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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Information Exploitation

Knowledge and information are power. The information explosion has been occurring over the last couple of decades creates a divide between those who can harness and exploit this information, and those who cannot. Here are my tips, tricks and advice for using information as a competitive advantage.

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If your products and services don’t serve the data science community; however, you’re using data science in your products and services for a competitive advantage, you’re in a popular but challenging situation with your customers. I call what I’ve just described: using data science as a supporting strategy. For instance, the people at Graze.com incorporate data science into their snack business to develop and deliver the next box of goodies their customer will get. Let’s be clear though: they’re in the snack business, not the data science business. In this situation, I recommend keeping your data scientists as far away from your customers as possible. If you’re using big data as a supporting strategy, make it a priority to keep your customers insulated from your data science.

Buffering

Buffering is an important strategy for leaders using data science as a supporting strategy. In short, buffering is structuring at least one organizational layer between your data science team and your customers. Contrast this to leaders using data science as a core strategy–selling products and services to other data scientists, like RapidMiner, Kitenga (now part of Dell), and Cloudera. In this case, it’s a great idea to put your data science team in front of your customers, because like attracts like. However, Graze.com’s snackers have no interest in data science, so in this case, keep the analytics out of the conversation.

Instead, have your customers interface with other people in your company who are like them. The same “like attracts like” concept applies. If you’re in the business of wearables for athletic people, put a layer of athletic-minded people between your customers and your data science team. A good friend of mine is a triathlete that runs analytics to help other triathletes compete. Although he’s an analytic, he wears his triathlete persona when addressing his customers. Since he’s a one-man shop, that’s his only choice. In a larger company, this concept should obtain as a sales and marketing layer comprised of athletes–not engineers.

Translation

One important job of the buffering organization is to translate what the data scientists are trying to accomplish, into terms your customers understand. The reason why you don’t put data scientists in front of non-analytics, is that they’re typically difficult to relate to. Imagine a group of pro football players showing up at Comic-con. The first time a trekkie introduces themself to a linebacker in Klingon, there will be a problem. Before a product or service is introduced to your customers, it must be sanitized from its analytic underpinnings.

When Progressive talks to its clients about its SnapShot device, there’s no discussion about analytics. Their marketing may allude to the scientific prowess that goes into their product for effect; however, in practice they call it usage-based insurance. This is a perfect example of translation. Most drivers understand the term usage-based insurance. You’ll quickly lose them if you start talking about behavior-based digital profiling using a synthesis of regression and machine learning algorithms.

It may take multiple layers within the organization to successfully translate your analytic-based competitive advantage into customer-facing language. I’ve worked with several organizations where the developers are three or four levels removed from the customer. When I worked with Visa, there was a product development group, product function group, business analyst group, and then developers and architects. Sometimes it takes multiple translations to get it right for the customer.

Curating

Curating is a special requirement for those integrating advanced analytics into their products and services. A special challenge the buffering organization has with their analytic brain trust is information overload. Curating sifts through the piles of brilliance to extricate the golden nuggets that will appeal to your customers. That’s no easy feat.

Consider a museum curator whose job is to process archeological findings into a display of wonderment. Piles and piles of ancient bones, tools, and artifacts must be reduced, organized, and displayed in a way the appeals to the masses. Curators do more than just translate–they manage and oversee their body of work, and interact with the viewing public.

In a similar fashion, your curators must own the body of work produced by your data science team. Whether or not you put your curators in direct contact with your customer (both ways work), they should synthesize the wealth of information produced by your data scientists into a concise, attractive package that your customers will relate to. Even if you translate well, if you don’t curate, you’ll hit your target market with too much information and they’ll find a competitor that’s easier to understand.

Summary

There’s no doubt your data scientists are brilliant; however, too much brilliance for your uninitiated customers will drive them away. If you incorporate fancy analytics into your products, but your customers aren’t really jazzed by math and science, save the tech-speak for your in-house design team. As you structure your organization, ensure there’s a buffer between your data science team and your customers, who can translate and curate their findings. If I’m a Graze.com customer, I don’t want a lecture on how to design the perfect meal–I just want a snack.

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.

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If you can’t get your data scientists and other analytics to be concise, you’ll never get anything done. To make the most effective use of your time, educate and coach your analytics on how to be concise.

As much as I love working with data scientists, this has to be the most frustrating part of my job. Analytic managers and consultants like me are responsible for getting things done; however, the very talented resources we deal with value brilliance over deadlines.

Notwithstanding their analytic disposition, everyone–including your data scientists–wants to succeed. To succeed, they’ll need to be concise: in their speech, in their writing, and even in their approach to solving difficult problems. Here are my seven best tips for making that happen.

Tip # 1: Analyze the Impact

You must do your homework before you broach the subject of concision. At the onset concision is very uncomfortable with analytics, so they’ll need to rationalize it for themselves before they unseat their de facto behavior. So when in Rome, do as the Romans. Do some research on the benefits of concision and the costs of not being concise, and prepare some analysis.

