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TDWI Upside - Where Data Means Business

It's Actions, Not Analytics, That Count

Executives who make decisions and act upon them face mainly organizational challenges to deliver business value. They need better technology support.

"Stop buying 'analytics' -- it's time to invest in answers" is Teradata's recent, somewhat tongue-in-cheek, marketing message. As an attempt to move customer focus up the decision-making value chain, it makes sense to me. Answers are more valuable to the business than analytics (and let's not even mention data).

For Further Reading:

AI for BI: Better or Faster Decisions?

How the Trust Gap Is Holding Back Data-Driven Decisions

Analytics is Only One Scenario in Decision-Making Support

Sadly, the slogan doesn't go far enough. It's only through actions -- committed, completed, and continued -- that the chain of value creation from first data capture or information definition to bottom-line impact is achieved. The first part of this chain links data and information to knowledge and meaning. The second part consists of decision, action, and confirmation.

Note that neither analytics nor business intelligence (BI) appear in the list. They are simply tools, and rather limited tools at that, focusing on the first part of the chain, that put data in context and offer deeper insights into what it may mean. For further details about the path from data to meaning, please see my previous Upside articles: part 1 and part 2 of "From Data to Information to Actionable Insight."

The last three links in the chain -- decision, action, and confirmation -- generally and unsurprisingly receive little attention from the analytics and data management industries. Technology has so far had a minimal role in these areas, although proponents of artificial intelligence suggest that will change rapidly. In this article I will focus on these last three links -- specifically, on the current makers of decisions and takers of action: managers within organizations.

Four Classes of Decisions

"It's the best and worst of times for decision makers," according to a 2017 McKinsey article "Untangling your organization's decision making ." The authors, De Smet, et al, noted that modern business leaders have far more useful information than ever, better analytics tools, and even an understanding of cognitive biases that need managing. However, growing organizational complexity, use (or often misuse) of cheaper and more pervasive communication tools, and poorly defined decision-making authority have more than offset the advantages. Seventy-two percent of senior-executive respondents said that "bad strategic decisions either were about as frequent as good ones or were the prevailing norm in their organization."

The authors provide an interesting 2x2 matrix, categorizing decisions against level of familiarity and scope/impact, defining four decision types:

  • Ad hoc: Infrequent, low-stakes decisions
  • Big bet: Infrequent, future-shaping decisions
  • Cross-cutting: Frequent, high-risk series of small, interconnected decisions made across different groups in collaborative, end-to-end processes
  • Delegated: Frequent, low-risk decisions effectively handled by individuals or small teams with limited external input

They note that this classification and its consequences, although largely self-evident, are often overlooked because "organizational complexity, murky accountabilities, and information overload have conspired to create messy decision-making processes in many companies."

Three Best Practices in Decision Making

In a follow-up survey "Decision Making in the Age of Urgency," published by McKinsey in April, De Smet, et al, define three best practices in decision making and suggest that they each have particular relevance to a specific decision-making class. In digital business, where decision urgency and quality are often seen to be in conflict, these suggestions are well worth deeper investigation. Unfortunately, the work did not address ad hoc decisions.

For big bet decisions, they recommend stimulating productive debate among executives by adopting three specific strategies in discussions and debates: exploring assumptions and alternatives beyond the initial proposal, actively seeking hypothesis-refuting information, and designating some executive(s) to play devil's advocate.

In the case of cross-cutting decisions, the focus should be on a well-coordinated process to help clarify objectives, measures, targets, and roles, both in management meetings and, more important, in coordination between the multiple players involved beyond the meeting rooms.

For delegated decisions, the key is to ensure that the managers closest to the work take responsibility and that executives pay more than lip service to empowerment by explicitly coaching the accountable managers in taking responsibility. Senior executives must avoid stepping in or overruling, which often leads to decisions being constantly escalated up the management hierarchy.

Across all three classes, it's vital to commit to taking action so that decisions arrived at through these processes are actually carried out. This hints at the sixth link in my chain, confirmation, a topic that merits an article all its own.

Mind the Information Gap

For those of us who approach decision-making support from an information-centric viewpoint, the paucity of recommendations for more or better data is notable. "Data driven" doesn't get a look in. "Information" gets the barest mention. In fact, information -- especially in overload -- is seen more as an inhibitor of decisions. "Interpersonal skills" and "organizational practice" are highlighted, together with the processes for maximizing benefit through them.

With process as the primary focus, the most useful tools are those that choreograph personal interactions, build adaptive workflows, and enable collaboration. Although some analytics and BI tools do address some of these areas, their focus is often too-much oriented toward the IT/business boundary as well as data preparation and information quality activities.

Though consultants such as McKinsey correctly emphasize organizational management best practices, BI and analytics practitioners (as well as tool vendors) will benefit from shifting their attention from delivering pretty data visualizations to helping managers get from insights to decisions and actions.

About the Author

Dr. Barry Devlin is among the foremost authorities on business insight and one of the founders of data warehousing in 1988. With over 40 years of IT experience, including 20 years with IBM as a Distinguished Engineer, he is a widely respected analyst, consultant, lecturer, and author of “Data Warehouse -- from Architecture to Implementation" and "Business unIntelligence--Insight and Innovation beyond Analytics and Big Data" as well as numerous white papers. As founder and principal of 9sight Consulting, Devlin develops new architectural models and provides international, strategic thought leadership from Cornwall. His latest book, "Cloud Data Warehousing, Volume I: Architecting Data Warehouse, Lakehouse, Mesh, and Fabric," is now available.

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