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The topic of planning is often left out of the definition of a data strategy. Planning is perceived as "too special" because it is strongly departmental, existing concepts can only be applied to a limited extent for planning, and the employed BI tools are often unsuitable for planning. As a result, potential remains untapped. This blog shows how you can fix this, even retroactively.

We have demonstrated in part 1 & part 2 how an AWS data lake is built from scratch and how the data is ingested in a Data Lakehouse. In this blog, we describe how to enforce GDPR law, the Right to be Forgotten (RTBF), in a Data Lakehouse. We make both the data lake and the data warehouse built in the previous blogs compliant with having a user exercise their Right to be Forgotten. Let us first understand what RTBF is.

As we have seen in the previous blog post, we should now have our transformed data in the data lake and have it available in the Glue Data Catalogue. In this blog post, we will first discuss what AWS Lake Formation is and see how we can use it to securely share access to the data.

Currently, a popular component of Cloud Data Platform Architectures is a Data Lake. If you are curious about the implementation and services for a Data Lake with AWS, have a look at those blogposts. An architecture which provides transparency about the Data in the Data Lake and makes it smoothly available for further analytics in a Data Warehouse, is called a Data Lakehouse. Click here.

The data mesh is a current technical and organizational concept to enable greater business proximity and more scaling for large organizations involved in data & analytics. Consistent implementation here proves revolutionary and requires change management.

Whether in storage, production or customer service – completely different business processes all involve a use of images which need to be analyzed and evaluated. However, manual evaluation of these images is time-consuming and error-prone. These procedures can be automated with the help of computer vision, i.e. machine analysis and processing of images. Thanks to highly mature methodology, machines are now able to carry out even complicated analyses.

Through creation of analytic applications, SAP Analytics Cloud makes it possible to produce complex reporting scenarios for customers. A building block which decisively complements this function is custom widgets which allow quick and unser-centred creation of visualizations. Here we will show you how to create these and embed them in an SAC application.

If successful proof of concept (PoC) for a data-analysis pipeline is to be followed by production, this often proves to be a long road. Ibis makes it possible to simplify this process and thus add value faster.

After successful local development of a data-analysis pipeline in Python, the code often needs to be rewritten to allow operation in production mode. But does it really have to be that way? Programmed by Wes McKinney, lead author of Python Pandas library, the Python Ibis library provides a fascinating solution for balancing data processing between the production and development environments, thus enabling analytics teams to achieve production faster. This blog post of ours shows how it works.

An MRM solution’s monitoring component offers analysis, evaluation and reporting functions providing transparency about marketing staff's activities with the help of dashboards or configurable reports, for example.

With the help of the workflow component of an MRM solution, marketers create tasks and can assign and control them to themselves or others.This makes timings, responsibilities and the processing status of tasks and campaigns recognizable, filterable and controllable at a glance.

Marketing resource management (MRM) is still too little known among marketers. This is unfortunate, because MRM software can take your marketing planning and management to the next level – provided you find the right software for your needs. To allow orientation in the MRM market, b.telligent as a technology-independent consultancy company has analyzed and classified relevant providers to create a market overview offering assistance, function overviews and provider profiles: b.telligent's MRM market overview.

The saying that "data is the gold of the 21st century" has been encountered by almost everyone in recent years. But there is a crucial difference between data and gold. Data only have value when you use them. In order to enable usage, something has to change in many cases – the data have to be democratized.