ERP data management solutions are business-led tools that create a single master record of all business processes and operations, including people and systems from various internal and external applications and sources.

The master data serves as a reliable and accurate source of business-critical information that can be managed and shared across the enterprise to make robust decisions. It also helps reduce errors, eliminate redundancy and facilitate reporting capabilities.

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Most organizations employ different systems that contain information on customers, products, sales, transactions and more, leading to data silos and an incomplete, disjointed view of critical information.

Since the data is stored in many different locations, it is hard to answer important questions like ‘Which products do our customers use the most?’ or ‘Who is the most profitable customer?’ So, it is imperative to employ a system to ensure consistent data visibility and enforce best practices to maintain integrity and quality.

This Guide Covers:

• Primary Benefits
• Key Features
• Trends

Primary Benefits

Data is a significant asset for a business. Increasing volumes of data make it difficult for organizations to manage it efficiently. ERP master data management has become a primary focus for most organizations to optimize their data usage.

Master data is a powerhouse of information that various departments across the organization use. Master data management involves:

• Data collection from multiple sources
• Application of business rules
• Compliance
• Validation
• Reconciliation

The benefits of ERP data management are as follows:

Boost Revenue Growth

If you want to provide customized cross-sell and up-sell offers, it is vital to access reliable, accurate and complete information across all customer touchpoints. ERP data management serves as a central repository for enterprise data, including customers, products, sales and relationships between entities.

It ensures the right cross-sell and up-sell opportunities or offers get to the customers at the right time through the right channels to boost revenue. A better understanding of customer demographics, preferences and characteristics makes it possible to provide a seamless customer experience and ensure business growth.

Improve Productivity

Drive operational efficiency and eliminate IT overheads and costs by providing a consistent and accurate data view across various departments. Improve visibility and control over business operations by managing relationships across products, customers, vendors and location entities.

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Supply Chain Optimization

Offer a centralized view of products, customers and sales-related data, thus providing accurate information on inventory, returns and out-of-stock items across the supply chain. In turn, it helps improve inventory management, develop accurate forecasts and provide impeccable customer service. It ensures accurate information is provided to the stakeholders to make better decisions.

Enhance Customer Experiences

Enhance customer experiences, gain loyalty, boost sales by personalizing interactions, provide consistent services across different sales channels or digital touchpoints and tailor products and services to meet customer needs and expectations.

Manage Supplier Relationships

Manage supplier lifecycles, collaboration and performance with role-based workflows and dashboards. Building robust supplier relationships helps you negotiate better pricing and payment terms. Speed up supplier onboarding to save time and effort.

Key Features

Let’s look at some data management capabilities:

Data Visualization

The data visualization component within the solution empowers users to create dynamic dashboards for robust data analysis. It also enables users to identify and fix quality issues, facilitate collaboration to make amendments and monitor processes.

Create intuitive dashboards to analyze data.

Master Data Matching and Linking

In addition to pulling data from diverse sources, ERP data management systems should possess matching and linking functionality to identify duplicates and combine multiple data entries into a single robust record.

Match and link data values from disparate sources.

For most organizations, data accumulates from different systems and applications. For others, mergers and acquisitions lead to silos, making duplicate records and variations possible. The matching and linking capabilities leverage algorithms to recognize duplicate entries. Data duplication imposes risks while providing customer service or results in skewed analysis.

Transforming data from different sources into a single unified record is a critical part of data management solutions. Data matching and linking ensure duplicate data elimination, feed accurate information into all systems, monitor integrity of sources and automate resource-intensive tasks.

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Set Specific Business Rules

The ability to set specific business rules is the primary feature of ERP data management solutions. They should be shareable across different business use cases, including data imports, to ensure process uniformity.

Create robust business rules to manage enterprise data.

Create, apply and manage enterprise-wide data governance policies with the help of business rules.

Data Localization

Customer information must be retained within the borders of a particular region. Data localization refers to information stored on a physical device present within the borders of the country where it was generated.

Localize data to protect it from malicious access.

This tactic is encouraged to protect data from unauthorized access. The GDPR states that data can only be transferred to countries outside the EU when an adequate protection level is established.

Due to high compliance costs, most organizations ensure their customer data remains within the country of origin. A data management solution enables you to standardize data locations with integration and quality tools.

Data Enrichment

Data enrichment refers to the use of tools and processes to improve the quality of data from different inbound sources. It helps achieve a 360-degree view of information.

