What does it take to become a more data-driven business? This is a question that every organisation, not just tech companies, needs to grapple with, or they will falter in the future. One of my customers is Shell – a company that is doing a great job of becoming more data-driven. One of the driving forces to make the company more data-driven is Dan Jeavons, Data Science Manager at the global energy and petrochemical company. With this article (and related videos), I want to share the learning experiences they had as they prioritised data-driven operations.
Why Must Organisations Become Data-Driven?
Does growing 30 percent annually sound appealing? According to Forrester, that’s what data-driven organisations can expect to achieve in addition to being profitable and acquiring and retaining new customers. Most organisations realise data should be central to decision-making; however, leaping from knowing to doing isn’t as straightforward.
Here are some best practices from Shell and other thoughts on how best to transition to a data-driven business.
Think Strategically about Data
The starting point for any data strategy must be to examine your business challenges—what’s your business strategy, overall business goals, your biggest challenges, and unanswered questions.
Before jumping into investing in data infrastructure and data-driven decisions, take a step back and determine how data can become relevant to the strategy of the business, this will help you to determine what data you need.
At Shell, Jeavons explained the value of a two-speed approach. “It’s very easy to think about culture and standards and technologies and try to get that all right, but at the same time, we have to make it matter to the business. What we try to do is to think about minimal viable products that are going to have a significant business impact immediately and use that to inform the KPIs that really matter to the business. Then, line up to that, which data really matters and how to invest in data quality, data standards, and technology to support doing this at scale.”
It’s important to review the data you have access to and determine how you will acquire data diversity, and what new sets of data must be acquired to solve your particular business challenges.
Learn from Tech Companies
Shell, and the entire oil and gas industry, has been incredibly data-intensive for a very long time. However, with the cloud, digitalisation, and new technologies emerging, the value and importance of data have gone up.
Jeavons shared, “What we’voe been trying to think through is how do we use data enterprise-wide. We recognise that some of the skills that cloud-based technology companies have developed and the way in which they think of data are quite different than the way we’voe thought about it historically, so we want to learn from them.”
Jeavons encourages organisations to leverage the best practices and learn from what’s going on in the market to bring expertise to your organisation.
Data Technology and Discipline
Once you have determined data sources and know where to get the data you need, you must look at the technology and some of the tools to help determine where to store the data, how to analyse it, and turn it into insights. Technology is another key enabler to becoming a more data-driven business.
Jeavons explained how Shell is disciplined about the data the company collects and stores, and they “learnt that when you start down this road, it’s very easy to get tempted to go after lots of data and then look for a problem. Our experience is that if you put all the data in one place, it’s harder to sift through.”
Shell built a phenomenal data set—likely the largest curated set of data on the planet Jevon asserts. “At the same time, we’re focused on not pulling all sets of data, but only sets of data that will drive business,” he said.
Data is an asset but also a potential liability. It’s important for organisations to only aggregate and integrate the data that is known to be of value, and that can be controlled. Your organisation needs to be clear on why you’re collecting data and the benefit you will drive from it. How can you envision that data being used in the future? How are you going to control access to that data in such a way that you deal with it in a secure, reliable, and well-managed fashion? When someone gains access to that data, how will they know what they’re looking at?
Skills and Organisational Culture
It’s important to make sure that people in your organisation have the skills to use the data and the ability to access the data, know how to use it, and gain insights. The organisational culture needs to support fact-based, data-driven decision-making, and make sure it’s encouraged and the norm.
At Shell, Jeavons explained, they invested in professionalising the core work of data scientists and others who would spend all their time working on the most complex problems the company had. They developed tailored training programmes and aligned these roles with other disciplines in the company and made data science as important as petrophysics, as an example.
Then, for the citizen data scientists—people who are in the business who need to use the data to solve their own problems—Shell had to make sure they understood how to access the data, develop their own algorithms and work with the data.
Another group, called the Shell AI community, that numbers roughly 2,000 people, is learning about what it means to use data in new ways to drive their own business performance. This is an area where there is a lot of excitement and real traction. People are starting to adopt this at scale and learning how to better use data in their areas of expertise. Shell schedules hackathons between data specialists and this AI community where they collaborate to develop small-scale rapid solutions in a very, very short timescale.
Finally, another essential role in the data-driven business culture of Shell spans the business and data communities. These business translators can speak the language of business as well as data technology. These roles are absolutely crucial. Shell invested heavily in training these people and worked with them to define ways in which the company can translate what they need in terms of a business outcome perspective into a valuable piece of work data scientists can do. These are essential personnel, and they tend to self-identify from the business world and have a key interest in using technology and applying it to solve business issues.
The last component of becoming a data-driven business regards data governance. Every organisation needs to make sure the data that’s collected is treated and stored securely, the right legal framework is established, and it is known who has access to the data and who can use it.