Are You Ready to Reengineer Your Decision Making?

There has been enormous progress in embedding the use of analytics at lower levels of companies. But according to Thomas H. Davenport, professor at Babson College and one of the best-known thinkers about analytics and business intelligence, the upper levels of companies haven’t kept up.

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When Thomas H. Davenport was writing about business analytics five years ago, the term du jour was “business intelligence.” Before that, it was “online analytical processing,” and before that “executive information systems,” and before that simply “decision support.”

Call it what you will, this thing he was looking at was essentially two activities: “There’s reporting, which is getting a handle on what has happened in your business,” Davenport says. “And there’s analytics, which, to me, is explanatory and predictive. It’s why this happened and what might happen going forward.”

Five years ago, Davenport says, analytics practitioners spent about 95% of their time reporting on the past and only about 5% on analysis. “Most companies were not really focused on the issue and, if they had analytics, it was a very siloed thing: a little bit in market research, a little bit in quality, maybe a little bit in actuarial for insurance companies.”

That’s changing. Some companies have gotten aggressive about analytics and become case studies for the smart use of analytics in their strategy: Progressive, Capital One, Harrah’s Entertainment, Google, UPS. But for most companies, the potential for analytics to become a critical tie to decision making remains an untapped opportunity.

The Leading Question

What challenges lie in the way of analytics being embraced by more executives for decision making?

  • Most companies still distinguish between transactional information systems and decision-oriented systems, although that distinction is breaking down.
  • The technical challenges of having analytics managers help executives in decision making pales compared to the cultural and political challenges.
  • Companies don’t yet embrace the science of collaboration and don’t yet view collaboration as a mission-critical activity to be measured and optimized.

Davenport, who holds the President’s Chair in Information Technology and Management at Babson College, says the link between analytics and decision making needs to be relearned: “What I’ve seen a lot with the proliferation of data and data warehouses and business intelligence systems is that the tie to actual decision making has been lost. We generate a lot of data, we supply a lot of tools and we say to people, ‘Okay, go at it, have fun, play, make better decisions.’ But we never actually ensure that they do.” In a conversation with MIT Sloan Management Review’s editor-in-chief, Michael S.


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Comments (5)
Adrian Alleyne
Great interview/article. Makes me wish I had taken a course with Professor Davenport when I was getting my MBA at Babson.  It’s interesting to see that this line of thinking is starting to make its way into the mainstream conversation about developing an analytical management mindset.  At DecisionPath we’ve been researching and writing about this for years. We spoke about it in our book, The Profit Impact of Business Intelligence back in 2006, and published findings from an empirical study on the positive impact of assimilating BI into core business processes in the Business Intelligence Journal in 2007 (“BI Impact: The Assimilation of Business Intelligence into Core Business Processes”). In a more recent, but related study, “Performance Management and Business Intelligence: A Power Combination”, we found a pretty startling statistic on the impact of coordinate BI with operational processes such as business performance management (BPM). We found that those with coordinated BPM and BI initiatives, companies are three times as likely to have achieved major business performance improvements. Really hoping to see you guys continue to evangelize the impact of analytics on companies’ bottom lines.

Adrian Alleyne
Director of Market Research
DecisionPath Consulting
Menes Rafael
What a delightfull topic!

Whether as a results of techology application or as result of acumen; results are all. Predictive models through CIM's and a DSS's make executive lives easier to manage what matter the most "Mission achievement", Lobbying is essntial when managing "A" staffs too, but accurate full of strategic content decision are mostly appreciated when looking for a leadership profile.

This are my smalls opinions.

Kind regards
Charles Anderson
I am impressed with your article.  I have been in health care for 30 years and there is a huge difference between collecting data and analytics. Most and I mean most managers are not trained in analytics.  Most don't have or don't want to spend the time.  In healthcare this issue is becoming even more critical as we move forward incorporating the total patient and family care experience.  
One point I think that is really important in this discussion is the notion of keeping the analytics at a level people can understand and relate to.  This doesn't, in the case of healthcare, pertain just to Doctors but to all members of the care team and at all levels, including the patient.  
I would like to hear more discussion about this issue as it relates to healthcare.
Mark Montgomery
Well congratulations on your new newsletter -- 'The New Intelligent Enterprise' -- appropriate topic and interview with Tom Davenport-- and Boris -- boy it's a small world these days.

I couldn't agree more on the need to re-engineer decision making -- we've gone so far as to say that evidence is overwhelming that many organizations need to re-engineer their enterprise structure (enter 'Structurally Engineered Enterprise'-- with 'integrity hard-wired' across the enterprise), particularly given that the vast majority of knowledge work is now digitized and stored.

Suppose there is a direct correlation between those org cultures that don't perceive a need to use (and improve) decision tools-- and those involved with systemic crises? Amazing that.

BTW, several months ago we started a series with a similar name, but much different format -- 'Semantic Scenarios for the Intelligent Enterprise'. In story telling format, very light on marcom and heavy on education -- we've been rather stunned at times frankly at the lack of understanding (and misunderstanding) at the highest levels surrounding this topic in many large organizations.

In our series-- after discovering that intelligence agencies and nuclear power plant operators (many leading universities, Pentagon, and WH among others) were visiting the site for the counter intelligence use case, but apparently afraid to register for access, we felt compelled to make freely available on the web. 

Based on the interest levels we see, from whom, and combined with need and technical ability, I am confident that we are approaching a fairly dramatic turning point in how organizations will be structured from an enterprise architecture perspective. We'll see a big leap in 'meaningful' quality of data, engagement across the enterprise, transparency and security, meritocracy, and decision making. 

Better late than never. 

Thanks for the work.

Mark Montgomery
Founder & CEO
Boris Evelson
Excellent insights. We at Forrester Research indeed see the same trend, where more advanced enterprises are venturing into combining reporting and analytics with decision management.  In my point of view this breaks down into at least two categories: automated (machine) vs. non automated (human) decisions, and decisions that involve structured (rules and workflows) and unstructured (collaboration) processes. Unfortunately current best practices and technologies to address these four distinct, but closely related requirements, come from different vendors, technologies and experts. The authors of the article are also correct in pointing out that a full loopback mechanism which measures decision outcomes is critical. Watch for upcoming Forrester research on this very important, but largely unaddressed (by analytics software and services vendors) topic.  I am also glad the authors pointed out a huge (often the key one) challenge, that I know my clients face every day, which is how does one convince a non-analytically oriented CEO that analytics and decision management are vital to enterprise success. One reason for such challenge, is that unlike any other enterprise application or a process, analytics and decision management are very hard (but possible) to build a business case around, with a concrete, tangible ROI. One way that we suggest our clients break through this executive logjam is with education on the benefits of analytics and decision management, often using competitive benchmarks. And guess what, analytics on analytics – or understanding when, who, and how analytics are used in an enterprise, and potentially correlating usage of analytics to decisions, good or bad - is also one of the emerging best practices.