In today’s data-driven world, all enterprises need to integrate data action nodes for their strategic and operational decisions. To enable data-driven decisions, the major criteria is to extract value from a continuously increasing structured/unstructured data volume and utilize this information (derived by data) on time via business and IT handshaking. Integration of cloud strategy for enterprise reporting can bring IT and business together in a best course.

The most important aspect of the digital transformation is that the data warehouse is flexible and has a democratized data usage with the help of self-service for the business and the seamless integration of external data sources. High agility is equally important here and, together with the other points, is the reason to future-proof the data warehouse. In addition, companies ideally want to take advantage of cloud data warehouses without losing their investments in on-premises systems.

Data Insights with SAP Data Intelligence

SAP Data Intelligence is a comprehensive data management solution. As the data orchestration layer of SAP’s Business Technology Platform, it transforms distributed data sprawls into vital data insights, delivering innovation at scale.

In these days, many organizations use data action nodes for strategic and operational decisions. The first criterion here is to enable such data-driven decisions to be made, to extract the amount of information from the growing volume of data and to be able to use it. The most optimal way for enterprise reporting can be brought to IT and business when the cloud strategy is integrated.

SAP Data Intelligence in our experience is known to be one of the well-suited products in SAP Integration suite. It provides data integration, data management and data processing, making it a comprehensive solution.

As per the current situation in a customer’s existing system landscape, the main question is:

Which use cases are preferred to realize this architecture and what are the advantages?

Advantages of Data Intelligence

Data intelligence helps with daily tasks and decisions. Here, we discuss the benefits of data intelligence and why companies should use it:

Changing demand

Data intelligence enables companies to adapt dynamic changes. Today, businesses are constantly facing the need to evolve. To compete and reduce the likelihood of failure, companies must accept and update according to emerging trends. Data intelligence helps understand customer behavior and changes. Companies are informed by intelligent adaptive dynamics about repeated changes and the pattern of their occurrence. This enables the business to make informed decisions based on the analysis.

Strong foundation of data

Data intelligence makes big data stronger and more powerful by restructuring the process of data arrangements. It enables the extraction of insights from big data and the provision of optimized applications.

Accelerate innovation

Data intelligence allows faster innovation through the intelligent use of data. It enables the use of data insights to drive business innovation and develop their services with customer preferences and requirements in mind.

Augmented analytics

Advanced statistical approaches are used in data intelligence to enhance visualized predictive and prescriptive analytics. Advanced simulations enable organizations to predict potential outcomes and make regulatory changes as needed.

SAP Data Intelligence Functionalities

With broad metadata management capabilities, SAP Data Intelligence assists us in understanding business data. To guarantee that everyone is on the same page, there are business rules, data lineage, a business lexicon, and a rules dashboard. Its centralized access to different data sources facilitates collaboration across many teams, including data engineers and architects, machine learning and data developers, and core IT teams.

SAP Data Intelligence Functionalities
Figure 1: SAP Data Intelligence Functionalities

Benefits of working with SAP Data Intelligence Functionalities

Today, making reliable forecasts and assessments based solely on internal data is difficult. Many businesses are gradually discovering the value of incorporating external data. Finding the right data supplier, acquiring data, and then laboriously analyzing and visualizing it using Excel takes a lot of time and resources. In the past, this resulted in lengthy and costly IT initiatives, with data integration issues frequently occurring. It was unclear if lucrative inferences could be taken from the data following this endeavor. Regardless, external data is required.

This is exactly what SAP Data Intelligence functionality solves. We can collect data from thousands of sources in one location and do comprehensive and up-to-date analysis.

Use case: integrating external data into DI in connection with DWC:

One example use case for integrating external data via SAP Data Warehouse Cloud with SAP Data Intelligence is our tool Camelot Supply Chain Resilience Cockpit. Based on historical and current data points, it analyzes how external influences impact supply networks. The solution gives businesses an always up-to-date picture of incoming and outbound delivery issues as well as real-time warnings. Eventually, this will provide more efficient transparency, resilience, predicting orders/events, active routes and supporting ordering process.

The SAP Data Intelligence capabilities for machine learning leverage accuracy by learning from the training data and applying the algorithms on the newly generated data. Once model training is done, it can transmit the predictions through DI and DWC connection management capabilities. The updated data can be easily integrated and linked into DWC data flows. DI can provide wide capabilities of incorporating the external geo-weather data and natural hazards warnings so the customer can have live updates as well as the best alternative options.

End-to-End Data Transformation
Figure 2: End-to-End Data Transformation

Advantages of data intelligence

SAP Data Intelligence can access virtually every data source such as relational databases, object storage, distributed files systems, data lakes but also HTTP systems, REST APIs, or Kafka event streams. With the use of operators based on python and other programming languages it is possible to realize access to every data source.

Integration for data science use cases is possible with all kinds of data, e.g., through usage of Jupyter Notebooks. This allows to dive into data along with pipelines that can be used to implement use cases.

  • The historical data present in DWC can be easily transferred via IP connection between DWC and DI using connection management.
  • Machine learning and training on data from DWC can be done efficiently in DI using ML Scenario manager.
  • Prebuilt operators enabling data handling in pipelines.
  • Metadata explorer functionality in DI can provide real time analytical information about the data under analysis.

Architecture of DI

SAP Data Intelligence Architecture
Figure 3: SAP Data Intelligence Architecture

 

The data management and orchestration layer provide management and data governance capabilities like Factsheet, displaying the basic structure of the data. This is a possibility to publish datasets to a catalogue to make it visible for others. It is possible to enhance the information of published datasets with tags and a glossary to describe quality rules and rulebooks to track data quality. Data orchestration helps to support everything around the management of data. Pipelines are powerful tools to manage data. There is a possibility to schedule their execution and monitor them. Visual pipeline modeling and data workflows provide end-to-end integration between DI and DWC via API access. Pre-trained modeling is governed by functional services. Core services, which contain model training, are a key component to fill a gap between SAP cloud platforms including DWC.

Meet Advanced Enterprise Reporting Needs

The solution we described here makes use of SAP Data Intelligence, SAP Data Warehouse Cloud, and data connectivity with numerous sources while SAP Data Warehouse Cloud serves as the primary storage for SAP Data Intelligence.

SAP Data Intelligence is the solution’s central coordinating point. It enables communication throughout the system landscape. As a result, SAP Data Intelligence hosts the Camelot sophisticated forecasting capabilities, which use machine learning to produce live forecasts and offer suggestions and alerts. Therefore, SAP Data Intelligence produces a probabilistic model, purchase orders are processed, and outcome predictions.

Overall DWC & DI is a complete package to fulfill all enterprise reporting needs in an easy way with the best collaboration between IT and business.

SAP S/4 Transformation: Survey on Expectations

The study "Expectations on S/4HANA in 2022" by techconsult and CamelotITLab shows possible painpoints in any migration and how they can be avoided. With data from 200 companies in Germany.

Download the complete study here

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