Every so often, opportunity knocks.
This year, opportunity started knocking and did not quit until I finally paid attention. Thanks to the persistence and support of several wonderful colleagues, I now find myself embarking on a new adventure.
On July 1, I became academic director of the Master of Science in Information and Knowledge Strategy (IKNS) program at Columbia University. It is a remarkable program that equips students to lead high-performing teams and unleash the power inherent in the knowledge assets of their organizations. In the words of one our distinguished faculty members, Jeanne Harris, our program teaches how to optimize organizational decision making and execution.
Who doesn’t need that?
Next week we will welcome a new cohort of mid-career executives eager to learn the things that knowledge management professionals know how to do. However, unlike most KM professionals, this cohort will have the benefit of a thoughtfully designed set of courses taught by industry leaders. This creates a wonderful path to the education that the rest of us gained painfully through the school of hard knocks.
We just released to our new students the online learning site for their first course. One of their initial assignments is to read a foundational book for knowledge management professionals: Working Knowledge by Tom Davenport and Larry Prusak. The site directs the students to read the book with a critical eye — not because there is a particular problem with the book but because they will have a chance next week to discuss it face to face with Larry Prusak. What an opportunity! And it’s only the beginning for them and for us.)
Together with the other members of the program’s leadership team (Dr. Ed Hoffman and Carolina Pincetic), our expert faculty, and dedicated alumni, I look forward to bringing the benefits of collaboration and knowledge sharing to more and more individuals and organizations.
We have some powerful tools in our IKNS toolkit that are just too valuable to hoard.
Jeanne Harris is executive research fellow and a senior executive at the Accenture Institute for High Performance in Chicago. She directs the global research agenda on information, technology, analytics and talent at the Institute. In the IKNS program, she is an instructor in the Business Analytics class.
[These are my notes from Columbia University’s 2013 summer residency program for its Masters of Science in Information and Knowledge Strategy. Since I’m publishing them as soon as possible after the end of a session, they may contain the occasional typographical or grammatical error. Please excuse those. To the extent I’ve made any editorial comments, I’ve shown those in brackets.]
- Lessons from Peter Drucker. Jeanne Harris had the privilege of working with Peter Drucker early in her career. Here are some of the data-related lessons she learned from him:
- Look for patterns. Analytics help reveal patterns, which in turn reveal what is happening below the surface of the organization.
- Don’t settle for just a single insight. Businesses are complicated animals so it is rare for a single insight to explain everything about an organization. Look for several insights that shed light on the business as a system.
- What gets measured gets done. Things don’t get implemented unless people and processes are measured and monitored.
- The Value of Analytics. Early studies indicated that companies that implemented ERP systems rarely received any value from the system. When Jeanne Harris and Tom Davenport dug deeper into the research, they discovered that reality was a bit more nuanced that the research indicated. In fact, while some companies found ERP systems disappointing, the companies that considered analytics to be a key part of their business strategy received great value from their ERP systems. The main differentiator was a company’s approach to and use of data.
- Competing on Analytics. People have been collecting data (and performing analysis) for centuries. What’s different is knowing how to use data analytics to (1) differentiate your company or product, (2) execute your business strategy, or (3) build data into your products. A perfect example of a company that competes on data analytics is Netflix. Data helps Netflix be a huge disruptor in its space. For example, Netflix initially used data via its Cinematch algorithm to help (1) customers choose films to watch; (2) Netflix manage its inventory; (3) Netflix negotiate its deals with movie studios (which are typically less data driven); (4) Netflix manage customers who tend to hold onto movies longer than average. (Netflix manages them by not sending them the first batch of new release movies.) In short, data analytics give Netflix a huge competitive edge.
- How do you Build Analytical Capability in Your Organization? It isn’t enough to use data to execute your strategy or achieve operational efficiency. In fact, the more effective use of analytics is to support better decision making. Ultimately, you need to use analytics to stay ahead of your competitors. This requires cultural change within the organization. Harris told us how Progressive Insurance Company used data to differentiate the safe motorcycle owners from the reckless ones. Next they offered preferential rates to the safe customers and pushed the bad risks to their competitors. In another data-driven effort, Progressive offered customers the opportunity through its “Snapshot” program to install a device on the vehicles that collected data on their driving habits. This data was then analyzed to determine each participating customer’s likelihood of having an accident. With this information, Progressive could offer a more appropriate insurance rate.
