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Big_Data_312_shadowYour big data, that nearly everyone is holding up like a golden calf these days, is completely useless unless it’s interpreted correctly.

Your organization has data, and it has information. Many of you use the terms almost interchangeably. All the talk from industry pundits…well, in nearly any industry…is that Big Data is going to save the day.

It’s now become something where I [sometimes] listen while picturing infomercial pitchmen plying their wares: “It SLICES! It DICES! It’s also a tasty DESSERT TOPPING!!”

To be sure, there are a lot of knowledgeable people teaching us some very helpful, useful things about big data. But, it’s been lost on many sales organizations the data is useless unless it’s used. By “used,” I mean the data is correctly interpreted and correctly applied for the benefit of your clients and your business.

Data even becomes destructive when it’s misinterpreted because in that scenario, none of the members of your sales team are aware your information is wrong.

The big foundational question you should be asking in today’s environment of instant access to oodles of data is this:

“What is the difference between data and information?”

This is, as they say, your lucky day. I say that because CEO, data quality improvement evangelist [and skiing enthusiast?] Martin Doyle (@DQMartinDoyle for @DQ_Global) wrote a post earlier this year titled, “What Is the Difference Between Data and Information?

Note: This post has nearly 27,000 views since February, so your competitors have learned from this post. [Don’t get left behind.]

In the post, Doyle answers this vital question quite clearly. His background is steeped in data. He’s the CEO and founder at DQ Global (the “DQ” stands for Data Quality), which has been running for nearly 20 years.

The post begins by defining “Data” so we’re all on the same page. In short, he explains that “Data is the 1s and 0s that fill hard drives, and it’s designed to be read by computers–not humans.”

The next section is something I found to be informative. It describes the properties of data in 8 bullet points. Here are 4 that caught my attention:

  • Data is, when clean, a fact.
  • Data can be misrepresented, depending on its interpretation.
  • Data does not mature, nor does it improve with age — in fact, data decays.
  • Data has no value until it is used.

“All data has to be interpreted to be useful to humans…”

Doyle explains that “we can use practically any meaningful unit” of measurement to understand data. The key is that information has context:

“We can see that information has context. It gives us a fact relative to something else.
It offers a yardstick for our decision making.
It lets us derive some kind of conclusion once we understand it.”

After defining information further through a half dozen bullet points, we then move into the meat and potatoes section of the post. It’s explained so well you’ll want to show this post to a colleague. Really.

Moore uses the DIKW Pyramid as a parallel to the process they followed in their process of deduction. Of course the pyramid describes how data moves from information to knowledge to wisdom. His examples are spot-on and they clearly show what he intends to convey.

The author wraps up the post with a section that pulls it all together under the header of, “Better Data – Better Business.” The kicker is a sobering reminder for all of us:

“Poor data leads to the loss of our competitive advantage.”

Yes big data is great to have, but you need to know how to use it correctly. It’s time to read the post and take the next step in becoming the leader among your peers in this area of your business.

If you sometimes feel as if you’re drowning in data, I highly recommend reading this recent post of mine.

QUESTION: Can you confidently say you have a strong handle on correctly interpreting your organization’s data? If not, what do you believe is the next step you need to take? Why haven’t you taken it yet?

Let’s talk about it…

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