As SAP consultants know, managing master data is one of the biggest challenges facing companies who want to run their business efficiently. Poor data quality leads to manual and repetitive workloads, incorrect reporting, and frustrated users.
The lack of a clean core also adds another complex challenge for companies considering digital transformation projects. Despite this frustration most companies still rely on periodic, manual data cleansing and take a reactive approach to maintaining data integrity.
Master data management needs to become an integral, ongoing process in SAP systems and for SAP projects. As systems get more complex with each new release and integration, manual MDM is no longer a viable option. Because the world outside of an SAP system is constantly changing, data drift is a real problem: so addresses, prices, products, and other core data objects also change constantly. This leads to duplicates, inconsistencies, and gaps in data.
SAP has repeatedly returned to the issue of data consistency with every new product and release, with a strategic focus on facilitating end-to-end transparency, but this problem also requires a new mindset among consultants and business users to match the new data products that are becoming available.
Platforms like SAP Business Technology Platform provide some MDM optimization tools but these are not a complete solution. More recent products like SAP Datasphere and the new SAP HANA Vector Engine look far more promising, but the secret to maintaining clean and consistent data is by changing human behaviors around data management, as well as using the right data tools.
This week IgniteSAP takes a look at ways in which SAP data management is changing, and how proactive data management strategies can ensure that data is clean, consistent and supports the use of new technologies like AI.
The key is utilizing automation with workflows, AI, and configurable rules, driven by carefully defined data policies to embed data governance into everyday business processes. This allows you to identify where core data goes off track and address root causes, driving a cascade of beneficial effects through the business.
Data stewards across business units can take ownership of managing master data, with IT acting as an orchestrator. The most important aspect is to have clear guidelines for SAP implementation teams and data stewards with an eye on making data easily accessible to support business processes. The right MDM platform also ensures compliance, tracking, and auditability.
SAP consultants who specialize in MDM should develop expertise in continuous, automated approaches to MDM, but they also need to understand human behaviors around data.
When faced with SAP systems that are inconsistent, with many data silos of SAP and non-SAP data sources, SAP MDM consultants should ask clients tough questions about how they currently maintain data integrity and offer better solutions. Hiring managers should look for consultants who can bring fresh thinking and automation to tackle the “dirty data” issues that have plagued SAP projects for too long.
There is also a role for change managers to champion data integrity as a key part of SAP projects: to help business users to engage with tasks involved in cleaning up business data during the early stages of implementations, and also to adopt everyday behaviors which are consistent with clean data landscapes.
Master data may not be glamorous, but clean, consistent data is the foundation of SAP success.
Data quality tends to degrade in SAP systems over time, not least because the data is a record of constantly changing real-world events outside of the SAP system: it must be constantly updated, and the amount of data involved necessitates automation.
Here a few reasons why business data needs constant correction:
Manual processes: Much of the master data management in SAP still happens through manual entry, cleansing, and updating. This redundant work is error-prone and time-consuming. Employees simply can’t keep up with the volume of changes.
Multiple systems/silos: Many SAP customers have data scattered across various systems, both SAP and third-party. There is no central “source of truth” for key data objects, leading to copies that diverge.
Lack of governance: Without strict data governance policies and stewardship, errors and inconsistencies creep in. Employees may take shortcuts that undermine quality.
Integration complexity: With new SAP acquisitions and custom systems added to the landscape, integrating data gets harder. More points of integration introduce potential anomalies.
Reporting gaps: Data issues may go undetected until spotty reporting reveals anomalies. If master data is not monitored proactively, problems perpetuate.
Incremental change: Master data drifts slowly over time. A product price may change here, a customer address there. No single change seems significant until the cumulative impact is felt.
Volume and pace: As mentioned earlier, the speed and volume of transactions make it nearly impossible to stay on top of data manually, especially with more real-time integration.
These dynamics create a situation where master data quality is constantly decaying unless conscious steps are taken to stop the entropy. Periodic cleanup is not enough. The reality is that data management needs to become ingrained into standard business processes.
SAP’s releases over the last few years include capabilities that can help organizations better govern, monitor, and fix master data on an ongoing basis. While not silver bullets, these tools show SAP’s increased focus on MDM. More recently SAP has been taking steps to provide solutions which address the problem more holistically, and make the data available for use with AI and ML with SAP Datasphere and the SAP HANA Vector Engine:
SAP Master Data Governance enables centralized master data oversight, lifecycle management, governance rule definition, and workflow automation. Data stewards can ensure adherence to policies.
SAP Data Intelligence functions as a data orchestration hub for ETL (data Extraction-Transformation-Load), cleaning, enrichment, and syndication. It provides self-service data preparation using SAP Data Services to load relevant data to the SAP HANA database, allowing data to be read at the application layer.
SAP Master Data Integration facilitates consolidation of master data from disparate systems into an MDG-managed central repository. This helps eliminate silos and discrepancies.
SAP Information Steward uses AI to automatically find, diagnose, and resolve data quality issues. Business users get data monitoring insights.
SAP Data Warehouse Cloud and SAP HANA provide platforms for efficient master data storage/management with data modeling, transformation, and analytics capabilities from SAC.
