed is profits.”

Gartner has estimated that perhaps 90% of budgets go on keeping the lights on, with just 10% of modernisation or innovation, he says, adding that this is similar for resource-strapped SMEs: “SMEs tend to have fewer software products, but their needs are still pretty extensive, especially if working outside their home country. The cost of admin is getting higher.”

He suggests reconsidering the need to be in the cloud at all, especially without bursty, elastic demand, and particularly with equipment costs for on-premise increasingly leveraged.

“We’ve seen all these companies that were ‘cloud-first’, finding out that they’re saving millions of dollars bringing it back,” he says. “Cloud is not always the answer.”

In Kyndryl’s survey, 76% of businesses reported investing in AI and machine learning, but only 42% had so far seen positive ROI. Yet benefits are there to be had. Kyndryl sees potential for automated resolution of up to 30% of IT issues, up from 8%, for instance – saving massively on maintenance and downtime.

Data-infrastructure modernisation of an ageing estate requires carefully examining every investment choice through the lens of ROI-driving business outcomes.

“You could easily spend enough money to actually have just about the perfect data set, but that could be incredibly expensive,” Henninger says. “Sure, the data infrastructure might be worth it, but only if it solves the right problem.”