Category Archives: Strategy

Become an Expert in Your Field

Source: Fast Company, Aug 2014

  1. Define each problem in detail before trying to solve it
    Take time to understand the problem, understand the criteria for a good decision, and generate some good options.

  2. Offer one or two firmly suggested solutions
    Offering too many suggestions will only confuse your client and allow him to become indecisive. Be very clear on the direction you offer with your solution and ask the person or team you are supporting to repeat it back so that it is clear.

  3. Prioritize your client’s action steps to help avoid overwhelm
    If your client agrees to take action, ask him to relax and focus on moving forward. Be sure that the action-steps requested are doable and achievable in a timely manner.

  4. Implement a step-by-step plan of action
    When you approach problems systematically, you cover the essentials each time–and your decisions are well thought out, well planned, and well executed. Provide a checklist and mark off each item as it is achieved so that others feel that they are achieving their goals and moving away from problems, obstacles, and challenges as they take action steps. This will keep them motivated and in motion.

  5. Look for more ways to improve upon the problem-solving idea to avoid future problems
    Continue to perfect your problem-solving skills and use them for continuous improvement initiatives to serve your clients’ needs. The more effectively you solve problems, the more value you create as the go-to authority.
    Develop a system to support more people in a timely manner by making note of your problem-solving process. Many of the problems you solve for others will be the same or similar problems you will support others with in the future.

3 Questions

Source: Fast Company, May 2014

Asking Why, What if, and How, in that order, can help one advance through three critical stages of problem-solving.

  • Why” questions are ideal for coming to grips with an existing challenge or problem–helping us understand why the problem exists, why it hasn’t been solved already, and why it might be worth tackling.
  • “What if” questions can be used to explore fresh ideas for possible improvements or solutions to the problem, from a hypothetical standpoint.
  • When it’s time to act on those ideas, the most effective types of questions are practical, action-oriented ones that focus on “how”: how to give form to ideas, how to test and refine them with the goal of transforming possibility into reality.

Difference between Analytics and Experiments

Source: HBR blog, May 2014

In their 2012 feature on big data, Andrew McAfee and Erik Brynjolfsson describe the opportunity and report that “companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors” even after accounting for several confounding factors.

One of the most important distinctions to make is between analytics and experiments.

The former provides data on what is happening in a business, the latter actively tests out different approaches with different consumer or employee segments and measures the difference in response.

 

Progress Depends Upon New Technologies & Norms/Laws

Source: HBR, May 2014

Paul Romer … helps make the point. He explains that the history of progress is a history of two types of innovation:

  1. Inventions of new technologies, and
  2. introductions of new laws and social norms.

We can make new tools, and we can make new rules. The two don’t always march in lockstep. In a period of time where one type of innovation flags, the other type can sometimes forge ahead.

Clayton Christensen has outlined how managers’ acquired habits in allocating capital are putting capitalism itself at risk. Noting the difference between empowering and sustaining innovations, he shows how a pressure for short-term payoffs will always drive investment toward the latter, which tend to increase the efficiency of current operations.

Unless at least some of the capital freed up by doing that is used to generate the truly “empowering” innovations – the ones that form the foundations of new businesses or even whole new industries – firms will not experience organic growth and society will not gain new jobs. (Christensen’s work is strong reminder that the most fundamental social responsibility of corporations and their managers is innovation, because it fuels not only their own future competitiveness, but the prosperity of the world.)

What new rules should managers be promoting? Clearly, investing in empowering innovations could be made more the norm, supported by revised approaches to everything from entry-level hiring to CEO compensation.

We would also argue for a different managerial mindset toward productivity and the best use of technology – specifically to adopt what Peter Drucker called a human centered view of them. Cowen is right when he describes today’s technologies as displacers of human work, but that is not the only possibility.

Managers could instead ask: How can we use these tools to add power to the arm (and the brain) of the worker? How could they enable people to take on challenges they couldn’t before?  The greatest problems of the world – such as ensuring abundant fresh water supplies, energy, health care, and schooling – will not be solved by placing human work in opposition to machines. They will require everyone’s best thinking combined with the staggering capabilities of digital technology.

5 Skills of Disruptive Innovators

Source: Business Insider, Dec 2013

The ability to look at problems in a non-standard way might be the most sought after competency of the future.

The five specific skills that are key to generating novel ideas are:

  1. Associating: Innovators associate ideas that are previously unconnected either to solve problems or create something new. This is how Gutenberg created the printing press. When forming teams, keep cross-pollination of experiences and perspectives in mind. But you also need the glue. You need someone in the room with loose associations who can pull ideas together.
  2. Questioning: Innovators ask a ton of questions. In fact, they treat the world as a question. Managers ask ‘how’ questions — how are we going to speed that up, how are we going to stop this from happening. Innovators ask ‘why.’ They are the kid at the back of the class the teacher hates (and often, the person in the meeting the manager hates.) Not only does this help you filter bullshit, but it helps jolt people from the status quo.
  3. Observing: You can’t learn if you don’t observe. You need to always be observing. This mindfulness is what allowed Sherlock Holmes to solve cases.
  4. Networking: Talking to people is a great source of ideas. People offer different perspectives. They may have just failed at something but you may be able to apply the same idea to a different problem. You need to be open to these perspectives, even if you just file them away for another day. (see #1)
  5. Experimenting: If the world is their question it is also their lab. Fail often. Fail fast. Fail Cheap. Try again. Never give up.

Successful People have these 8 Traits

Source: Business Insider, Dec 2013

  1. Stay Busy
  2. Just Say No
  3. Know What You Are
  4. Build Networks
  5. Create Good Luck
  6. Have Grit
  7. Make Awesome Mistakes
  8. Find Mentors

Data Science Requires Creativity

Source: VentureBeat,  Dec 2013

… the importance of creativity as a key trait to look for in people who work with the data. That means relying on proven algorithms might not always cut it.

On the data science competition site Kaggle, some people who do well tend to “spend all their time being creative” as they comb through and pull ideas out of the data they’re given, said Jeremy Howard, Kaggle’s former president and now a data science faculty member at Singularity University.

Rogati said she thinks it’s interesting that sometimes in academic work, unusual hypotheses result from mere accidents. It could be that accidents have the power to give companies brilliant insights, too. So data scientists would be wise to appreciate accidents and consider ideas they hadn’t initially considered.

… data scientists shouldn’t forget about the power of their eyes. “My favorite algorithm is the vision, because it’s just so powerful, and, believe it or not, it’s underestimated,” Rogati said.