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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Posted by on in Information Exploitation

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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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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Posted by on in Strategy

Happy Tuesday, folks! I hope everyone had a fun and relaxing Memorial Day weekend. I sure did; Kim and I lounged around all weekend, usually doing something close to nothing. If you know me at all, you know cooking was on the agenda; however, this year I went a little mellow compared to some of the other holidays. I need to drop some pounds, so I started on Atkins a few weeks back. It’s going pretty well so far; I’m down about 7 or 8 pounds. One adjustment that I thought was going to be tough was eating more vegetables; however, it has pleasantly turned out well. The key I’ve found is this: the right ingredients make all the difference—that’s true with diets and it’s true with strategy.

One thing I’ve really grown to love is tomatoes. Before starting the diet, I would rarely eat tomatoes; however, now I eat two to three every day. And now that I’m a tomato connoisseur, I’ve noticed that not all tomatoes are created equal. Sure, Roma tomatoes will not taste like Beefsteak tomatoes. What matters more though is where I get the tomatoes from. The tomatoes from Safeway aren’t as good as the same type of tomatoes from Whole Foods; and these aren’t as good as the same type of tomatoes from Windmill Farms (they carry a lot of fresh produce from local farmers). The tomatoes on the vine at Windmill Farms are awesome and the same type of tomatoes from Safeway are barely okay, even though they look similar.

Selecting people for your strategy—whether they’re full-time employees or consultants—is like selecting tomatoes. The talent differential between average, good, and great is sizable; and looks can be deceiving. It astounds me every time I come across someone from a big-name firm like McKinsey, Deloitte, or Accenture, who doesn’t know the difference between strategy and long-range planning. Or, an Executive Vice President with an MBA who cannot make a decision.

One of the critical elements of executing a successful strategy, is making sure you have the right people on your team. Selection is crucial—you don’t want to end up with a bunch of sour tomatoes.

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Posted by on in Leadership

We planted some beautiful Hibiscuses (Hibisci?) earlier this year and with the amazing spring that we are experiencing here in Silicon Valley, the blooms are just enchanting. As a Hawaiian, these flowers have a special meaning for me—they are the state flower of Hawaii (okay, the state flower of Hawaii is actually the Yellow Hibiscus, but these are close enough). Now, when I step outside, I’m often transported to my favorite spot on on Waikiki beach, the house in Pearl City where I lived with my family as a teen, or the beautiful grounds of the Hyatt in Maui where my wife and I were married. This all comes from a simple flower. Symbols have the power to be transcendent in the message they convey to your big data strategy team.

Symbols are objects, acts, or events that convey a special meaning. I was talking with a colleague yesterday who did some consulting for Apple, and she mentioned that contractors and consultants had badges with muted, grey apples; whereas, all the employees had bright, colorful apples. This and other rituals made her feel like an outsider.

Along with rituals, stories, and the infamous grapevine; symbols are a component of your organization’s informal system. Your informal system exists whether you like it or not and has greater power than your formal system (e.g. mission, vision, stated policies) to influence the people in your organization.

As a leader, the most important thing you can communicate to your big data strategy team—both formally and informally—is your support. What symbols do you have in place for this? Here are some questions to help you figure that out:

  • Do you hand out special awards to them?
  • Do you have special ceremonies for them?
  • When they walk around campus is their status on the big data strategy team conspicuous and respected?
  • Does your office layout make you accessible?
  • Do they feel comfortable approaching you for help?

The symbolism in your company is working for you or against you. It’s up to you to figure out which force is in play and make adjustments if necessary.

So, while you do that, I’ll take some time to smell the … Hibiscus.

Aloha!

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Posted by on in General Comments

With all the high-tech, analytic software tools that I play with on a daily basis, one of my favorite tools is actually my shoe horn.

It is a gorgeous day in Silicon Valley today, and I’ll be spending part of it at lunch with a good friend Julian, who I met when I was consulting for PayPal. As I’m getting dressed, I realized how often I use my shoe horn, and it made me think about why this is a great tool. Great tools are simple.

I see a lot of companies choosing the wrong tools for their strategy. I’m one of the key contributors on TechRepublic’s Big Data Analytics blog, and someone made a comment the other day on one of my posts indicating that executives are erroneously trying to use Big Data to solve everything. He’s absolutely right; I see the same thing. It’s a very expensive mistake; Big Data resources are not cheap.

The bigger problem with most Big Data tools is that they’re complex. Sure, data scientists know what’s going on, but from the executive perspective, it seems like an alligator that you need to feed with fancy technology and fancy people. This isn’t good.

In fact, in many cases, you can get to where you want to go without big data. Most companies don’t even have their small data under control. And even if it makes sense to use big data for your strategy, you don’t need to dive straight into the deep end of complexities that you don’t understand.

If at all possible, keep your tools simple. Sure, I can hire a team of professionals to design a fancy, electronic device that will get my shoes on in sub-second time—but I’ll just stick with a shoe horn.

Tagged in: big-data strategy tools
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Posted by on in Strategy

How many management consultants does it take to change a light bulb?