- Big Data. One definition of Big Data is “data too big to be analyzed by SQL.” Harris noted that we have only begun to scratch the surface of Big Data’s potential to realize major business outcomes. The technology is finally up to the task, but we don’t yet have enough people able to derive insights from that data. As a result, there is incredible demand for trained data analysts. The other problem is that many decision makers in organizations are essentially innumerate and, therefore, don’t understand how to use Big Data and the insights it can reveal. Harris quoted Tom Davenport who was concerned that “a lot of Big Data is low analytics” and, therefore, people are missing the great insights that could be attained by using more sophisticated analysis.
- What Skills are Necessary? Harris recommends the following skills:
- You need to be numerate. This means not only understand numbers, but also statistics.
- You must understand scientific methodologies (e.g., forming a hypothesis, A/B testing, the scientific method generally).
- You must understand what the data are really telling you.
- You must understand how the data relate to your strategy.
- You must know how to translate the data analysis into operations/execution and, ultimately, how to use the data to change the organization itself.
- Analytics Makes s Difference. Big Data isn’t only for Big Business. Even simple analytical applications can help small businesses and nonprofits. For example, Harris and her team helped a theater company use their sales data to materially improve their ticket subscriptions. For organizations large and small, there now is a drive to data monetization.
The Florida Supreme Court wants lawyers to behave nicely with respect to their opponents. Here’s how the court’s recent action is described in a press release by the American Board of Trial Advocates:
Commenting that `concerns have grown about acts of incivility among members of the legal profession,’ the high court noted ABOTA’s efforts to stress the importance of civility in the practice of law. The Supreme Court emphasized to Florida lawyers old and new that practicing law is an honor that comes with responsibilities, paramount among which is civility, an often overlooked cornerstone of the legal profession. The Court added to the Oath of Admission the following: `To opposing parties and their counsel, I pledge fairness, integrity, and civility, not only in court, but also in all written and oral communications.’
It’s well and good that some lawyers will show their kinder and gentler sides to their opponents, but what about colleagues within their own firms? The hard truth is that law firm knowledge management faces some rather particular challenges based on the population we serve. If you doubt this, take a look a some of my earlier posts on lawyers and lawyer personalities:
Now, let’s consider some specific challenges that all knowledge managers face. Jack Vinson has done a terrific job of gathering in one place some things we know to be true about how people share knowledge. In Rules of Knowledge Management, Jack starts with a summary of Chris Collison’s amusing take on Tom Davenport’s “Kindergarten Rationale” for sharing:
- You share with the friends you trust
- You share when you’re sure you’ll get something in return
- Your toys are more special than anyone else’s
- You share when the teacher tells you to, until she turns her back
- When toys are scarce, there’s less sharing
- Once yours gets taken, you never share again
These observations of kindergarten children are entirely consistent with what we know about “mature adults” operating in a work context. In fact, the lack of trust coupled with some nasty lessons learned about the downside of sharing can lead to an epidemic of information hoarding within an organization. If this is what happens in the general working adult population, what can you expect from a lawyer population? Given their natural skepticism, high degree of autonomy, low sociability and resilience, and adversarial natures (see What Makes Lawyers So Challenging?), this group may find it even harder to share than your typical kindergartener. While I’m not sure it is possible to change anyone’s fundamental nature (and that certainly is well beyond the capabilities of a knowledge management group), we can work with senior management to change the environment in which lawyers operate. Taking guidance from Fighting the Knowledge Hiding Epidemic, I’d suggest the following strategies:
- Build trust — emphasize positive relationships among employees
- Demonstrate the mutual benefits that result when colleagues share information
- Treat all workers fairly and respectfully, thereby reducing feelings of injustice and the need for retaliation
At the end of the day, perhaps we are really about trust management rather than knowledge management (to the extent either trust or knowledge can, in fact, be “managed.”) [Photo Credit: Kathy Cassidy]
In Tom Davenport’s terrific post, Microdecisions for Macro Impact, he reminds us that fortunes can be won and lost in the little decisions we make every day. As he astutely notes,
What many companies don’t realize is that microdecisions — small decisions made many times by many workers at the customer interface — can have a major impact on the business. How they are made can be the difference between sloppy and effective execution, and between profit and loss.
Equally, small decisions made in the course of routine procedures can have a profound effect. If you’re not sure about this, think about the huge beneficial change in health care derived from the simple act of hand washing. Or, imagine what would happen if your pilot decided to “wing it” and disregarded the standard take-off checklist?
In knowledge management, we regularly spend time thinking about work flow and business process. And, especially when we’re considering bringing technology into that flow, we have an opportunity to ask whether the individual steps within a process are sensible given current circumstances. Do they yield the best possible outcome on a predictable basis?
The fact that something is routine does not mean it is optimized. As you go through your day, take a closer look at the many repeatable acts you perform and consider whether there are small decisions you could make differently to yield much better results.
[Photo Credit: Wisconsin Historical Society]