If every employee hits their goals, will your organization execute its strategy?

“If every employee hits their goals, will your organization execute its strategy?”

That was the key question in today’s webinar by Donald Sull from MIT’s Sloan School of Management. It’s an incredible insight, as collectively we’ve all embraced goal-setting in the 100 years or so since General Motors popularized the idea.

But does it work? Is there enough transparency corporately to ensure that each contributors goals are in line with the overall strategy, and that if all the individual goals are met that the executive strategy would actually be executed?

Goals have been SMART (Specific, Measurable, Attainable, Realistic, Timely). Dr Sull et. al. present FAST (Frequently Discussed, Ambitious, Specific, Transparent). FAST goals seem more suited to today’s fast-paced high-tech world. They provide the agility needed for organizations to thrive.

Specific and Measurable are redundant, and together lead to Specific.

Attainable and Realistic are redundant, and perhaps wrong, and are replaced with Ambitious.

Timely is modified slightly to become Frequently Discussed, allowing the freedom for the goals to evolve as the needs evolve.

Transparent is missing from SMART, and helps to ensure individual alignment with the big picture.

FAST (Frequently discussed, Ambitious, Specific, Transparent) deserves more attention as businesses struggle to keep pace with innovation.

Check out more at https://sloanreview.mit.edu/article/with-goals-fast-beats-smart/ and http://www.donsull.com/

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Next-Gen Business Intelligence for Healthcare and Life Sciences

Amazon’s webinar today highlighted several ways that Big Data is being used in the HCLS industry. One of the key insights was the self-realization within one of the featured medical practices that they were “data rich, insight poor.” Having tremendous amounts of data isn’t much value to anyone without the appropriate tools and skills to visualize, interact and explore the data, which of course needs to be coupled with people who are empowered to act on the insights revealed.

Several of the tools demonstrated seemed to use Shiny and R to create interactive graphics. This is a perfect combination of developer skills preparing the right kind of visualizations so that self-service data exploration becomes meaningful. So many self-service reporting tools end up being huge time sinks because the fundamental data organization was incomplete or inconsistent.

One of the featured labs highlighted how expenses were reduced and customer satisfaction increased by the appropriate use of dashboards and educated data exploration. They were able to reduce repetitive testing and quickly identify trends in unreimbursed testing. Then with data at their fingertips they were able to discuss the trade-offs between economic and medical goals.

Some technologies to investigate further:

  • Amazon QuickSight: self-service analytics visualization
  • Amazon Macie: for discovering, classifying and protecting sensitive data
  • Amazon SageMaker: infrastructure to simplify machine learning at scale

Is Missing Talent a Hindrance to Effective AI?

Today I joined IBM’s Machine Learning Everywhere: Build Your Ladder to AI webinar to hear about some of the latest trends in adopting Machine Learning into an organization, and of course plugs for several of IBM’s products in that space.

Rob Thomas is General Manager of Analytics at IBM. His main topic was that there is a ladder that must be climbed for the effective use of Machine Learning and Artificial Intelligence. Per Rob, the technology has finally caught up and “2018 is the year to implement Machine Learning.” Part of that is instilling a data-driven culture.

The first rung is that AI is not possible if your data is not simplified and collected into a data lake. Traditional business analytics with a solid data strategy is essentially the prerequisite for a shift into ML. As part of this, the point was made that AI is perceived is being only for hyper-scaling big players like Uber, Airbnb and Facebook but that recent advancements really make it available to all. An insight that really resonated with me was that 80% of the world’s data is sitting unused and neglected behind corporate firewalls.

The second rung is that data must be accessible. Policy and governance must be in place so that data is at the fingertips of the users that need it most. Of course, there are complications with financial data and HIPPA protected data. The idea is to install the data driven culture by making as much data available to anyone who needs it in a self-service manner.

The top rung is built upon traditional analytics and business intelligence—the actual utilization of Machine Learning in every decision. Once data is widely accessible, decisions based on hunches become less viable and people learn to interact with their data using ever more sophisticated tools. This is the destination of all successful companies in the future: data driven decision making.

MLLadder

Rob mentioned the 40/40/20 rule about companies adopting a data driven culture:

  • Stage 1: 40% are data aware and are building out incremental efficiencies in their data processing, using both proprietary and open-source tools to create their data lakes. Data collection and data cleansing are the current investments.
  • Stage 2: 40% are actively examining their data looking for new ways to grow. Regardless of their actual industry, they are becoming a technology company.
  • Stage 3: 20% are exploring Machine Learning with the intention of being data led. It’s the end of opinions. They are evolving to use ML to automate tasks they never wanted to do in the first place.

