Creating Leaders for the Knowledge Age: Douglas Weidner

By: Yadira Y. Caro

In the field of Knowledge Management, becoming a Certified Knowledge Manager or CKM, is a symbol of credibility. It shows the certificate holder has an understanding of the field, which focuses among other things, on managing intellectual assets of an organization, creating a culture of sharing and of course, using tech to achieve this.

Douglas Weidner, Chairman of the KM Institute, has certified thousands of people around the world for over 20 years. Furthermore, he was the one who develop this certification. As a certificate holder myself, I was curious to know what drove him to create a CKM and how he sees its future.

In this interview, Douglas shares insights on his journey, addresses the debates in the KM field and why it is important to be passionate about what you do.

Why did you choose KM as a career?
Good question, applicable to almost everyone. My answer is in two parts.
The first part is more generic – about how to choose a career by paying attention to the drivers of success. The second part is about how I personally chose KM. So, here’s a principle. It’s all about passion, the key driver of success.

I believe that there are many aspects to choosing a career that will become successful for you, if you have the luxury of choosing. By success, I mean you ‘love what you are doing’ and are very engaged in doing it, not that you make a lot of money, though if you love what you are doing you will no doubt outperform your peers in that career field.

The proven drivers of success include: You have a passion for doing the kinds of tasks associated with it. That encourages you to perform those tasks very well. By kinds of tasks, I mean at the granular level of your very personal set of traits. Are you analytical vs not so much so or not at all? Do you crave being with other people rather than independent work? Do you love developing other people? Do you love to learn? Are you very self-confident? There are over 30 such traits, but about five to ten define you. Traits don’t define an industry sector, but do define what types of jobs you will love within any industry.

Another (driver of success) is to have a real passion for what the job allows you to accomplish. For instance, do you really believe in the organization’s mission and objectives? Would putting an astronaut on Mars excite you? Would curing at least some form of cancer satisfy your life’s ambition? Would transforming the world from the computer-driven Information Age into the human (knowledge) -driven Knowledge Age, challenge you?

Douglas Weidner

Those two drivers will enable you to have a good shot at a very successful career.

Now on to how I personally chose KM. The answer to that question is more complex than your followers might expect. It is a story in three parts.

In 1994, I was working for a think tank and designed for the Department of Defense (DoD) a very granular, Knowledge Base Tool (K Base). It housed DoD’s Business Process Reengineering (BPR) methodology, which I had helped define and document. To me, a K Base had the capability to provide a K Nugget to the right person at just the right time. I was doing one aspect of KM, but I didn’t really know it.

In early 1995, when Knowledge Management (KM) definitely rose above the horizon, I realized what I had been doing was at the very core of the systems-oriented KM of the late 1990s. But a granular, process-oriented perspective, which is what I had worked on, was different than a traditional repository/portal, which was emerging as the dominant KM system initiative. A repository is a digital library of the organization’s policies, processes, statutes, regulations, marketing info, etc. Think documents.

So, I joined a large commercial IT consulting firm to lead their KM market entry. The KM consulting team had a staff of one, which was me as Chief Knowledge Engineer. We had some consulting success, not so much in terms of billable hours, but rather as a marketing arm, what I often described as the point of the spear. I was the point of the spear. I introduced KM, after which the profitable spear shaft (IT projects, whether KM or not) often followed.

By the late 1990s, when I might have retired, I had developed an abiding passion to make the KM discipline a real success, to change the world, so-to-speak. I saw a specific need for rigorous KM training, not just a KM101 but specifically a KM certification program, which I first offered as the Certified Knowledge Manager (CKM) in 2001.

It became the core product of the International Knowledge Management Institute, which is now the de facto leader in KM certification.

You started in KM over two decades ago, how has KM shifted since you started? And, is there any misconception you commonly see regarding KM?

When I started in KM in the mid-1990s and long after, KM was all about KM Systems. For many that is still true. In the late 1990s, KM was primarily about repositories and expert locators, which are still dominant applications today. Little was known about other KM initiatives, such as best practices and lessons learned though such techniques had been publicized by Ford Motor and BP.

By 2000, other enterprise wide initiatives were gaining notoriety, such as Communities of Practice (CoPs). But many non-KM Systems applications were emerging as well, e.g., ‘Rethink Learning’, Customer Satisfaction, and especially Knowledge Transfer and Continuity. By the late 2000s, K Transfer was becoming critical to address the Baby Boomer retirement surge.

As to are there any misconceptions, yes! Everyone knows the trilogy “People, Process and Technology.” The KM Systems approach, driven by IT naturally focuses on ‘technology’. The KM Transformational approach, of necessity, focuses on people. So, there is a major divide in KM today. Many still believe or at least act as if KM is all about technology, aka KM Systems. Some believe KM is about an episodic change in human occupations, which requires a shift from traditional change management to a focus on transformational change management.

