Importance of Big Data Analytics

Big data has become somewhat of a buzzword in recent years. But it offers organizations a wealth of insight when analyzed and harnessed for decision-making. The volume, variety and velocity of incoming data that businesses face today means that specialized tools are needed to make this data useful. Big data analytics makes sense of it all.

With 80% of the data today being unstructured, deriving knowledge from it was previously impractical and at times impossible. Business analysts focused entirely on structured data that was neatly stored in relational databases. However, in the age of big data the playing field has changed drastically. With big data analytics, both structured and unstructured data can now be accessed, analyzed and reported.
Analytics can be applied to text documents, social media, website logs and sensor information in order to correlate this information with transactional or operational data. Decision-making is no longer restricted by the limited insights derived from structured data alone. Big data analytics expands the dimensions of business intelligence in ways that were never seen until now.

Business Intelligence to Big Data Analytics

Analytics is not new technology in business intelligence. It has existed for some time in one form or the other to visualize data, identify patterns and draw conclusions for decision-making. Manual reporting was essentially the only way to access data for analysis. However, analytics changed the way and speed in which business analysts could view data in a relational database containing structured data. It became a fundamental part of business intelligence and indispensable tool for analysts.

BI analytics tools may have been faster and more flexible than manual reporting but it did not always deliver the complete picture. When big data analytics came onto the scene there was now larger datasets included that contained vital information which could assist with better decision-making. Once again there were limitations – big data had to first be collected and sorted in a database warehouse before the analytics software could utilize it. It also had to be analyzed and visualized to be useful in BI.

As with any field in information technology, big data analytics continues to evolve. Some big data analytics tools of today offers full continuity from accessing unstructured data all the way to visualizing it for decision-making. It has even removed the need for manual coding to extract, transform and load (ETL) data from a variety of sources with a diversity of attributes. Graphical interfaces means that business analysts can access data almost instantaneously from any source with the click of a button.

Business Decisions Today

Big data analytics is mainly promoted for its ability to access large volumes of structured and unstructured data from a variety of sources that are flowing in at different speeds and then make sense of it all. However, one of the main strengths of big data analytics is the time factor – it is ability to access, report and visualize relevant data almost instantaneously. It gives business analysts insights that are current, relevant to within a month, week, day or even hour.

This was previously not possible with traditional BI intelligence analytics. Even if it could access data that was streaming in to give real-time reporting, it did not include the unstructured data that was relevant to the business. For example, customer sentiment could not be reported on without collating data after extensive market research. Big data has therefore changed the landscape entirely.

Business decisions today are not only dependent on analyzing the most recent transactional data but also factor in various different metrics such as customer behavior and sentiment in real-time. It translates into faster business decisions, almost on the fly, from data that is relevant for today. More importantly, it has become the heart of better business decisions.