Silicon Valley State of Mind, a blog by John Weathington, "The Science of Success"
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    Welcome to a Silicon Valley State of Mind, thoughts tips and advice based on the consulting work of John Weathington, "Silicon Valley's Top Information Strategist."

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Silicon Valley State of Mind

Tips, thoughts, and advice based on the consulting work of John Weathington, "The Science of Success."

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Archive, April 2015. Switch to list view

    Posted by on in Information Exploitation

    Does it feel like you're spinning on your next big product idea instead of moving forward? That's a very expensive scenario when a data science team is involved.

    I'm often called into companies to organize them and move them forward. Most of the time, they have an idea of what they want to do, but for some reason, they just can't move things forward. There's a lot of activity, and a lot of meetings, but no real accomplishments. Does this sound familiar?

    There are several reasons why this happens, but all comes back to execution excellence, which is not intuitive or intentionally developed as a capability in most organizations. Even with great thinkers and doers, if you don't have a good frame for moving an idea into action, you'll probably spin. However, if you're focused and organized, your data science team can begin work on your next big idea in just five days.

    It Starts With Leadership

    The first day starts with you--the leader. If your organization is spinning around, my guess is that you're trying to get too many things done at once. If your next big idea is really important, your first job is to decide that it takes priority over everything else. You must resolve this for yourself before engaging with the rest of the team.

    Once you've resolved that this is where your organization will focus, develop logical and emotional reasons why everyone should make the development of this product their priority. I had a leader tell me if they don't differentiate somehow, they're going to die. That's compelling and emotional! This is the message that you want to move forward with.

    Start With the End In Mind

    On day two, in the spirit of the advice given to us by the late Dr. Stephen Covey, start with the end in mind. Define what success looks like with your leadership team. This can take an hour or it can take all day--but it shouldn't take more than a day. The outcome of this exercise is more than a vision statement; it's a vivid depiction of how the future will look. I recommend doing this in three cycles: macro-environment, competitive environment, and internal environment; in that order.

    In the first cycle, paint an outline of your future macro-environment, including political, economic, social, technological, environmental, legal, and other factors that affect your company. Fill in this outline on the second cycle with your competitive environment, including: customer, suppliers, new entrants, and alternative offerings. Finally, complete the vision on the third cycle with how your organization will look, including size, composition, culture.

    You've Got The Brains, Now Start Storming

    On day three, involve your entire data science team in a brainstorm. The goal is to understand how the team will achieve the vision. The pre-work on days one and two are important. Open the meeting with the logical and emotional reasons why this effort is more important than anything else they're working on and clearly articulate your vision.

    During your brainstorm, let the ideas flow. Encourage free flow of thought, and capture ideas in an organic fashion (in a mind mapping tool) and not in a linear fashion. Most brainstorms like this will last a few hours, so make sure to incorporate breaks. When I reach most organizations, they've started here and they're stuck here because nobody's defined a cutoff period. You're cutoff period is the end of the workday--after day three, there will be no more brainstorming.

    Making Sense Of It All

    Bring the team back on day four to organize everything. It's important to reinforce the sequence--we're done with guidance, we're done with visioning, and we're done with brainstorming. Don't let the team regress at this point--that's how everything goes circular. The team must mentally switch modes from brainstorm to organize.

    Organizing is about grouping and removing duplicates. This can be time consuming for some; however, it’s easier for data scientists. They are naturally adept at separating ideas into affinity groups. You should reduce the ideas in your brainstorm into tangible deliverables; this will be the basis for your action plan. One more day to go.

    Moving Forward

    Bring everybody back on day five to build an action plan. Set the expectation that by the end of the day, work will begin. Divide the day into two parts. The first part of the day is spent identifying the top priority deliverables (from the action plan) and when they will be done.

    The second half of the day is a working session to get started on the top priority deliverable. While the data scientists are moving forward, the analytic manager completes the action plan and the change leader is starts on the stakeholder map. If you want to move forward within five days, schedule it into the agenda for day five.

    Summary

    If you have a great idea, and you have a data science team, you should be getting things done and not meeting to schedule more meetings. I've given you a simple, five-day agenda for moving forward. It starts with a resolution you make with the man in the mirror--so take that first step. If everything's a priority then nothing's a priority. Make this the priority, and in five days you'll be well on your way to the next level.

