The ‘big data revolution’ is a recent phenomenon and still an ongoing endeavor. Companies are collecting data on a large scale, but in order to extract real value from it, they need to bring the organization, collection and extraction of data to the next level. Capgemini helps its clients to simplify this process. During the first .BLD() Tech Talk on September 8th, Marcus König will take you into the world of search solutions, here applied to log centralization via the powerful Elasticsearch. Marcus is a managing consultant with a mathematical background, working with the Java cluster. He is involved in the engineering of analytical data solutions for complex architectures.
Creating insight on complex environments
“Data is continuously collected, exchanged and stored automatically. Take governmental institutions like Belastingdienst (the Dutch IRS) for example, their operations fully depend on their critical IT infrastructure. This requires a complex mix of many servers, applications and interoperabilities between disconnected systems. But what if something goes wrong? Then they need to be able to locate the issue and fix it quickly. For example, say a mechanism is running slow or seems to be clogged in some way, then you first need to figure out why the issue occurs. In essence, you need to have an analytical insight on what is happening in this complex technical environment. That can be quite challenging… usually systems are not setup for inference, they are setup for production.”
Computers are everywhere these days. Everything runs in heavy automation, at best collecting runtime diagnostics. However, no implementation is perfect, and complexity creates problems. “Faulty operations or delayed system behavior will burn a hole in our client’s pockets over time. That is where we step in. They need people who are able to react in day-to-day operations to these circumstances. Accessible runtime diagnostics, as provided by a combination of Elasticsearch and Kibana, allow for more effective measures in tracking down situations and shortening reaction times. It all translates to money saved on every incident, one of the key ingredients to a good optimization strategy.”
Pure insights and real engineering
In order to achieve optimal results, it is important to have a multidisciplinary team, in which knowledge about mathematical inference as well as strong engineering backgrounds are represented. Supplemented by developers and AI specialists we can make data work for the customer in a practical and reliable way. A large benefit for client operations. "I am in a rather sweet spot between pure analytical approaches and real engineering. In that role I form the link between the problem and the client. Using Elasticsearch and Kibana to understand, analyze and manage all the data that applications generate and ensure that the client gains insight on the operation of the application as a whole… that is what makes my job so exciting.”
Why you don't want to miss the .BLD() Tech Talk
During the presentation, Marcus will show you how to apply Elasticsearch to customer problems and generate insights -from an analytical point of view- into their operative systems. Citing examples of typical customer problems faced by Capgemini in the past, he will provide you with great business opportunities. “We regularly encounter clients with these problem sets. They have trouble understanding what is going on in their own infrastructure. We provide solutions to them and fix their predicaments.”
“You will be asked about this topic by our clients, or you will hear about it from others in our field of business. Our clients will need higher level thinking when it comes to organizing, extracting and analyzing data. Therefor it is rather interesting to get informed at this stage. Clients are losing money on systems that are not working properly or are not working at all. They will need someone (sooner rather than later) who extracts, analyses and reads the data generated by their infrastructure, so they will be able to operate with a trustworthy and stable system.”
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