At a recent event, IBM Performance, we heard success stories from some of the best analytic minds. Harry Markopolos presented the best argument for analytics I’ve heard in a long time.
If you don’t know who he is,1 this ethical and brilliant person is now forever linked with one of our generation's most notorious sociopaths—Bernie Madoff. Mr. Markopolos uncovered Madoff’s Ponzi scheme in 2000,2 although many of us may not have heard of him until 2008. At that time, he stated on the news that it took him only five minutes to figure out that Madoff was running a Ponzi scheme.
It was great listening to Harry’s talk and later I had the opportunity to speak to him directly and thank him for educating the public about how to be more attentive to our investments. Besides being a genuine human being, he pointed out that his confidence in what he learned was simple. “We knew we were right because we did the analytics.” It wasn’t ego; he ran the numbers!
Visualizing the data, Harry had that immediate aha moment, observing that the investment gains stated in Madoff’s reports were a nearly perfect 45-degree angle. Markopolos knew that the markets don’t deliver that. They have modest or often huge gyrations (depending on the economy, sector, company, etc.). The government and other investors had not done the analytics, when data was there for the plucking. Thus the power of visualizing data.
You have to step back from this whole drama and ask, “What can our firm do to uncover the critical information that can make a huge difference in our performance?” “We knew we were right because we did the analytics.”
Across the enterprise and across the market, there is untapped opportunity to gain new, actionable insights about your environment, your markets, your customers, your operations. In spite of a generation of packaged apps that promised these insights, end users still have an urgent need for more.
Analytics is defined as the ‘layer’ that sits on top of the data, but we often talk about analytics and data inclusively. Data acquisition through corporate databases, data subscriptions, auto-ID and search, is a whole issue unto itself. The foundation of the acquired information must be relevant: as we discussed in the big data article, your purpose should drive your data acquisition.
Data acquisition is a diverse set of activities which often is not part of a consistently managed, prescriptive process. Add to this the sheer size and complexity of the data and you can see the need for some help with the process. But whatever the source, insights often come from beyond the standard transactions and reports. Understanding original source data and having a consistent methodology for acquisition and organization is essential.
You Need a ‘Hot Holder’
Last week we had meetings and discussions with several companies that offer analytical solutions: Creative Computing, First Insight, IBM, Ironside Group, NetSuite, QueBIT, SAP, SAS (in alphabetical order) to name a few. In talking to these companies it became clear to me that so many of these analytics projects require the assistance of third parties—at least initially.
Figure 1: ‘Hot Holders’—support required for analytics
At the SAS Global Forum Executive Conference, Angelia Herrin, Editor, Research and Special Projects, Harvard Business Review showed their research, and it was interesting to note that most enterprises did not have a cohesive enterprise-wide analytics strategy. Most analytics activities were functionally deployed. That is an interesting data point, considering that many ERP systems connect operations to the C-suite reporting. (In our webinar, you can hear from firms who do leverage this integration.) Most of these ERP companies also sell analytics technologies and services (SAP or Oracle); whereas some, like NetSuite, Syspro, HarrisData or Epicor, provide it all-inclusively.
SAP told us that besides the hundreds of thousands of Crystal reader downloads and more than sixty thousand purchases of Crystal reports (Crystal mostly services the mid to smaller markets), they are also encouraging their partners to build content and reporting by industry and present these offerings in SAP’s marketplace to sell to each other and to end-customers. Amazingly, there are over five hundred thousand partner-developed reports yearly.
The profusion of requirements is so vast that even the largest firms often can’t provide a standard offering. Large firms can afford consulting firms, big data centers, and lots of software packages. But this can be a problem for smaller firms, who can’t afford the teams of service providers and the brigade of spreadsheet and reporting magicians to create and manage all this. (See a short list of products and services you might need in Figure 1, above.)
In the brief, Bill McBeath has an ongoing series of spend analytics. His contention is that ultimately we do create enough standard approaches and rules to standardize analytics, thus making it an application. This takes analytics from being ‘yet another custom coding project’ into becoming a more standardized technology that is usable as-is by all companies—large and small. Inventory Management, Spend Management, Supply Chain, some corporate performance management, and some risk management come to mind as areas where apps are in the market.
Beyond Data Acquisition, Beyond Generalities
First Insight, aptly named, is a firm that goes beyond mining data. Their premise is to create intelligence by testing consumer’s reactions to products and pricing before products are produced and pricing is set in the market.
This approach is the Holy Grail and future of the analytics road map! It doesn’t just mine existing structured or unstructured web-sourced data, but is a true predictive and preventive approach3 with new information. This is a critical trend that many companies will pursue. Part of the problem with using secondary sources such as your channels’ data or data obtained from searching the web is that often the data is not relevant or precise enough for your purposes. And even when this data is meaningful, your competitors may have access to the same data. Companies must go beyond generalizations to specifics about their customers, their products, their ideas on pricing, offerings, etc. Going beyond generalizations or mere demographics can get you closer to a real definition of who your customers are and what they are likely to buy.
IBM, in the IBM Performance 2012 event, also did a first rate job in the Customer Analytics track. They walked us through many important issues and provided frameworks for approaching this new social and mobile world.
One critical point was the issue of ‘widening the aperture of your lens.’ This gets back to thinking about the questions you might want answered before you define your data-acquisition strategy. Many companies think narrowly, I think, about feeding the application, focusing on planning, scheduling, or order management, and not about the broader topic of insight. So a wide lens plus embedded intelligence is in order. We also discuss Intelligent Things and Intelligent Environments, which contain that embedded intelligence, in this issue of the brief.
It’s a Journey
It can be a bit overwhelming when you think about all the things you want to know and explore. Creative Computing provides a useful roadmap to think about on the data-analytics journey (Figure 2).
Ken Gustin, from Creative Computing, walked me through a road map which I feel is pragmatic and understandable: as the process goes on, value increases. However, users can gain value early on; they don’t have to wait years for results.
Value and confidence building is critical to ongoing investment in technology. “We knew we were right because we did the analytics.” Analytics can let you discover critical things that can change the world—or your business.