I believe we will always have better and faster computers, but they are becoming mere commodities in terms of capability, price and especially ubiquitousness. The primary discriminator for the future will come from substantive increases in personal human potential and performance, which we call personal knowledge management..

Douglas Weidner

Let me quickly explain. The world has gone through many episodic changes, but if you think about careers (aka human occupations), there have been five. Humans have progressed from ‘Hunter-Gatherers’ to the ‘Agrarian, Industrial and Information’ Ages.

For millennia, until the Info Age, most all human occupations were labor intensive. As computers emerged, they enabled information management which further enabled its ultimate end-game – the Knowledge Age, and hence ‘Knowledge Management’.

We still grow food, but farms are run by machines, even combines guided by GPS. We still make and move stuff, but increasingly that is being done by robots, drones and artificial intelligence. In the Knowledge Age, human brainpower will dominate, not muscle power.

What are other implications? Here’s one. If KM is just a KM System, traditional change management is applicable – communicate the new system and train folks as to how to use it, preferably before it is installed.

If KM is much more than just a system, transformational change management is applicable. Transformational change management is much more complex than traditional, including ‘Call-to-Action’, quick wins, and much more emphasis on employee comprehension and involvement, and top management’s transformational leadership. But, ultimately, KM’s success will be about a major change in human occupations and motivations, which I call Personal Knowledge Management (PKM)™.

What is Personal Knowledge Management?
In a nutshell, PKM is about both aptitude and attitude. We have always focused on human aptitude, ability to do the task, whether hunting, farming, or on the assembly line.

In the Knowledge Age, we must focus as well on human attitudes, the love of your career and the motivation to do an outstanding job. That is why when answering about my career choice I talked about aligning your own traits with what you do and the resultant passion and high performance you will have.

Most everyone loves new and innovative technologies, but few want to take the time and effort to develop and instill best practices, which is much more difficult than just buying the latest technology.

You teach people in multiple industries and countries. Is there any example of any company or industry that does KM right?

Please understand, I have a high bar for ‘doing KM right’ and there are a number of viewpoints.

First and foremost, since KM is still an emerging discipline, I applaud organizations that started early and achieved some notable financial results. Not many organizations publish their results, though Shell Oil published their 2002 financial results using their own in-house CoP software, when CoPs were in their very formative stages.

I especially applaud those who invented new initiative types. Ford did best practices in early 1990s, BP did Lessons Learned in late 1990s, Lockheed Martin did Knowledge Transfer & Continuity in mid 2000s.

There are many people and their organizations around the world that are doing KM. We’ve had about 10,000 CKM students since 2001, from at least 25 different countries. Of course, the US and Europe were early birds to KM based on their more mature economies, but we saw early interest in many geographical pockets, such as Australia, Singapore, Hong Kong, India and Malaysia. The Middle-East is rapidly increasing its interest in KM, often due to central government guidance, if not mandates.

I’d like to report someday soon that many organizations have begun to transition from a traditional KM Systems approach to transformational KM approach; to actually focus on using KM to optimize their personal and organizational performance in the Knowledge Age.

I believe we will always have better and faster computers, but they are becoming mere commodities in terms of capability, price and especially ubiquitousness. The primary discriminator for the future will come from substantive increases in personal human potential and performance, which we call personal knowledge management

What are some tendencies of KM you see coming in the next few years?
Most everyone loves new and innovative technologies, but few want to take the time and effort to develop and instill best practices, which is much more difficult than just buying the latest technology.

For instance, in Learning, which should be of keen interest to KMers because the goal of KM is often claimed to be: ‘Create a Learning Organization’, there are many underutilized technologies. By underutilized I mean, not that the technology doesn’t work, but that humans don’t have proven ways to best use the technology, or worse – aren’t motivated to even use it.

Consider virtual training. The technology capability is obvious, a real-time (synchronous) class, but where everyone is virtual rather than face-to-face. But, the real benefit of virtual learning is not the savings of travel time and expense but the much increased interaction and learning among students. So, to gain the ultimate benefits of virtual technology, it is less about the technology than about optimizing other aspects of the learning process.

Consider K Bases, one of my favorite technologies. From a training perspective, the future should be less about learning processes and methods that could be well-documented in a K Base, and more about learning how and why you should use the K Base, which will teach you what you need to know, when you need to know it. Such an approach could be called ‘Performance Support’.

Consider Mobile Learning. We all have cell phones, but do we have established best practices about how to integrate them into online collaboration and learning?

What three resources (podcasts, books, websites) you recommend which have helped you in your career?

There are certainly exceptions, but I have found my robust formal education was extremely important to my career success. In high school, I loved business, and have an MBA – Business Economics and an MS – Operations Research. So, when I say robust I mean really robust. Getting such an education may be out of the question for some already buried in their careers and committed to their families, but it certainly helped my career.

