Most technology projects inevitably involve innovative uses of data, whether through analytics, IoT, artificial intelligence, or machine learning. In fact, most digital transformation projects are more about data-driven change than anything else. So how can professionals develop data-driven strategies that make the most of the information their organizations collect?
1. Build the right foundations
Brandon Hootman, Caterpillar’s director of digital data, believes there has been a tipping point in the use of data in recent years. While data wrangling – the process that transforms raw data into more easily usable formats – is still important, some executives are focusing more on business needs to accelerate experimentation.
“I think the companies that you’re going to see that are really successful in this space have taken a more mature approach to managing and building data capacity. So when it comes time to do these experiments, bring the business to the data instead, bring the data to the business,” he says.
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At Caterpillar, Hootman used Snowflake’s data pipelines, management capabilities and data lake technology to create a foundation for analytics that enabled his organization to leverage consolidated sources of information to support new business use cases. He advises other digital leaders to take a similar approach, but acknowledges that it’s not always easy.
“It’s a change – and unfortunately there is no easy button,” he says. “I’m very fortunate that we’ve made the investment that we need to do this work and I feel like we’re doing it in a way that means we’re starting to see the benefits.”
2. Understand where better data can make a difference
Access to the right data is important across the enterprise—from analyzing customer sentiment to security. Prabhath Karanth, director of security compliance and assurance at travel management company TripActions, encourages everyone to view security from the perspective of data that can be shared with management.
“When you set up your environment so that you build your security practices and programs on top of the data layer, it becomes much easier to scale and it becomes much easier to produce the metrics that your management expects,” he says.
“It also becomes a lot easier to give your C-suite deep, data-driven insights to drive further security investments into your program.”
Karanth also encourages professionals to carefully consider the technology solutions and vendor partners that help them think about security in a data-driven way.
“This movement is here to stay,” he says. “The teams and programs that adopt a data-driven mindset for security and compliance will be tremendously successful.”
3. Give business users control
Salim Syed, vice president of Slingshot Engineering at Capital One Software, says companies need to focus on democratizing data expertise and giving more control to industry users. The faster the company can do more, the more likely it is to succeed.
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“Data needs to be democratized – data engineering needs to be democratized,” he says. “What companies want is to get to the insights they need, the models they want to run, and move at the speed that modern work demands.”
According to Syed, companies that get stuck with engineering and backend integration waste a lot of time. Instead, build a working platform, put in place the right policies and processes, and then empower business users to experiment with data.
“Treat data as a product and have an owner and manage everything effectively,” he says. “Something I’m saying is: ‘central policy, central tooling, federated ownership’ — that’s the model.
4. Be prepared to optimize systems
Daniel Smith, Head of Analytics at fashion house PANGAIA, says it’s critical that professionals who want to get the most out of data have the right source systems in place. It is then much easier to think about how to use insights for decision-making processes.
“You have to have a ‘Fail Fast, Learn and Iterate’ culture. You will never build the perfect solution, version one. You have to iterate and, even when you think you’ve got it right, important changes that you have to make are constantly moving,” he says.
Smith works with Board International to transform the sales reporting process, consolidate multiple data sources and improve the company’s analytical capabilities.
The key message, says Smith, is that any attempt to get the most out of data is an ongoing work: “I wouldn’t say we’ve ever had a dashboard that wasn’t optimized. The longest a dashboard has gone without being tweaked is probably two months ago.”
5. The goal is to build an information ecosystem
Milena Nikolic, CTO at Trainline, says professionals shouldn’t limit their data-driven transformations to just internal knowledge. Your company uses data — some of which it collects internally and others from across the industry — to build features that improve its customers’ experience.
“We use data a lot for decision-making — it’s very important,” she says. “Sure, the data we gather from users’ use of our products, which we obviously collect in a consensual and privacy-conscious way. We use that amount internally to make sure we’re counting and measuring the right things, we’re setting the right goals, we’re setting the right ambitions and recognizing when things are going wrong.”
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She says that Trainline’s long-term approach is to develop application programming interfaces with external parties. The aim is to create benefits for the company, its customers and other organizations throughout the industry.
“Rail is an ecosystem and we understand that we are part of the ecosystem and that the only way for us to be successful is to work in partnership with the rest of the ecosystem, which is train operators as well as everyone else. So, it’s all about partnerships. It’s all about doing the right thing to get more and more customers onto the rails.”