SAP Datasphere is the next generation of SAP Data Warehouse Cloud. It combines data from almost any source in an open data ecosystem, allowing data users to access meaningful data in a business context.
These capabilities demonstrate SAP’s recognition that manual master data management will no longer cut it. Tighter integration between products will help close gaps that allow data issues to emerge. SAP still has work to do to make MDM seamless, but the building blocks are falling into place with the release of the most recent products.
Of course technology alone cannot fix poor data practices. Embedding continuous data governance through workflow automation, stewardship, and monitoring is critical. SAP consultants need to utilize these new tools to implement MDM as an automated, business-driven process.
Rather than bolting on master data management after the fact, smart SAP consultants and architects should build it into the fabric of their solutions from the outset. Some best practices include:
Designing workflows and processes that include data monitoring, issue escalation, and automated remediation where possible.
Assigning data stewards for each domain who are accountable for data quality. Business and SAP services partners should provide MDM education and support, and increase team awareness of data management best practices.
Leveraging a master data hub or repository to centralize authorized versions of core data objects.
Implementing data governance policies (e.g. mandatory attributes, valid formats, error tolerance). Enforce with automated validation.
Installing data quality KPI tracking and dashboards to expose issues early before they proliferate.
Building in master data reconciliation routines and exception handling for inbound data feeds.
Utilizing intelligent data profiling to detect abnormalities and identify root causes for rectification.
Promoting reuse of authorized master data across systems versus allowing copies.
The goal is to make master data management an integral part of day-to-day workflows instead of a separate IT workload. Delivering this requires SAP consultants to understand clients’ processes and data pain points and address them in creative ways.
Today’s complex SAP landscapes often include various bolt-on systems and external data feeds. Integrating data from these sources cleanly into core SAP master data is another common challenge, something which SAP Datasphere is intended to address.
SAP consultants should emphasize automating consolidation of disparate data through:
Data mapping of fields/objects across systems to allow matching, and resolve semantic differences.
Automated ETL routines to migrate and transform data into target formats.
Data validation checks and reconciliation mechanisms to catch discrepancies.
Exception workflows to handle non-standard cases needing special data handling.
Master data merge logic, such as automated duplicate checks, hierarchies, and survivorship rules.
Data enrichment from supplemental systems to augment core MDM.
By designing structured integration processes guided by business and IT policies rather than occasional manipulation, external data can flow into SAP master data smoothly. This eliminates human errors and many reconciliation headaches.
Beyond MDM-focused products, SAP continues innovating platforms that allow organizations to integrate, analyze, and extract value from their data. There is some overlap between products and more recent offerings are combining capabilities. For example:
SAP Data Warehouse Cloud offers a PaaS data warehouse with tools to ingest, transform, enrich, and analyze data at scale in the cloud. This facilitates building analytics apps on integrated, cleansed data.
SAP Analytics Cloud provides intuitive BI tools to business users needing insights from their data. It also enables data storytelling and sharing.
SAP HANA Cloud brings the power of the vendor’s in-memory database to the cloud for faster performance. It allows real-time analytics on transactional data.
SAP Data Intelligence leverages AI to handle data wrangling at scale, delivering insights to business users.
SAP’s recently announced the SAP HANA Vector Engine which aims to revolutionize data processing further by using “three-dimensional” data storage. It allows super-fast, real-time analytics directly on operational transactional data across multiple datastores without duplication. This could eliminate large batches of ETL since live data synthesis is handled in real-time.
SAP HANA Cloud’s new Vector Engine also gives generative AI the long-term memory it needs: providing the contextual understanding that AI models require to grasp business complexity and change over time. With this vector support, AI systems can now generate accurate, business-relevant responses to users’ natural language queries.
In addition, the Vector Engine allows developers to embed generative AI securely into intelligent data apps built on SAP HANA Cloud. Rather than relying on external AI services, organizations can leverage HANA’s vector capabilities to implement responsive, conversational AI natively. This close integration of vector-powered generative AI unlocks new possibilities for streamlining work and deriving insights.
The Vector Engine makes AI more accessible and impactful for enterprises. By introducing vector processing, SAP removes a key technical barrier to contextual conversational AI for business contexts.
While the Vector Engine will take time to mature, it exemplifies SAP’s drive to make data open, unified, and more consumable for business users. This requires clean integrated master and transactional data at the core.
Ensuring high quality master data should be priority one for any SAP implementation. Clean core data is the foundation from which useful insights and transactions grow. Messy data endangers all downstream efforts to digitally transform businesses using SAP solutions.
SAP is now demonstrating commitment through new products and services dedicated to optimizing the value that can be gained through holistic data management. With carefully data policies and planning, no organization has to remain mired in manual processes and bad data practices.
While solutions from SAP are becoming far more able to manage data for meaningful applications in business, SAP consultants and SAP business users need to adopt behaviors as part of their daily working practice that treat data with the respect it deserves as the fundamental aspect of a successful SAP system,
SAP consultants and those assembling SAP project teams should make master data integrity a rallying cry. Promoting solutions and data integrity initiatives that embed continuous data management into business workflows through automation and stewardship pay huge dividends in the long-term. Don’t allow poor data to compromise SAP projects. SAP consultants must encourage clients to invest upfront in master data excellence. Their long-term success depends on it.
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