Boost your data quality with Data Quality Service
Importance of data quality (Image Source: SAP)
This article is intended to provide an overview of the Data Quality Service (Service ID: 50109586) and how it can be used to improve data quality.
The goal of the blog post is to introduce the Data Quality Service, offered by the Data Management and Landscape Transformation (DMLT) team at SAP and its scope options. The post will provide a short introduction of the motivation for this service, followed by an example. Following this, the different scope options and the delivery approach with a typical project schedule will be introduced.
Motivation and Value
The most important IT asset of our customers today is data. They are the basis for efficient processes and analyses. Also, the driver for the Intelligent Enterprise is data; therefore, the data quality plays an important role. Wolfgang Albert Epting explaind this well in his blogpost: Week 2: Master Data – lifeblood of the Intelligent Enterprise.
But if the underlying data is wrong, the analytics will be wrong, and if the analytics are wrong, the decisions made based on those analytics are wrong. It doesn’t matter how pretty and modern the analytics dashboards look. Additionally, SAP customer systems have grown enormously in size and complexity over the years. This also has an impact on the state of the data. In conversations with our customers, we often hear that they are unable to complete their workflow without disruption across their line of businesses (LoBs) due to process interruptions and unreliable analysis results. These inefficiencies in the processes lead to higher transaction costs, data. The following graphic shows some examples about problems we hear from our customers.
Many of our customers know that there is something wrong with their data. For example, incorrect, outdated, incomplete and/or duplicate data. But very often they just do not address those data issues for multiple reasons.
Our Data Quality Service provide customers with a comprehensive view of the current state of their data and helps them find and fix the foundation of their problems. With our capabilities we want to help our customers to improve their data quality today and in the future.
What can be improved by this service?
Let’s see below how this service can improve the state of the data. This can be from non-standardized address data to bank data or to missing information on master data such as suppliers and materials. These can cause intelligent analysis to be useless because attributes on the objects are missing. This service improves the data in order to fix the above-mentioned shortcomings.
The possible improvements of this service are now considered in an example. First, let’s look at the state of the data beforehand.
State of data before the Data Quality Improvement Service
Initially, it is obvious that the field contents are not standardized or follow certain guidelines, for example, names, addresses, etc. are written in different ways, which makes it difficult to find duplicates in the system. With the help of the Data Quality Improvement Service, the field contents are standardized, and possible duplicates are identified.
State of data after field standardization
After running the tool, the customer receives a dashboard where they can review possible duplicates identified by the tool.
Possible duplicates presented in the tool
The customer can now decide what are real duplicates and what are not. Subsequently, these duplicates and the associated transactional data can be merged into golden records. This significantly improves the state of the data so that for example intelligent analysis delivers much better results.
State of data after the Data Quality Improvement Service
The Data Quality Service provide a rich out of the-box content, which leads to a short service ramp-up time. Once the customer has decided to use our service, we always start by jointly defining the scope and determining which scope options we can use to cover the requested scope. The initial scope is defined in cooperation with SAP consultants and the customers data management team and related business line representative(s). We cover master data, transactional data and open items.
Data Quality is a journey, but there is no clear starting point, because every customer begins from a different starting position. With our comprehensive end-to-end service portfolio, we flexibly adapt to the needs of the customer. We start by identifying the issues by completing a root cause analysis of the data through our data inspection to find and uncover the wrong data. Through this we provide a “Get Clean” action plan with recommendations and best practices, followed by the data quality improvement activities that form the basis of the “Get clean before stay clean” approach.
The Data Quality Service includes the following scope options (as of 17.04.2023).
Data Quality Service portfolio (Image Source: Own Image)
In the following this scope portfolio will be described in more detail. The process graphics serve for a better understanding. At this point, the process flows are not specifically discussed, in further blogposts the scope options will be considered separately.
1. Data Quality Assessment
Data Quality Assessment Process Flow (Image Source: Own Image)
Data Quality Assessment (DQ Assessment) starts with a comprehensive questionnaire based on SAP data quality best practices to evaluate and measure the quality of a customer’s data. The assessment acts as a root cause and fit-gap analysis. Moreover, the questionnaire also aims to understand customer business, scope, and pain points even better. As a result of the assessment, SAP provides customers with a comprehensive as-is report and action plan (recommendations and roadmap) to remediate issues short-term, mid-term, and long term.
2. Data Quality Inspection
Data Quality Inspection Process Flow (Image Source: Own Image)
The Data Quality Inspection (DQ Inspection) is a system-based data analysis to uncover data quality issues such as duplicates, data inconsistences as well as incomplete or wrong data by pre-defined rules (provided by SAP). This scope option uncovers low quality data as a first step towards cleaning up and improving data using our complementing service offerings.
