Data analytics is a domain in constant motion. Early in 2020, it seemed clear that organisations would continue to invest heavily in analytics to support their digital transformations. The COVID-19 pandemic emerged as a major disruptor.
Early in the pandemic, it seemed organisations might waylay data and analytics advancements to retrench and focused on other pressing priorities like enabling a remote workforce.
But, in many cases, organisations accelerated their adoption of data and analytics capabilities and AI. In July 2020, a KPMG study found that 67 per cent of respondents increased the pace of their digital transformation strategy due to the pandemic, with 63 per cent increasing their digital transformation budget.
Things haven’t slowed since. Research firm Fortune Business Insights predicts the global big data analytics market will grow to $549.7 billion in 2028 at a CAGR of 13.2 per cent between 2021 and 2008.
As IT leaders focus attention on data analytics and AI in 2022 and beyond, they should keep the following three closely related trends top-of-mind.
It’s all about the supply chain
The pandemic has put an enormous strain on the global supply chain. The past year has seen ships waiting endlessly to enter ports, containers stacking up at distribution centres, and empty shelves in some cases. For many organisations, supply chain analytics are becoming an essential component of doing business.
“Most organisations only focus on a single level of the supply chain: Who are the suppliers and how to gain some alternative suppliers,” said Doug Laney, innovation fellow of data and analytics strategy at West Monroe.
“I think more and more organisations are going to start looking at multilevel supply chain visibility to be able to predict the price indices. Not just my suppliers, but my suppliers’ suppliers’ suppliers, and so forth.”
Laney said organisations can find a great deal of data to gain such visibility, including gathering data from their website, monitoring LinkedIn for turnover, social media for complaints that go into pricing and availability, and so on.
Mike Giresi, chief digital officer at manufacturer Molex, said understanding the supply chain is currently a big pain point.
“Supply chain right now is a massive challenge on so many levels,” he said. “We’re trying to leverage data and AI and ML — we’re trying to do all kinds of things there to make us more advantaged in terms of how we deliver our supply chain capability.”
Organisations will assign real value to their data
“The greatest success that a chief data officer has is when they’ve actually productised or commercialised their data in some way,” Laney added. “This is starting to get noticed by a lot of companies.”
Laney, a former distinguished VP analyst at Gartner, outlined that Gartner did a study of chief data officer success that found CDOs were 3.5 times more likely to achieve success in their role when they met data monetisation initiatives, versus only 1.7 times more likely when they demonstrated ROI on their BI or data analytics investments.
Gartner also found that companies that productise or commercialise their data are also more valued by investors. Indeed, he said, the value of a company’s data is becoming an important element of M&A activity.
“We found that companies that treat data more as an asset have a market to book value ratio that’s nearly two times higher than the market average," Laney added.
"And companies that sell data products or data derivatives of some kind have a 3x market to book value ratio. So, there’s something that investors really favor about companies that are more data savvy, data driven, or data product oriented.”
In 2022, companies are getting serious about assigning value to their data and leveraging that value to drive revenue. It’s not just about selling data; it’s about understanding how to bake data into an existing product or service, or even using data internally, to generate demonstrable value streams for the organisation.
Alexandre t’Kint, data scientist at Collibra, and Sarvenaz Rahmati, automation developer at the European Centre for Clinical Research Training, recently published a blog post on the process they developed to determine the value of a Collibra data product.
They calculated the cost of the resources used by the data product (including development, maintenance, and the licenses involved) and the revenue generated by the data product to determine its net value. The calculation was not straightforward, as the data product in question was a tool that supports Collibra’s sales engineers rather than one that generates revenue directly.
t’Kint and Rahmati say the process can help organisations understand which data products will bring them the most bang for their buck and evaluate whether the data team’s resources are being used effectively.
“It is true, it is a lot of effort to calculate the value of your data product,” they write. “If you measure as many costs and components as you are able, it is well worth the effort. An effective data product leads to accurate decisions.”
Disney Advertising Sales is an example of an organisation leveraging the value of its data to connect with customers. It is providing advertising customers with access to its audience graph via a data clean room.
“It allows our advertisers to be a lot more innovative beyond traditional demographics,” said Lisa Valentino, executive vice president of client solutions and addressable enablement at Disney Advertising Sales. “That provides more relevant, contextual opportunities, and makes our guests and our viewers happier because it’s a more relevant environment. And the performance goes up for our clients.”
Sustainability is key
Awareness of environmental, social, and governance (ESG) issues was on the rise among corporate leadership in 2021 and that trend continues in 2022.
Paige Morse, sustainability and strategy expert for process industries at Aspen Technology, joined that company as director of industry marketing focused on AspenTech’s chemicals and energy businesses.
AspenTech is a provider of software and services for the process industries born of a joint project of the Massachusetts Institute of Technology (MIT) and the US Department of Energy. In August 2021, she was appointed the company’s sustainability lead.
“This new role was created with a sustainability focus just this summer,” Morse said. “I think we’ve seen how important sustainability is.”
AspenTech is driving the use of simulation and digital twins for sustainability. Early on, she said, it was using simulation to help customers look at various options, like different ways to approach a particular chemical process. What if the process takes place at a different temperature or a different separation technique is applied?
“Mostly it was focused around cost and profitability,” Morse stated. “How can I scale up this process?”
Today, though, customers are increasingly interested in efficiency.
“We used to measure efficiency in dollars or euros,” she said. “But now we’re saying we better look at it in terms of CO2 avoided, waste not made, feedstock that didn’t get lost in the process.”
For years, manufacturing services company Jabil has been pursuing its Factory of the Future initiative. The company operates more than 100 plants in more than 20 countries, and the Factory of the Future initiative seeks to optimise and future-proof those plants. May Yap, senior vice president and global CIO of Jabil, said factory optimisation and sustainability go hand-in-hand.
“At the time when we set up the Factory of the Future initiative, we did not actually have a big fancy name for it. We called it IT factory optimisation,” Yap said. “Once we can digitalise something in the factory, we can visualise it. When we can visualise it, we can think about how to optimise the processes within the factory.”
Among other things, the initiative uses digital twins to monitor the operations of Jabil’s sites and identify potential waste and then Jabil seeks to utilise that waste product elsewhere in the factory.
One process in a factory might generate waste steam, for instance. The Factory of the Future uses digital twin to identify the source of the waste steam, which can then be captured and used to power another process in the factory.