Vitaly Tsivin of AMC Networks and Alissa Hess of USAA both shared examples of how Machine Learning and data science are being used to transform their companies from the inside out. However, both noted that the biggest barrier to success is finding talent. The combination of understanding data, understanding technology, and understanding business is rare in a person and difficult to hire.

The webinar ended with a presentation by Garry Kasparov, the first chess grand master to be defeated by a computer, and his opinions on the limitations and direction of artificial intelligence.

The first hour is well worth the time.

Day One of Essential Practice Skills for High-Impact Analytics Projects

Today I met Emory University professor Patrick Noonan  at the INFORMS seminar on Essential Practice Skills for High-Impact Analytics Projects. The day included many great gems that will guide some thought processes for years to come, but an early idea was about the new nature of work and that businesses need to “figure it out as we go.” The underlying assumption is that business practices and advancements in data collection and analytics are changing at such a rapid pace that there is really no opportunity to rely on prior techniques. Of course, that’s not universally true—there are many situations where our skills and experiences are directly applicable so that some problems can be solved quickly. However, there are many complex business questions that have never before been answerable as the data and tools required to answer them did not exist. This is new territory.

The bulk of today’s discussion was about decision making processes and how to formulate a plan to answer key questions based on analytics. The advantages and limitations of existing frameworks such as SWOT and SMART were discussed, and how their inherent limitations naturally lead to the use of Issue Trees. What is the Key Question to be asked? Because the first step in the process is always situation specific, this becomes a highly customized process instead of a generic framework. Once the Key Question is agreed upon by key stakeholders, a plan of attack can be developed.

The most interesting exercise today was generating a task list from terminal questions based on the Issue Tree. After formulating the questions, we collectively created Proto-Graphs. I don’t know if Proto-Graphs is a Noonan invented term or borrowed from someplace else. Proto-Graphs are sample layouts of graphs that should be created by the data team to help answer the Key Question. The creation of these Proto-Graphs led to a significant amount of disambiguation. e.g., would a scatter plot or a histogram best represent the data? What time frame should be analyzed? What units are expected? It was surprising to me how many of the “obvious” assumptions were made differently by different team members. The process clarified the result before the expense of creating the graphic with real data was incurred. Another advantage was that our paper sketches were not dependent on the capabilities of any specific tool—our sample visualizations were not guided by what can be done. All of my new data visualization projects will use this technique.

We also discussed how to clarify the Key Question. A highlight of my day was when Professor Noonan asked to use one of my quotes. We were defining ideas to clarify the Key Question and I said that it was essential to ensure that everyone understood the true goal. When asked about this, I replied that “our biggest failures at Blue Ridge Solutions were when we delivered what the client asked for.” What’s that mean? Often in the rush to get something done, business requests are made from a starting assumption of let’s move quickly. Failure to investigate the true goal often leads to the wrong deliverable.

The E-Myth Revisited

The E-Myth Revisited has changed my perspective on owning and growing a small business.

There are parts of the book that are an obvious advertisement for Michael’s consulting business, but who can blame him for that? He’s on to something.

Thinking of myself in terms of the Technician, the Entrepreneur, and the Manager has been insightful. I started Blue Ridge Solutions as a reluctant entrepreneur. There was an immediate need to supplement my cash flow and a shortage of available high-tech jobs in Western NC. Thus, I created an environment where I could pursue my craft.

At some point the transition was made from the Technician (a highly skilled web developer) to Manager directing a team of qualified developers personally recruited. It was a good transition and allowed the company revenue to grow well beyond that of a single person.

Later, the transition to Entrepreneur occurred, though I’m not quite as conscious about when that happened. Instead of managing my team, I was implementing processes (Systems, per Gerber) so that the team knew what was needed whether I was present or not. During this time, I definitely experienced the Entrepreneurial Seizure and experimented with different management styles somewhere between delegating and abdicating responsibilities.

Many of these ideas were brought to my attention as I went through the ScaleUp program. Sometimes it’s hard to see what’s directly under your nose, especially in the crisis and busyness of the moment. I wish I had read this before I started a business. The lessons learned will definitely apply throughout the rest of my career.

Pick up The E-Myth Revisited: Why Most Small Businesses Don’t Work and What to Do About It before going out on your own!