Sailing Smooth Through Data Vortex:
Master Data Management
ALL YOU NEED TO KNOW ABOUT ERP MDM
What is MDM?
Why should you worry about MDM?
The pain that top companies are experiencing in consistent reporting, regulatory compliance etc once they have moved to new/upgraded ERP platform has prompted a great deal of interest in Master Data Management. But contrary to general belief, MDM is not a recent phenomenon. This has been a problem long in the making but its impact has been realized only a few years back. With ever evolving and increasing inventory, supplier, product and customer databases the need for MDM continues to grow and it is poised to help organizations meet the business challenges of the new decade.
This paper will provide you with an introduction to MDM what it is and why it is so important. It will also explore role of stakeholders and discuss best practices, ending with a brief about key masters and ancillary masters.
According to industry experts MDM is a set of disciplines, processes and technologies, for ensuring the accuracy, completeness, timeliness and consistency of multiple domains of enterprise data - across applications, systems and databases, and across multiple business processes, functional areas, organizations, geographies and channels.
An Elementary view is, MDM comprises a set of processes and tools that consistently defines and manages the non-transactional data entities of an organization. It is the core data that links together and integrates an organization's business processes, application and
information systems.
MDM is not just about technology and MDM program managers should use a business driven, holistic framework to ensure that all the component parts of MDM are being addressed. Some of many benefits of MDM are:
In a long run, any MDM cleansing activity improves the trustworthiness of the data, thereby increasing business people's confidence in using the data. As more unreflective copies of the same data instances are consolidated into a unique representation, thereby reducing the number of errors and rework, also it allows the resources to focus on productive
development.
1.Consolidated Data Informative Data
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2.Reducing the Need for Cross-System Harmony
3.Reducing Operational Complexity
4.Making life easy for ERP design and implementation
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5.Easing Integration
Accessing the organizational data sets and copying them locally to configure departmental reports is a common practice. What business clients don't realize is that this creates a situation where there are discrepancies that are related to the time that the data was copied or how it was manipulated locally. It's important to investigate the variations and pulling the source data from different origin leads to mad scramble to reconcile numbers, sums, accounts etc. Reconfiguring the report generation process to be driven off the master data asset reduces the need for cross-system reconciliation.
MDM addresses the issue of proliferation of application and corresponding internal
representation of each data entity by providing a master data object model that can be used for both information persistence and application communication. This problem is common to any organization which grows organically or through mergers-acquisitions. This issue must be rectified at the earliest to reduce the overhead and management complexity associated with the multitude of connectors to be put in place.
There are three ways an MDM initiative makes life a little easy for any ERP implementation activity:
A consolidated data repository captures the whole story associated with data element use. Instead of a glorified data dictionary, this repository integrates the semantic analysis associated with names, shapes, structures, formats, associated reference data, and, most important, definitions of data elements collected from across the organization.
It helps the application programmers to uniquely identify entities and reduces the effort wasted in sorting through the set of records which are often diverse and inconsistent. Standardizing the process reduces the need to design the process at the application level and instead allows the developer to reuse the capability engineered into the master data environment.
Ability to define and standardize many different kinds of master data services provides a means for unifying the enterprise application architecture, thereby freeing the
developers to focus on supporting the application business requirements.
Simplifying the core representative models and standardizing metadata and access services makes it easier for applications to talk to each other. Reducing complexity and harmonizing metadata and exchange interfaces will better enable applications to conform to an enterprise application architecture driven by business expectations instead of line-of-business functional needs.
Why Current Systems Fall Short:
What makes Data Consolidation activity worth the pain!
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1. Operational Efficiency as a Competitive Differentiator
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2. Bolster Governance, Risk Management, and Compliance
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4. Revolutionize decision making at enterprise level
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Organizations typically have complex data quality issues with master data, especially with material, customer and address data from legacy systems
There is often a high degree of overlap in master data, e.g. large organizations storing customer data across many systems in the enterprise
Organizations typically lack a Data Mastering Level which defines primary masters, secondary masters and slaves of master data and therefore makes integration of master data complex
It is often difficult to come to a common agreement on domain values that are stored across a number of systems, especially product data, material data.
Poor information governance (stewardship, ownership, policies) around master data leads to complexity across the organization
Optimize account coverage with multidomain MDM by identifying the gaps and conflicts that exist among and between channel partners and direct sales teams.
