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By 2025, global data creation is projected to grow to more than 180 zettabytes. One zettabyte is equivalent to a trillion gigabytes. Swaths of this data will be held by organizations – yet poor data management will leave much of it outdated or unreliable.

To ensure accurate and consistent information across systems, applications and processes, organizations rely on master data – core data that is essential for business operations. It includes critical information about customers, products, suppliers, employees and other entities.

Successful strategies rely on accurate data

As the single source of truth, master data provides a reliable reference for informed decision-making, reporting and analysis. Outdated or incorrect data can lead to flawed decisions and operational inefficiencies. Without accurate customer data, for example, your targeted marketing campaigns, personalized customer experiences and sales strategies won’t be effective.

Your master-data framework should therefore eliminate data inconsistencies and errors arising from outdated or duplicated records. Duplication can arise from data-entry errors, system migrations or a lack of data governance.

If your organization comprises multiple systems, departments and offices or branches, maintaining data standards can be challenging. Issues such as inconsistent data formats, naming conventions and classification schemes can hinder your data integration and analysis.

Prioritize data quality and governance

Data quality-management through regular profiling, cleansing and enrichment strengthens the integrity of your master data. To achieve this, you need robust data-governance practices that define data ownership, establish quality standards and validation rules, and enforce data-management policies.

In a sprawling organization, integrating disparate systems and centralizing your master-data repositories will also help to eliminate data silos.

A master data management (MDM) solution provides a centralized platform for governing your data and making it easier to manage.

Managing data quality in SAP with AI and automation

When organizations migrate to SAP enterprise resource planning (ERP), their master data is usually in pristine condition because it’s just been cleaned and set up correctly. Then, disorder starts creeping in because employees with master-data access don’t fully understand the implications of changing certain settings, for instance.

This is a universal problem – especially in organizations that don’t have or don’t enforce master-data governance policies.

Sometimes, organizations will allocate data-management duties to whoever is using the master data – the finance team looks after the financial data, for instance. But this strategy can fail when some master-data sets relate or belong to multiple departments and no one takes responsibility for maintaining the data, leading to deteriorating data quality.

GLASSWING is an MDM solution from NTT DATA that uses AI to implement rule-based automation and better data governance. It cleans your data, including getting rid of duplication, and keeps it in sync across locations.

It also supports data-management workflows and delivers analytics that help you make informed decisions about your data management.

3 reasons to use GLASSWING

There are three major advantages of using GLASSWING:

  1. Simplification: We show users only the data fields they need to maintain. While the material master (for data about your products or materials) may have hundreds of fields, you’ll typically use only a subset of those – and even fewer fields will be relevant at departmental or user level.
  2. Governance: We assign ownership at data-field level to a person in your organization who has the expertise to maintain the data values. This includes control over workflow processes and approvals.
  3. Rule-based automation: Say your organization has 120 data fields to maintain. Based on rules that we set up after analyzing your data and processes, we can automate much of this maintenance. So, instead of users having to type something into 100 data fields, they can focus on a far smaller number while the rest are determined automatically.

Take the first step to clean data

Master data serves as the backbone of your organization. By recognizing its importance, you can make the most of your data assets and gain a competitive edge in an increasingly data-centric corporate landscape.

We can work with clients on SAP ERP Central Component (ECC) or SAP S4/HANA to implement GLASSWING, or to add GLASSWING back into their SAP environment after a move from SAP ECC to SAP S4/HANA. (Of course, GLASSWING will also be useful in cleaning up master data before such a migration.)

It’s never been easier to get clean and stay clean in SAP.

Read more about GLASSWING by NTT DATA to learn how we can transform your master data management.