A beautiful fall late morning in Southern California has my family contemplating an afternoon at the beach, just 20 miles away as the crow flies. As I ponder the idea, I remember that the Santa Ana winds blow strong in the fall (from the inland areas to the Pacific), it could be warmer, windier and even a bit smoggy. Quickly I move to the calendar, seeing as it is a Sunday on a holiday weekend the traffic will be tough (not to mention the parking). Of course, now I needed to weigh the overall feelings of my family; my daughter not a fan of the sun, my wife would go to the beach anytime regardless of conditions. Finally, my red Dixie cup may not go by unnoticed as it does on non-holiday weekends — additional law enforcement officers are likely to be present at the beach areas.

In a matter of seconds, my brain assessed numerous points of information to come to an informed decision, consuming both subjective and objective data points. The human brain is an impressive and contextual data factory. Let’s break this down into a high-level data acquisition exercise, taking advantage of modern approaches to data:

  • Weather service, with location services
  • Traffic service, with location services, route, and travel preferences
  • Calendar, with a micro-service connection to U.S. holidays
  • Police presence, pulling data from social feeds and attempting to establish a probability
  • Empathy and feelings, a uniquely human computing function
Our context was simple: an afternoon at the beach. Thus, all the data points are seen from that perspective — an exercise much easier said than done. Without a clear understanding of objective, it will be impossible to gather the right amount of data points and, more importantly, the inputs which have the greatest impact on one another.

For all the brain’s power, the flow of data can be a bit tricky. Four of the five citations were processed in the rational side of the brain. However, your brain first processes the information through the feeling’s sides of the brain, BEFORE traveling to the right side of the brain. Now, depending upon the information being crunched, the situation, your personality type, and inputs from other parts of your body, decisions may stall or shoot through the left side of the brain. This is where is gets a bit dicey — in order to contextually maximize the value of your data, you must have a crystal-clear understanding of the objective. In most cases you will need to have access to a team of experts, some of the roles may include:  data scientist, data modeler, networking and storage, security and authentication, graphing (modern integrations), code developers, visualization designers, anthropologists, business owners and practitioners. Not always the case; however, rule of thumb dictates that the more human inputs you can wrangle the greater your perspective.

Our world is overflowing with data, most of it out of context. Yet, at the cost of storage, strategies should be put in place to grab as much data as you possibly can from numerous sources, structured, unstructured, pulled from your enterprise and from third party sources. While the statistic seems to hover around 97 percent of data is never used, keeping that data for a rainy day, when you are looking for deeper perspective, will be worth its weight in gold. Consider for a moment the gigabytes per hour of data from a Rolls Royce jet engine from Los Angeles to Tokyo. You can bet that this information is sliced, diced and pumped into algorithms and transactional systems. The trick is to make sure that you have chosen tools which can perform some fundamental actions:

  • analyze the streaming data while being gathered
  • data lakes for storing mass amounts of data not used today
  • edge devices capable of local processing to manage data costs
  • encrypted transaction level mechanisms to the edge and from the edge to the device
Rethinking your data brings about some of the tensions found with other enterprise environments: what is the best time to rebuild, expand, or pivot from your existing approaches to modern data management practices? Regardless of tool sophistication or age, the assessment must start with a multi-year plan on how this data impacts the business. If the data you are collecting cannot lead to some form of action, then I recommend you find another hobby. Producing a color-filled dashboard with all sorts of arrows, pies, and graphs may be nice eye-candy, but what does it all mean? As future decision makers enter the workplace, with their digital-savvy backgrounds and life experiences often fed by young perspectives on the meaning of data, your data outputs will need to provide clear action.

All too frequently I find myself in a cold sweat when I think about the future of decision making. Odds are you played the game of “operator” as a child, the first person whispers a phrase into another ear, that person whispers into the third persons ear, and so on. The last person shares the phrase with the group and, almost without fail, it is completely different than the phrase that started the game. Now, imagine data processed in “black box” algorithms spitting out assumptions about the true meaning of the information, watered down by limited perspective. Put the cherry on top, and all this kind-of-accurate data is feeding the opinions of our future leaders. Digital savvy? Sure, with much of their experiences consumed by the outputs of other data elements, feels a little uncomfortable. Rethink your data — your company’s future depends on it.

Greg Lush

Founder at Last Mile Worker Solutions