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Thomas Davenport published an article in Harvard Business Review entitled “Competing on Analytics.” He concludes the article with a checklist of ten key points he feels are important to creating a analytics-based business.

We disagree with quite a few of these points and even where we agree, we want add real-world nuance.

The challenge of analytics is communication and creating a shared understanding. It’s about focusing on high impact areas, moving forward one step at a time, being skeptical, being creative, searching for the truth. Any company can compete on analytics, and you certainly don’t need to satisfy a checklist to do so.

Here’s Davenport’s checklist, with Juice commentary. We’re putting together a list of practical steps anyone can take.

1. You apply sophisticated information systems and rigorous analysis not only to your core capability but also to a range of functions as varied as marketing and human resources.

Analytics is hard. Analytics takes resources. It takes effort for an organization to create and assimilate learnings from analytics. You need to focus your analytics at the key leverage points of your business. As Davenport points out in the HBR article, UPS focuses their analytics on knowing where packages are, Marriott focuses on revenue management. If you try to do everything, you won’t do anything well.

2. Your senior executive team not only recognizes the importance of analytics capabilities but also makes their development and maintenance a primary focus.

Of course analytics are good. But so is branding, innovation, operational excellence, customer focus. Companies are defined by what they don’t do just as much as what they do. If you’re going to make analytics a primary focus, you will need to make sacrifices elsewhere. Which of the above are you willing to de-emphasize?

Capital One, oft cited as the credit card king of analytics, aren’t customer service champions nor are they particularly innovative.

3. You treat fact-based decision making not only as a best practice but also as a part of the culture that’s constantly emphasized and communicated by senior executives.

This is hard to argue with. However, it’s easier said than done. In our experience, getting to a culture of decision making requires your business to have real, solid wins using analytics to make people care from top to bottom.

4. You hire not only people with analytical skills but a lot of people with the very best analytical skills—and consider them a key to your success.

The problems raised by the Mythical Man Month apply to analytics. Just as doubling the number of programmers on a project won’t halve the time it takes to complete a project, doubling the number of analysts won’t make your company twice as smart.

What you need are well placed and versatile analysts – analysts that are in constant communication and debate with key decision makers.

5. You not only employ analytics in almost every function and department but also consider it so strategically important that you manage it at the enterprise level.

What does this mean?

One thought: This refers to having a Chief (Analytics|Knowledge|Data) Officer. This may be a good idea. Here’s an interesting interview with Usama Fayyed, Yahoo’s Chief Data Officer about the value of having a chief data herder at a data intensive company.

If, on the other hand, this means centralizing analytics and building a single data warehouse, we disagree. For most companies, building a big “atomic baloney slicer” for analytics is not going to work out. These approaches take too long, are inflexible, and don’t adapt to your business.

6. You not only are expert at number crunching but also invent proprietary metrics for use in key business processes.

Why is “proprietary” a good thing? What you do want is to develop a few metrics which are core to the success of your business. If you are in a well established industry, it’s likely those metrics have been defined and are well understood. There’s a lot of value in well understood metrics that everyone in your business understands. The challenge with analytics is communication and creating a shared understanding.

7. You not only use copious data and in-house analysis but also share them with customers and suppliers.

Insight is not measured by volume. As for sharing with customers and suppliers, it’s a rare company that has evolved that far (e.g. Toyota). Focus analytics where you have the most leverage to change your business.

8. You not only avidly consume data but also seize every opportunity to generate information, creating a “test and learn” culture based on numerous small experiments.

There’s lots of ways to build insight from data. It can be test and learn, it can be customer visualization, it can be scoring systems.

9. You not only have committed to competing on analytics but also have been building your capabilities for several years.

Yes. Analytics is a learning process – a journey, not a destination. The best companies have been working on learning for a long time. You can compete on analytics without having worked on it for years. Just get started.

10. You not only emphasize the importance of analytics internally but also make quantitative capabilities part of your company’s story, to be shared in the annual report and in discussions with financial analysts.

You risk hypocricy if you follow this advice. Culture starts with internal stories. External stories will arise naturally and organically from internal stories. If you focus on external stories the best you can hope for is to find yourself in a Harvard Business Review article.

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  • http://www.jimnovo.com Jim Novo

    Sorry Juice, I ain’t buying what you are selling.

    Most of the 10 counterpoints made above focus on one of three things:

    1. Reporting versus analysis – reporting and reacting, reporting and reacting, (repeat cycle) often does not result in “root cause”, real analysis.

    Let’s say marketing reporting shows customers generated by a certain campaign are of low quality. Marketing starts tweaking the campaign so it generates higher quality customers, but it doesn’t work; they waste a lot of time and money.