Well, it depends; let’s first understand why you feel light bulbs are necessary.

(I’m kidding)

Actually, I had a light bulb moment yesterday—literally. We have a small chandelier in our entry way that blew its last bulb this past weekend, so my first order of business was to shed light on the situation (pun intended). Once I got up on the ladder, I realized I had a situation. I could not reach the light bulbs because there was a grey, metal diffuser in the way. It’s there so that people upstairs looking down don’t get blinded by staring directly into the bulbs. The only solution that came to mind was to remove the large, heavy, glass base of the contraption. So that’s what I did.

Before long, I was screaming to my wife for help. I’m balancing on the third step of a ladder holding a heavy, delicate ornament in one hand and the knobs that hold it in place in the other. Fortunately, Kim quickly came to the rescue and I was able to change out the light bulbs without breaking my neck.

Later that day, I stopped into the lighting store where we bought the chandelier and told my story to the owner. He patiently waited for me to finish my story, smiled, paused, then explained to me that I should have removed the diffuser—not the huge glass bowl at the bottom.

Good information not only increases strategic effectiveness and efficiency, but it also reduces risk. I talked about this yesterday when I was commenting on the awful bombings at the Boston Marathon. In my chandelier episode yesterday, I got the result I was looking for—light where there was no light. However, I could have arrived at the same result with much less risk, had I known about removing the diffuser instead of the base.

I’m not making that mistake again. Fortunately, I see the light now.

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Posted by on in Strategy

I’m still struggling to process how anyone thinks it’s okay to set off a bomb in the middle of a crowd of innocent people over a difference of ideals. I was in the dentist’s chair this afternoon when my wife sent me a text succinctly detailing the awful Boston Marathon bombing. I couldn’t believe it—and still can’t. It’s unfortunate that a plot like this actually succeeds; however, I’m thankful for all the terrorist plots against our people that don’t. Although I talk a lot about using information for strategy and innovation, information prowess is also a powerful tool to mitigate critical risks.

It’s hard to notice non-events because they aren’t conspicuous; however, it’s remarkable to think about all the terrorist plots that were attempted and failed. Our intelligence agencies work with our enforcement agencies around the clock to monitor and intercept all the crazy schemes devised to harm and kill Americans. At times like this, President Obama reminds us, our friends, and our enemies how serious we are about justice around these matters. The combination of leadership and information prowess keeps critical risks from surfacing. The unfortunate event in Boston today is the exception that makes the rule.

All strategy is vulnerable to the effects of critical risks—not only those that involve Big Data or some other form of information exploitation. Your degree of analytic capability has a direct impact on how well you mitigate these risks. You can see this in action with Santam, South Africa’s largest short-term insurance provider. With big data and predictive analytics, Santam was able to save millions that were previously lost to insurance fraud.

Mitigating critical risks is an important part of any leader’s strategy. If the stakes are high enough, it may make sense to assemble a big data team for the sole purpose of making sure nothing happens. Regardless, take some time today to see where advanced analytics might neutralize your biggest risks.

My sincere condolences to those affected by the Boston Marathon bombings

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Posted by on in Strategy

My wife just bought me a great cookbook by Faye Porter called, At My Grandmother’s Table: Heartwarming Stories & Cherished Recipes from the South. When I’m not out helping executives turn their chaotic data into strategic wisdom, I’m often found in the kitchen or the backyard cookin’ up something good. I love to cook and I like to experiment with new ideas; but I absolutely love old-fashioned cooking (methods and recipes). Although you must always be innovating, some of the best ideas come from the wisdom that precedes us.

For instance, look at the way I define Big Data for use in a competitive business strategy:

Big data is the massive amount of rapidly moving and freely available data that potentially serves a valuable and unique need in the marketplace, but is extremely expensive and difficult to mine by traditional means

I opened up TechRepublic’s Big Data Analytics Blog with my seminal post, Big Data defined, wherein I systematically explained this definition using the underpinnings of Michael Porter’s five forces analysis. Although Porter put out these ideas in the 1980s, they’re still relevant for academic discussions on strategy and for evolutionary derivatives as I did with defining Big Data for strategic competitive reasons.

With all the charm brought about by the novelty of Big Data, it’s easy to lose sight of the the past—this is a mistake. We have a wonderful repository leadership and management theories and ideas that date back to Taylorism in the early 1900s: and all the way back to the history of time if you study leaders qualitatively. Bringing these ideas current is a talent you should embrace.

Now, I’m off to embrace Grandma Elizabeth Robertson Smith’s Crumb Top Apple Pie.

Have a great weekend, everybody!

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Posted by on in Strategy

We’ve recently been entertaining some uninvited visitors in our cement pond. A couple of months ago, a cute avian couple landed in our swimming pool to hang out for the day. I knew it was a couple because male ducks are much more colorful than females. At the time, my wife felt compelled to feed them; they ate well, feasting on our 9-grain bread and stoneground wheat crackers. Well, the word got out, and now our pool is a popular hangout among the duck community. Although it was a bit surprising at first to be greeted by ducks in the morning, the truth is—they were here before we were here. Survival requires adaptation.

