Decoding Data: The Numbers Speak, Can You Hear Them?

by Jenny on November 9, 2016 Comments Off on Decoding Data: The Numbers Speak, Can You Hear Them?

An important aspect of finding value in the world of Big Data is the ability to understand what the numbers are saying. Just as with any written or spoken language, if your translation is inaccurate the message will be misinterpreted. This can lead to a variety of problems, and when dealing with business objectives there’s little room for error.

While learning a new language, there’s typically a process involving vocabulary, sentence structure and proper grammar. By putting these things together, the language can be used to communicate a message. Using Big Data to guide business decisions is no different; a language is being spoken, and the message must be clearly understood.

Approaching Big Data as a language, rather than learning vocabulary or grammar, analysts conduct their own process to read, translate, and comprehend the messages within complex data sets. When looking for solutions within the complex language of big data, there are four attributes we like to consider:

  1. Data Availability
  2. Data Quality
  3. Data Literacy
  4. Data Action-ability


Imagine for a moment that you’re driving down your favorite rural highway. There’s no traffic to speak of, your favorite song is on the radio, and all is well in the world. That is, until you hit a pothole and the engine starts to sputter. There’s a noticeable lack of power and as your car begins to slow, and you’re miles from the next service station. Something is obviously wrong.

Data Availability & Data Quality

After drifting to the highway shoulder, you notice the check engine light is on. Your dashboard is also flashing seemingly random digits over and over: “P0304 – P0304 – P0304…” You have no idea what these numbers mean. Miles from help with limited cell service; you shake your head in disbelief and wonder what could have possibly gone wrong.

Overwhelmed with the feeling of helplessness and frustration, your options are limited as you can’t identify the cause of your troubles. The raw data is there flashing, “P0304…P0304…” yet there you sit, wondering what to do with this information.

This is a similar feeling to having issues or obstacles within your business and not knowing how to resolve them. While the necessary data may be available and of good quality (it’s coming directly from the car), this alone cannot decipher the error code and therefore you cannot effectively address the problem.

Data Literacy & Data Action-ability

There you are, stalled on the side of the road. Data surrounding the problem is available and the quality is good (Error Code P0304). The vehicle is clearly telling you there’s something wrong, and it’s telling you the exact root of the problem. But without the data literacy to know what the flashing numbers mean and no way take reasonable action as a result, this data is essentially useless.

Similarly in business, where terabytes of raw information are available, there is an ocean of data in which many companies drown. Whether its customer satisfaction, product attributes, warranty claims, historical sales volume, or any other recorded metric, without the proper context of understanding and a supporting system to take action decisions makers are unable to leverage these sources of information. As a result, the most likely outcome is inaction and the sheer volume of data overwhelms and paralyzes a company’s decision makers.

Let’s revisit your car scenario, this time adding context to see if that helps. Assuming at least low-level knowledge of on-board diagnostic codes, or at best spotty mobile internet service, you have a lead on the core of the problem. The error code indicates an ignition misfire on one of the cylinders. So through deductive reasoning, your gut tells you the problem is under the hood. Having a 4 cylinder engine, it becomes a guessing game.

Having reliable data AND some basic data literacy, a good guess is as far as you can go resulting in an unproven hypothesis. With a deeper understanding of your car’s problem codes, troubleshooting skills are better honed. Furthermore, data literacy also requires some exposure to context if it is to be truly effective.

Decoding Data & Finding a Solution

Considering the series of events that led to your current situation, you remember hitting a pothole just before coasting to the highway shoulder. So, perhaps this has something to do with the big bump before the engine quit?

Having developed a hypothesis, you decide to take action; the first step is opening the hood. You look at the engine and notice that one of the sparkplug wires came loose. Maybe you find that another part is cracked or damaged. If nothing appears to be wrong at first glance, you consider the possibility of having run out of gas without noticing. Whatever the problem may be, the data is now ACTIONABLE. You can reconnect the wire, you can seal the crack, or you can realize the only option is to pick a direction and start walking to the nearest gas station. The situation may be dire, but now that the cryptic problem code has contextual meaning you can DO SOMETHING with it.

This highlights the difference between simply having data and having data intelligence. Circling back to its business application, terabytes of data will not overcome data illiteracy which requires context and actionable insights. To best leverage your data while making business decisions, the aforementioned attributes must exist and work together for ultimate success; data availability, data quality, data literacy and data action ability.

Looking for more information on data literacy? Have a specific question? Just bored and want to talk? Don’t hesitate to reach out! 

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Jenny Dinnen is President of Sales and Marketing at MacKenzie Corporation. Driven to maximize customer's value and exceed expectations, Jenny carries a can-do attitude wherever she goes. She maintains open communication channels with both her clients and her staff to ensure all goals and objectives are being met in an expeditious manner. Jenny is a big-picture thinker who leads MacKenzie in developing strategies for growth while maintaining a focus on the core services that have made the company a success. Basically, when something needs to get done, go see Jenny. Before joining MacKenzie, Jenny worked at HD Supply as a Marketing Manager and Household Auto Finance in their marketing department. Jenny received her undergrad degree in Marketing from the University of Colorado (Boulder) and her MBA from the University of Redlands.

JennyDecoding Data: The Numbers Speak, Can You Hear Them?