Contents
What is Data Analytics
Definition of Data Analytics
Over time, data analytics has evolved into a multi-layered term that refers to the process of analysing data. Numerous methods and techniques are used to identify certain behavioural patterns within data, which can then be interpreted depending on the context. Finally, a clear presentation of the final results is also of central importance in order to create the greatest possible added value for the company.
In the area of data analytics, a distinction is usually also made between the following sub-areas depending on the focus:
- Descriptive analytics: What happened (in the past)?
- Diagnostic analytics: Why did a certain event happen?
- Predictive analytics: What will happen in the future based on past data?
- Prescriptive analytics: What measures need to be taken to achieve certain goals?
These relationships are visualised again in the following diagram and, with regard to the level of maturity (analytics maturity), it can be seen that the individual areas usually build on each other and become increasingly sophisticated. In addition, the use of data analytics also has a positive effect on the company’s competitiveness, depending on the level of maturity.
Brief overview of data analytics´ relevance in today's business world
Decision-making based on data
Nowadays, business decisions in particular are affected by many complex and partly interdependent influencing factors and can therefore be made more precisely with the help of data analytics based on relevant business data.
Optimisation of business processes
When analysing existing processes within the company, data analytics can also be used to identify weak points. Based on these findings, solution proposals can then be derived that are helpful for optimising the corresponding business processes in the next step.
Increasing efficiency and reducing costs
As already briefly mentioned above, the use of data analytics can generally also reduce costs in the company, for example by identifying irrelevant expenditure or other cost-relevant factors through analyses. Based on these findings, efficiency can also be improved in the relevant areas, for example through more accurate forecasts in relation to resources or the identification of additional potential for cost savings.
Application areas of data analytics with SAP solutions
Financials (CO/FI)
In the area of finance, there are numerous application areas that can be efficiently solved with the help of data analytics and SAP solutions. In cost centre accounting from internal accounting (CO), for example, the company’s finances are monitored through plan/actual comparisons or the creation of cost centre reports. Another example is bookkeeping from Financial Accounting (FI), which is responsible for recording business-related transactions. These and other processes can be supported by using the corresponding modules in SAP S4/HANA, resulting in relevant KPIs such as cost centre efficiency, liquidity or equity ratio.
Based on this, the SAP Analytics Cloud can be used to plan and analyse financial data (e.g. forecasts of individual key figures) and is characterised above all by the creation of dashboards that provide numerous interactive functionalities for the user and are also suitable for visualising important key figures in the area of financials.
Production (PP)
The SAP solution for production (PP) includes S/4HANA Manufacturing, which is based on the regular S/4HANA platform and has been specially optimised for use in the manufacturing industry with additional helpful functionalities. Typical processes such as production planning, monitoring of manufacturing processes or quality inspections can be addressed here, taking into account relevant key performance indicators (e.g. throughput time, capacity utilisation or reject rate).
With solutions such as SAP Analytics Cloud or Embedded Analytics, the process data from the aforementioned areas can be analysed in real time and visualised in the form of dashboards to provide an overview of all key production figures.
Sales and Distribution (SD)
Sales planning, order management and the creation of reports are typical application areas in sales. By mapping these processes in the ERP solution S4/HANA, a number of KPIs develop, such as the number of incoming customer orders, sales forecasts or customer profitability. It is important to consider these and other KPIs in order to fully exploit the potential in terms of customer loyalty and optimisation of existing sales processes.
Various SAP solutions from the Customer Experience area, such as SAP Sales Cloud, can be used for this purpose. With the help of AI-based functions, customer data can be analysed with the aim of clearer segmentation, for example, or forecasts can be made regarding trends and sales figures.
SAP solutions for various Data Analytics challenges
Data Analytics challenges
Data quality and consistency
Data quality plays an important role in data analytics, as incorrect or incomplete data, for example, often leads to inadequate results with little meaningfulness. A further problem can also arise if data records are stored inconsistently, which usually leads to complications in the analyses and should be prevented by data cleansing and consolidation.
Data integration
Company data can be available in numerous formats and systems, which can make it difficult to provide for analysis depending on the number and type of different data sources.