I had this epiphany a few months back when I saw a band of coyotes emerge from a nearby creek. It startled me at first, then I quickly realized that there were children playing at the grade school a block away that would not be thrilled to have coyotes join their fun, so my wife immediately called the school and I called animal services. That’s when I received my education. After assuring me that the coyotes were no threat, the gentleman kindly explained to me that we moved into their territory—not the other way around. The wildlife around here has had to make some significant adjustments over the last few decades to accommodate the disruption we call suburban progress. It was an unwelcome change for the incumbent fauna; however, survival requires adaptation.

To survive today, leaders must focus on keeping their organizations adaptable. Martin Reeves and Mike Deimler, partners at the Boston Consulting Group, assert that today’s companies must adjust their strategic focus from building out one strong competence to learning new things quickly (Reeves & Deimler, 2011, p. 137). I agree, provided your organization is suspect to wildly changing external conditions. This ideology ostensibly flies in the face of traditional strategy planning where a long-term vision is articulated; however—for some companies—survival requires adaptation.

If you’re trying to run a company where the rules of engagement keep changing, think about where your focus is. To be successful on a traditional program, you must firmly focus on what will be delivered; however, to be successful on an agile program, you must firmly focus on how it’s being delivered—with a deep respect for the effect change will have on the evolution of your product and how this change will be managed.

Take some time to evaluate your environment. If you sense radical disruption, you may want to focus more on adaptability than your articulated vision. This doesn’t mean abandoning your mission—this is your reason for existence. However, a radically changing external environment has engendered a different attitude in leaders who are running successful organizations. If this is your situation, adaptation is your only answer. If ducks and coyotes can do it, I’m sure you can figure it out.

References:

Reeves, M., & Deimler, M. (2011). Adaptability: The new competitive advantage. Harvard Business Review, 89(7/8), 134-141.

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Posted by on in Strategy

I went into my bank last week, and I thought I walked into the wrong building. I’ve banked there for at least 10 years now, building relationships with all the tellers and account managers. I go there frequently, at least a few times a month; however, last week when I walked in, I didn’t recognize one person. You cannot build a relationship with your clients if you’re constantly introducing them to new people.

The foundation of your relationship strategy is the people that interact with your customers. There are other things that contribute to customer loyalty like your brand; however, any real relationship involves at least two people: your customer and the person in your organization that comes in contact with your customer.

Banks have a bad reputation for turnover, so seeing one or two people leave is disappointing and demeritorious, but doesn’t really come as a surprise. Last week however, was jolting. There were at least a dozen people working in the bank, and I didn’t recognize anybody. I made a comment to the teller asking, “Where did everybody go?” I started naming names, and one-by-one, she notified me that they had either transferred to another branch, or left “for a better opportunity.”

For me, this isn’t my bank anymore. I’m not leaving or canceling any of my accounts, but it’s just another place for transactions now. I can get the same experience at the grocery store, and since I shop more often than transact with the bank, I probably won’t go to my branch anymore. It’s a shame when I feel more comfortable picking up Chinese food than sitting down with my banker to discuss a loan.

Employee retention is vital in all areas of your organization, but especially where these employees touch your customers. Whether you like it or not, your customers are more loyal to the people in your company than your company’s brand or values. If your employees are leaving for better opportunities, your customers are probably walking right behind them.

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Posted by on in Strategy

Ebay announced earlier this week that it was shutting down the sale of metaphysical goods and services on its site, including psychic readings, potions, spells, and fortune telling. They should have also included some strategy formulation in this category. When you hire big consulting firms to build fancy strategies for you, are you buying a real vision, or a very expensive love potion?

In addition to information strategy formulation, I deal with all strategy execution, so I clean up these messes all the time. A small army of nicely-dressed consultants brandishing impressive credentials and expensive pens swoop in to patronize your company with an “environmental analysis,” then proceed to pontificate through glimmering, buzzword-ridden PowerPoint about the latest management theories coming out of Harvard. Then, with one of their hands shaking your hand, and the other holding your big check, they’re out the door to repeat the process with your competitors. That’s about the time you call me to ask, “John, how do we make this work now?”

Thanks a lot.

When I step into a situation like this, I don’t automatically look for problems with the strategy; however, it must be vetted. That’ doesn’t mean I correct their paper, it just means that to mobilize a strategy, certain things must be in place.

Strategy execution is very different from project or program execution. With program execution, you execute tasks to complete a relatively short-term objective. With strategy execution you monitor assumptions and consider possibilities and alternatives to move toward a relatively long-term vision. So, to vet a strategy, you must document and support your assumptions about the future. Until you do this, there’s no way to responsibly mobilize your strategy.

Unfortunately, in going through this process, you often see the big-strategy spell dissipate. It’s a painful realization; however, it’s better than spending the next three to five years committing resources to a strategy that won’t work.

Although the sale of metaphysical services has been part of Ebay since its inception, they finally decided to pull the plug, because customer service is tired of people complaining that their $20 love potion didn’t work. Who are you going to call when your $250,000 love potion doesn’t work?

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Posted by on in Information Exploitation

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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