1. Problem description
Data is the fuel that drives digital business. Everything depends on data and its quality – whether we measure and monitor our fitness with mobile apps and the cloud; let algorithms decide how to optimize our domestic energy consumption; or (perhaps soon) let our car’s computer take over the driving. Customers and providers do not want this data to fall into the wrong hands or be assigned to the wrong person. As an example, online commerce in the pharmaceutical sector can only work responsibly if the data quality is so reliable that drugs and medications are sent and delivered to the right address and the right patient.
Digital business only functions properly when the underlying data is correct. Data quality is one of the major prerequisites for traditional companies to succeed in their digital transformation initiatives. Banks and insurance firms are one example of that. On the one side, they are undergoing an intensive phase of consolidation, while on the other the core of their business models is being challenged by various digital start-ups. Their continued success and path to digitalization depends mainly on data quality in general as well as on the quality and consolidation of master customer data in particular.
The reality for many companies can be summarized in a short sentence: one customer, multiple data sets. There is a typical situation that has emerged almost unavoidably: a customer calls, reports a problem and their data is captured in a system. At a different time, they call with a complaint and their data is captured a second time. But unfortunately the service person spells the customer’s name wrong. The customer contacts the company a third time to order a product but has moved house since the last contact. As they can’t be found under the new address, the sales department creates a new data set. So now there are three data sets for one and the same person – and they are all different. Quite understandably, the company assumes that they are three different customers as it cannot establish any relationship between them.
If we include the various archive and legacy systems in this situation – even if they are used rarely or not at all in everyday business – it soon becomes clear that this phenomenon is pervasive throughout the company’s entire stored history. Yet this history is the biggest asset that established companies in traditional industries can leverage in the race to a digital future. Historical information is tremendously valuable for the value creation of the future, comprising intellectual property and the history of whole generations of customer relationships. And that is something that newcomers and challengers will not yet have been able to build up to the same extent. But this treasure trove is hard to excavate when it is buried under a mountain of data trash, so to speak.
This situation is also problematic from the data protection perspective. Germany’s Federal Data Protection Act, for example, stipulates that companies should use data sets sparingly and minimize the quantities they store. This is made even more difficult by the EU’s General Data Protection Regulation (GDPR) requiring proof that companies are adhering to these principles – whether or not they have suffered a data protection incident.
The problem of defective data quality and redundant master data is not limited to the duplication of manual work. The biggest challenge for companies on their journey to a digital future is their ability to analyze data in detail in order to draw the right conclusions for optimized digital processes, new digital services, and new digital business processes.
Companies that do not have the correct overview of their customers’ buying histories will not be able to target them with the right products and the right level of personalization. In addition, customers expect to be recognized by a company, regardless of the channel they use or where they are located when they contact it. This, too, is only possible if the master data is correct and up to date.
Companies need complete master data sets about their customers that give them a 360° view of each one. To obtain this, the data needs to be enriched with information about liquidity, the products they purchased, revenue levels, home address, customer category etc. and this information must be assigned to the right customer. The vision of a smart digital enterprise can only become a reality when the business can run analyses on customer-related data across all its various systems and the whole customer history.
Companies wanting to consolidate customer data will need to search all the systems in question, extract the data, and store it on a neutral platform. Once there, it can be analyzed for redundancies, consistency and correctness. Automated procedures check for duplicates, zip code errors, bank information etc. and undertake corrections based on predefined rules. The data can also be enriched with data from other systems, such as sales and service solutions.
The name of this modern and centralized Information Management Platform is JiVS IMP. It was designed for the consolidation and quality optimization of master customer data. It offers, as standard, a wide range of interfaces to legacy systems from multiple providers, including Baan, Microsoft Axapta, Oracle ERP, Peoplesoft and of course SAP. The standard version of JiVS IMP supports more than 2000 business objects from various providers’ solutions; for SAP, it supports over 1200.
The Java-based platform is system-independent and has been certified by auditors. It acts as a centralized information collection and provision point, known as a data staging area, as part of a data quality optimization project. It helps companies analyze their data and its quality and optimize it, such as by enriching and harmonizing it as well as by creating analyses on the potential reduction in size of the information pool. Companies can predefine the filter rules for this reduction before they import the data and convert it to a neutral format. This facilitates the transformation and migration of the information in preparation for export to operational systems’ analysis tools or to analytics solutions. Once the export is complete, JiVS IMP ensures legally-compliant information access that is device-, time- and location-independent. It also subjects the legacy information pool to an end-to-end retention management process to ensure the seamless lifecycle management of all the historicized data and documents. In addition, the business object-oriented approach of JiVS IMP offers companies the option of integrating the platform in the target environments, such as SAP S/4HANA or SAP C/4HANA.
5. Customer benefits
Data is the fuel that drives digital business. JiVS IMP ensures the necessary quality in customer data and associated information. It is a centralized, neutral platform and data hub that allows companies to extract data from a wide range of systems, then clean, enrich and optimize it on the platform. From there, the data can be sent to relevant tools for real-time analyses of IoT scenarios or for deep-dive analyses prior to the development of digital business processes and models. These are often performed within the framework of big data use cases. As well as reducing the number of errors, this process reduces the overall number of data sets.
As an integral part of the destination architecture, JiVS IMP plays its strategic role to the fullest extent in the operational phase. JiVS IMP enables companies to avoid many problems right from the outset that are often typical of existing ERP environments, such as the ever-increasing need for resources. This is solved by regularly moving data and documents to JiVS IMP once they are no longer required for everyday business.
This is a specific scenario for which JiVS IMP offers existing SAP customers integration on the business object level. It enables changes to business objects in live systems like SAP S/4HANA and SAP C/4HANA, along with the associated changes to the data structure, to be automatically synchronized in JiVS IMP. As a result, data and documents from the operational systems can be continually transferred without generating additional project cost or effort. The application’s interface is the second integration level. From within the SAP GUI or SAP Fiori, SAP users can directly search for and view information in the operational systems as well as in JiVS IMP. In the case of the historical information, it is displayed exclusively in read-only mode.
In this way, JiVS IMP ensures that operational systems – ERP solutions as well as analytics systems – are always working with current data sets, but with no need to keep extending them due to growing resource requirements. As the legacy systems can be shut down completely once JiVS IMP has extracted and optimized the data from them, companies save 80 percent of the operating costs compared to keeping the legacy systems running.
JiVS IMP also helps companies comply with the world’s strictest data protection standard in that it meets the EU’s GDPR principles and regulations. The platform ensures permanent and legally-compliant storage for data – together with its business context – that is no longer needed in the original systems. This helps customers save costs by adapting the systems’ resource levels to the lower data volumes. As such, JiVS IMP also creates the foundation for projects involving migration to modern software generations.
In conclusion, JiVS IMP ensures the lowest overall operating costs, highest data quality and minimized project costs for projects involving data quality, migration and historicization.
6. Price and availability
JiVS IMP is available now. The functional scope and pricing are defined on a project-specific basis. Customers can choose to subscribe to the platform’s functionality as a service, enabling them to transfer capital expenditure (CAPEX) to operating expenditure (OPEX).