Big data has been a buzzword for some years now. Companies collect as much data as possible and are now sitting on a huge store of data, often without knowing exactly how they can use it profitably. The technology operational intelligence (OI) provides an answer to this problem by converting underused information into operational knowledge — i.e. converting big data into smart data.
What is Operational Intelligence?
Operational intelligence (OI) is an innovative technology that works with a combination of in-memory computing and parallel real-time data analyses. This allows it to save, update, and analyze an unlimited number of rapidly changing live datasets. This real-time data is enriched with historical data from every available source (referred to as data silos). This produces what is known as a data lake completely without silos, which enables a 360° view of a business as a whole and its processes.
Using correlation-based big data real-time analyses, operational intelligence brings previously unrecognized relationships from this data lake to light — without modeling or simplifying the data. For example, anomalies or trends in the business processes are displayed live with just a click. Advanced analysis methods (“Advanced Analytics”) such as predictive and prescriptive analyses, then make automated decisions about the best way to implement the findings of the analyses to achieve pre-defined company objectives. If desired, they initiate the required measures independently. The success of these measures is tracked in closed feedback loops and incorporated in future analyses (“self-learning system”).
Operational Intelligence thus enables the consolidation of big data across silos, intelligent real-time analysis of the data, and automated optimization of processes which are constantly being refined — if desired, completely without human intervention.
From big data to smart data for a competitive advantage
Operational intelligence merges all your data and allows you to use it profitably (“actionable”). The findings obtained can be used for automated optimization, for example.
The Distinction between Operational Intelligence and Business Intelligence and Machine Learning
The disruptive OI technology thus answers the problems of classic business intelligence (BI) which is lacking in terms of man-power, know-how, and technical possibilities: while BI is still limited to descriptive analysis and leaves the modeling and derivation of conclusions to the business user, operational intelligence combines descriptive and predictive analyses with prescriptive analyses which can initiate the required measures automatically based on a previously defined objective.
Operational intelligence is also distinguishable from the currently very well-known machine learning (ML): although ML works independently and without any human intervention, it emulates human thought processes which are very strongly prone to error and — much more importantly — have limited capacities: ML can only process large quantities of data offline “overnight”, i.e. over a significant amount of time and with a high degree of simplification and modeling. In contrast, the decisions taken with operational intelligence are based on exact calculations without segmentation. They are made in real time.
Application Areas of Operational Intelligence
The progressiveness of operational intelligence is a great opportunity for all sectors in which the optimization of processes or the automated adaptation to live events is important:
The benefits of operational intelligence are only beginning to be realized. The opportunities are mind-boggling¹.
Operational intelligence is being used for example in manufacturing industries to optimize manufacturing processes: sensors on products as well as along process and supply chains collect data and make the processes as a whole visible. Any irregularities are detected immediately and can, if necessary, be corrected automatically the moment they occur. The logistics industry (see image) is a similar use case.
Operational intelligence can also provide great advances in fraud prevention. In online banking for example, it can be used to detect fraud at several levels: at the level of the end device, features such as login data, location, or account authorizations are detected; at the navigation level, the online behavior is registered; and at the transaction level, the transactions are compared with the “normal” business of a user. If there are any anomalies, the system intervenes and prevents the fraud.
In the logistics industry, sensors and control modules on vehicles capture data such as the fuel consumption, data related to wear and tear, or position data. Operational intelligence systems merge this data and enable timely transport planning, optimization of routes and loading, as well as minimization of maintenance costs and downtimes.
Operational Intelligence in E-Commerce: Personalization Par Excellence
With operational intelligence, online merchants can meet the needs of their customers by fulfilling their expectations of personalization and customer focus: the technology allows them to adapt the structure and content of their shop to each and every visitor in real time. Every customer therefore sees their own individually relevant shop displayed, with the content and products that precisely meet their current needs placed prominently (“Me-Commerce”). Just like in a Google search, it is not the top sellers (based on all buyers) that appear at the top of the list, for example, but rather the products with the (probably) largest individual relevance for the current user (1:1 personalization). The customer can thus enjoy a personalized shopping experience, with the retailer benefiting from higher conversion rates and higher sales.
The main focus is not on the personality features of the user, but rather the features of the user’s current situation: the day of the week, time, channel, and device or referrer (origin) say a lot about a person’s needs. This data can be enriched with weather data, product information data, inventory, and returns, enabling personalization that complies with data protection requirements, even for unknown first-time visitors for whom no personal information is held (“situation aware personalization”).
Promising examples like this personalization of the shopping experience are pioneering but still only the beginning of operational intelligence: the application of the technology is still in its infancy and waiting for many other fascinating use cases.
From e-commerce to me-commerce: in online shops, operational intelligence allows situation aware personalization of the shopping experience and creates a personal shop for each customer.
¹ from: In-memory computing brings real-time intelligence to operational systems
