Does Your Data & Analytics Strategy Have These 10 Crucial Elements?
Imagine you’re the captain of a boat moving upstream. Your crew of five struggles to row against the current. You then add a new member to the team.
This person jumps into the boat, picks up an oar, and enthusiastically begins to row—but in the opposite direction.
Their efforts aren’t just in vain; they’re slowing the entire boat. By rowing backward, this new arrival cancels part of the effort spent by the rest of the crew.
This is exactly what happens when your data and analytics (D&A) efforts don’t align with your organization’s business goals. The big-ticket investments and actions don’t just go down the drain, but they kill your business momentum.
Many organizations venture into D&A without any data strategy. The results are equally disastrous.
I’ve often observed this unfold while working with chief data and analytics officers (CDAOs) in my data advisory engagements across industries. To pinpoint why this happens, we’ll look at the ten critical elements of a winning data & analytics strategy using examples from industry experts.
Why do organizations drive blind on the data highway?
McKinsey found that just 30% of organizations align their data strategy with their organizational strategy. By implication, 70% of leaders are burning money in the name of data.
Why do organizations make this fundamental mistake?
“Truly impactful data strategies are only just coming into their own due to the advancement of technology and maturing of things like enterprise artificial intelligence (AI),” says David Benigson, CEO of Signal AI.
Despite the buzz around data analytics and AI, leaders often view these elements tactically. They turn to analytics projects to solve localized pain points across the organization. Not surprisingly, a tactical approach with data just delivers marginal results for a business.
Benigson has seen such misalignment at legacy organizations that operate with an old-world mindset. They fall short on innovation, fail to implement iterative sprints, and struggle to pivot into an open culture with a data-first mindset.
Orchestrating an organization-wide data strategy takes time and effort. “However, failing to plan is planning to fail,” quips Maya Zlatanova, CEO of FindMeCure.
What is data strategy?
Data strategy is another buzzword in the industry, quite like AI, Data Science, or Big Data. It can mean a variety of things, ranging from the most strategic to the very operational. What is the purpose of a company’s data strategy?
Strategy is defined as a plan to achieve one or more long-term goals under conditions of uncertainty. Similarly, a data strategy is a plan with a set of choices that help achieve long-term business goals.
Benigson adds that “a data strategy should enable better decision-making through the use of data. It should define how best to capitalize on, manage, analyze, and act upon data to realize organizational goals.”
What are the ingredients of a winning data and analytics strategy?
Let’s look at the ten critical elements of an effective data strategy framework. We’ll cover the questions you must ask to elicit the correct details for each component.
1. Key Business Goals:
Since a data strategy is crafted in service of the business goals, there must be absolute clarity on the organizational vision and key business priorities. Ask the leadership team, “what are the long-term business goals?” Review and internalize the organizational strategy.
Consider the business goals of Janssen Pharmaceutical Companies owned by Johnson & Johnson: “We aspire to transform lives by bringing life-saving and life-changing solutions to people who need them. We’re committed to providing safe and effective medicines as well as the services and support that contribute to healthy outcomes.”
We’ll next see how data can enable this goal.
2. Data & Analytics Vision:
Once you understand the business goals, find how data and analytics can help you achieve them. This vision for data can guide your choice of stakeholders, selection of initiatives, and validate whether they deliver the intended outcomes.
“At Janssen, we’re applying data science end-to-end across our portfolio by focusing on finding solutions to big questions that can advance our impact on patients,” says Najat Khan, Chief Data Science Officer & Global Head of Strategy & Operations for Research & Development at Janssen.
“It’s helping us better understand diseases and the patients impacted by them. It [helps] us to select the most promising compounds to advance into clinical development; design more efficient, diverse, and targeted clinical trials; diagnose rare and difficult-to-detect diseases earlier; and connect patients with treatment sooner,” she adds.
3. Target Stakeholders:
While picking business objectives, ask, “who do you want to enable through your data initiatives?” While it’s tempting to serve everyone, this isn’t realistic—particularly at the start of your data journey. Pick a set of target departments and roles.
For example, you could start with Research & Development and Commercial teams in a pharma firm. The organizational data plan could help accelerate drug development and achieve targeted business growth. Then, you plan to expand coverage to the entire organization.
