Recent Snowflake workshops and roundtables have started with the question: “Does your data strategy match your AI ambition?” It certainly sparks customer engagement, but is that the right question to ask? Right now, it seems appropriate with all of the interest — dare I say “hype” — around AI. But it merely reflects the current darling of the tech world, focusing on the technology itself, rather than the ultimate goal. I would argue that the better question is, “Does your data strategy match your organization’s ambition?” Or even, “Does your data and AI strategy match your organization's ambition?” Ultimately, both the data and AI are means to an end.

What does your organization want to achieve? What are your business goals? That’s the starting point for your data strategy. The workshop we’ve conducted with customers and partners follows a simple outline, which starts with business goals. We often hear that AI will be the fuel for growth, but data will be the fuel for AI. If that’s the case, we can also conclude that data is, and likely will continue to be, the fuel for growth and other organizational goals. So the workshop on data strategy follows an appropriate acronym: G-R-O-W. 

The better question is, “Does your data and AI strategy match your organization’s ambition?“

When applied to data strategy development, the G-R-O-W framework explores the following questions: 

• What are the organization’s business goals that the data strategy will support?
• What is the current reality within the data organization? How mature is the organization, in terms of data and analytic capabilities? How are the data teams organized? 
• Where is the opportunity to align the data strategy to the business goals, and what options exist?
• What is the way forward? Which actions can be taken to seize these opportunities and achieve the goals?

Each of these questions opens a dialogue that extends well beyond technology. 

A strategy starts with what you want to achieve. But some would say that there is really no such thing as a “data strategy.” A veteran data leader once explained that it's not a strategy per se but rather the execution of initiatives that support a broader business strategy. He was not wrong per se. But let’s not split hairs. Data leaders today are tasked with defining, and frequently refining, their strategies. The key to building support for that strategy and securing funding for those initiatives is to align with the goals of the organization. 

To achieve the best data strategy alignment, an organization’s goals must be S-M-A-R-T, a framework to improve goal development: 

• Specific. Clearly define what you want to accomplish, providing as much detail as possible. For example, increasing revenue across specific business units, geographical regions or product categories, improving customer satisfaction with products or services, or expanding market share of specific products or in certain geographies.
• Measurable. Establish quantifiable criteria to track your progress and determine when the goal has been achieved. This could include metrics like sales numbers, customer retention rates, website traffic, time to market or reduction in carbon footprint.
• Achievable. Set goals that are realistic and attainable within the resources and constraints of your business. Consider factors such as available budget, manpower and market conditions, along with industry regulations. “Market domination” might not look good to regulators.
• Relevant. Align your goals with the overall vision and strategy of your business. Ensure that they contribute to the long-term success, growth and direction of the company.  
• Timely. Set a specific timeframe or deadline for achieving the goal. This creates a sense of urgency and helps prioritize actions and resources. It also allows for periodic evaluation and adjustments if needed.

Abiding by these criteria, organizations can develop clear and actionable goals. That applies to all types of goals. The sustainability goals of a large multinational food and beverage manufacturer provide an example; it has established very specific targets. Goals include reducing emissions by 20% by 2025 and reaching net zero by 2050, using 2018 as a baseline. Actions to achieve this goal include use of 100% deforestation-free primary supply chains for all products by 2025, and use of 100% recyclable or reusable packaging by 2025, as well as 200 million trees planted by 2030. That’s all pretty S-M-A-R-T.

At Sainsbury’s, a strategy update, “Next Level Sainsbury’s,” outlines goals, through FY2027, which support its “Food First” strategy. Ultimately, the company wants to be the first choice for food by offering more options in more locations. It plans to open about 75 new Sainsbury’s Local convenience stores in the next three years, and is investing in data and analytics capabilities that will improve processes. More specifically, the data teams are working toward specific targets to reduce out-of-stock items and improve substitutions in online orders. And, for greater convenience, Sainsbury’s has even introduced a Stock Checker tool so customers can check on availability before going shopping or placing an order.

Alignment with strategic business goals is merely the first step. Refinement requires adding detail to ensure that the goals can be met with the data and analytics tools available. How can customer experience be improved? If customers hate it when their favorite products are out of stock, that’s the metric to choose: “Reduce out of stock products.” And, then set a target and a time frame. That’s a S-M-A-R-T goal.

The next step is to decide which goals to address first. Returning to the existential question of the data strategy, how does the data team translate those business goals into their own initiatives? Defining them ultimately requires not only alignment but prioritization. How do you choose one thing over another? Another data leader once asked me, “Is a dollar of cost savings the same as a dollar of revenue generated?” My response was a resounding “no.” Data and analytics initiatives must align with business objectives. If a company is in growth mode, hell-bent on capturing mind and market share, data and insights teams prioritize revenue generation over cost savings. In a downturn, when survival mode kicks in, priorities might look a little different. The data strategy must focus on identifying, prioritizing and executing initiatives that best support a company’s business strategy and strategic objectives. 

Adopt a rigorous and transparent prioritization framework to ensure the view is worth the climb.

Keep in mind that not all proposed initiatives originate through this top-down process. Ideas come from all parts of the business, and that’s a good thing. Diversity of ideas should be encouraged. However, ultimately, those ideas must be put to the test. As one data leader advised, “Ensure that the view is worth the climb.” Adopt a prioritization matrix to ensure the goals of the data and analytics team match those of the business. 

Lastly, like most things, there is trial and error involved. Not every project will succeed. Why would we expect them to? Does every shot mean a swish of the net in a basketball game? Does every science experiment get it right the first time? Even Ricky Gervais tests jokes before filming a Netflix special. A data strategy should always include the goal of building a foundation to test multiple approaches to addressing challenges. 

A data strategy should include the goal of building a robust foundation to test multiple paths to success.

Stay tuned for the next installment of how to G-R-O-W your business with data and AI!