Technology has added more choices and complications
At one time, comparing statistics and analyzing data for business insights was a manual, often time-consuming exercise. Spreadsheets were usually the top tool of the trade, so to speak. Along came electronic technology, including relational databases, data warehouses, machine learning (ML) algorithms, web searching solutions, data visualization, and other tools with the potential to facilitate, accelerate, and automate the analytics process.
Yet, advances in technology and market demand have brought on many challenges. These challenges include a growing choice of competitive, sometimes incompatible analytics and data management solutions, creating technological silos within departments and organizations and with outside partners and vendors. Not only that, some of these solutions are so complicated that they require technical expertise beyond the average business user, which limits their usability.
Modern data sources have also taxed the ability of conventional relational databases and other tools to input, search, and manipulate one large category of data. These tools were designed to handle structured information, such as names, dates, and addresses. Unstructured data produced by modern data sources, including email, text, video, audio, word processing, and satellite images, can’t be processed and analyzed using conventional tools.
Accessing all those data sources and being able to determine what is valuable is not easy, especially since the majority of data being produced today is semi-structured or unstructured. And the amount of data continues to dramatically increase.