Scaling your data strategy will inevitably result in winners and losers. Some work out the system to apply in their organization and skillfully tailor it to meet the demands and context of their organization, and some don’t or can’t. It’s something of a game.
But how can you position yourself as a winner? Read on to uncover how you can win the game. By scaling your data strategy through leadership, management, culture change, innovation, creativity, and understanding people, you can prevail. Remember, the better you complete one level, the better set up you are for the next.
What Hinders Scaling Your Data Strategy?
Several roadblocks may hinder an organization from achieving its data strategy goals in the desired way. This could be caused by a lack of clarity about what to do, what to focus on first, who to recruit, what technology is needed, unclear investment needs, the threat of AI and its impact, or a lack of leadership. I could go on. All these lead to a lack of buy-in and engagement across a business, which slows down change and minimizes impact. So while there’s great promise in what data and AI can provide, it falls to business leaders to solve these challenges.
The Five Levels of Scaling Success
The art of solving the challenges of this game is to apply systems, best practices, and the right approaches that give you a better chance at driving towards a successful result. The key is to prioritize around the incremental or transformational business outcomes that can be delivered, rather than doing data or AI for data or AI’s sake (which happens more often than you’d like to imagine).
Level 1 — Understanding the Opportunities
In the early stages of your strategy, you must get very clear about the opportunity that data and AI present and the impact, both positive and negative, they can have on your organization, strategically and operationally.
Identifying the business needs for data and AI aligned to your strategic objectives is the number one way of building a robust starting point, shaping your strategy around the correct outcomes by aligning everyone around a common goal.
You can use this to create desire, excitement, and buy-in — one of the challenges that data leaders face, according to a recent report from my company. This helps to secure the investment required for the next level.
This is all achieved through reading and conversation — reading company strategy, city announcements, annual reports, risk registers, and conversations with business leaders, managers, and front-line staff about the real day-to-day operational and strategic challenges.
Level 2 — Building Confidence by Proving Value
A mistake many make is going from strategy to broad and deep investment in building out capabilities straight away. This has led to many spending money and time on things that the organization doesn’t need or isn’t well-equipped to cope with.
Early on, you are in theory mode, so what’s needed is to make that theory into reality, test assumptions and bets made as part of the strategy. Before pushing hard on further deep investments, it’s vital to prove to yourselves that by applying data or AI to a real business need, it adds value. This is particularly true if there is resistance from the organization, a lack of certainty among leaders, or no previous proven track record of success.
For example, you might identify that supplier costs have increased this year vs. last year, so you put together a small group to look into this and how you can rationalize to bring costs down. What you find in the data is that there are suppliers being paid by multiple different departments all on different commercial terms. As a result, you rationalize not the number of suppliers, but the efficiency of purchasing from that supplier, bringing the costs down in the process. This gives an incredibly simple but powerful test case for data to demonstrate value.
With these value cases tested, you will be able to refine your strategy, have a better idea of what success will take, and have built some champions, credibility, and confidence in the investments needed to scale.
Level 3 — Scaling Your Strategy
With value proven and credibility growing, this stage is foot-to-the-floor time. The investment required here is about growing out your team, platforms, data capabilities, and building the right skills, organizational structure, culture, and mechanisms that allow you to create a data-guided organization. With the foundational work in place with establishing the agenda properly and proving value through testing and iterating solutions earlier on, scaling up capability is done so with good roots.
Critically, the focus needs to remain on delivering business value, not just investing in capabilities. Aligning around key business problems, priority strategic objectives and operational improvements you are looking to make, ensures the investments are directed in the right place.
This is typically the meatiest stage with the broadest investment and careful consideration needs to be made about the order to invest in the capabilities. The roadmap here requires regular iteration to learn the impact that the work you’re doing has made to the organization. Opportunistically, you may pivot to jump on a new project that will deliver better value than where you thought you’d focus.
Level 4 — Accelerate Your Strategy
While you always want to be moving at pace, moving fast at scale isn’t always easy. It’s only possible if you have scaled on solid foundations through the earlier stages. Business acceleration comes from making faster and better decisions. This is possible when insights are available quickly and can be acted upon at pace. The more integrated those insights are into the workflow of decision-making, algorithms, applications, and processes, the more benefit you can achieve.
This all comes from maturing the technology you use to be more accessible, real-time, and integrated — this might be more sophisticated Generative AI technology, or a more API-driven architecture. It comes from automation and the ability to make rapid deployments and changes to data and AI products and platforms — being able to make changes to live systems in minutes rather than months means you can get to market more quickly. This is helped by building out capabilities using reusable components, almost like Lego building blocks — this means that sharing across teams is possible to avoid duplication of effort or different people using different data sets. And importantly, having the right culture, driving the right behaviours by people with the right skills – being able to be this adaptable to agile is tough for legacy organisations, but the shift in culture is worth it.
Level 5 — Optimize, Optimize, Optimize
Reserved for the marked few that make it to a point where data and AI are embedded seamlessly into their organization. It’s not a separate thing. It aligns tightly with the way a business is run, and strategy is defined. Digital and data natives, like Netflix, Uber, and Amazon, are here. It’s where small changes and improvements to data and AI products can make a big difference to the returns.
For example, a retailer through their metrics tracking spot that the conversion from baskets being updated to checkouts has dropped. They run some scenarios to predict the impact of changing the checkout journey, then A/B test the variants with live customers. They use tracking to identify which journeys improve conversion the best and apply the winning checkout journey across the customer base. These small optimizations, data guided, create a fast iterative commercially minded business.
The idea here is to continually optimize your business through the application of data and insights: It’s how innovation wins and reshapes what ‘business as usual’ means. Decision-making in many instances is hands-free, but should be made by considering the data and insight available every time.
Is Your Data Strategy Ever Complete?
Even when you have reached level 5, the game is certainly not complete. There will always be new levels and different challenges. As the organization evolves, team members change, technology capabilities increase, business performance fluctuates, and cultural norms shift, so should your data and AI strategy. You must be open-minded to the necessary changes. While there are helpful checkpoint levels, there is no finite end point where the process is complete. As the landscape changes, you need to change with it. That’s the common thread amongst the data winners: They recognize that adaptability is at the heart of scaling success.