My next most important resource, which is possible for all regardless of past education, has been books, hundreds and hundreds of books. But that has been over many decades and many disciplines. Note, books don’t have to be new, they can even be used if not too marked up, especially since I like to highlight key insights for future reference.

I love websites, especially informational ones. For many knowledge needs a Google search can provide the answer, especially if your background body of knowledge is robust enough to comprehend and evaluate the content. A typical website feature is podcasts: any multimedia presentation whether a talking head or an animation with voice-over, etc. I love podcasts (think Ted Talks) but have two concerns. I have found that I’m unlikely to be able to commit to a long scheduled broadcast, so being able to replay a prior broadcast is key.

Given my two expressed biases (Granular K Bases and need for transformation to create Personal K Managers in the K Age), I look forward to the future where various knowledge domains will be richly defined and categorized, with both K Nuggets as the leaf nodes, but also the ability to collaborate with others at that level of granularity. As mentioned, mobile technology, and associated K Base, is no longer just for random conversations, but for structured (threaded) conversations around an entire curriculum of brief K Nuggets. That brings me full circle back to K Nuggets, where I started in KM – getting the best Knowledge to the right person at the right time.

Do you have questions, feedback or suggestions of people to interview? Contact me!

Driven by Data: Kristen Kehrer

By: Yadira Y. Caro

Data Science has become very popular term in the world of technology careers. But what does this term really mean? How can you start shifting your skills to become a data scientist? Kristen Kehrer wants to help with that.

With a Bachelors in Mathematics and a Masters in Statistics, Kristen has worked in fields such as Health, Communications and eCommerce. Her roles have included analyzing data, conducting research and developing technical models as coder. When she started, she did not knew these were roles would be ascribed to a Data Scientist.

Today, as a founder of Data Moves Me, she focuses on teaching others about the field through online courses, speaking engagements and helping people build their resume towards a job in Data Science. She is also a Data Science instructor at UC Berkley Extension and EMERITUS Institute of Management.

In this interview she describes what Data Science is and shares some of the required skills to get into this career.

How do you describe Data Science and what you do?
This completely depends on the context and who I’m talking to. The definition I typically use for data science is: “It is the understanding and utilization of tools, data and methodologies that enable you to effectively solve problems utilizing data.” Someone who self identifies as a “data scientist” is often using machine learning and writing code, however the umbrella of the “data sciences” also involves analysts and other data wranglers.

It is certainly a multi-disciplinary field including a bit from programming, statistics, and business. There are no unicorns, everyone has their own strengths in the field and may be doing quite different tasks depending on industry.

What are some of most common misconceptions about it?
Again, the misconceptions depend on who you’re talking to. There are people who think everything is “AI”, there are the people who aren’t as data literate but still making decisions based on data, potentially the most dangerous (people). There are those who do not understand what the real pipeline looks like and only focus on machine learning.

I think there are a whole lot of misconceptions and it’s exacerbated by the “hotness” of the field. Lots of buzzwords and hype that make it difficult for people to fully grasp what the reality looks like. There is a huge focus on machine learning, but this is one tool.

I often hear people say “I need to hire a data scientist.” This is an incredibly broad statement. Think first about what you really need someone to help you with, nail down how they’ll contribute to strategy and what skills that will actually require, and then hire for those specific things, rather than listing the kitchen sink in terms of skills on a job description.

Why did you choose it as a career?
I definitely didn’t know that I was seeking out “data science.” The term wasn’t really being used when I started my career. I had finished a BS in Mathematics in 2004, realized I was in a dead-end job and decided to go back and pursue a MS in Statistics. I had seen that statisticians made good money. Then it was through a series of job changes and career moves that I really found myself in the data science space. It also involved some rebranding, as I considered myself a statistician who does “advanced analytics”. Then one day it was “oh wait, I’m a data scientist”.

Can you describe a project you worked on which you enjoyed or learned from?
The amazing thing about this field is that I’ve found most of the projects enjoyable. This industry requires continuous learning. Even after I’ve implemented an algorithm one way, the next time I go to do something similar there is probably a new library or package that makes data cleaning or model building easier, so I learn those.

One of my more favorite projects was using customer’s subscription data to find customers with seasonal usage patterns. So instead of saying “hey, these customers are using our product less and may be a retention risk,” I was able to say “hey, this customer has a seasonal business and we expect less usage from them in these months, we can use this information to speak to them differently and infer there needs.”

I used the TBATS algorithm to take these people as seasonal or non-seasonal. Although I’m very well versed in econometric time series analysis and forecasting, this was my first time researching this algorithm and the pros and cons that went along with it. It was also sort of an off-label use case for the algorithm. That is where I find the most enjoyment: developing a methodology that will work for a problem I haven’t solved before.

Because Data Science is so interdisciplinary, there are many competencies that transfer well from other careers if you position them for the Data Scientist role. I want to educate others to be able to use this to their advantage.

Kristen Kehrer

What drove you to focus on helping others with resume building?
I was laid-off in 2017 a week and half after returning from my second maternity leave. Although I was quite happy with my resume as is and was frequently getting calls from recruiters, I picked up some amazing additional tips from a career coach. I saw so many people trying to “rebrand” themselves or make a career change to data science. These people would ask me to review their resume and it was clear that they were highlighting the things they had done previously, but not how that would translate to them being an effective Data Scientist.

Because Data Science is so interdisciplinary, there are many competencies that transfer well from other careers if you position them for the Data Scientist role. I want to educate others to be able to use this to their advantage. People often bring fantastic skills to the table that they’re not highlighting to their full potential.

What trends do you see coming up in the field?
Well I hope that there will be more of a standardization between terminology, roles and responsibilities so that we can all use a common language and understand each other. I think as Data Science matures it will be clearer that it is a team sport and not a single person sport.

What are two of the absolute must-have tools you use in your day-to-day for your job?
I always say that SQL is a must. People often get distracted by shiny objects, learning new algorithms, etc. But on your first day as a data scientist you’ll most likely be told about your new job’s data warehouse. That is where you’ll extract your data from. Although you can do joins and connect to a database from R or Python, you’ll still need to understand relational databases to navigate the schema where your data lives to be effective.

I also like to stress the importance of communication skills. Give your deliverables love and care, think about how to best present to a non-technical audience. Your ability to build relationships where stakeholders trust your work and see you as a valued partner will be instrumental in your career.

What are some of the plans for near future?
I’m currently working on a book Mothers of Data Science with Kate Strachnyi. I expect the book to be available in 2020. I’m also teaching a course through UC Berkeley Extension called “Practical Data Science.” This is a foundations of Data Science course in R. I’m also currently consulting and offering in-office training for Analytics/Data Science teams that want to take their skills to the next level. I also intend to keep blogging at https://datamovesme.com.

What resources (books, podcast, websites, etc.), do you recommend which have helped you in your career?
I try to give useful tips on my personal blog https://datamovesme.com. I also think finding a community on LinkedIn, Twitter, or other social platform helps you to keep up with the trends, new programming libraries that will make your life a little easier, and help you to gauge what might be most relevant to learn next. Because again, it is continuous lifelong learning as a Data Scientist that will help you stay relevant. You can also become involved in things like “Makeover Monday” or “Tidy Tuesday” and the community will give you feedback on your work. This is one of the greatest forms of visibility, and networking is diving right in and contributing.

Do you have questions, feedback or suggestions of people to interview? Contact me!

Women Empowerment Podcasts

By: Yadira Y. Caro

Podcast junkies like myself always seek recommendations of what to listen to next. That is the beauty of podcasts. The enormous variety allows you to pick based on you interests. Plus, you can listen to them at any time, especially on the move. As you may infer from this blog, I love to learn from other people’s journeys through interviews. Therefore, interview podcasts are my favorite.

Working in an environment dominated by men, I look for stories of professional and successful women to find inspiration. Unfortunately, most of my favorites go-to podcast lack women as guests. Therefore, podcasts with an emphasis on interviewing successful women are a great choice to find fascinating stories and valuable lessons.

Here are some I listen to:

No Limits with Rebecca Jarvis – Journalist Rebecca Jarvis’ podcast features company executives, entrepreneurs and celebrities turned entrepreneurs. Her questions are insightful to elicit guests to share their stories and many times, unconventional path to success. At the end of each show, she highlights a listener who has launched their business and gives them the opportunity to pitch their product to the audience.

Wall Street Journal Secrets of Wealthy Women – Want to know how millionaires became financially successful? In this podcast you learned about their journeys. Host Veronica Dagher interviews successful women in top level jobs sharing ventures and most importantly money secrets related to investing. It also helps women lose fear in investing and take control of their own finances.

Girlboss– Former Nastygal CEO Sophia Amoruso was the subject of many headlines after her company went bankrupt. Instead of letting that experience shadow her efforts, she launched Girlboss Media. This is a business and lifestyle company producing content geared to millennial women. In her podcast, she attempts to redefine the definition of success as climbing a corporate ladder, and she interviews women in media, business, arts and many other industries. They share their stories and provide advice. Even if you are not a millennial (or a woman), there is great advice for entrepreneurs in every interview.

Latina to Latina– I started listening to this per recommendation from interviewee Univision’s Selymar Colon. In this podcast, journalist Alicia Menendez talks to many successful Latin women around the world about their challenges as minorities making it to the top. Anyone regardless of ethnicity and gender can take away valuable advice.

Have any other recommendations? Let me know!