    Over in customer service, they are doing their own reporting showing that this same campaign generates a ton of customer service problems because of the way it is worded; they use this reporting to defend additional requests for staff.

    This is analytical failure due to “reporting” without any real analysis. Both silos waste time and money, and nobody gets to “root cause” because there has been no true analysis. Low value customers continue to be generated and costs spiral up. I’ve seen this exact scenario repeated over and over.

    2. Micro versus macro analysis – if a silo wants to keep an analytical “lead” in it’s own little box to do the navel-gazing, silo-focused analysis that impacts it’s own little box, then that’s OK. Just know that this analysis, while meaningful to the little box, cannot be used or trusted anywhere else in the company and so is of very little value in a macro way. But it’s safe; the silo can proceed with the $10 “micro tweaks” and have full accountability while the competititon is making macro process changes worth millions using centralized analytics.

    3. Centralizing analytics will be “hard” – Sure, change is hard. New thinking is hard. Staying inside the box is easy; silo thinking is easy.

    Reporting on one’s own little domain so one can control their own accountability is not only a structurally weak approach prone to data torture, it is also wildly inefficient from a corporate perspective.

    How is it possible to “focus your analytics at the key leverage points of your business” when the analytics are coming from a micro perspective, or worse, are not really analytics at all but simply silo-based reporting? Meaningful, truly impactful “leverage” comes with cross-functional analysis, not silo-based reporting.

    Jim

  • Zach

    Jim,

    Thanks for your comments. I doubt we disagree on the application of analytics as much as it may appear. I’ve enjoyed your web site and newsletter and know that you are very interested in practical approaches to making better decisions. Let me see if I can give a fuller view of our perspective in response to your comments:

    1. Reporting versus analysis.

    Coincidentally, I was just working on a blog post about the over-emphasis on reporting (executive dashboards, KPIs, balances scorecards, etc.) vs. digging into data to understand root causes and finding opportunities. As far as we’re concerned, reporting should be focused on situations where a process or system is well understood and “under control.” I agree with you that hypothesis-driven analysis is far more likely to lead to substantial innovations than gazing at the weekly report for your division.

    2. Micro versus macro analysis

    While we didn’t mean to imply that silo’d analytics is the way to go — I think every organization faces an important balancing act. On the one hand, centralized analytics ensures a multi-faceted view of the problem (to your point about marketing vs. customer service misalignments). This is critical to fully understanding your business. At the same time, there are sacrifices to centralizing analysts. First, they will lack a deep understanding of the problems. Many times I’ve seen lack of intimacy with the data, processes, and unique issues of a particular business area undermine the success of an analysis. More importantly, good analytics is an evolution of thinking and deciding. We’ve seen much more success when an executive responsible for something has analysts nearby who can help them make data-driven decisions. The typical asymmetric communication — i.e. presenting a fully-baked analysis/recommendations to executives — is far less effective than the continuous, informal questioning and answering between managers and analysts. All that said, I’m not sure certain about the precise balance that works best.

    3. Centralizing analytics will be “hard” – Sure, change is hard. New thinking is hard. Staying inside the box is easy; silo thinking is easy.

    Here’s what we are reacting to: companies recognize the need for more data-driven decision making then embark on a crusade to make it happen. They bring in a technology-focused consulting company which promises to build them the business intelligence system to end all BI systems. This approach is: 1) too big; 2) too slow; 3) too expensive; and 4) neglectful of the organizational mindsets that have to change. I know from your work that you recognizes that analytics isn’t about the tools. I just haven’t seen a successful version of this “big bang” approach in any of the companies I have worked with.

    Thanks again for your comment. I’d love to hear more of your thoughts.

  • Chris

    Jim,

    You might not be buying what we’re selling, but I don’t think you’re reading what we’re writing either.

    I prefer the tone and scope of your more reasoned argument at http://insideanalytics.blogspot.com/2006/02/research-competing-on-analytics.html#comment-114035810728586370 and recommend anyone who’s gotten this far to check out the thread at insideanalytics.

    “The VP marketing presents analytics based on the company having 10,000 customers. The VP product area presents analytics based on the company having 11,500 customers. The VP customer service presents analytics based on the company having 9,500 customers…”

    “If corporate life & death decisions are being made based on the analytics, the above situation is outrageous, deadly.”

    Sure, there are cases where analytic discrepancies can be dreadful (M&A for instance). We believe that many times plurality of results is real and helpful rather than harmful. Most of the time, analytics is not about drama (life and death decisions!), but about understanding enough to make a directionally correct decision. “Siloed” analytics–now that’s a loaded term–can be about people better understanding their world. It’s cynical to believe that the reason people would resist centralized analytics is to evade responsibility.

