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Pattern-driven Performance: Should You Start with Tools—or with Talent? That’s one of the questions addressed by Deloitte at Real Analytics:
Companies everywhere are catching onto the wisdom of mining information for patterns of performance. Using a combination of advanced statistical tools and good, old-fashioned experience, they’re discovering and dissecting hidden patterns that can help guide their choices in operations, talent, technology, financial strategy, you name it. For those looking to drive performance in this way, there are two paths forward: start by investing in tools or start by investing in talent.
In Analyst First terms it’s a contest between human and electronic infrastructures. Deloitte frames it as a debate, presenting a set of rhetorical, stylised point / counterpoints to which a panel of its Directors and Principals respond. The case for tools first cites scale, automation, efficiency, transparency, and some degree of insulation from undesirable human subjectivity. It also talks up the scarcity of good people and talks down the difficulty of analytics. The case for talent first argues for the importance of business knowledge, an appreciation of context and nuance, interpretative skill, big picture understanding, the ability to ask the right questions, and the soft skills required to build cross-functional communication, coordination, trust and support networks.
Three out of the four Deloitte contributors prioritise talent over tools; the fourth elects both. As Janet Foutty, National Managing Director, Technology, Deloitte Consulting LLP puts it:
[T]here’s a big problem with the “buy technology first” approach: What if you’re not asking the right questions. I know it might sound strange coming from a person who leads Deloitte’s IT services, but I’m “talent first” all the way.
One of Analyst First’s key principles is our advocation of investing in the human over the electronic infrastructure. Simply recommending “both” is appealing, but the reality is that investment decisions are always taken at some margin at which a trade-off is being made, so “both” is never a real choice. A decision to spend any amount of money on commercial software is always a decision to not spend that money on alternative uses—such as hiring more or superior analysts. In comparing the marginal utility of commercial tools with alternative investments, the following should be added to the case put by the Deloitte panel, which further strengthen it:
- Although some instantiations of analytics are process-based, analytics itself is not a process.
- Most of the analytical tools any organisation will require, especially at the outset of its exploration of analytics, are readily available. Much can be done—and is being done—with the commodity tools already available on analysts’ desktops (e.g. Excel and SQL) and with open source tools such as RapidMiner and R.
- Many different tools exist—commercial, commodity, and open source—and sensibly choosing between them means becoming an educated buyer. This entails leveraging experimentation and experience—either in-house in the form of trial and error, or that of outside help.
- Prominent expenditure on commerical tools—while it may perversely benefit individuals—erects a number of barriers to organisational success.
- Tools without sufficient expertise are not harmless. In fact, they may act as risk multipliers.
This does not mean that the marginal utility of commercial software is always less than the marginal utility of analysts. This is of course possible. However, it is empirically the case far less than outsiders to Business Analytics—and many insiders—intuitively expect.
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