Separate, Simplify, Automate: What To Do With Historical Data

Digital transformation is posing a challenge for business leaders: They have to have intelligent answers that lead to sustainable solutions – despite the current global uncertainty. COVID-19 is forcing a lot of industries, markets and businesses to restructure or start from scratch. Acquisitions, mergers, divestments, partnerships, and so on will be an everyday reality for managers in order to tackle this crisis. Furthermore, companies need to save money, and they need to save money fast. IT projects that enable short-term advantages, however, need to align with long-term strategies and measures to be effective.

A company’s true value is crucial for short-term as well as long-term gain. The more intrinsic value, the better the negotiating position. A large part of this value stems from corporate data and documents. Renowned analyst firms have been highlighting the importance of information for some time now, urging companies to become more agile and increase their value. In this case, the term information especially refers to X-data and O-data. O-data means transactional data from operating systems, X-data means information about user and customer experience (with the X standing for experience). Looking at successful data-driven business models of online companies, the focus on X-data and O-data seems reasonable.

Online companies are comparatively young, especially in traditional industries like engineering, automotive and textile. The know-how on e.g. how to build an efficient, high-quality car is older than the design of electric cars and steering software. The human body, its size and measurements are not as fleeting as viewers’ tastes in TV shows and movies, making cutting patterns from the 1950’s valuable intellectual property in today’s textile industry. Machines and plants have a lifecycle of many decades, and blueprints as well as maintenance reports can lead to innovative insights in the development of new products. Industries that struggle with digital transformation, like banks, insurances or healthcare, sit on treasure troves of historical data that they legally cannot change nor delete.

In all of these examples, historical data might have valuable answers to today’s digital challenges. Consequently, the true value of a company is determined by its data, recent as well as historical.

Digital meets reality

Digital transformation means combining the new and the old. Lessons from young online business models fuse with decades of corporate experience. Beneficial short-term changes combine with the long-term value of intellectual property. Tried-and-tested blueprints are enhanced by adding up-to-date data from machines and plants.

For example, textile companies leverage comprehensive analytics capabilities to be able to quickly and flexibly react to changes in the market and new trends. Automotive companies can use their process and production know-how to accelerate digital transformation and be at the forefront of innovation. Knowledge and experience from decades of customer interactions can give banks and insurances an edge in designing personalized digital offers.

Digital meets reality, history meets zeitgeist. Intelligent companies would do well to not only concentrate on X-data and O-data, but to also incorporate historical data, H-data.

For IT departments, accessing and combining X-data, O-data and H-data is a challenge. All of them (but especially H-data) are often dispersed throughout many different and sometimes very old systems – not to mention that their sheer volume is already hindering agility. Corporate information that needs to remain immutable because of legal or other reasons typically makes up about 80 to 95 percent of data volume in productive systems.

Separate, automate, save

Historical data do not have to be a burden, however, as they hold immense value. Companies have to separate X-data and O-data from H-data and manage the lifecycle of historical data on an independent platform. Because legacy systems can be decommissioned after separating and transferring the data, operation costs can typically be reduced by 80 percent compared to the continued operation of legacy systems. If the platform and its functionalities are available as a service – as should be the norm in times of cloud computing -, companies do not have to invest in additional hardware.

Furthermore, by transferring legacy information– including the business context in which it was created – to a separate platform, the amount of data and documents that have to migrated to new operational systems is reduced drastically. For example, the efforts associated with data migration to SAP S/4 typically reduces by 50 percent.

Over time, these short-term gains become long-term advantages. On the one hand, historical information can continually be transferred to a separate platform for information management – not only out of legacy systems but also operational systems. Realistically, the TCO (total cost of ownership) of a new S/4 environment could be reduced by 25 percent with this approach.

On the other hand, this approach has long-term benefits for other business scenarios as well. Companies can harmonize and consolidate their heterogenous system and application landscapes. If a subsidiary has been sold, data can quickly and easily be selected and presented to the buyer in a modern format. Furthermore, this approach promises high level of security for a company’s intellectual property as well as legal certainty. The requirements of recent or new regulations, such as the European GDPR (General Data Protection Regulation), often mandate seamless management of the lifecycle of historical information, which the platform takes care of with comprehensive retention management.

Moreover, historical information and its business context stay 100 percent accessible on the separate platform. Companies thus do not lose insight into a customer’s or a supplier’s history, and they can tackle internal revisions or external audits with ease.

Last but not least, an independent information management platform lays the foundation for all kinds of big data scenarios. Before transferring the data to the platform, they can be optimized. Duplicates can be deleted, information from other sources can be added to enhance incomplete data sets. This is important to fulfill the potential of data-driven business processes and models. Decisions based on data analysis are only as good as the quality of the data, and this is especially true for big data scenarios.

Simplicity instead of complexity

Automation is key to reduce complexity caused by the heterogeneity of application and system landscapes. Being the result of too much information in operating solutions, this complexity usually causes inadequate data quality, thereby calling for immediate optimization. During migration and transformation projects, this complexity requires additional personnel and financial resources to become manageable.

Focusing on lifecycle management of historical information and enabling the right connections between a suitable platform and operating systems lay the foundation for automation. This is especially important considering automation is the key to the treasure trove that all companies sit on – recent as well as historical data. Regardless of which industry they operate in, automation also significantly reduces costs.

Historical data are brimming with potential and value waiting to be unlocked, and they may well determine a company’s future success. This is a fact, not a result of the current economic crisis – but the realization of how valuable data is might help companies tackle current challenges.