4. Strategic Initiatives:
Once you define your destination, find which big initiatives will get you closer to your goals. These strategic programs will help your chosen stakeholders achieve their business objectives. (Check out my earlier article on picking initiatives and building a data strategy roadmap.)
“Start with the question you are trying to answer and work back since there is no point having data for data’s sake,” advises Benigson. “For example, in the communications function, we now have quantifiable reputation data on the association between brands and topics of interest in the global media.”
5. Measures of Success:
For each strategic initiative, ask, “what will success in these initiatives look like?” People often pick initiatives based on urgency rather than business impact. Documenting the desired outcomes also helps validate whether the chosen initiatives are the most important.
“Use key performance indicators (KPIs) and objectives & key results (OKRs) to track progress with your business goals,” recommends Zlatanova. “This could help uncover blind spots along the journey.”
6. Sources of Funding:
Clarity about the initiatives and their outcomes will inform the next question, “who will foot the bills for these programs?” Some organizations make big strategic plans but soon realize they are unable to secure the needed funds midway. Identify your likely sources of funding.
For example, some firms assign a fixed percentage of the departmental budget toward organization-wide D&A initiatives. Others allocate a portion of their centralized technology spend for such programs.
7. Top Enablers:
Strategic plans may be easy to make but are tough to execute. Leaders who manage this successfully ask, “what are the strategic ways to support the efforts?” They spot tailwinds within the organization and capitalize on them.
For example, a firm with an innovation-friendly culture might see lower resistance to user adoption. Zlatanova shares that “a good strategy should have all the answers for the why, what, and how questions.”
8. Top Challenges:
While identifying the tailwinds, savvy leaders must also watch out for the likely headwinds or challenges. Find the biggest roadblocks to anticipate and plan ways to mitigate them. Articulating the support needed with the organizational data strategy is an excellent way to secure the necessary resources.
For example, firms often vary in the sophistication and maturity of technology implementation across departments. This could hamper rollouts and must be factored while planning initiatives.
9. Governance Plan:
To ensure a data & analytics strategy is executed throughout the year, ask, “what mechanisms will track and review outcomes from D&A?” Plan to review the progress not just of business projects but also of technology initiatives such as platform upgrades or upskilling programs.
For example, organizations may set a steering committee that convenes quarterly to review the progress of programs, validate outcomes, and greenlight new initiatives. Plan for such interventions.
10. Capabilities to Build:
While detailing the data and analytics strategy, you must invest in capabilities across people, process, and technology perspectives. This is crucial for building the strategy execution muscle. Ask how to onboard and empower users, how to rewire your business processes to integrate your D&A initiatives, and how technology strategy can enable D&A efforts.
Organizations are often reactive and don’t plan for these capabilities. Benigson adds that “companies must make the critical investment of building competencies, adopting new tools, and helping teams keep an open mind to experiment and adopt.”
What does it take to execute an organizational data strategy roadmap?
Executives play a crucial role in the crafting and realization of an organization’s data strategy. Benigson shares that “leaders must help define the most important business problems to solve.” They should help set measurable goals for the D&A initiatives.
To nurture an environment that is conducive to decision-making with data, “[Leaders] must lead by example and show that they use data for their own decisions,” advises Zlatanova.
Bringing about a cultural shift with data is easier said than done. “But, what we’ve found is that early successes generate momentum,” reveals Khan. “When you show people what data science can do for patients, it helps them see the possibilities and gets them excited about the potential for impact.”
She shares an example from the development of their COVID-19 vaccine. “We leveraged data science to identify where the ‘hot spots’ would be when we were ready to launch our Phase 3 clinical trials. This had a 90% accuracy, down to the county or province level, four months in advance. It helped accelerate our development timeline by six to eight weeks. Examples like these generate significant momentum.”
We’ve seen how to craft an effective data and analytics strategy and pave the way for meaningful execution. A focus on both aspects is indispensable.
To quote the famed military strategist Sun Tzu, “Strategy without tactics is the slowest route to victory. Tactics without strategy is the noise before defeat.”
Check out this whiteboard video where I walk through these ten elements of an effective data strategy.