“Building the Stanford of Denver at the speed of an entrepreneur”
I was thrilled to see so many of my favorite professional topics in one article! Moreover, as a graduate of both Stanford and the business school at DU, I am pleased to see acknowledgement of the role of the former and the potential of the latter. This article from The Denver Post is a must read for my colleagues from Stanford, DU, and the Denver tech scene:
“Building the Stanford of Denver at the speed of an entrepreneur”
Just published the new Bodhi Tree Solutions website. Check it out at http://www.bodhitreesolutions.com/.
I came across this interesting article on transformational change initiatives and it prompted a couple of thoughts / questions. Here's a link to the article:
First, the article includes a brief (but interesting) perspective on the role of social technology in what the article calls "change platforms." According to the authors, change platforms take advantage of the large-scale collaboration that social technologies enable, but it's "the encouragement individuals are given to use the platform to drive deep change" that makes the platform effective. The specific examples given in the article reinforce the notion that attention to business process and cultural change are at least as important as technology in transformational initiatives, which is a constant theme in this blog.
Which brings me to my second thought / question. A theme of the article is the need to move away from top-down approaches to transformational change, but at the same time the authors acknowledge that "few [bottom-up] efforts effect systemic change across an entire organization." Moreover, the authors suggest that "responsibility for initiating change needs to be syndicated across the organization." But who is responsible for driving this syndication and initiating the “change platform” in the first place? It seems that such broad, and for many (most?) organizations profound, change in business process and organizational culture requires significant top-down executive leadership. So, which came first, the chicken or the egg?
Please leave a comment and share your thoughts on the article and experience with transformational initiatives. Who / what drove the initiative and what were the key factors in its success or failure?
I often think of the public sector as lagging the private sector in the area of information technology (IT), so I was pleased when I read Mike Flowers’ interview with McKinsey Global Institute: Learning from New York City’s open-data effort. The key points made by the former NYC chief analytics officer are as relevant to private sector companies seeking to maximize the value of information assets as they are to other municipalities embarking on open data initiatives.
Essentially, Flowers says, “technology… is the easy part.” While we all know that “easy” is a relative term, I agree with Flowers that the bigger challenges often relate to optimizing business process and managing cultural change. Flowers gives some great examples specific to municipal open data, but leaders in the private sector would be wise to apply the learning to their own information initiatives.
Please share your own thoughts and experiences around the challenges and opportunities of open data.
While the article “How to Pick a Business Partner” in the current issue of Analytics magazine is aimed at selecting a good analytics partner, it provides excellent guidance for any organization seeking to realize value from analytics regardless of whether the capabilities are built internally or sourced externally.
All 10 of the factors identified in the article contribute to maximizing value from analytics, but four stand out in my experience as the most critical. Unfortunately, these factors are often overlooked as companies rush to get on the big data and analytics bandwagon by focusing investment primarily on technology solutions.
First, fact-based decision-making is ultimately driven by company culture, not technology or quantitative analysis. Investment in big data infrastructure and data scientists will not deliver the appropriate return if the organization isn’t culturally prepared to act on the resulting business insights. Developing this culture of data-driven decision-making (what the article calls “analytics DNA”) throughout the organization is often the most difficult part of realizing value from big data and analytics.
Second, analytic capability requires an interdisciplinary approach. Business, math, technology, and behavioral science are among the key disciplines noted in the article. Not only does an organization need to source all of these components, but it must also bring them together in a way that fosters synthesis of the various disciplines. Facilitating this synthesis requires very specialized leadership, which is perhaps the most difficult ingredient to find.
Third, the entire analytic lifecycle must occur to realize business value. The article describes this as creation, translation, and consumption of analytics. Any analytics program which doesn’t focus on all three of these stages will ultimately fail to realize maximum value. If building analytic capability is seen primarily as a technology project to enable creation, for example, the business results of the initiative will likely be a disappointment.
Fourth, the convergence of cross-industry experience is critical to realize maximum value from analytics. While it is reasonable for an organization to seek out analytic resources with experience in its particular industry, an outside perspective can mitigate group-think and industry bias in the search for innovative insight. After all, simply doing what everybody else in your industry is doing doesn’t create competitive advantage. So, in developing its analytic capability, an organization should give strong consideration to resources with cross-industry experience.
I highly recommend reading the entire article in Analytics magazine and thinking about the key factors as important considerations for any analytics program. Also, the entire digital magazine from the folks at INFORMS is excellent, and I believe it is offered to non-members via free registration.
Please add a comment and share your thoughts and experience on key factors in realizing value from analytics.
I really enjoyed this New York Times article: “What Data Can’t Do”
While it is a cautionary tale for those of us in the big data space, I think the author’s story about a CEO having to make a difficult business decision also reinforces the value of analytics and decision science. Essentially, the CEO conducted an analysis which factored not only the risks he could quantify, but also those that required his qualitative assessment.
Effective business decisions often demand the analysis of both quantitative and qualitative information, and enlightened solutions emerge from the synthesis of knowledge and experience.
A thought provoking HBR article entitled “Why IT Fumbles Analytics” is making its rounds in the blogosphere. While I agree with much of the content in the article, I can’t help feeling that the title perpetuates the problem of realizing value from IT.
A primary assertion of the authors is that big data and analytics projects are “completely different animals” from other IT projects. Analytics projects do have a set of special considerations (which are very nicely covered in the HBR article), but the broader organization must take responsibility for the success or failure of any project that involves technology and cross-functional users. Perhaps a better title would be “Why Organizations Fumble Analytics.”
Just as I pointed out in my previous post that IT leadership needs to be a part of the solution, so do top leaders in other business functions (and where is the CEO in all of this?). The McKinsey Quarterly article “Competing in a digital world: Four lessons from the software industry” makes some great points about the need for better cross-functional perspective from managers and executives in all business functions and at all levels.
If you don’t already have full access to the HBR article or McKinsey Quarterly, consider free registration with each organization to read the full content.
Please add a comment with your thoughts on this topic, and what the HBR article gets right and what it gets wrong.
As I’ve been surfing the current wave of chatter in the Business Analytics space, I see that the gulf between IT and the realization of business value is, unfortunately, alive and well.
This morning, for example, I attended a presentation by one of the industry's premier trade organizations on “operationalizing” big data analytics. I was looking forward to hearing how organizations are embedding big data analytics into their operational processes and systems. Instead, the presentation focused on the use of BI to make reports, dashboards, and scorecards available to a broader and deeper audience in order to overcome analytic "silos."
Don't get me wrong – I’m a firm believer that driving information and insight to a broader and deeper audience is an important piece of the puzzle. However, simply delivering insight and information to more people is not enough to realize business value and does not, in my mind, equal operationalizing analytics (from big data or any other source!).
To me, operationalizing analytics must include completing the value chain, which means ensuring that action is taken based on the insight. As IT leaders we must play a role in this critical link between insight and value!
First, we must stop presenting the delivery of information and insight as the end of our initiatives. Rather, we must stress to our stakeholders that delivering information and insight is necessary, but not sufficient, to realize business value. Action based on the insight is the missing link.
Second, we must become part of the solution and help find the missing link. In other words, we must work with our stakeholders to ensure that the right processes, people, and downstream systems (automation is often the best way to ensure appropriate action!) are in place to operationalize the insights.
Some great examples of finding the missing link already exist. Harrah’s, Amazon, and credit card companies, to name a few, have realized significant value by embedding analytics based action into their operational processes and systems. Now that, to me, is operationalizing analytics!
I could stand on this soapbox all day, but I’d really like to hear my colleagues’ take on this topic.
Please add a comment and share your definition of, and experience with, operationalizing analytics!