Data science, steroid trading information

Data science and business intelligence gathering are sometimes mistakenly used as interchangeable terms. Both business intelligence gathering and data science provide a host of additional capabilities and benefits to your business, even if they are different.

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

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

Data analysts, who until recently were the luxury hire of large companies, began to be more sought after. Using the appropriate software, they could integrate the mass of Big Data and find not only KPIs and decision-making reports, but also predictive information with high levels of accuracy. The ability of data analysts to not only obtain past information, but also future predictions, meant that companies with data analysts had much more useful information to run and grow their businesses. Truly information that was BI on steroids.

BI will ask “what has happened in the past?” Data analysts will ask “what happened in the past and will this happen in the future?” and both will obtain accurate and verifiable supporting information. BI works only with past information, while Data Science analyzes trends, predictions and potential activities to make its reports. BI needs structured, often static information, while data science can also deal with unstructured, hard-to-find, and fast-moving information. While both use software, companies are moving from BI to data analysis.

Of course, this now meant that data analysts became a rare commodity and this role is now known as one of the highest paying jobs in the IT market, so you would expect well-trained data analysts to start to be available. Data science software is also rapidly improving, but it is also changing as data collection matures. The models that underpin data analysts are much more complex than those used by BI and are evolving as both data science and Big Data collection mature.

So what is the challenge of working with Big Data? It’s those V’s: the speed of data coming into the business, the volume of data is often huge, especially if using social media data, and lastly, the variety of data, much of which is not structured data. You are looking for BI software.

When companies move from BI to Data Science, they can also interrogate unstructured data, and this means they don’t need to pay or go to the trouble of forcing unstructured Big Data into a structured warehouse. Saving costs, data problems and ensuring that the information is viable.

Using data science also means the company has an advantage over its BI-only competitors. They can make predictions on a much larger data set, and these predictions are based on actionable information. A huge advantage and a real reason to use Data Science – BI on steroids.