Business Intelligence

Game changer at all levels

The global economy, the markets operating there and the flow of goods are moving and changing at a breathtaking pace. There is an abundance of information, market data and indicators. This is why the ability of companies to draw the right conclusions from the multitude of data and act in real time is becoming a real competitive advantage. The trend towards real-time analyses and decisions now plays a central role in the modern business intelligence landscape. This is made possible by technological advances such as the IoT (Internet of Things), cloud computing, modern data management platforms and new methods such as ELT (Extract, Transform, Load), which is used in the course of implementing data lakes and with the help of which so-called data scientists immediately recognize errors and derive reliable developments and trends.

Traditional reporting, classically located in the data warehouse, is more of a retrospective process. The IT department is responsible for this. It collects historical data, fixed key figures and hard facts periodically from the source data and evaluates them according to clear specifications for classic company reporting. The result: companies use OLAP (Online Analytical Processing) to make quick and accurate decisions along predefined evaluation paths, also known as dimensions.

Sophisticated AI models use numerous methods

While reporting serves to make complicated key facts visible, data and business analyses rely on a direct, event-based evaluation of as broad a database as possible, which also includes metadata in order to identify trends and derive forecasts. Both reporting and data science are used to make decisions based on the reports, forecasts and big data analyses presented and support companies in various ways to improve operational efficiency and thus their added value.

However, reporting alone is often no longer enough in today’s dynamic economy. Cloud-based real-time analyses enable companies to analyze information directly as soon as it is available. This allows business processes to be monitored and quick decisions to be made, paving the way for an agile and adaptive business strategy. And this in turn creates a considerable advantage for company managers: In future, modern BI solutions will enable them to identify undesirable developments, exploit opportunities and avoid risks.

As an IT consultancy that is independent of manufacturers and financial markets, we will be happy to work with you to evaluate the right business intelligence solutions for you. No matter what your requirements are: We carry out extensions to standard software through to the creation of customized solutions. Our strength lies in the holistic view of customer-related analysis and reporting processes and their transfer and mapping in the appropriate BI systems.

Almost inexhaustible application scenarios for AI

Companies now have a wide variety of systems, some with huge volumes of data. With the trend to no longer operate systems in-house but to use an application service provider, the heterogeneity of data continues to increase. In addition, users are demanding ever more sophisticated analyses of ever larger volumes of data. Companies are therefore well advised to establish a data culture and a central data platform including a data lake. In this way, both structured and unstructured data can be processed and reliably evaluated: While a company’s operational systems maintain core processes in a regulated and stable area that provides very stable and automated processes, the systems provide information and data from a wide variety of source systems, ideally event-based. Using data lakes, the ELT method and the appropriate analysis tools, simulations or even forecasts can be created very flexibly in near real time. For the successful development of such agile BI architectures, it is essential to collect the data on a use case basis and then make it available for flexible use. The challenge here is to logically decouple the central data storage in the company’s own data memory from the actual evaluations without losing focus on the user. After all, it makes a difference which reports and forecasts a Chief Data Officer needs, for example, and which data is relevant for a data scientist or business user.

The key to modern real-time analysis: the extract-load-transform process

Extract Load Transform (ELT) is a modern data processing method that is becoming increasingly important, particularly in the course of preparing and structuring big data for BI scenarios and data analytics. Especially with the increasing spread of tools such as Google Dataflow, Cloud Storage, BigQuery, Azure Synapse, AWS S3, Redshift, etc., sometimes combined with Snowflake or Databricks, ELT is becoming increasingly important. Using ELT, the data is first loaded into the target data system or a data lake, where it is collected, cleansed, supplemented and only then transformed. This more error-tolerant approach uses the powerful computing capacities and modern data catalog systems and enables fast and agile processing of large volumes of data. ELT is particularly efficient in cloud-based environments, which are characterized by high scalability and flexibility.

Companies are therefore increasingly extracting raw data from various sources, which they obtain from their own core system, but also from their application service providers via previously defined service level agreements and load into a designated staging layer, such as a data lake or data warehouse. Once there, they are transformed into meaningful information in the so-called evaluation layer. This is based on three steps, which we will briefly describe again here:

1. extract (extract):

The raw data records to be processed are initially obtained from the entire IT landscape and via loosely connected systems. These are usually integrated using predefined interface contracts and service levels. If personal data is extracted, it can be masked in compliance with data protection regulations.

2. load:

In order to transform the acquired data quickly, it is loaded directly into an in-house, usually relational structure, if necessary with the help of a master data management system. This reduces the time between extraction and delivery enormously.

3. transform:

According to relevant use cases, the data is made available in the form of reports and forecasts in the output layer. Data scientists can flexibly analyze the raw data or the already processed data. Storing such large amounts of data is time-consuming, but BI evaluations generated in this way can be called up in real time. This distinguishes such forecasts and evaluations from time-controlled reporting from conventional data warehouses. In the output layer, large volumes of data can be transferred directly to an end-user tool for analysis using in-memory technology.