In my experience, you can double or triple your productivity, when your team effectively practices concise behaviors. You’ll need the numbers for your specific situation to make it relevant. High-level studies are interesting, but when the analysis is brought into their reality, it becomes impactful.

Tip # 2: Communicate the Need

Armed with your analysis, you must let them know what your intentions are. You can do this formally or informally, depending on the structure of your organization. I like informal–it’s better for engagement; however, do whatever you feel works best. Double- and triple-check your analysis; remember, you’re dealing with people who can spot a hole in your analysis a mile away.

This should be an engagement, not a communication. Engagement implies dialog and discussion. Listen to what they have to say: their feedback and concerns. Make them understand that you understand. If they don’t voice any concerns, they’re either not listening or not internalizing the implications of the message. Continue the dialog until they stop head-nodding and start sharing.

Tip # 3: Teach Them How

Concision is a skill that needs to be taught. Work with your team coach, Human Resources, or an external consultant to design a program that teaches concision. The facilitator should be familiar working with analytics—they are a special breed when it comes to this type of instructional design. Analytics have always been good at whatever they try to learn; you’re asking them to learn something they won’t initially be good at. It takes finesse to navigate through this human dynamic.

Tip # 4: Show Them How

Modeled behavior should follow education. Once your analytics have some guidelines to ponder, they’ll want to see it modeled in exemplars. The analytic manager on a data science team should be the paragon of concise behavior. Shorten one hour working sessions to thirty minutes and eliminate status meetings altogether. When documents are created or reviewed, focus on communicating the most amount of information in the least amount of space, with the question, point, or thesis within the first few sentences.

Tip # 5: Help Them Build

Be encouraging and supportive, not critical or condescending. Analytics are especially sensitive to skills they can’t quickly master. Give them time to grow and they’ll eventually come around. In addition to modeling concise behavior, I suggest introducing them to a well-written newspaper like the New York Times or the Wall Street Journal. I receive regular email alerts from the Wall Street Journal. They’re usually a hundred words or less do a great job of communicating breaking news within a few seconds.

Tip # 6: Give Them Feedback

Give them positive feedback when concision is done right. They won’t do it right for some time, so here’s where you have to be very careful. Criticizing an analytic for rambling or producing a tome when a brief will suffice, is a natural tendency that should be avoided. Even when it’s in the spirit of improvement, highlighting any shortcomings should be done with care. In this situation, just ask them to produce a more concise version, and be specific. I once had a data scientist give me a 50-page PowerPoint of all words. My feedback was that it had a lot of great content, but I’d need a 2-page process visual to call it done.

Tip # 7: Give Them Kudos

When you see your analytics exhibit concise behavior, whether a brief response or a quick turnaround on a priority deliverable, make a big deal out of it. Constructive feedback should always be done in private, but exemplary behavior should be well publicized. Leaders should support analytic managers in this effort; even a handshake from a higher-up is a big deal to most people. Everyone appreciates kudos, but more importantly when analytics see their peers getting rewarded, they take notice.

Summary

Be concise, and coach your analytics to do the same–enough said.

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.

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Are you noticing anything wrong with your data science team?

I'm sure you are; it's human nature. A client recently told me that she came home one day and noticed that there was a water-filled glass sitting directly on a wood table. She asked her husband, "Where is the coaster for this glass?" Her husband responded, "That's what you noticed? I just finished cleaning the entire house!"

I see a lot of leaders frustrated with their data science team. They've spent a lot of money so the have very high expectations. In consulting, we call that White Knight Syndrome, and I deal with it all the time. So when things don’t go as expected, they go down a very classic route of identifying gaps and solving problems. Not only is this enervating, but it's a reckless abuse of your data science team's potential. It's far better to build on the strengths of your data science team, than it is to improve on their weaknesses. Here are five things to absolutely love about your data scientists.

They Fuel An Uncatchable Competitive Advantage

Your data science team is a key ingredient for a breakthrough competitive advantage. This is no joke; so don't ever overlook this fact. They tackle unsolvable problems for fun, in a way no other profession can. Most people take for granted how the data scientists at Google have changed the world, with a search engine that was late to the party. Sure, the leaders had the vision that powerful search capabilities would equate to market domination; however, it was the data scientists that figured out to jump into our brains, figure out what we were trying to find, and bring back the most relevant results. Google's data scientists made it one of the most powerful organizations in the world.

They're A+ Students In School and Life

Data scientists learn fast and retain extremely well. They've done it their whole lives. Most data scientists you encounter excelled in school—4.0 GPA in high school and college. And although you would expect them to get good grades in computer science and math, remember that a computer science degree has more than just computer science classes. Data scientists don't only get good grades in math and science; they get good grades in everything. Don't be shy about bringing them into your business world. They'll start contributing real value faster than you realize.

