Competing With Data & Analytics
What to Read Next
The growing use of analytics in organizations is powered by analytics teams, which are often underfunded, misunderstood, and starved for talent. Extracting business value from data depends on nurturing the development and effectiveness of these groups. Yet, despite the demand, just a quarter of analytics teams operate as “business drivers” today, proactively leveraging data to unearth business opportunity. Most are seen as internal “consultants” (41%) or “service providers” (31%), suggesting opportunity on the side of both analytics teams, and the leadership they serve.
The confluence of factors arising from burgeoning data availability, open source, more affordable technology, and consumer demand for betterment on every front suggests it might be prime time for analytics teams. But for a host of reasons, analytics teams are still at the cusp of delivering the breakthrough value their discipline is capable of. The current demand for analytics outstrips supply — there is simply too little talent, and much of it turns over too quickly to fill the barrage of demand. Additionally, management itself is not yet well versed in analytics, while analytics teams are not effective translators of data. Lastly, the promise of technology is not yet at pace with the reality of the data, plagued by continued issues of cleanliness, connectivity, and availability.
Management faces a shortage of analytics talent, an uphill battle to work with analytics teams in a way that extracts maximum value amidst mounting cost pressures, and the promise of what new data and technology delivers. All the while, the immediate need for more insights persists.
Analytics Talent — Where Demand Outpaces All Hope of Supply
It’s not uncommon for analytics demand to be up 30% to 50% year over year, and for analytics leaders to be in perpetual hiring mode as talent pool migrates from company to company, lured by the promise of better environments, higher pay, and cooler jobs.
Consider the fact that analyst hires can take up to one-third longer than average, with counteroffers the norm. From experience, I know it can take up to a year for them to learn company data, systems, and the business itself. At 18 months — just as analysts become value-generating — they’re also highly marketable and vulnerable to poaching, while corporate pay scales and compensation policies struggle to keep pace. Moreover, the growing need for translation layer roles (those embodying that rarefied business/analytics hybrid skill set) persists, and is in even higher demand. Compounding matters is the scarcity of women in technology amid pressure for diversity. This perpetual analytics talent quest plagues companies competing for the coveted talent pool while they struggle to meet increasing demand for business analytics.
Naturally, analytics teams need to do the dutiful: rationalize, ruthlessly prioritize, standardize, automate, and build self-serve solutions for ongoing reporting. They need databases to track their own work, standard intake processes, and analytics on the analytics they perform, and the value generated. But most, drowning to manage business requests amid double-digit vacancy rates, aren’t able to keep on top of this foundational work. Yet an assessment of analytics work — how it ladders to strategic priorities and the time and human resources it takes to get it done — is crucial for management to understand the yield on analytics teams and the need to invest.
Management also needs to invest in customized acquisition, development, and succession plans for analytics talent. Beyond the monetary rewards, career ladders, technical and non-technical training, exposure to technology advances, direct involvement in solving business problems, and seeing the outcomes are essential to attract and retain analytics people. Without them, they walk. And with them, a company’s best chance for data monetization.
Email Updates on AI, Data & Machine Learning
Get monthly email updates on how artificial intelligence and big data are affecting the development and execution of strategy in organizations.
Please enter a valid email address
Thank you for signing up
Improving How Management and Analytics Work
Insights teams, whether operating as centralized functions (50%), decentralized functions (26%), in hybrid models (20%), or some combination (4%), must continuously align to shifting organizational priorities. It’s not uncommon for two-thirds of analytics teams’ volume to still be ad hoc (not offloaded through automation or self-serve), and for it to take several turns between analysts and leaders to arrive at what’s needed. This is a function of both the leaders’ “analytics IQs” still being low, and the analysts themselves needing to develop stronger business acumen and translation skills.
To increase analytics IQ, some companies have sought formal training from business analytics schools who teach executives a base level of analytics knowledge, while working to foster a fact-first culture. There is growing recognition that analytics — while a functional discipline — is fast becoming an essential business core competency, and this knowledge investment is helping organizations with aspirations to compete analytically develop the analytics skill to do that en masse cross-company and at every level.
Management must also work to increasingly embed analytics talent in the business in an effort to foster cross-pollination and, ultimately, data monetization. Increased investment in communication, influence, and persuasion skills is important to fuel a better exchange and teach historically technical analysts the value of translation. This enriched value exchange between management and the analytics function is essential to propel the business forward, producing the right kinds of insights, which will be well understood and ultimately leveraged for improved business performance.
The Promise of Technology
There is often a disconnect between the promise of what technology will bring, and what it takes to monetize it through analytics. Whether essential building blocks like the 360-degree customer view, business intelligence (BI) solutions, or newer advances like Apache Hadoop, migration to open source, and artificial intelligence, for them to become value-generating the data itself needs to be clean, complete, and integrated — and it still isn’t (although progress is being made). The daily data gymnastics analysts perform still consumes 30% to 40% of their time, as they move, clean, and prep data to be analyzed. This robs skilled analysts of the higher-order analytics work, and the embedded inefficiency is still not widely understood by the leaders who clamor for their output.
Classic IT-driven analytics solutions are still most often cost and efficiency plays, getting all groups onto common environments or upgrading platforms. They’re not driven by revenue, opportunity cost of time, or incremental uplift, though increasingly management is focused there, attuned to the power of data. The impact to the impending technology changes and enhancements (which are numerous) to analytics teams needs to be carefully orchestrated, especially given work demand analytics teams face. The same analysts conducting analysis are often the people who know the data and are involved with new technology plans, so balancing priorities becomes essential.
As data and analytics work are distinct but interdependent, heads of both teams need to partner with the business to ensure alignment against the right business priorities to drive toward data monetization and smooth execution, given the state of the current and emerging talent base.
The Path Through Analytics Disillusionment
Organizations today find themselves at a crucial intersection where the promise of analytics and access to new technology are imminent. We test the potential of technology, while beholden to the scarce talent that mine our data today, and the growing and evolving demands of those who consume it. We are navigating this great in-between space, and rather than toil in analytics disillusionment, eternally starved for more insight, and reorganizing ourselves to get at it, we can acknowledge the state of the union, and manage through it practically.
Foundational data, and a good, integrated technology plan is one key ingredient. Increased management investment in analytics, including developing an analytics talent strategy, will become crucial to extract the value out of our greatest, most underleveraged organizational asset: our information.