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The intelligent company of the future?  Only possible with information!

Only possible with historical information and with the current information, which can then provide information for the future. The information/data have been available in the companies for years and these are still not applied and used with the best benefit. Certainly, excluded are the big high-tech companies, such as Google, Amazon, or the finance and insurance industries or even start-ups.

Furthermore, the energy industry, especially in open markets, use the strengths of

Data for their predictions. In addition, throughout trade (energy, finance, raw materials, etc.), people use the information and the scenarios derived from it.

Which they do not, but what companies continue to do! One gets current financial relevant figures or performance figures very painstakingly, with any heavy BW tools or new also Cloud Tools, by means of comparison, loads, queues, and so on from the systems (double data storage). Thus, you can export and display them again in Microsoft Excel! Then the reports will be sent by mail to everyone. No!

SAP offers an overview with the campaign #GiveDataPurpose. It shows nicely, according to the diagram by Simon Sinek, the WHY; HOW, and WHAT of the data.




What is the benefit of the company and the customer? What benefit would like to be achieved? How does something like this work? So how does one proceed in order to exist? What should one pay attention to?

Here again the type: Find a little of "Proof of Concept" and make a prototype. Then show the usefulness of data!



  • Why do we have too little information today when we need to be more effective and efficient?

  • Why is the quality of information not sufficient?

  • Why do we know too little about the market and our customers?

  • Why do we...


  • Digitalization and transformation, where does the company stand?

  • What is the current situation in the market? Where does the company stand?

  • Are there regulations and laws?

  • Data quality and availability?


  • How do we get the relevant information?

  • How does the organization handle information?

  • How do we keep the data?

  • Operational data?

  • Experience Data?

  • How...


  • Data and it's quality?

  • Historical data and current data?

  • Information sources and how to obtain them?

  • Simplicity?


  • What is our strategy?

  • What resources are available to us today?

  • What can we do?

  • What added value and benefits can be generated?


  • Do we have a strategy?

  • The landscape for historical data and current data?

  • Data quality and security


Let's familiarize ourselves with some more technical issues regarding data and information.The topic of platform and data management is covered in blog number 5.


This is data that is accessed very frequently, for example, for reporting or processes.


This data is no longer accessed or is accessed only rarely (write-optimized DataStore objects from the corporate memory or persistent staging areas or write-optimized DataStore objects from the acquisition layer).


This is data that is no longer actively needed and is backed up via nearline storage - this data is used for history or partial improvements!


Database types and manufacturers

Relational databases (SQL, DBMS)

In the relational database model, data is stored in structured tables. These tables are related to each other (relation), which are implemented using a foreign key. The database system is the DBMS executed along with the database data to be managed. A database ensures persistent storage and consistency of an institution's user data and provides interfaces to the database applications that use the DBMS to query, evaluate, modify, and manage this data (Sybase ASE, IBM, Informix, dBASE, Microsoft SQL Server, and Oracle; OpenSource: MySQL and PostgreSQL).

Object-oriented databases:

Object-oriented databases are based on the fact that data is stored along with its functions in a single object. The object in which the data is stored is responsible for managing the data internally. For this purpose, there are suitable object languages for the database model. With their help or via the object functions, the data can be read (ObjectStore, O2, EntropyDB, db4o).

Non-relational databases (NoSQL, BASE)

NoSQL means "Not Only SQL" and describes database systems that follow a nonrelational approach. These databases, which can be based on different database models, are horizontally scalable and can be used for large data applications (document-oriented databases).



In-memory database (IMDB):

An in-memory database (IMDB) is a database management system that uses the working memory of a computer as data storage. This distinguishes it from conventional database management systems that use hard disk drives for this purpose

(Document oriented databases) ( (SAP HANA, SQLite, Redis).


So now we can answer further questions: 

Big Data

What does this mean for Big Data? Is there a Big Data database? To answer this question, it is worth asking another question: Does it make sense to write a massive number of individual data records into an SQL database and wait for the transaction to be completed each time? The answer is: No. Now you know why Big Data - actually a term for massive data storage - is often used as a synonym for NoSQL databases.

Data Lake

The term "data lake" stands for very large data memory. Unlike normal databases, it contains data in its original raw format. Data Lake can be fed from a wide variety of sources. The data can be structured or unstructured and does not need to be validated or reformatted before storage. In addition to text or number-based data, Data Lake can also capture images, video, or other data formats. Only when the data is needed is the structuring and, if necessary, reformatting of the affected data performed.


Regulators, security, and data protection are also needed for data and information. The law provides the basis, but it makes sense to record this company internally in a document!

