Data science and business intelligence gathering are sometimes used incorrectly as interchangeable terms. Bringing together both data science and business intelligence offers a great deal of additional potential and benefits to your company, although they are different.
A few years ago, business intelligence, also known as BI, was the king of information used to differentiate your company from your competitors. Business intelligence was collected through a sophisticated program that probed company databases and pulled relevant information and key performance indicators that were used to make management and manager level decisions.
However, big data came knocking on the door with countless unstructured information coming in from everywhere, and business intelligence started to struggle because it needed more structured data to work through.
Data analysts who until recently were the luxury hire for larger companies, are starting to be sought after more. With the right software, they can integrate the mass of big data and find not only key performance indicators for decision making reports but also predictive information with high levels of accuracy. Data analysts’ ability to not only gain historical information, but also future predictions means that companies with data analysts have more usable information to manage and expand their companies. Really information that business intelligence was on steroids.
BI will ask “What happened in the past?” Data analysts will ask, “What happened in the past and will this happen in the future?” Both will have accurate and proven supporting information. Business intelligence works on historical information only while data science looks at trends, forecasts, and potential activities to prepare their reports. Business Intelligence needs structured, often static information, while Data Science can also operate on fast-moving, hard-to-find, and unstructured information. Although both use software, companies are moving from business intelligence to data analysis.
Of course, this now means that Data Analysts are becoming a rare commodity, and this role is now recognized as one of the best paying jobs in the IT market, so hopefully well-trained Data Analysts will start to become available. Data science programs are also improving rapidly, but they are also changing as information collection matures. The models that support data analysts are much more complex than those used by business intelligence and they evolve as both data science and big data collection mature.
So what is the challenge of dealing with big data? It’s V – the speed of data entering the company, often the amount of data is large, especially if social media data is used and finally a variety of data, a lot of which is not the structured data that BI software is looking for.
When companies move from business intelligence to data science, they can also interrogate unstructured information, meaning they don’t need to push or go through the trouble of forcing unstructured big data into a structured repository. Savings on costs and data problems and ensuring that the information is applicable.
Using data science also means that the company has an edge over its competitors who only use business intelligence. They are able to make predictions on a broader set of data and those predictions are based on actionable information. A huge advantage and a real reason to use Data Science – BI on steroids.
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