What are this year's business intelligence trends?

AI-driven predictions based on data

Current AI-driven business intelligence platforms can assess the data quicker and more accurate than ever before. As a result, AI-powered business intelligence will slowly start to not just assess what had happened in the past but also predict future outcomes. In general, while the whole business intelligence sector will continue to be even more AI-oriented than it is today, the quality and amount of insight the data can provide will drastically improve, too.

Together with the growing amount of data to crunch, the infrastructural needs will also become increasingly important.

“As a network infrastructure provider, we are already seeing the increasing technical demands of our business intelligence customers,” said Vincentas Grinius, CEO ofHeficed, a cloud, dedicated server and IP address provider. “Business intelligence enterprises depend on storage to gather and retrieve the data, and the storage needs to be high in IOPS (input/output operations per second). The computing power of the server matters equally, so does the network speed. As certain types of business intelligence operations are time-sensitive and carried out while competing with rival enterprises, they need high speed, low latency connection to be able to gather and process the data quickly enough.”  

Data management

The process of collecting and processing data is increasingly becoming a target of public scrutiny, and not without reasons. Growing fears and reports regarding mismanagement of personal information are influencing the BI sector, too. 

After the EU’s General Data Protection Regulation (GDPR) became implemented on May 25^th, 2018, companies handling extensive amounts of data were forced to take their data governance seriously. Also, consumers are becoming increasingly aware of how their personal data is dealt with. In 2019 and beyond, the business intelligence platforms, as well as other businesses, will have to be aware of how they collect, store, share, and monetize the data.

Failing to ensure that their data is well managed, business intelligence organizations could face painful backlashes. On the one hand, the mismanagement of data can lead toenormous fines andsettlements. On the other hand, while AI and business intelligence engines are developing side by side and are becoming more capable, poorly managed data would exponentially increase the risk of data misinterpretation.

Data gravity: software following the data

The final trend is closely linked to the concept of data gravity, which suggests that applications and data attract one another. If the data is moving to the cloud, that is where the software will follow.

“When it comes to business intelligence, the theory of data gravity appears to be true for two simple reasons: latency and throughput. The higher the latency, the bigger the delay before data travels; the higher the throughput, the larger amount of data travels through the network successfully. In essence, to run an efficient operation, business intelligence platforms prioritize low latency and high throughput, and that is why these companies are migrating their software to the cloud storing their data,” added Grinius.

Both good latency and throughput can be best achieved if the data and the application processing the data are located not too far apart from one another. Given the continuous increase in amounts of data collected and analyzed by business intelligence platforms, the software will continue to follow the data to the cloud where it is stored.