Data Science and Business Information gathering are sometimes, erroneously, used as interchangeable terms. view website Both Data Science and Business Information gathering provide a great deal of added capabilities and benefits to your company, even though they are different.
Many years ago Business Information, also known as BI, was the king of information used to distinguish your company from your competitors. BI was gathered by sophisticated software that investigated a company’s listings 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 numerous unstructured information coming from everywhere, and BI begun to struggle as it needed more structured data to work from.
Data analysts that had until more recently were the posh hiring of larger companies, begun to are more sought after. Using appropriate software, they could integrate the mass of Big Data and discover not only KPI an decision making reports but also predictive information with high numbers of accuracy. The ability of data analysts to not only gain past information, but also future prophecy 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.
BI will ask “what has happened in the past? inch Data analysts will ask “what has happened in the past and will this happen in the future? inch and both will get accurate, provable supporting information. BI works on only past information whereas Data Science talks about trends, prophecy 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 tight item and this role is now known as one of the best paid jobs on the IT market, so hopefully well trained data analysts will quickly be around. Data Science software is also rapidly improving, but also changing as information gathering grows. The models that underpin data analysts are far more complex than those employed by BI and these are growing as both Data Science and Big Data gathering grows.
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 when social media data is used not only that Variety of data, a lot of which is not the structured data that BI software tries 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 making unstructured Big Data into a structured storage place. Saving on costs, data problems and ensuring that the information is viable.
By way of Data Science entails that the company has an advantage over its competitors that merely use BI. They are able to make prophecy on a far broader set of data and these prophecy use viable information. A massive advantage and a real reason to use Data Science — BI on steroids.