The outcome of this scope option is a detailed report on current master data state along with a road map and strategy for improving master data quality with recommendations for the next steps. The final list can be used by the customer, for example, to identify and define the golden record based on the duplicates so that the duplicates can be merged afterwards.
3. Data Quality Improvement for S/4HANA Conversion
For the transition to S/4HANA, data must partly meet certain requirements. In addition, many customers use the opportunity to clean up the data in the transition project in order to start with a clean core on S/4HANA. This scope option eliminates data issues that can impact the transition to S/4HANA. In addition, deduplication can be used to prevent duplicates at customers and vendors from being passed on into the Business Partner. Through our data enrichment, cleansing and deduplication, we help our customers start cleanly on S/4HANA.
Data Quality Improvement for S/4HANA Conversion Process Flow (Image Source: Own Image)
4. Data Quality Improvement for S/4HANA Data Migration
During a classic (a.k.a greenfield) data migration our data enrichment, cleansing, and deduplication services allow our customers a start with clean data on S/4HANA. In the specific case of a migration, our activities can reduce and streamline data that is no longer used or needed in S/4HANA due to new processes. Data quality or completeness issues that may have an influence on the transition can also easily mitigated “on the fly”.
The process shown in “Data Quality Improvement for S/4HANA Conversion Process Flow (Image Source: Own Image)” is also valid here. In both cases, in the migration, data quality improvement will take place in the ECC in the preparation phase of the S/4HANA transition project.
5. Data Quality Improvement for S/4HANA Selective Data Transition
The Data Quality Service can also be used in the third transition approach, the Selective Data Transition. The advantage here is that no separate data quality project is required, as the data quality project becomes part of the Selective Data Transition project. Enrichment, deduplication, and cleansing can eliminate data issues that can impact the transition to S/4HANA and further enable a clean start on S/4HANA.
Data Quality Improvement for S/4HANA Selective Data Transition Process Flow (Image Source: Own Image)
6. Data Quality Improvement in Source
The Data Quality Service can also be used before or after a transition project to optimize data quality. Due to cleansed and enriched data, customers have optimized business processes and no more incorrect reporting caused by incomplete and/or wrong data.
Data Quality Improvement in Source Process Flow (Image Source: Own Image)
7. Data Quality Improvement for SAP Master Data Governance Implementation
The focus of the Data Quality Service is on the get clean before you stay clean approach and can therefore be optimally integrated into an SAP Master Data Governance implementation. The services improve and enrich master data before it is used in SAP Master Data Governance in order to ensure data quality in the long term.
Data Quality Improvement for SAP Master Data Governance Implementation Process Flow (Image Source: Own Image)
Which tools are being used?
The Data Quality Service are pure “as-a-service” offerings. They do not require customers to purchase additional licenses or major infrastructure investment. Service uses a set of expert tools developed by SAP for this specific purpose. Those tools will be deployed via a combination of SAP BTP cloud and on-premises components. On premise components are delivered via transport requests and SAP notes. As all tools are included in the SAP service fee, they do not require separate product licensing, which strongly distinguishes the Data Quality Service from other service offerings.
Typical Project Schedule
The Services are following an agile approach with a flexible adaption to customer needs. The actual project duration is highly dependent on the selected scope option as well as on the customer and its speed in processing activities.
|Data Quality Assessment||2-4 weeks|
|Data Quality Inspection||4-8 weeks|
|Data Quality Improvement for S/4HANA Conversion||Depending on number of systems and master data domains in scope|
|Data Quality Improvement for S/4HANA Data Migration|
|Data Quality Improvement for S/4HANA Selective Data Transition|
|Data Quality Improvement in Source|
|Data Quality Improvement for Master Data Governance Implementation|
Duration per scope option
The service delivery will follow the applicable parts of the SAP Activate methodology which has the following phases:
- Prepare: The Services are formally initiated and the schedule, project plans and resources are agreed. The Project System Landscape is created.
- Explore: System-based analyses and workshops are conducted to define the (transformation) requirements and create the (transformation) specification. All areas that are impacted by the transformation have to be identified.
- Realize: The transformation solution is being implemented. Several test cycles are being performed in order to verify and fine-tune the transformation approach.
- Deploy: A cutover plan is being created and final preparations are made before the cutover to Production Environment. Ongoing support is put in place by the Customer and the Services are closed.
The typical process flow looks as follows.
Typical project plan, main service phases (Image Source: Own Image)
Thank you for reading! I hope you find this post helpful.
As mentioned earlier, this is the beginning of a series of blogposts about the Data Quality Service. In the near future, more blogposts will be published introducing the scope options in more detail.
The service is constantly evolving so that custom solutions can be developed that are not explicitly mentioned in the above scope options, but still fit the context of this service.
For any questions or feedback just leave a comment below this post. If you need more information about this blog or the service, please send an email to: mailto:email@example.com
Find more information and customer success stories on the website of Data Management and Landscape Transformation Services.
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