The replacement of IS-U is just around the corner
Companies within the energy industry need to take action. As with SAPs legacy ERP systems, time is running out for SAP IS-U (Industry Solution for Utilities) by the end of 2030. By then, providers must have switched at the latest, e.g. to the SAP IS-U replacement SAP S/4HANA Utilities (On-Premise) or SAP C4U (Cloud for Utilities). There is also the option to choose a non-SAP solution such as TAP, the billing platform for the energy industry which has been collectively developed by Thüga and Accenture. Regardless of who companies give ‘their heart’ to when it comes to the replacement of SAP IS-U, they should attach particular importance to the quality of the master data which needs to be migrated. In the following article you can read why companies should do that.
SAP IS-U is SAP's business solution for the energy industry. Among other things, it serves as the basis for billing within energy services, device-management and -maintenance, sales processing and customer service. The longer such a solution is active, the more data will be accumulated. Most of the time, the quality of the data is not satisfying, because ensuring data quality is often an unpleasant task. Moreover, there are usually no suitable tools for data quality to proceed efficiently in this context. The result is obsolete, incomplete and dublettes or multiple data. Not to mention the vast amounts of archived data that, upon closer inspection, are no longer needed. In this respect, the database of an old system to be replaced is like a “basement room”. Time by time more and more was stored in this room. In the end, you don’t even know what’s in this room that you might need later. You don’t even know the texture. A reliable overview is missing. Clearing out and cleaning up will be difficult. At the end of the day, you don’t even want to go into the basement and look for the crowd of old remains.
Replacement of SAP IS-U: Without data quality, there is a riks of 'arbage in, garbage out'
The replacement of SAP IS-U leads to a migration project, independent of the new solution. Its complexity should not be underestimated, especially with regard to the quality of the master data. An important sub-task is their migration. Particularly in the energy industry, these data and their quality are of special importance. Master data can be used to manage business partners, contracts in general and contract customers. It can also be used as managing tool for connection objects and the contained consumption points, annex, and device locations. The data quality must be sufficient, so that the system and the processes work efficiently. Ensuring data quality is a sub-project within the master data migration. Because it goes like that: ‘garbage in, garbage out‘! When you don't migrate your data in top shape, you risk your investment, you put yourself in the hands of the principle of hope, according to the motto 'It will be fine!' If you migrate well planned with valid data, you will enjoy a quick migration and get the full performance of the new system right away. Since hope is not a tactic, it is not a good solution to rely on hope. And it also doesn't have to be, if you rise to the challenge early on. To stay in the picture: At some point, you have to go down to the basement and look for the existing material to analyze it.
Get a jump on data with quality, accelerate the replacement of SAP IS-U
IT landscapes in the energy industry are characterized by core and satellite systems. What these are, depends on the tasks which are perceived in addition to energy supply. Core systems mostly cover traditional gas and electricity customers. For instance, satellite systems capture pool customers, provided that the operation of public pools as a municipal task is the responsibility of the municipal utility. An utilitie’s responsibility may also include photovoltaics, wallboxes and e-mobility as an example. The expansion of the fiber-optic network is another recent development. These are all highly relevant issues involving large amounts of customer and business partner data. These data are constantly in motion, due to weddings, removals or deaths for example. New housing areas, street renamings and incorporations also play a role in this context. Archived data, which is sometimes no longer needed and stored improperly, is added to this. If this data were transferred to the new system without being checked, the migration would indeed be completed quickly, however the bad awakening would come later. After all, how can a billing system and its processes work reliably and efficiently if the data stock contains many duplicates, is incomplete, outdated and implausible? To top it all, some doubles are even desired because the address of the connection object and the billing address are not the same.
There is also the aspect that SAP is working with a new data model. According to this, there will be one central business partner in the future, and there will no longer be a distinction between customer and vendor. With verified data, the transformation to this new model can be done reliably.
Even the basics bring a noticeable added value
To do well in data quality, at least the basics should be considered. This includes the observation of a changed data model of the successor solution as well as the basic functions of address validation and identity resolution.
Address validation ensures postal data is up-to-date, complete and correct. With Identity, you can find and identify duplicates and multiple existing data records – you can continue to maintain purposeful doubles, such as those with different billing and shipping addresses.
This means your data is valid and largely free of overlaps. Postal-checked data which is free of overlaps is a prerequisite for transforming data into a new data model. The data also provide a good indication of the reliability of the dataset.
The sooner the better
To create the best conditions for a successful migration, you should deal with the quality of your data as early as possible. You lay the foundation for a successful project with data quality, which is an integral component before the migration. Asked differently: When building a house, would you first build the floors with the roof and then build the basement? Probably not – because this would be complex, expensive and time-consuming. It also delays the completition enormously. In the worst case, it could even prevent the completition as a whole. That means, applied to the migration project it is better the other way around: the data with the required quality needs to come first. The data from customers and business partners must first be qualitatively optimized so that the system works reliably from the start – then the migration will be successful and satisfactory.