Cognitive analytics: Creating a roadmap for smart C-level decisions
“Cognitive analytics” is expected to be a common phrase used in C-suites around the world. Both Forbes and Forrester included cognitive technology as one of their top predictions for data analytics in 2017, and this trend should only continue into 2017. C-level executives are increasingly turning to this type of technology to provide the answers they need to grow their companies. And, in the mobile development world, these questions often center on enterprise performance.
The main difference between cognitive analytics and data analytics is that data analytics technology is programmed to perform a specific algorithm each time. Cognitive analytics actually understands natural languages and grows smarter over time. The algorithm learns more information each time it is run and becomes a better predictor in the future.
Mobile technology has dramatically increased the amount of consumer information collected, and many enterprises are now unable to effectively use this data. The key to being able to use cognitive analytics is having a high volume of data, so unlike other technologies, this actually increases the effectiveness of the system instead of burying it in data. Cognitive analytics solves this problem and provides usable insights by combining the thought processes of humans with the speed and precision of a computer. The result is concise answers to strategic questions.
Customer satisfaction often largely hinges on enterprise application performance, making it challenging to retain customers or gain new customers if performance is subpar. By using cognitive analytics to analyze application performance in a way previously not possible, C-suite executives have the insights they need to make the right decisions to grow their business.
Many companies simply fall into the pattern of fixing enterprise application performance issues after they happen. However, at this point, it’s already a problem. Customers are upset, the call center is flooded and productivity is down. The solution is using cognitive analytics to understand the root causes of issues and take preventative action instead of corrective action after the fact. The result is happier customers and increased productivity.
The following are two ways C-suite executives can use this technology to assess the performance of enterprise applications:
1. Analyze log data
Log data often contains the answers to performance issues. Cognitive finds performance patterns that are likely affecting application performance. Once you understand why a problem is happening and where the issue is, you can then fix the performance issue. Enterprises often find that log data allows their company to reduce repair time and eliminate downtime, which can lead to a significant reduction of expenses.
2. Use predictive analytics
By combining cognitive and predictive analytics, enterprises can gain insights into what is likely to happen in the future. The technology determines patterns and anomalies in the data and then determines a typical baseline of application performance. You can then set up business rules for notifications if performance indicators go outside a set norm, allowing you to predict a potential enterprise performance problem and prevent it from happening.
Enterprises that start to implement this technology can now provide a level of performance and service that their competitors not using this technology simply can’t match. However, if you wait, you’ll be playing catch-up down the road. The choice is yours.