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Data Driven Decision Making & Camping

I was recently reminded of the importance of data driven decision making. I spent 6 days kayaking and backpacking in the wilderness on the US and Canada border. After living as a hyper connected technologist, disappearing into the backcountry was amazing and it lead to an unexpected realization.

I realized that building and using technology is part of being human. Even when disconnected from modern society, we use technology stay warm; capture, gather and cook food; and store and purify water. Without technology, modern or primitive, humans can’t survive. We often take technology for granted and demonize it as unnatural.

I carried roughly 50 pounds of gear, food and water into the backcountry. I was limited to packing only what I needed to survive. The only “luxury” I carried into the backcountry was a deck of playing cards to pass time. I found it strange that what I missed most wasn’t my bed or car, but data. I wanted to know:

  • My location: I wanted to know where I was. In some places their was no trail, or the map was inaccurate.When traveling I had to stop every 15 to 30 minutes to mark our location on the map. However, I was not always sure where I was.
  • The weather forecast: I slept lightly and never traveled far from camp. I never knew if a storm was coming, how fast the wind would blow and how much it would rain. It would have been great to not worry about a storm preventing travel or flooding our campsite. With a little data I could have traveled confidently and slept soundly.
  • Where to find food: During the trip most of my food came from fishing. I would drift into bays looking for fish. Some bays had a lot of fish, some had none. It took at least an hour to explore each bay. In addition to scouting fish, I spent a lot of time trying to figure out what the fish were eating. While this is a part of the sport of fishing, when my dinner was online it would have been nice to have some data.
  • What was going on with my family: At the farthest point during the trip, if their was an emergency it would have taken 2 days of paddling and hiking to get to the nearest person; not including transportation to a hospital. Also, I missed my family. It would have been great to call my girlfriend or at least send her a message that I was safe.

These are things that I normally take for granted, but when stuck in the backcountry I realized how much I use “business intelligence” and “data driven decision making” in everyday decisions. Using data to make decisions doesn’t have to be complex. You don’t have to have terabytes of storage and petaflops of processing power to make data driven decisions.

With all the hype around Big Data, Business Intelligence and Data Science it is easy to think that making Data Driven Decisions is akin to science fiction. It is important to remember that the hype represents the cutting edge of what is possible and that most businesses aren’t ready for the cutting edge.

SAS recently surveyed 339 organizationsonly 12% of organizations are currently implementing or executive a “big data” strategy. Nearly 51% of companies that are not pursuing a “big data” strategy are doing so because they don’t understand the benefits, need more information, or lack support from the business and executives. Another 11% provided no reason for not pursuing a “big data” strategy.

As the CEO of Contemporary Analysis I know the power and potential of Big Data, Data Science and predictive analytics. However, my goal isn’t to push the most sophisticated tools – my goal is to make data driven decision making ubiquitous (Read Case Study).

The ubiquity of data driven decision making requires that we make accessing data so easy that it is taken for granted. The gold standard of Data Science, Big Data and Business Intelligence should be that people ask for results not tools. We don’t ask for a thermometer, but ask what the temperature is. Learn how we are making data driven decision making ubiquitous download our case studies on Dashboards and Data Visualization and Business Intelligence Data Warehousing.


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