We now have the topic of storage of data and data types and thus the basics. What can you do with the data? How can you do it?


In the previous blogs, the users but also the data producers are dealt with very precisely:

1. Ambient Experience - Multiexperience - User Experience

User Interfaces: text input, chatbot, speech, and sensory input, Augmented Reality (AR) and also Virtual Reality (VR), Ambient User Experience

2. Hyper-automation

Intelligent Robotic Process Automation, Intelligent Business Process Management

3. Intelligent and autonomous things (IoT)

Human-Machine Interface (HMI): Text inputs, voice, and sensory inputs, sensors, controls, guidance systems

And the upcoming topics

5. Application runtime, data storage, and computing power

6. Security

The new WLAN technologies, Low Power Network (LoRa) and 5G also promote the emergence of even more data and data traffic through increased performance.


The way to get there:

I can now obtain, edit, and prepare the data from sources with various tools.

Here we are talking about data management and orchestration services, which also help me to ensure data quality and data governance.

We also need an environment that allows us to map models, recognize patterns, and train them. There are several tools that SAP combines under the heading of Machine Learning Services. The data scientist can let off steam with tools, Liberia, and methods that are well-known on the market (Python, R, SQL, Jupyter notebooks, TensorFlow, and so on).

The SAP service for intelligent applications, SAP Data Intelligence at a glance.

The following applications are already based on data and intelligence.

The embedded applications

This also includes business cases already prepared for the application.


Review and future, this could be a future


The presentation of information, findings or evaluations can happen in various ways.

SAP Fiori


Tiles can display different types of content based on the data provided by the app. They can include an icon, title, informative text, KPIs, counters, and charts.


Overview page

Each task or topic on a summary page is represented by a map. The overview page serves as a framework for the user interface to organize multiple maps for a function on a single page.


Analytical List Page

The Analytical Listing (ALP) page offers a unique opportunity to analyze the data step-by-step from different perspectives, to investigate a root cause by drilling down, and to influence the content of transactions. All this can be done seamlessly within one page.


Analysis Path Framework

The Analysis Path Framework (APF) is a framework for the creation of interactive, graphically oriented, configuration-based analytical drill-down applications. Applications based on the APF allow users to display and analyze data for multiple key performance indicators (KPIs) from different data sources.


SAP embedded Analytics Cloud

 The SAP Analytics Cloud, integrated into SAP solutions, provides immediate value by enabling you to create operational reports customized to your specific application.

  • SAP S/4HANA 1911 Cloud

  • C/4HANA

  • SAP Cloud Platform

  • SAP success factors

  • SAP Concur, Ariba and Fieldglass H2 2020

  • Cloud SAP HANA Q2 2020


SAP Analytics Cloud

Die SAP Cloud Analytics bietet eine grosse Auswahl an Möglichkeiten; von Beschaffung der Daten, konsolidieren und aufbereiten der Daten. Dabei helfen diverse Standard Konnektoren und auch die Möglichkeit mit R zu programmieren.

  • SAP Analytics Cloud for business intelligence

  • Augmented Analytics Capabilities

  • SAP Analytics Cloud for planning

  • Extend Analytics

  • SAP Digital Boardroom


Artificial intelligence also leads to regulation. Legal guidelines and codes of conduct emerge. It is important to make the information known within the company and also to create a code of conduct specifically for the company.

Some examples of guidelines and codes of conduct:

We are committed to the use of artificial intelligence (AI) and...


Confidence of data in the data flow with business partners:

Furthermore, the digitalization of processes, information, and products, artificial intelligence leads to the networking of data, which increasingly requires tracing back to the source.

In addition to legal requirements, technologies such as Blockchain can also help to ensure the trust of data and transactions.


Business partners who do not know each other can safely interact in a digital environment and exchange data and information. Data and their products can also be traced back to their origin through a chain of blocks, ensuring traceability.


With SAP Blockchain Suite, products, information, and data can be securely interacted and tracked from start to finish.


How can you build your smart business?

As mentioned above, find a small Proof of Concept and create a prototype. Demonstrate the benefits of data and results!

SAP supports you with products, in all the topics covered. Take the products, because they are optimally coordinated and lead quickly to success. Avoid lengthy technical integration projects with different manufacturers!

Your budget, your company, and your management will thank you and your success will follow.



Machine Learning and Artificial Intelligence


Applications are already based on data and intelligence

SAP Analytics

SAP Blockchain


In the next blog: The distribution and connection of various cloud services, without bridges and hurdles, is in full swing today. Setting up diverse computing power and data storage of the Public Cloud / Private Cloud at popular locations solves the challenge of data sovereignty, technical hurdles and regulatory requirements.
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