They Deliver No Matter What

Data scientists are extremely loyal under the right conditions--sometimes to a fault. I can't count the number of times I've been roped into an all-nighter because of situations far out of my control. We dig in and we deliver anyway; it's part of that excellence gene that I referenced earlier. The only thing you need to do is setup the right conditions, which has more to do with job satisfaction than money (although a good paycheck doesn't hurt either). Data scientists love to create data masterpieces with people they enjoy. With the right environment and the right challenge, they'll stay with you all the way.

They Are A Magnet For Other Talent

It seems like everybody's having a hard time finding good data scientists, except for other data scientists. If you're a leader, you probably know a lot of other leaders; so, guess who data scientists hang out with? You guessed it--other data scientists. This is important to you on a number of levels. If you ever need to extend your team, the best source for finding more data scientists is the team you already have. Also, the data scientist community is very supportive. So if your team actually gets stuck on a problem, there's a huge brain trust at their disposal that's ready and willing the help.

They Save Your From Yourself

Data scientists think through everything before making a decision. This will and should drive you crazy if you're an impulsive leader. Impulse is good for immediate action, but like all things the best results come from Aristotle's golden mean--the desirable middle between two extremes. At one extreme is a knee-jerk reaction that gets you into trouble (sound familiar?) and at the other extreme is analysis paralysis. The trick is to get the right balance, and you won't do that without the counsel and reason of your data scientists. You may think you have a good idea, but it won't sit right with your data scientists until there's data, research, and analysis. This voice will save your assets more times than not.

Summary

Identifying problems and closing gaps with your data science team will only bring you status quo; however, identifying strengths and raising the bar will catapult you to a place nobody can catch. Instead of obsessing about what's wrong; invigorate your organization by using the strengths within your data science team. There's a lot to love: they're extremely bright, loyal, and precise. Make this your starting point and enjoy your immaculate house, instead of worrying about a missing coaster.

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.

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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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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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Banks need an overhaul in their lending practices and I think big data can help. There’s little chance I can get a bank loan right now (not that I need one) even though I’m probably one of the lowest risks in the country. I say that because I emerged from the financial meltdown of 2008 without missing a single payment on anything: loans, credit cards, office rent—even the gardener got paid on time. Compare that to all the FICO superstars that collapsed after two months of no work. Consumer behavior is not easy to model, but if your business relies on it, you better be good at it.

Banks lost a tremendous amount of money because they relied on dubious and ineffective scoring models and now they’re not sure who to lend to. This is bad news for banks—lending money is how they stay in business. I never understood why lending institutions—with all their core competence in analysis—would rely so heavily on FICO scores and lightweight scoring instruments. For instance, I can’t understand how two years of tax returns demonstrates your ability to pay on a 30-year mortgage; however, this still seems to be the gold standard for income verification. And don’t get me started on FICO; I’ve seen my credit score swing 121 points over the last five year period. First, they say I’m a very high risk—then they say I’m a very low risk. All the while, I really haven’t changed a bit.

My advice for banks is to bring their core competence for understanding consumer behavior in-house and reinvent their lending model. Big data and predictive analytics are in a place right now where very sophisticated modeling can be done on consumer behavior. Throw away the arbitrary rules of thumb and forget about FICO—it’s not effective. And even if a new, fancy consumer behavior modeling company opened its doors, why would you outsource something that’s so important to your survival?

Exigent innovation is painful; however, what’s the alternative? The good news for banks is that big data presents an opportunity to pull out of this mess. The question is whether they see it.

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The right information is probably available, but are you sensing it? If not, this information is doing you no good. How in touch are you with your common sensors?

I just walked into my chiropractor’s office five minutes late. This is unusual for me, I'm usually very punctual. Unfortunately, I fell victim to my own informal control plan. No, I don’t track statistics on how long it takes to drive to my chiropractor; however, after going for several years I have an internal sense for the central tendency and variance of the drive time (a little Six Sigma lingo for you this morning). For good measure, I always leave 30 minutes prior to my appointment, which I did today.

As soon as I hit the freeway, I was in gridlock traffic. I thought there may be an accident; however, I didn’t see anything. It took a total of 35 minutes to make it to my appointment today; fortunately, my chiropractor wasn’t too upset.

What’s important to note, is the information for my travel time was available, I just wasn’t tuned in. Whenever you get directions on Google Maps today, not only does it tell you distance, but it also tells you driving time based on the current traffic. If I had a sensor to this information tied into my workflow engine, I would have known to leave a little bit earlier today.

Fortunately for me, this particular bridge isn’t critical to my daily operation or my strategic objectives; however, do you know what information you need to collect, and how timely it needs to be?

These are what I call common sensors. Sensors are the devices used to collect information. What makes them common is the fact that they should be baked into your organization. Don’t let the word common take away from their criticality. In fact common sensors are the most critical sensors you have. They drive your strategy and they drive your operations.

Know and instantiate your common sensors. It’s one thing to be late to the chiropractor—it’s another thing to be late to the market.

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