Taking the Lead with Analytics: Hard-Won Lessons in IT

How is functional alignment more critical in driving the value of analytics than the technologies directly powering analytical insights? As discussed in our recent white paper, Taking the Lead with Analytics, those delivering sustainable business value from analytics– who we’ve defined as “leaders”—commonly exhibit trust in their actions, from data collection to data-driven decision making. Within that piece, we offer a deeper look at the beliefs and behaviors that separate leaders from laggards in establishing trust. This blog series aims to extend those insights with a focused look at key themes in the white paper. We kicked off the series by looking at the role that trust plays in organizational structure. Today, we’ll explore key success factors for the analytics-business relationship, applying decades of experience helping analytics teams, often part of IT organizations, become strategic business partners, insights from our research of over 200 analytics decision-makers, and advice from organizations leading on analytics.

Our research and insights from leading organizations illuminate several actionable principles that analytics leaders can apply to help forge a strategic and value-driven business partnership that nurtures trust and is right-sized to the needs of the business:

Lead from the Business Side

Over the past 30 years, IT organizations, and the role of the CIO has evolved as demands from the business escalate and technology-based innovation accelerates.  What is new in today’s climate, is the demand for CIOs and senior IT leaders to serve as transformational leaders, partnering with the business to uncover new sources of technology-enabled innovation and competitive advantage. Taking a “build it and they will come” mentality centered on a promising new analytics platform or technology without clear business engagement to ensure the adoption of  new capabilities in real, meaningful ways is at best, a waste of financial and human resources, and at worst, produces a patchwork of disconnected solutions that inhibit innovation and undermines any attempts to build an insights-driven culture.

Instead, sponsors should be identified (or recruited) and coached on their key responsibilities for building organizational trust: active and visible leadership, building coalitions across the business, and frequent communication to stakeholders affected by the change. Sponsors must be “true believers” responsible for owning a clear vision of the future state and the roadmap for the change. A clear understanding of the current state should guide a pragmatic approach, using quick wins to gain momentum and planning for the journey ahead. Active and visible executive sponsorship is the single most important element to the success of change initiatives and can’t be skipped or compromised on.

Organize for Action

The all-too-common state of entrenched IT and business silos carries baggage of poor quality and speed, but equally important is how this engagement model hampers the ability of initiatives to reach their full potential. Leading companies create a central home for the analytics function, while embedding or aligning very closely to the business. Failing to do so poses two threats that are especially relevant for the analytics journey. The first threat is that the view of analytics, is constrained by the capabilities of existing reports and tools and, as a result, requirements coming from the business are limited to existing, or historical limitations in reporting or analytics. This is a problem of defaulting too strongly to an IT agenda that is often driven by the need to standardize and simplify the technology landscape, which often stifles the very innovation needed to realize full potential of analytics. The opposite threat is that the desire of business to innovate and change wins out: requirements are highly specialized to specific functions or applications, leading to a fragmented patchwork of systems that work against enterprise-grade analytics.  A situation best characterized as “data anarchy” as shadow IT spending on analytics proliferates.

When closely aligned, the analytics function can serve each partner with their specific needs while also enabling those partners to break out of today’s paradigms. Eric Colson, Chief Algorithms Officer for Stitch Fix, explains to Kathryn Hume how his team prompted the development of genetic algorithms to design new clothes by recombining elements of past styles. “Sometimes things are not even asked for, and that's because it's not always obvious what data science can and can't do... So these are ideas that you really need to expect your data scientists to bring to the table: they're not going to be asked for, they're not obvious, they're very esoteric….” Our survey shows that this is a broadly-held belief: when asked to rank factors in order of importance to establishing a data-driven culture that fosters the use of data and analytics to inform business decisions, 56 percent of respondents included “Understanding of the business and metrics by analytics team(s)” in the top three, and 29 percent of leaders ranked this as the most important factor.

Take a Disciplined Approach

On the same question, 40 percent of leaders and just 29 percent of laggards included organizational change management in their top three responses. The importance for the analytics journey of change management practices such as developing a change agent network, embedding change by developing and reinforcing habits, and communicating clearly and frequently is increased compared with traditional IT transformations because an even higher degree of the payoff relies on people doing work a new way.

Indeed, the promise of analytics capabilities is more about equipping individuals to use data to creatively increase their added value to the organization rather than “just” adopting and becoming proficient in use of a new tool or process. In this way, a successful change program will be more akin to “way of thinking” change initiatives like Six Sigma or the imperative to cross-sell a newly acquired portfolio, and less similar to traditional technology platform implementations emphasizing “process of working” changes associated with major ERP and CRM programs. 

Co-Create Value and Trust

While the previous points focus on elements subject to central planning and control, teams leading the analytics journey can’t stop at “handing down” change. In today’s flat and decentralized organizations, even the most dynamic sponsor with the most sterling business case can’t go it alone: shared ownership is a requirement, and moreover it is consistent with keeping close to business value and with the imperative to change minds, not just actions. This means engaging broad sets of stakeholders to co-create for design of the transformation, for the delivery, and through engagement at all stages of the journey.

While it requires more effort, this deep engagement helps analyst job satisfaction as well. Colson describes how business partners at Stitch Fix engage one of his analytics directors: “they're really coming to her for her ideas, her partnership…often there are just ideas that need to be fleshed out and the two can then collaborate from there.” Adding business value (and seeing the analytics outputs acted upon and making a difference) boosts the relevance of the analytics team’s work – and when an analyst or data scientist becomes a trusted advisor, he or she feels valued and is more satisfied with the work.

Putting it in Perspective

The transformative potential of analytics commands a partnership between business and analytics teams that is right-sized to drive strategic objectives. In fact, in April 2018 North Highland-sponsored research with over 300 cross-functional technology decision-makers, respondents most commonly cited “Data & Analytics” as the technology most likely to have a high impact on the business in the next two years (66 percent)—even exceeding security tools (59 percent) and artificial intelligence (45 percent) in relevance.

Forging this partnership won’t be easy. The path to analytics value requires tearing down IT and business silos and applying these lessons to the new analytics journey – a journey that requires not just a new processes and systems but new systems of thinking and believing, deeply imbedded in an organization’s makeup. In the next part of the series, we’ll look more closely at how to apply principles of cultural change to the analytics journey.

For a complete picture of the landscape of trust in analytics, take a look at the other pieces in our series: