The Real Reason Organizations Resist Analytics

While discussing a Harvard colleague's world-class work on how big data and analytics transform public sector effectiveness, I couldn't help but ask: How many public school systems had reached out to him for advice?

His answer surprised. "I can't think of any," he said. "I guess some organizations are more interested in accountability than others."

Exactly. Enterprise politics and culture suggest analytics' impact is less about measuring existing performance than creating new accountability. Managements may want to dramatically improve productivity but they're decidedly mixed about comparably increasing their accountability. Accountability is often the unhappy byproduct rather than desirable outcome of innovative analytics. Greater accountability makes people nervous.

That's not unreasonable. Look at the vicious politics and debate in New York and other cities over analytics' role in assessing public school teacher performance. The teachers' union argues the metrics are an unfair and pseudo-scientific tool to justify firings. Analytics' champions insist that the transparency and insight these metrics provide are essential for determining classroom quality and outcomes. The arguments over numbers are really fights over accountability and its consequences.

At one global technology services firm, salespeople grew furious with a CRM system whose new analytics effectively held them accountable for pricing and promotion practices they thought undermined their key account relationships. The sophisticated and near-real-time analytics created the worst of both worlds for them: greater accountability with less flexibility and influence.

The evolving marriage of big data to analytics increasingly leads to a phenomenon I'd describe as "accountability creep" — the technocratic counterpart to military "mission creep." The more data organizations gather from more sources and algorithmically analyze, the more individuals, managers and executives become accountable for any unpleasant surprises and/or inefficiencies that emerge.

For example, an Asia-based supply chain manager can discover that the remarkably inexpensive subassembly he's successfully procured typically leads to the most complex, time-consuming and expensive in-field repairs. Of course, engineering design and test should be held accountable, but more sophisticated data-driven analytics makes the cost-driven, compliance-oriented supply chain employee culpable, as well.

This helps explain why, when working with organizations implementing big data initiatives and/or analytics, I've observed the most serious obstacles tend to have less to do with real quantitative or technical competence than perceived professional vulnerability. The more managements learn about what analytics might mean, the more they fear that the business benefits may be overshadowed by the risk of weakness, dysfunction and incompetence exposed.

Culture matters enormously. Do better analytics lead managers to "improve" or "remove" the measurably underperforming? Are analytics internally marketed and perceived as diagnostics for helping people and processes perform "better"? Or do they identify the productivity pathogens that must quickly and cost-effectively be organizationally excised? What I've observed is that many organizations have invested more thought into acquiring analytic capabilities than confronting the accountability crises they may create.

For at least a few organizations, that's led to "accountability for thee but not for me" investment. Executives use analytics to impose greater accountability upon their subordinates. Analytics become a medium and mechanism for centralizing and consolidating power. Accountability flows up from the bottom; authority flows down from the top.

That's where resentment arises. The emerging cultural challenge for leadership is whether analytics-driven accountability cuts both ways. Are business unit leaders and top executives using analytics to make themselves more transparent and accountable? Should "accountability analytics" be internally branded as a something "shared" rather than "imposed?"

Transforming the culture and practice of analytics inherently transforms your culture and practice of accountability. The mathematics and technologies of sophisticated analytics are increasingly well understood. The cultures and challenges of accountability are not. Going forward, which do you think will matter more?

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