Big data and its challenges
The term ‘big data’ refers to large and complex data sets that businesses bring together from a wide range of sources. The volume, velocity and variety of these data can make it difficult for them to be used effectively by businesses. Some of the most important challenges of big data are:
(1) Data security – Given the risks with respect to the storage and transmission of large quantities of data, safeguarding data security is of great importance. Businesses have to ensure that their data are protected from hackers and cyber criminals. Here, access rights, data encryption and anti-tamper security, regular data backups as well as recovery options in the event of data loss play a major role.
(2) Data quality – The quality of the data that is collected is of crucial importance for accurate analyses because poor data quality can result in flawed insights and, thus, to wrong decisions. The focus here is on issues such as data integrity, compliance with data standards and the currentness of the data.
(3) Data integration – Data can originate from a wide range of sources, including internal systems, social media and the interaction with customers. The integration of these data requires the very different data formats to be combined and merged. Inconsistencies and duplicates should be separated out and efficient data processing ensured.
(4) Data analysis – Prior to the extraction of insights, big data go through a number of analysis processes that help to map the data in a way that makes them usable. Data exploration and descriptive statistics are first used to gain an understanding of the data and their correlations. Data visualisation technology is deployed to graphically represent the data and to identify patterns, trends and relationships.
(5) Ethical aspects and communication – Compliance with ethical principles, especially those related to personal data and the use of AI, as well as continuous communication with the company’s executives are also key elements in order to avoid misjudgements.
Synergies of ERP systems and BI solutions
While ERP systems and BI solutions achieve different purposes, nevertheless, synergies of both systems are able to generate considerable added value for businesses and, in doing so, help to realign a business or allow it to continue down its chosen path with renewed vigour. For example, ERP systems are able to provide internal company data in real time and BI solutions can then link and visualise these data in order to ultimately enable the optimisation of business processes through these insights into the business operations.
Using BI solutions to extract data from an ERP system and displaying the different business divisions in a visual presentation exposes influencing factors that frequently remain concealed in the ERP system (for example, SAP or Navision). Moreover, BI solutions are able to integrate not only one or more ERP systems, but also additional data from information sources independent of the business (for example, weather information or market data) to show a more comprehensive picture of a company’s business operations and also include external influencing factors.
The difference between ERP systems and BI solutions is ultimately that ERP systems are geared towards transactional data and processes while BI solutions focus on analytical data and processes.
Recommendation: The exploitation of synergies makes it possible to enhance the forecasting of business developments, identify risks more quickly and to optimise processes. Furthermore, new services and digital products could be developed or serve as ideas for submissions to facilitate decision-making about business development.
At the start of a BI project there is a proper objective or also the prioritisation of a long list followed by the choice of the necessary data. Here, it is vital to carefully consider the objective because otherwise the statements that are made will be too general. As in the case of metrics, here too there also needs to be proper definitions and a basis for interpretation. The data quality here will determine how meaningful and detailed the project set up can be. It is not uncommon for the project to fail already when it comes to maintaining the master data, which is necessary in order to be able to build on a valid basis.
If the data basis from the ERP system(s) and other sources has been properly prepared then implementation in the BI tool follows and then the linking of the data. A dynamic dashboard will then be able to map the sales metrics in such a way that a visualisation is created that is ‘tangible’. Interactive clicking through quarters, locations, products, times of the day, customers or other features will result in revealing the linkages that would simply be difficult to determine in other ways.
A specific example of an application
Depending on the data basis, business monitoring can be displayed in a wide variety of ways. The above example shows a practical application case. The graphic shows sales revenue generated by a company in 2022 broken down by industry. For example, the construction industry accounted for the largest share of sales revenue, while agriculture generated relatively few sales. It is likewise evident that notably in March and April the contribution margin was especially strong, while in October it was rather weak. The so-called ‘heat map’ in the lower part of the dashboard clearly shows which products generate most of the sales revenue (field size) and, specifically, in relation to the number of documents attributable to these product groups (field colour). If it is now of interest which products are doing particularly well in the respective industries then, for example, it would be possible to select the construction industry and then all the other diagrams would be adapted according to that choice.
Linking the data from different business divisions thus enables an interactive and varied way of viewing the business processes and, with just a few clicks, highlights optimisation potentials. This facilitates in-depth engagement with the efficiency of individual departments. Moreover, forecasts, 1-to-1 comparisons with previous year values or multi-year comparisons can also be easily displayed.
Summary: Developing a big data model and determining analysis objectives can present huge challenges. At the same time, BI solutions help to monitor data and processes more effectively and to gain valuable insights into business operations. While ERP systems and BI solutions serve different purposes, nevertheless, by combining them it is possible to generate a high amount of added value for businesses by providing data in real time and dynamically visualising it. This however requires the selected solution to be compatible with the DNA of the business and tailored to its needs. It will ultimately be up to the users and a company’s executives to interpret and implement the results in order to design more precise structures for the business and optimise it in a future-oriented way.