The data laboratory at Data Lake: Raising and utilizing unexpected data treasures

The data laboratory at Data Lake: Raising and utilizing unexpected data treasures

You know that your company’s data volume is growing drastically, but you also know that there are even greater volumes of data out there waiting to be analyzed. These data piles are invaluable assets. But how can you gain added value from this mass of data? This is where data lake comes in.

Data Lake is an architecture that enables your company to manage large amounts of data, including unstructured data, efficiently and purposefully. At the same time, the data lake is built so that new information can be obtained from processed, stored and previously unprocessed data. So, a data lake architecture offers much more than a data warehouse-based business intelligence solution.

The advantages of a data lake

In contrast to a data warehouse, which makes limited data available in an end user-oriented and easily consumable manner, the data lake holds a huge amount of raw data in its original format. This makes more meaningful and in-depth analyses possible - especially in the explorative area.
In addition, a data lake is best accompanied by a fast-data component with which data streams can be evaluated practically in real time.


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Application scenarios

Data lakes are ideal repositories for diverse masses of data, particularly sensor data, which have to be processed quickly.

360-degree support is the latest standard in customer support. Answers to the following questions have to be available for service & support purposes in near real-time: What order was placed a few minutes ago? How satisfied was the customer with the order? Which websites did the customer visit before and after placing the order? What traces did the customer leave on your company’s website? etc.
If you don’t know the answers, you can’t optimize the customer experience.

Quality inspections often necessitate insights to the immediate and distant past by way of big data analytics. An increase in your defect rate drives up costs and impairs customer confidence, and it sometimes takes a great deal of data to identify and solve the problem. Sometimes it is necessary to analyze data from many years relating to production parameters, batches, test results, telemetries, returns, defect descriptions and many other things. This of course requires the methods and skills to draw the right conclusions from these data.

Vice versa, you can use analytical methods and a large database to predict failures and emerging quality problems, known as predictive analytics. This enables you not only to identify risks more reliably, but also to minimize them in advance.

Make your job and your colleagues’ jobs easier by offering self-service analytics. Costs are reduced and additional benefits are gained if power users and data scientists can quickly and easily compile their own databases for analysis from data in the data lake. This is only possible with a well-planned, automated analytics platform and a governed data lake, plus the right tools to maximize your flexibility. To create that platform, you need the help of experts and realistic expectations. You don’t have to reinvent the wheel for your enterprise. But you do need a stable platform with reusable components that have been extensively tested in various combinations.

It’s always best to put your data lake and your data lab in the cloud. Data analytics platforms are predestined for operation in the cloud. Standard solutions from Microsoft, Oracle, Amazon or Google provide you with efficient tools and services for a wide range of applications. Especially the possibility to build up an environment almost without delay and the enormous elasticity of the resources, i.e. the ability to use hundreds or thousands of times as much computing power for certain tasks or times, makes this option so attractive and the ideal environment. Your own IT department doesn’t have to keep these resources available on a permanent basis because you have access to them in the cloud.

With an appropriate data governance approach to data management and ownership, as well as advanced metadata solutions and analytical methodologies, your organization's workforce will always find and interpret the right information.
Productivity increases when a simple search delivers a clean and correct list of all the relevant reports, data sources, data descriptions and processes, and also provides direct access to them. It gets even better when your colleagues don't have to manually collect and process this information about data first, but when the software solution itself does this and also discovers and suggests suitable cross-connections. The Trivadis experts can realize solutions like these on your premises with AI technologies such as natural language processing or semantic web processes.

To remain successful, your company has to be in a position to meet the requirements of digital customer, supplier and partner relationships in digitalization projects. What you need are architects, technologies and platforms that help you manage the data and experts to support you. We’ll be happy to help you build a data lake for your enterprise.

Trivadis has more than 25 years of experience and collective know-how in managing data. Our proven approach to the planning and development of data lake architectures and data labs help you to establish a platform that can be successively enhanced at reasonable cost. Data warehouses and data lakes are more or less industrialized with automation tools to minimize the associated costs and workloads.

If you'd like to learn more about how a Data Lake can help you get more out of your data, contact us directly.

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