Data security and data protection
The topic of data protection with regard to sensitive data is also relevant for almost every company and the protection of data should always be handled in accordance with the rules and regulations in the area of data analytics, for example by anonymising the data used.
Complexity of the analysis
The effects of big data mean that correlations and dependencies are becoming increasingly complex in day-to-day business, which can also be reflected in data analyses. The complexity of the resulting models has a negative impact on comprehensibility and, depending on the requirements, more advanced data analytics methods that require specialised knowledge may also need to be used.
Data Analytics solutions in the SAP environment
SAP Analytics Cloud
The SAP Analytics Cloud provides some functionalities (e.g. creation of dashboards or predefined business content packages depending on the industry and business area) for data analysis and visualisation in the cloud and can also be used for planning. This makes it possible to provide complex facts and models in the form of stories in a clear and user-orientated manner.
SAP BW/4HANA
By using an SAP BW/4HANA system, the challenge of data quality and consistency is addressed first and foremost. Company data from different sources can be consolidated, cleansed and transformed in the system to ensure a high quality standard for the data that can be analysed and interpreted in the next step. With this approach, BW/4HANA as the central data platform corresponds to the single point of truth in many cases, which is usually a decisive factor for companies with regard to data analytics.
The security and data protection requirements are particularly important for sensitive company data and can be resolved within BW/4HANA with the authorisation concept for standard operations and analyses by mapping an internal company structure. This determines who has access to which data and reports and which actions may be performed on corresponding objects in the system and by whom.
SAP Datasphere
SAP Datasphere, as the successor to SAP Data Warehouse Cloud, is characterised in particular by its advanced data integration with regard to various systems (SAP and non-SAP), thereby enabling the central consolidation and management of data from internal and external sources. To ensure data security, SAP uses encryption for network communication in the cloud and other functionalities such as space management in combination with user authorisations also help to ensure data protection.
Added to this is the focus on self-service and the general user-friendliness within the platform, which also assists users with more complex analyses and the creation of data models, making it easier to get started. It is also possible to use machine learning in the form of Python scripts, for example, to uncover more in-depth relationships between data and processes.
As a current, modern cloud service, Datasphere therefore demonstrates numerous potentials in business-relevant areas and should therefore also be established by SAP as the data warehouse solution of the future in the long term.
SAP Embedded Analytics
With the help of Embedded Analytics, a solution for operational reporting, numerous data analytics functionalities are directly integrated in the SAP environment, in this case in the ERP solution S/4HANA. The integration enables, for example, real-time access to data with subsequent analyses or the use of provided SAP Fiori apps depending on the user and context. In addition, numerous performance indicators, reports and other planning and forecasting scenarios are made available to the user in the form of business content.
SAP Business Objects
SAP Business Objects is the name for a collection of tried and tested tools in the field of business intelligence (BI), which are designed to support companies in areas such as data analysis, visualisation and reporting.
Successful implementation of Data Analytics in the SAP environment
Best practices
Steps to successful implementation:
Definition of requirements and business objectives
At the beginning of a strategy, the business requirements should be examined more closely and defined in the form of clear objectives, e.g. process optimisation in a specific area or a 10% increase in turnover.- Identification and provision of relevant data
In preparation for the next steps, all relevant data sources must first be identified in order to subsequently integrate and provide company data with a suitable software solution (e.g. BW/4HANA or Datasphere).
- Ensuring data quality and consistency
The consistency of the stored data plays an important role, particularly with regard to analyses, and data quality must also be ensured in this context, e.g. by preparing and cleansing the data. - Selection of data analytics solutions in the SAP environment
To simplify the selection process, some areas of application and solutions in the SAP environment have already been outlined in chapters 3 and 4.2. The selection of tools for data analytics generally depends on many factors, such as the size of the company, the industry and the business objectives defined at the outset.
- Application of data analysis methods depending on the focus
Depending on the data analytics tools selected, various analyses and techniques (e.g. predictions of turnover through machine learning) can be carried out at this point in order to gain deeper insights into data sets.
- Evaluation and visualisation of the results
Finally, the results of the analyses must be evaluated and clearly presented in order to derive recommendations for future action.