  • http://opensourceanalytics.com Nishith

    I’ve been reading and then re-reading the discussion here and have been trying to reconcile the two opposing view points.

    While I am inclined to go with Zach’s views on how Analytics ought (not) to be done, I also agree with some of the points raised by Tom in his article and post.

    If I visualize a mature organization that has been doing analytics for years now it is quite likely that what they have are multiple silos that do not collaborate or cooperate. In such an organization, probably the CEO or the CFO would (and should) stand up one day and force the silos to merge by aligning their strategic objectives into a coherent organizational strategic objective. The end result would probably be some kind of a Analytics Center of Excellence, which makes sense.

    On the flip side, if I look at another organization that does not have such a long history of analytics (or has never done it), then getting the CEO/CFO to set up a single team and force BI downwards onto the businesses might be a recipe for disaster. The business heads would feel threatened by a change that they do not understand and that is taking place outside of their control. Such an initiative is very likely to get sabotaged and die a silent death after sometime. It might be better for such an organization to first do Analytics at a departmental level, and then merge them into an organization wide initiative when there is momentum and demonstrated value.

    Maybe both the approaches are valid, and we need to choose one depending on who we are looking at.

  • Zach

    I just ran across a very similar discussion over here: http://customer.corante.com/archives/2005/10/30/competing_on_analytics.php

    Neil Raden also seems to have a similar queasy feeling about this high-level, out-of-touch analytics talk. He wrote up a compete retort to Davenport (http://www.hiredbrains.com/davenport01.htm). I like this point in particular: “as an organization becomes more “agile,” which is a definite trend, decision-making, even for the big decisions, will become more decentralized. Imagine how difficult it will be to buy or sell pieces of a company if the “brain,” the centralized analytical capability, stays with the parent and there is no local expertise?”

  • Priya

    At the end of the day, Analytics is “decision support” function. It can only SUPPORT decisions/ not support them on the basis of data. While one can find “Insights” hidden in numbers, its not simple to find them – as it is critical for an analyst to also understand the market reality like a sales/ marketing person does. Putting together the jigsaw pieces is a function of numbers+gut-feel+ pulse of my customer (which comes from my front end teams).

  • Zach

    I agree with the notion of analytics is a part of a jigsaw puzzle as we try to piece together reality. And there are definitely limitations in the ability to drive decision-making through data.

    That said, I’m a little uncomfortable with the notion of “decision-support” function. I think most companies place analytics out to the side as a separate shared service to inform decisions — then are happy to disregard the data when it contradicts their “gut-feel” or “pulse of the customer.” We have run into many instances where those gut-feels are really just deeply ingrained and untested assumptions about how the business works. Faulty assumptions can become a crutch that decision-makers lean on way too much.

  • Ajay Kelkar

    This is a fascinating discussion. As an executive who is in the thick of trying to make analytics happen in a leading Indian bank ,I can empathize with almost all the comments being made. My only addition would be that in my view,to truly drive this capability ,you need to invest serious $ in Change management. Without changes in process,incentive and structures..competing in analytics would be an impossible hurdle!!

  • http://www.juiceanalytics.com/weblog/?p=281 » What about the (Analysis) Grunts? – Juice Analytics

    [...] Meanwhile, Davenport minion Jim Novo responded to our criticism by stating: “if a silo wants to keep an analytical “lead” in it’s own little box to do the navel-gazing, silo-focused analysis that impacts it’s own little box, then that’s OK. Just know that this analysis, while meaningful to the little box, cannot be used or trusted anywhere else in the company and so is of very little value in a macro way. But it’s safe; the silo can proceed with the $10 “micro tweaks” and have full accountability while the competition is making macro process changes worth millions using centralized analytics.” [...]

  • http://opensourceanalytics.com/2006/10/31/competing-on-analytics-webinar-with-tom-davenport-today-at-100-pm-est/ Competing on Analytics Webinar with Tom Davenport today at 1:00 PM EST | Open Source Analytics

    [...] Tom’s HBR article titled “Competing on Analytics” is based on his profiling of early adopters of Analytics that compete today based on data driven strategies. The research is also expected to be published in a book format in spring 2007. Tom’s article led to a fairly strong debate in the blogger community. Some see it as learning from the successful experiences, while others point out that while it may be true for the organizations that have been doing analytics for long, it may not factor in some other realities. You can see some of the interesting discussions here: Juice Analytics: 10 Ways Not to Build an Analytics-based Business, Juice Analytics: The Heart of the “Competing on Analytics” Matter, and Neil Raden: Power to the People: Analytics for the Masses. [...]