The purpose of business intelligence (BI) is to collect and analyze large amounts of noisy business data and make it coherent and structured enough to be used in decision-making. Artificial intelligence (AI) is often used in conjunction with big data (that includes BI data as well) to generate artificial yet human-like insights that can drive greater profits and efficiency.
This statement alone already explains how these two enterprise tools can be (and are) linked together. In a nutshell, AI aggregates and digest BI data and breaks it down into manageable, tailor-made insights. Several next-gen BI apps and dashboards have implemented in-built machine learning (ML), such as SAP HANA, Domo’s Mr. Roboto, or Avenade. But that’s not all.
BI data captured by sensors and Internet of Things (IoT) devices can be leveraged by AI to improve the efficiency of equipment and vehicles. Any data that is mined by BI can be later used to feed AI and create integrated tools. The heavy industry makes large use of these matched techs for predictive maintenance.
Examples include General Electric’s Predix operating system that draws data from trucks and drilling machines to determine when they must be stopped for maintenance, or Siemen’s MindSphere that monitor machine fleets.
Another robust use case of a link between AI and BI that goes beyond “intelligent” analytics platforms, is the one provided by Fetch.AI. BI data is often stored in third-party cloud databases such as those offered by Amazon or Microsoft.
However, if losing the privacy of key business data wasn’t a pressing enough issue, cloud services are still suffering from a serious case of lack of data security that makes them vulnerable to outside attacks and hacks even today. The blockchain represents the ideal solution to guarantee privacy and security since the data stored in decentralized databases is practically inviolable.
By merging the safety of a decentralized database with a collective learning AI/ML architecture, the company implemented a tokenized metals exchange with a consortium of Turkish iron and steel manufacturers.
The integration with AI and ML allows for advanced BI analysis — the quality and quantity of metals within the trade flow are monitored and analyzed in real-time, for example. Electronic documents are also processed in a few seconds, and data is used to automate the shipping process and enable insurance costs dynamically. In this example, the whole process is streamlined and optimized without having to split BI and AI into two separate entities.
Another potential application of AI to BI is ensuring the security of data. Advanced Encryption Standard (AES) protocols are the industry standard to encrypt sensitive data used in BI. However, key stores can be hacked and once a user name and password are stolen, whether the data was encrypted with the best level of AES encryption won’t matter anymore.
AI can be used to create keys that are then immediately destroyed so no human has access to this data anymore. If you’re thinking “this is really stupid,” well, the idea is that the algorithm is also able to hack itself later on since it has kept two vital pieces of information to recreate it.