A ‘questions first’ approach that starts with consulting with business stakeholders to identify their most pressing challenges and ensure alignment with corporate goals can set a data strategy up for success
For Disney, it’s “to be one of the world’s leading producers and providers of entertainment and information”. For Tesla, it’s “to accelerate the world’s transition to sustainable energy”. For Levi Strauss & Co, it’s “to be the world’s best apparel company, famous for our brands and values”.
Every brand has a mission that defines where it’s heading and helps shape its corporate strategy – and a good data and analytics strategy needs the same kind of strategic ‘north star’.
In fact, in today’s digital-first business landscape, a company’s digital, data and AI strategies are often critical to the success of its corporate strategy. Since they are so strongly connected, ensuring alignment is key.
“Digital, data and AI must serve the holistic vision and strategic priorities and opportunities for the company”Katia Walsh PhD, Chief Global Strategy and AI Officer, Levi Strauss & Co
“You cannot separate data from digital and you cannot separate data from AI,” says Katia Walsh PhD, Chief Global Strategy and AI Officer at Levi Strauss & Co. “And you cannot separate this integrated digital, data and AI capability from the vision for the whole company.”
“The vision for digital, data and AI has to be in service of the strategic vision for the enterprise,” she adds. “What drives success in this transformation is focusing digital, data and AI on the overall strategic problems to solve and opportunities to seize for the company.”
AI Success Starts with Asking the Right Questions
For Wendy Zhang, Director of Governance and Data Strategy at consumer bank Sallie Mae, the primary cause of misalignment between a company’s corporate and data strategies is a poor understanding of what data-driven technologies such as AI can do.
“There are a lot of different reasons [AI projects fail],” she says. “But it all starts with a lack of fundamental understanding of AI, what it is and what it can or cannot do.”
Zhang warns businesses against doing AI for the sake of AI. She argues that companies must start with the business challenges they need to solve before considering how AI might be applied to solve them.
Rogayeh Tabrizi PhD, Co-Founder and CEO at AI-focused consultancy Theory+Practice, agrees that this is the first step data-focused executives should take when developing their strategies.
“It requires people to really look at a lot of your business processes and to think about different possibilities”Wendy Zhang, Director of Governance and Data Strategy, Sallie Mae
“Developing a successful data, digital or AI strategy always starts with asking the right questions,” Dr Tabrizi says. “It’s the only way to challenge pre-existing assumptions and ensure data-focused executives truly understand the business challenges they are facing.”
Once an AI-focused executive has identified projects that could benefit from data-driven technologies, they must consider what they need to deliver these projects successfully. This includes assessing what resources, funding, datasets and support they’ll need for each project.
“It’s really got to become the company’s DNA,” Zhang adds. “It requires people to really look at a lot of your business processes and to think about different possibilities, and that requires mindset change.”
“It’s not so much working and doing the same things over and over and just automating a few things and having AI on the side,” she concludes. “If you really want to get massive benefits, you have to be able to experiment and fail and also incorporate that into your core business.”
Consulting with Stakeholders to Discover their Needs
Morgan Stanley has a huge number of databases in many different types and formats. Meanwhile, users of data across the company have very different needs, depending on their role. For example, pricing analytics, client analysis and risk reports all require different datasets and different kinds of analysis to help users achieve their desired business outcomes.
Russell Barker, Global Head of FID Data Strategies at Morgan Stanley, says his top challenge is the sheer scope of the firm’s ‘digital transformation’ project. This makes it hard to design a single framework that can ingest all the necessary data and serve all these needs.
“We wanted to build a single consistent framework across the whole department, encompassing all these diverse data sources that could be used to satisfy the needs of the entire user base,” Barker recalls.
“We spent a long time talking to people about their data uses and issues, all the way from senior management to the individual salespeople”Russell Barker, Global Head of FID Data Strategies, Morgan Stanley
To overcome this challenge, Barker spent a great deal of time consulting with business stakeholders about their data use. In the process, he identified a range of common needs and pain points.
“We spent a long time talking to people about their data uses and issues, all the way from senior management to the individual salespeople, traders, risk managers and our partners in finance,” Barker says. “Doing this, we identified some common themes.”
First, Barker found that users needed data discovery tools so they could know what data was available and which sources to use. Access to data also needed to be simplified, as some data was kept in complex systems that required specialist knowledge to navigate.
Barker’s team also needed to ensure the data sources analyses were based on were high quality, to support trust in data-driven insights. Finally, data controls were needed to ensure staff knew what they could do with data and who they were allowed to share it with.
These four priorities became the strategic ‘north star’ that guided Barker’s data and analytics investment roadmap.