Big Data is not new, yet it is just becoming mainstream now. Large data providers have been collecting data for a long time, so what is different now?

Today, Big Data is in the form of LinkedIn, Facebook, Twitter and Google. It’s coming at us from all directions. To learn more about why Big Data is big now, you must understand the continuum of getting at Big Data.

In other words, today's data must meet specific Big Data prerequisites to be of value to you and your business.

Here are the requirements:

1. The data is available

This is the most exciting tipping point. In being the CEO of a data-mining software company, I’m still dumbfounded when users expect to get information off the web that is not there. To begin, the data must actually exist.

2. You can flag the data

You can’t store everything, therefore, you must make choices. What is important? When is it important? In the absence of a real-time data stream, you must be able to search through the data to find and flag what you are looking for.

3. You can parse the data

This is the analysis of relevant grammatical constituents, identifying the parts of what you need, from within potential noise. For example, parsing out the name of an inventor from within an article on nanotechnology.

4. You can extract the data

This is not the same as parsing. What if the data is in a PDF file or HTML code? In many cases, extraction is about access. Is the data across five links within a single web page? Extraction as it relates to the Internet also encapsulates web crawling.

5. You can process, store and index the data

This takes CPU cycles. Big Data takes up disk space. If you ever want to find the data after you store it, the data needs to be indexed, which also means more disk space. Prepare for the data ahead of time and the space it will take up.

6. Data experts are involved

7. The data is clean

All of your data efforts are utterly useless if you cannot ensure your data is clean. Data decays at an average rate of 4% per month, and with phone numbers and email addresses changing by the second, as well as your sales teams and other teams manually entering data into your databases, there will be incorrect, missing, outdated and/or duplicate data.

8. You normalize the data

If you have multiple pieces of data on “The Container Company”,  “Container Company, The”, “The Container Co”, etc., how do you merge that data? You must normalize similar entities to a standard “canonical form”. Without it, you’ve got the Data Tower of Babel.

9. There is payoff

Putting Big Data together is expensive. Without an end goal in mind, it is pricey to collect. Google and Facebook collect, process, index and store data for profit. Make sure you have a goal and profit plan in place for your data efforts. Only then will it pay off.

Time to get started...

While it is tough to predict where Big Data will go next, we can start by looking at the requirements of Big Data, and where it comes from in the first place.

Donato Diorio is the CEO of RingLead with the primary responsibility of combining the teams, technology and vision of RingLead and Broadlook Technologies, a data technology company founded by Donato. Donato started his career as a software engineer and quickly became a specialist in process automation. A recognized thought leader and speaker on data quality and recruitment; two diverse areas technology dependent and process-driven, Donato’s mantra is, “Build technology that is the right balance between automation and human interaction.” Executing this vision has delivered consistent innovation for Broadlook and now the RingLead-Broadlook combination.