Syniti

Syniti’s SKP can extract over a hundred different types of source systems, with an extensive range of connectors to assorted databases and file systems. It is important to understand that in a migration project it is not just a matter of a mass copy of raw data from one system to another. Legacy systems will have assorted data quality issues as well as duplicated records, and it is a major exercise to identify incomplete or missing data, and decide amongst candidate records which data is the most trustworthy – the rules for such decisions are known as “survivorship rules”. For example, some data may be more recently updated than others, some may be more complete, and some may be from highly trusted systems. Rules need to be established and refined to take into account which data records are to be migrated to the target systems. SKP has full data profiling and data quality capability, including a high-performance matching engine (an acquisition). This includes machine learning capability for more efficient merge/matching of data records. Master data that emerges from this process is stored with a master data hub, ready for load to the target systems. Although the target is often SAP, the SKP software is agnostic, and can be used for any data migration project. All metadata, rules, policies, and intellectual property developed during a project are saved in the Syniti Knowledge Platform (SKP) and are ready for re-use. Accelerate future data quality, master data management, data migration, and data governance projects using the intellectual property from your data migration.

The master data management component of the suite uses a SQL database to store master data records and is domain-agnostic, so it can handle data about customers and products but also master data about suppliers, locations and materials etc. There are examples of deployments with many tens of millions of master data records. It is likely that, in time, the core master data hub will be switched to a graph database, which has advantages over relational when it comes to quickly dealing with complex relationships between data. Data load routines are generated within SKP, and do not require additional programming, for example with SAP’s ABAP proprietary language.

SKP has prototyping capabilities and can be deployed in an agile manner, taking advantage of pre-built templates of rules and processes to accelerate migration projects. This enables iterative implementation, for example including simulated load routines to ensure that any data issues are resolved prior to data going live in production. At the time of writing Syniti’s software has been deployed at over 570 customers, including many major multi-nationals, which are the core of its Fortune 1,000 target market. These include companies like Levi’s, British American Tobacco, Zespri, Merck, Organon, Exxon, FrieslandCampina, and Carlsberg. All new customers are deployed in public cloud with transactional data staying in the VPC or hyperscaler of the customer’s choice.

Data migration is a fact of life for large companies and organisations whether that is related to mergers, acquisitions or divestitures or critical business transformations like the move to SAP S/4HANA. The business world is undergoing a secular movement of applications and data from an on-premise environment to cloud-based environments of various flavours, whether public cloud, private cloud or hybrid. This is in addition to migration required by mergers and acquisitions. Migrating major business critical systems and their data is a non-trivial exercise, and not just a “lift and shift” exercise. Data needs to be quality checked and consolidated, duplicate records removed and business rules set up and applied to ensure that only high quality, trusted data is loaded into the target environment. For that exercise to be successful a range of capabilities is needed, from basic extract of data through data profiling, validation, de-duplication, record merging and matching, data movement and scheduling. This process needs to be carefully managed, with extensive testing. Syniti’s data platform has proven capabilities across this range of tasks, including simulation of test data loading to ensure a smooth transition to a live state. If you are involved in such a project then you need to ensure that you have the whole life cycle of data migration covered.

With increased utilisation of generative AI, organisations need to have high quality data in order to build accurate AI models. High quality data foundation is essential for developing accurate generative models. Building high-quality data sets is a fundamental step to achieve accurate and trustworthy AI models. By following the data curation and cleansing steps provided by Syniti, data scientists can create better models that have real-world applications.

The Bottom Line – Go Data First

Syniti provides a data-first approach, which is crucial for achieving first-time success in any data migration or digital transformation project. Syniti is known for its extensive data migration capabilities, especially with SAP S/4HANA as the target. However, the Syniti Knowledge Platform is a fully fledged data management platform that handles access to source systems, data extract, data quality, master data management and extensive testing and simulation of data migration. It goes well beyond standard extract/transform/load capabilities, aiming to take existing data sources and deliver high quality, validated, business-ready data to a new platform. Although it has a strong heritage with SAP, the proven data migration capability is platform agnostic, and should be considered by any company planning a major data migration project. Syniti’s approach of addressing data early lays the foundation for predictable, on-time migrations and removes project risk related to data.