    Submitted for Publication in TechRepublic’s Big Data Analytics Blog

    This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

    Update: TechRepublic published this article on April 27th under the title, “From big idea to action in 5 days: A step-by-step guide”

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    Posted by on in Information Exploitation

    If you're creating a product or service that incorporates data science and big data analytics, you might be paying too much attention to artificial intelligence and not enough attention to superficial intelligence. Data science is filled with mystical algorithms reminiscent of spells chanted by wizards of yore. Armed with this arsenal of prestidigitation, zealous leaders eagerly present their market with new and improved widgets, powered by artificial intelligence. However, many times they take an egocentric view of the world, relying myopically on their internal capabilities for advanced analytics. If you flip this around to a customer-centric view, you'll see intelligence doesn't need to be artificial to be valuable. To get the most value from your artificial intelligence application, combine it with the superficial intelligence obtained by involved communities.

    The Wisdom Of The Crowd

    There's a wealth of valuable data available in plain sight and happening right now--I call this superficial intelligence. When I was in grade school, my neighborhood friends and I would occasionally start a pickup football game in the middle of the street. We would post the girls on the corner to signal us when a car was coming, so we could move out of the street. This was great superficial intelligence for us. Without the benefit of this information, a wide receiver might be tackled by an unwelcome, automotive defensive back!

    Superficial intelligence is a great addition to your bag of data science tricks, as it adds to your existing base of artificial intelligence and it represents a more customer-centric marketing approach. This primarily applies to leaders who are using big data analytics to support their core products and/or services: similar to Progressive Insurance's Snapshot device, where analytics supports a traditional product (insurance) to gain a competitive advantage. The value of data and information doesn't need to be artificial or involve sophisticated analyses to be valuable. Just knowing that a car was turning down our street was great to know. Where this starts to get exciting for data scientists is when you combine superficial intelligence with artificial intelligence. That will take your game to whole new level.

    A great example of this is an application I just downloaded on my iPhone called Waze. If you haven't heard of it yet, you really should. Like Google Maps or MapQuest, Waze is an application that helps you navigate the streets of your locale. You give it an address, mount your phone in your car, and it gives your real-time navigation instructions to your destination. What's different about Waze though, is the Waze community, which is actively involved in feeding you superficial data. For instance, with the help of your local community, Waze tells you where there's an accident, construction that requires a detour, or even a cop hiding out under a bridge. Waze combines this information with real-time analytics to determine your best route. It's amazingly powerful and accurate. I don't say this often, but it actually puts Google to shame. That's what the wisdom of the crowd can do for you.

    The Human Machine Synergy

    To apply this principle of combining artificial and superficial intelligence, consider the evolution of data into wisdom. I'd say superficial intelligence gives you a good base of data to start with. Remember, data is just raw, uncultured insights. If there's an accident a half-mile away or a car around the corner, that's really good data that someone could use. You can combine this with non-crowd-sourced data. Waze obviously has geographic data at its immediate disposal and I'm sure the team at Waze curates of wealth of other information as well. This data becomes useful when it evolves into information.

    Information is analyzed and applied data. When Waze analyzes all the stock and superficial data coming from the Waze community and tells you to "turn right," that's information. Information tells your consumer what to do with all this data, based on their objectives. So again, you must transcend the pure data paradigm and think about what your customers might be trying to accomplish. Then, using a mix of base data and superficial data, perform a real-time, big data analysis to prescribe their next step. This strategy alone puts you at a distinctive advantage, but there is one more level you can take it to.

    Information evolves into knowledge, which further evolves into wisdom. Knowledge is when you take information from disparate sources and combine them for new insights. With superficial intelligence, you're already going down this path; however, for more impact, you'll want to explore related but very different sources of information. I used to live next to an arcade, which would sometimes host special events that drew a lot of traffic. So, it wouldn't be a good idea for a pickup game on one of these days due to the traffic. Wisdom comes from maturing knowledge over time. The first time we tried a pickup game at 5p when everyone was coming home from work, we learned our lesson. If you apply these ideas to your next product or service, you will probably be approaching breakthrough territory.

    Summary

    Artificial intelligence is great, but when combined with the superficial intelligence of the crowd, your product or service goes to a whole new level. Take some time to consider how your existing data can benefit from additional, crowd-sourced data, and what your analytics would look like at that point. Then, survey your customers and see if they would be willing to form a community around your offering. With the wisdom of the crowd on your side, you can't go wrong.

    Submitted for Publication in TechRepublic’s Big Data Analytics Blog

    This is the sneak peak of my latest contribution to TechRepublic’s Big Data Analytics blog. As editors do, when this gets published, some of the words and content may be arranged or deleted for a variety of reasons including SEO. What you’re looking at here is the uncut, unabridged, unedited version of the article that was submitted.

    Update: TechRepublic published this article on April 23rd under the title, “Crowdsource data science to add superficial intelligence to AI”

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