More and more businesses are on the verge of adopting data-driven processes. Being data-driven means making decisions backed by information gathered from analyzing high-quality data.
Making the best use of this data requires continuous analysis and visualization.
There are two paths to achieve this goal:
1. Crunching data the hard (and not so efficient) way
Having implemented efficient ways to collect data, company A is banking on a large team of data analysts and data scientists to transform this flow of raw data into meaningful and actionable information.
Data mining is vital for a data-driven business, so it is important not to overlook this step. However, crunching the data into a comprehensible format – reports, data visualization presentations, etc. – is probably the most resource-heavy step in the process. This kind of reporting is very time-consuming to set up and maintain.
2. Letting AI do the crunching, so you can focus on decision making
Company B is tackling data mining from a different angle. They have hired a small team of data analysts and data scientists and decided to leverage artificial intelligence (AI) tools with two goals in mind: to process data faster, and to extend the capabilities of data analysis and data exploitation.