Improve data quality to derive valuable insights.

Gain a complete and accurate look at your customers by cleansing and streamlining information. The cleansed data can be combined with external sources to derive useful insights.

With data enrichment, you can access customer data and profiles to gain insight into their preferences and personas to make proactive decisions. It facilitates better risk management with higher confidence in business data.

The enriched information can be used to analyze trends and patterns, mitigate risk exposure and tailor products according to customer preferences.

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Notable Trends

Data management is an integral component of mid-size organizations. Soon, organizations will make investments in scalable platforms to secure, manage and analyze data across business units through a centralized platform. It will help organizations gain valuable insights and make better decisions. Leveraging advanced technologies like machine learning and artificial intelligence will support dealing with disparities across different environments.

Let’s dig deeper into the trends that are likely to materialize in the future:

Adoption of Hybrid and Multi-Cloud Strategies

The rise of hybrid and multi-cloud architecture and advancements in AI and ML technologies is leading organizations to move their workloads to the cloud. Many organizations prefer multiple cloud environments over a single cloud provider as storing information on multiple cloud platforms guarantees security and integrated ML capabilities.

The data and applications that need the most security can be kept in a private cloud, while less important items can run on cost-effective public cloud networks. This type of infrastructure proves to be successful for organizations as it offers multiple cloud options and saves money.

The major challenge in adopting a multi-cloud cloud strategy is managing data across multiple locations and systems. In a hybrid cloud scenario, most organizations use a combination of multi-cloud and on-premise deployment. To overcome the challenges of managing data across multiple platforms, the organizations will choose to adopt an end-to-end management platform to gain visibility and control across cloud and on-premise environments while ensuring security and governance.

A modern data management platform as defined by the IT giant IBM as something that should ensure complete accessibility regardless of the source and format, support various deployment options, democratize data access and leverage advanced analytics with ML capabilities.

Data Fabric Technology

Data no longer exists in a single environment; it disperses across multiple clouds and on-premises environments, which indicates businesses are adopting the hybrid cloud strategy at a rapid pace. With exponential growth in data formats, sources and deployment methods, it is imperative to look for ways to better manage and optimize it.

Data fabric technology can be defined as a weave that can put together or connect multiple types, locations and sources of data with robust methods to access it. It is designed to manage disparities across on-premise and cloud environments through a centralized platform.

The main focus of this technology is efficient management across different platforms. Create data environments that provide centralized access to enterprise-wide data residing in different clouds or on-premise platforms while inheriting access and governance restrictions regardless of format.

Empower users to streamline ETL processes by connecting to different sources and eliminating the need for copy-pasting from various applications with an interconnected architecture.

Data fabric technology allows organizations to harness the capabilities of hybrid clouds, provide seamless hybrid multi-cloud experiences with frictionless data consumption and modernize storage through advanced management capabilities.

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Augmented Data Management

Data analysts and scientists spend the majority of their time accessing, preparing, cleaning and managing data. Augmented analytics and data management utilizes AI and ML capabilities to automate manual management tasks. It helps businesses simplify, automate and optimize operations related to data quality, master data management, metadata management and more.

It leverages machine learning and natural language processing to provide smart recommendations to professionals to automate manual tasks and apply business rules, policies, classifications and tags to data catalogs. It can also help them deploy artificial intelligence models for specific tasks to increase productivity and efficiency while gaining a competitive advantage.

Knowledge Graphs

A knowledge graph is a graphical representation of real-world entities, including events, objects, situations and concepts. It also defines relationships between these variables. This information is stored in a graph database and visualized as a graph.

A knowledge graph is made up of three critical components such as nodes, edges and labels. An object, place, person or organization represents a node, and an edge defines a relationship between them.

Knowledge graphs help organizations understand their customers, product lines and business decisions. They derive useful insights from underlying data and let data scientists uncover and analyze complex heterogeneous relationships between datasets.

Knowledge graphs are particularly useful in finance, entertainment, retail, finance and healthcare industries.

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Conclusion

ERP data management is critical to overcoming challenges like redundant information and discrepancies in master data. These solutions manage data irrespective of its source and streamlines business processes.

Organizations are adopting multi-cloud and hybrid strategies to move their information into the cloud instead of managing it on-premises. Businesses will build scalable platforms leveraging AI and ML technologies to connect and manage data regardless of its source and formats.

Do you foresee any other upcoming trends in ERP data management? Let us know in the comments below.