Over the past decade, GlueData has actively collaborated with numerous organisations, focusing on maximising data value through the use of technology. Amidst the challenges faced by large SAP-utilising enterprises, one persisting challenge stands out: how to leverage technology to improve master data quality without inflating costs, all while aiming to achieve three vital goals:

  1. Attain scalability while improving master data quality.
  2. Adapt to changes and use technology to support these changes.
  3. Make informed business decisions based on trustworthy data.

According to a report by Forrester Celonis, 71% of organisations employ up to 10 different technologies to carry out a single process. The process of creating, maintaining, and distributing master data does not require much fewer technologies. Moreover, external parties also supply master data, thus influencing its overall quality. This underscores the complexity faced in managing master data effectively.

In the current business landscape, characterised by rapid innovation, technology disruption, and a volatile global economy, organisations face multifaceted challenges. To remain competitive, businesses must ensure the resilience of their value chain and underlying processes to unlock every potential value. However, efforts to optimise operations and related processes often do not entail rapid changes. One variable organisations can leverage to enhance any software equation is master data quality.

The rapid advancements in Artificial Intelligence, Machine Learning, and Cloud capabilities prompt business leaders to continuously reassess their IT strategies. Implementing these decisions in large organisations is often a slow and budget-restricted process. Yet, the competitive edge can be gained or lost by embracing speed and agility.

In an SAP article on industry ecosystems, Joseph Miles fittingly emphasised: “Organisations need to leverage an entire ecosystem to maintain the pace of innovation required to simply survive, let alone disrupt, in today’s digital economy”.

Poor data quality can directly hinder speed, agility, and scalability while diminished trust in reporting leads to compromised decision-making. Moreover, poor data quality can disrupt or delay business processes, and integrating new technologies or replacements necessitates the migration of legacy data, thereby reducing the ability to fully leverage technological investments.

How can organisations do more with less? 

Organisations must consider the following key objectives:

Goal 1: Achieving scalability while improving data quality

Identifying the need:

  • Focus on the current technological landscape, system performance, and integration to identify areas for optimisation.
  • Mitigate technology redundancy and poor system alignment.
  • Recognise that technology is a carrier of data, and integration always has room for improvement.

Addressing the challenge: Mitigate data noise by shifting the focus away from integration and consistent process replication challenges due to poor data quality and variable process execution.

The business can:

  • Standardise and simplify business processes while reassessing how master data is managed, focusing on data quality improvement.
  • Enhance key field data quality which significantly impacts both process efficiency and financial outcomes.
  • Ensure implicit responsibilities are made explicit and optimise the data steward’s time.

How:

  • Define clear stewardship responsibilities and field ownership, implementing measures for managing data quality.
  • Foster process consistency while documenting acceptable process variations.

Goal 2: Adapting to Change with the Support of Technology

Identifying the need: The necessity for a high degree of flexibility.

The challenge: the discrepancies between rapidly changing market conditions and slower-changing business processes and equipment.

The business can: Control business rules and roles swiftly in response to changing business demands while maintaining data quality investments.

How: Embed business rules, stewardship, screen control, role-based reporting, and cleaning capabilities.

Goal 3: Making Informed Business Decisions based on Trustworthy Data

Identifying the need: Establish a direct link between Master Data quality and each business process.

The challenge: Understanding the critical role of data quality in the chain of business decisions, analytics, transactional data, and master data.

The business can: Control Master data through various functions, such as embedded Point-of-Entry control and systematic data cleansing.

How: Improve Data Steward capabilities to elevate data quality, embedding this capability wherever feasible.

While many large organisations integrate a suite of applications to control and manage data quality, a single technology solution built around the data steward that effectively ensures clean and reliable data is a rare find.

In a landscape shaped by rapid technological advancements and dynamic market conditions, the convergence of technology and data quality stands as an imperative for businesses. GlueData’s journey illustrates the pressing challenge faced by enterprises aiming to move faster and achieve business goals without inflating costs. The trifecta of scalability, adaptability to change, and informed decision-making hinges on master data quality and leveraging the best suited technology to support this process.

If you want to discover how technology like SimpleData Management can help you reach your business goals in a simple and cost-effective way, let’s have a discussion to understand your SAP Master Data needs.

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