Data Science, Business Information on Steroids

Data Science and Business Information gathering are sometimes, erroneously, used as interchangeable terms. Both Data Science and Business Information gathering provide a great deal of added capabilities and benefits to your company, even though they are different.

A few years ago Business Information, also known as BI, was the king of information used to differentiate your company from your competitors. BI was gathered by sophisticated software that investigated a company’s databases and pulled out relevant information and KPIs that were used to make management and director level decisions.

However Big Data came knocking on the door with its myriad of unstructured information coming from everywhere, and BI began to struggle as it needed more structured data to work from.

Data analysts that had until more recently were the luxury hiring of larger companies, began to be more sought after. Using appropriate software, they could integrate the mass of Big Data and find not only KPI an decision making reports but also predictive information with high levels of accuracy. The ability of data analysts to not only gain past information, but also future predictions meant companies with data analysts had far more useable information with which to manage and expand their companies. Truly information that was BI on steroids.

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BI will ask “what has happened in the past?” Data analysts will ask “what has happened in the past and will this happen in the future?” and both will get accurate, provable supporting information. BI works on only past information whereas Data Science looks at trends, predictions and potential activities to make their reports. BI needs structured, often static, information whereas Data Science can also work on fast moving, hard to find, unstructured information. Even though both use software, companies are moving from BI to Data Analysis.

Of course, this now meant that data analysts became a scarce commodity and this role is now known as one of the best paid jobs on the IT market, so hopefully well trained data analysts will begin to be available. Data Science software is also rapidly improving, but also changing as information gathering matures. The models that underpin data analysts are far more complex than those used by BI and these are evolving as both Data Science and Big Data gathering matures.

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So what is the challenge of working with Big Data? It is those V’s – Velocity of data entering the company, Volume of data is often vast, especially if social media data is used and lastly Variety of data, much of which is not the structured data that BI software seeks out.

When companies move from BI to Data Science they can interrogate the unstructured information as well and this means that they need not pay or have the problem of forcing unstructured Big Data into a structured warehouse. Saving on costs, data problems and ensuring that the information is viable.

Utilising Data Science also means that the company has an advantage over its competitors that merely use BI. They are able to make predictions on a far wider set of data and these predictions are based on viable information. A vast advantage and a real reason to use Data Science – BI on steroids.


by Lee Lister