Things have changed a lot in practically all organizational environments. The way we work, make decisions or analyze data have evolved greatly in recent decades. And an example of this is the use we make of the so-called operational analysis, but what is it really and how does it help us in our day to day?
Previous generations -but also companies that even today are completely reluctant to abandon certain habits and fully trust in the benefits of digitization- based decision-making on the analysis of quarterly or annual reports to set the actions and the path to follow. continue in the short and medium term. Nevertheless, with the irruption of operational analysis everything has changed.
There are companies that use methods similar to operational analysis or data processing such as: hybrid transaction and analytics processing (HTAP), hybrid operational/analytics processing (HOAP), translytics or continuous intelligence. Although they are not quite 100% examples of operational analysis.
We can define this as a business strategy that aims to harness real-time information to improve or automate decision-making. Or in other words, it converts business intelligence and analytical knowledge to make the company a more competitive organization that makes much more accurate and appropriate decisions. At the end of the day, it is about using more and better the data available to the company and that often goes unnoticed.
This is how operational analysis works
One of the keys to the success of this method is the timeliness and freshness of the data it uses.. It must be taken into account that each one of them reaches the company in a different way: through mobile applications, forms, documentation created on collaboration platforms, CRM data… so that the different departments of the company can both share and use this data for faster and more agile decision making.
Examples of operational analysis in sectors
energy providers
The energy sector pioneered the use of operational analytics, processing large volumes of analytics and responding almost instantly, often with the benefit of artificial intelligence (AI). Energy consumption takes thousands of data every nanosecond over kilometers of infrastructure and automatically makes adjustments so that nothing fails in its operation.
video game developers
Video game developers also use operational analysis when presenting their different products. In fact, some developers collect large volumes of data on player trends and preferences, which levels are the hardest or easiestgame times, most common errors… some data that undoubtedly help to offer improvements in new versions of the video game or future new games of the brand.
online retailers
Online retailers are among the professionals that make the most use of operational analysis. They use the data to know how their customers behave, to present personalized recommendations of products and advertisements adapted to their preferences, to change prices and that these fluctuate according to the geolocation of the client’s IP… Recommendations powered, of course, by AI-enabled recommendation engines for machine learning or ML.
In fact, companies like Amazon or Walmart use real-time operational analytics and data integration to meet the demands of their supply chains and automatically trigger replenishment orders from their fulfillment centers in times of shortage.
The curious case of Amazon
A few years ago, Amazon fully embraced operational analytics in its day-to-day, using real-time data, carry out a same-day delivery service in those regions with a higher number of Amazon buyers. The objective was to reinforce customer satisfaction, increase revenue or avoid customers having to go to the store, among others. Everything indicated that the union between data collection and artificial intelligence would be a success.
Nevertheless, Amazon’s AI produced a map of the same-day delivery service that prioritized wealthy neighborhoods and conspicuously excluded poorer ones. So the company received complaints about what was considered a discriminatory service map, and Amazon had no choice but to change its use of it.
In short, operational analysis can bring countless advantages to companies, an advantage for decision-making and for the competitiveness of the important company. But we must not forget the human value and planning before how it can help us and how we can put operational analysis into practice so that it is profitable. Strict data analysis may lead to errors or peculiarities that we must take into account.