Beispielhafter Aufbau einer Datenplattform im Bereich Business Intelligence
Example of a data platform structure

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Whichever BI scenario you choose: We develop BI solutions from the business process. The question of WHY is always at the forefront for our BI experts in order to cover your individual requirements in the best possible way. This is because powerful analysis and reporting systems integrate seamlessly into the user’s working environment. Many BI systems fail to gain company-wide acceptance because certain departments or specialist employee groups are often left out. Modern BI systems enable specially prepared evaluations and “views” and the necessary data integration for all relevant stakeholders in the company, such as CEOs, CDOs, business analysts, AI developers and data scientists. They are all dependent on individual information and therefore all benefit equally from the single source of truth approach. Modern BI solutions provide information where information, forecasts and reports are needed and are self-explanatory and intuitive to use in the front end. The right amount of information is always crucial: user-specific filters adapt dynamically to the required content.

However, information alone is not enough: modern IT landscapes can support the decision-making process or the derivation of necessary measures. On request, our BI experts can make your data clearly accessible using specially developed information and analysis cockpits or printable reports, which we tailor to the requirements of the respective users and stakeholders in your organization. Forecasts and scenarios based on the latest data provide important insights for portfolio analysis or reliable risk management, for example.

How we are introducing an AI solution

Data forms the basis for business decisions and is therefore of central importance. But more data does not automatically mean more information – and as digitalization progresses, the volume of data is growing exponentially. As a result, companies are now dealing with very large volumes of data. These are often complex and heterogeneous in their structure, composition and quality, making it almost impossible to process and utilize them using conventional methods. The volumes of data generated by the Internet of Things alone can hardly be processed effectively with a conventional data warehouse. In this context, it is important to establish modern data lake strategies, efficient data management and a clear data governance structure in order to maintain an overview and be able to make reliable statements about which data originates where and which of it is important at all.

A fundamental problem for many companies in this context is the lack of a standardized database. The result: those responsible make decisions based on data that is only of limited value. In the worst case, such decisions can jeopardize a company’s business success. As part of a Statista analysis, we have identified the biggest hurdles that companies face when it comes to using their data effectively. We are happy to support you in optimizing your data processing in order to exploit the full potential of your data.

Our BI service portfolio has many facets

In addition to a process-oriented requirements analysis, we design and implement company-wide data warehouse and modern BI solutions that enable real-time analyses using agile data lake strategies. We also advise you in detail on data governance, IT and data strategy and work with you to define the necessary role concepts, which are of central importance in many BI scenarios. Our BI experts are characterized by a high level of technical expertise and many years of experience. We support you from data integration (ETL, ELT, data mesh) to aggregation and presentation in the form of reports, forecasts or dashboards. We are happy to support you in selecting the right tools and also provide suitable user training as part of a rollout. But even after a BI project, we won’t leave you on your own – we will be happy to provide you with full user and system support.

We support you in these analytics and BI areas:

  • Requirement analysis
  • Concept
    • Flexible individual solutions
    • Integration into operational systems/enterprise data warehouse/data lake/data mesh
    • Cloud-based BI solutions
    • Multi-country solutions
    • Authorization management at national/international level
    • Data governance, IT security and data protection
  • Realization
    • Performance optimization
    • ETL,
    • Data Lake Strategy (ELT)
    • OLAP, Data Vault, E-R
    • Event-based real-time forecasts
    • Key Performance Indicator
    • Easy-to-use, user-oriented dashboard/portal solution
    • Reporting
  • Rollout
  • User training
  • Support

We use tried-and-tested, high-performance BI tools:

  • ProClarity
  • Microsoft Reporting Services
  • Microsoft Integration Services
  • Talend Data Fabric
  • Confluent, Apache Kafka
  • Seeburger
  • Mulesoft
  • Microsoft Analysis Services
  • Microsoft Synapse, Data Factory
  • Snowflake Data Cloud
  • Google Data Cloud
  • Microsoft Power BI
  • MS Excel
  • Excel Calculation Services
  • Business Objects
  • Tableau
  • BEx Report Designer
  • BEx Analyzer
  • SAP BW Portal
  • SAS Data Integration
  • SAS Visual Analytics

Research in our IT projects in the area of Business Intelligence

Anchor a holistic BI strategy in your company with our wide-ranging BI expertise. This forms a solid and indispensable basis for successful and company-wide data analytics projects. Together, we consider all the steps involved in such a strategy, from data management to analysis and evaluation using suitable BI tools to clear visualization using meaningful dashboards.

Would you like to get an initial overview of our project successes? Then you’ve come to the right place. In our project database you will find a selection of meaningful BI projects:

As part of the project, the reporting requirements from the new processes in the distribution centers, regional warehouses and cross-docking centers will be implemented. The basis is the parallel implementation of these …
The existing system landscape is being raised to the next generation. The central component is the move to S/4 HANA. The existing SAP BW on HANA will continue to be operated and …
In the previous project, process-oriented KPIs were developed for a pilot area. The data originates from SAP (EWM, Retail, BW) and non-SAP sources (TMS, WinSped) and is collected in SAP BW as …
The existing system landscape will be renewed in the coming years. The main component is the migration to new SAP products such as S/4 HANA, Service Cloud and Sales Cloud. This will …

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Dr. Andreas Schneider

Sector Manager Energy

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