Understanding Data

It is obvious that the world is creating a lot of data.  Much of this data lies dormant, stored in countless databases, where it is often untouched or underutilized.  Organizations have a propensity for using data to serve a specific purpose, and often do not realize its full potential.  In a world with emergent data-driven decision-making, this is unacceptable.  Insufficient use of data resources hinders an organization’s performance, increases costs, and widens risks.  Unlocking the power of data in today’s world is paramount.

In order to appreciate data and have the imagination to unlock its potential, one must first understand its purpose.  In its primitive state, data is raw and has no significance beyond itself.  It is only when it reaches maturity, that data is enlightening.  The process to reach this maturity can take several paths, and incorporate various techniques.  The diagram and explanation below illustrate the progression from raw data to actionable information.  In the end, the objective is to have such an intimate understanding of the data (wisdom), that using it to make decisions becomes second nature.

Data: The data level is the most basic level. It is factual and comes in the form of raw observations and measurements.

Information: Puts the data into context, defines it within a structure, and analyzes the relationships and connections across the data. Answers the questions: Who? What? Where? When?

Knowledge: Transforms information into knowledge, giving it meaning by interpreting it based on context.  It answers questions related to how?

Wisdom: Applies personal interpretation to determine the why; that is, why something is important.

In my experience, in order to claim an intimate understanding of data, one must first demonstrate that knowledge in several ways.  In my opinion, data-crunching individuals (robots) that are only capable of using data to complete a simple task fall short of this standard.  Organizations (clients) tend to look for more individuals that “understand” rather than ones that can simply follow instructions.  It is only with this understanding that individuals can help make effective data-driven decisions.  In my opinion, here are some key differentiators:

Understanding the essence of the data

Data takes on several shapes.  Data can be qualitative and quantitative, numeric, textual or image-based.  Effective data users take the time to learn about the nature of the data, as well as where it came from so that they learn to trust it.

Ease of data manipulation

The type of analysis required for a given problem shapes the necessary data manipulation.  This can only occur when a data user actively studies the data, and “gets their hands dirty” by playing with it.  Individuals with a thorough understanding of data are then able to transform, shape, and filter it in every way imaginable to fit the requirements of the problem.

Incorporation of Multiple Datasets

Often time, users of data must merge seemingly disparate, but interrelated, data in order to solve a problem.  Data users must be able to find the common link or “common denominator” between datasets in order to marry them.  Individuals with an intimate understanding of data can do this with ease.

Data Mashups

The human mind processes images effectively, so users should find ways to visualize data whenever possible.  Using various tools at their disposal, users that can create charts, graphs, sketches, etc. not only learn the data better, but also demonstrate that they can highlight the story that the data is trying to tell.

Data Presentation

To effectively use data, users must have a thorough understanding of the problem that the data is helping to solve.  Good data users are able to grasp the problem, understand the stakeholder’s needs and wants (by putting themselves in the stakeholder’s shoes), and present it to them using the most efficient available method (in the form of a written report, presentation or data dashboard).

Learning and gaining wisdom from data can be difficult; but with difficulty comes rewards.  Once an individual makes the effort to understand data, they can translate their understanding into knowledge.  And, as the old adage goes – knowledge is power — the power to improve decision-making, the power to maximize use of scarce resources, the power to contribute to your organization’s mission.


Posted on Monday, July 18th, 2011 at 1:45 pm and is filed under Broadband Map, crowd-sourcing.

By: Paul Salasznyk

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Computech and the FCC at the ESRI Federal Users Conference


Last week, FCC Geographic Information Officer Michael Byrne and I presented new products built from the FCC’s Consumer Broadband Speed Test API at the ESRI Federal Users Conference in Washington, DC.

Computech has been behind the scenes in many of the FCC’s recent successes. Among our contributions:

- Computech created the tools necessary to transform the data into geographically aware information that can be exploited by a GIS. These routines update a GIS layer on a daily basis and make it available for the APIs
- Our team developed and deployed the Speed Test API mentioned in the presentation, available in fcc.gov/developer, which listens for latitude & longitude as parameters, and gives broadband speed information at the county level
- Our team built the speed test system for the FCC as part of broadband.gov

For last week’s demo, Computech worked with ESRI staff on how to better present some results from the speed test; the big story for both the FCC and ESRI was the crowd-sourcing aspect of this information. They prepared the environment using their software and created the different scenarios and both Michael Byrne and I provided input.

Watch the video of our presentation below.


Posted on Friday, January 28th, 2011 at 10:19 am and is filed under News, crowd-sourcing.

By: Computech

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