- Monitoring and optimising the process
Data analytics is an ongoing process that must always be monitored and continuously optimised. Company data should be checked regularly and topics such as performance can also be important in order to possibly improve individual steps.
Case studies and success stories:
The German company Wörwag Pharma GmbH & Co. KG employs over 1200 people at more than 20 locations worldwide and focuses on the development, production and distribution of pharmaceuticals for the prevention and treatment of lifestyle diseases.
With a view to the future, the focus was particularly on modernising the existing system landscape and using data analytics tools, and a number of requirements and questions had to be clarified first. This included, for example, analysing the current data analytics strategy and deciding which tools would be used in the near future. As part of a workshop, Wörwag’s requirements were analysed and, based on this, the functionalities and areas of application of Microsoft Power BI and SAP Analytics Cloud (SAC) were presented and evaluated.
An additional pilot project in the area of controlling with the Analytics Cloud and the dashboards created, which could then also be used productively, ultimately led to the decision in favour of the SAP tool. This was also in line with other SAP solutions in the company (e.g. S/4HANA or BW/4HANA) and generated high added value for Wörwag in terms of data analytics.
Another success story can be seen in the introduction of SAP BW/4HANA at SSI Schäfer Shop GmbH. The mail order company was founded in 1975, is represented in 19 countries and has a range of around 85,000 items in various areas such as office and promotional products.
Previously, the company used SAP Business Warehouse as its reporting solution and the BEx environment was used for the front end. Through a new BI strategy with associated modernisation, SAP BW/4HANA and SAP Business Objects were introduced as a visualisation tool for reporting and analysis applications in order to create a company-wide basis for the implementation of challenges in the area of data analytics.
Data sources
The number and type of data sources vary depending on the context, company, requirements and existing IT structure. Nevertheless, there are some relevant data sources that play an important role in terms of data analytics. These include, for example, an ERP system such as SAP S/4HANA, which is responsible for recording, automating and managing company processes, among other things, and is considered one of the main sources of data. In addition, there is a data warehouse, for example SAP BW/4HANA or Datasphere as a current solution for integrating data centrally and thereby creating a basis for reporting and further analyses. There are also numerous other data sources, for example SAP HANA as a database or external sources such as Excel files, which can also be important for data analytics depending on the use case.
IT architecture
A structured IT architecture should ensure that all necessary systems are correctly set up and networked with each other. As mentioned briefly above, the following components should ideally be in place:
- Operational system as data source (e.g. ECC or S/4HANA)
- Central data platform (e.g. SAP HANA, BW/4HANA or Datasphere)
- Tools for data analysis and visualisation (e.g. SAP Analytics Cloud or Business Objects)
When designing the IT structure, attention should also be paid to the fact that topics such as data governance, scalability and performance are important with regard to the software solutions used and must be solved with a view to the future wherever possible.
Conclusion
Summary of the main points
The application area of data analytics is a major challenge for companies today and can be simplified with the support of suitable solutions from SAP, depending on the context, industry and maturity level of the company. Business decisions based on data, optimisation of business processes and cost reduction are typical scenarios that once again highlight the relevance of data analytics in various departments such as production (PP) or sales (SD).
In order to guarantee a successful implementation at this point, clear goals must be defined in advance and challenges such as data quality, data integration and the complexity of analyses must be solved in the best possible way. SAP products such as BW/4HANA or Datasphere, for example, are helpful for consolidation, data preparation and integration from various sources, while SAP Analytics Cloud can assist users with data analysis and visualisation of complex issues with the help of dashboards and key performance indicators.
Future prospects
When it comes to data analytics, dealing with topics such as artificial intelligence, big data and data complexity will become increasingly important in the future. SAP is therefore currently focusing more and more on cloud solutions (e.g. Datasphere or Analytics Cloud), which are characterised above all by scalability and performance. Current trends such as AI are also increasingly being incorporated into individual products, such as the integration of machine learning functionalities in Datasphere. With future-oriented features and product roadmaps, SAP is also constantly developing existing solutions in order to provide the most up-to-date technology possible, also with regard to challenges in the area of data analytics.
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