Streamline business processes such as order-to-cash and new product launches.
More easily comply with regulatory initiatives such as spend reporting with multidomain MDM by improving the tracking, transparency, and audit ability of financial data.
Improve risk management by aggregating customer risk exposure across product lines and business units.
Optimize capital reserves by aggregating counterparty risk across financial instruments.
Drive more confident decision-making across the global enterprise with multidomain MDM through increased accuracy, reliability, and timeliness of business-critical data. Boost the productivity of business analysts by empowering them to efficiently analyze business performance with highly accurate and easily accessible information about customers, products, partners, suppliers, and more.
Roadmap for a successful MDM implementation
Any MDM data cleansing initiative will be influenced by requirements, priorities, resource availability, time frame, and the size of the problem. Most initiatives include at least these phases:
This step is usually a very revealing exercise. Some companies find they have dozens of databases containing material, vendor data at different ERP instances that the IT department did not know existed.
Once we know what the source is, it's very important to identify the consumer application. This step might not be necessary if all changes are detected at lower level itself.
To make the data cleansing activity easy it's always advisable to have a metadata prepared for your master data, as we don't have to consult database tables and source code. This should include attribute name, data type, allowed values, constraints, default values, dependencies, and who owns the definition and maintenance of the data.
These are the people with the knowledge of the source data of that industry vertical and have the ability to transform source format into master-data format. Generally they are data stewards, business users, architects responsible for MDM systems.
Decide what the master records look like: what attributes are included, what size and data type they are, what values are allowed, and so forth. This step should also include the mapping between the master-data model and the current data sources. This is normally both the most important and most difficult step in the process. If you try to make
everybody happy by including all the source attributes in the master entity, you often end up with master data that is too complex and cumbersome to be useful. It's important to work out the decision process, priorities, and final decision maker in advance, to make sure things run smoothly.
1. Identification of the source.
2. Identify the producers and consumers of the master data.
3. Metadata to master data.
4. Involve people with domain knowledge.
6. Choose a best of breed service.
7. Come up with infrastructure blueprint.
8. Generate and map the master data.
9. Redesign the consumers and producer application.
10. Implement Data Governance Strategy.
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While undergoing the data cleansing activity, you will need to either select a vendor or do it in house.
You can use a single vendor for all of these functions, or you might want to take a best-of-breed approach. In general, the techniques to clean and merge data are different for different types of data, so there are not a lot of tools that span the whole range of master data.
Once you have clean, consistent master data, you will need to expose it to your applications and provide processes to manage and maintain it. When this infrastructure is implemented, you will have a number of applications that will depend on it being available, so reliability and scalability are important considerations to include in your design. In most cases, you will have to implement significant parts of the infrastructure yourself, because it will be designed to fit into your current infrastructure, platforms, and applications.
This step is where you test the process you have built in house or involved 3rd party vendor. This is often a ceaseless method requiring tinkering with rules and mapping to get the matching right. This step involves lot of manual inspection to ensure that results are correct, we can't expect 100% accuracy so must see if results are meet the requirement of the project. Also weigh the consequences of false matched verses missed matches to determine how to tweak the solution according to project's need.
As part of MDM strategy, all three pillars of data management need to be looked into: data origination, data management, and data consumption. It is not possible to have a robust enterprise-level MDM strategy if any one of these aspects is ignored.
The source systems generating new records should be changed to look up existing master record sets before creating new records or updating existing master records. This ensures that the quality of data being generated upstream is good, so that the MDM can function more efficiently and the application itself manages data quality. MDM should be leveraged not only as a system of record, but also as an application that promotes cleaner and more efficient handling of data across all applications in the enterprise.
As we stated earlier, any MDM implementation must incorporate tools, processes, and people to maintain the quality of the data.
All data must have a data steward who is responsible for ensuring the quality of the master data.
The steward might also want to review items that were added as new, because the match criteria were close but below the threshold.
It is important for the data steward to see the history of changes made to the data by the MDM systems, to isolate the source of errors and undo incorrect changes.
Maintenance also includes the processes to pull changes and additions into the MDM system, and to distribute the cleansed data to the required places.
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To whom it matters the most: The MDM Stakeholders
The MDM vision has to be all about how MDM can enable the organization's business vision, as opposed to an IT infrastructure project that the business doesn't buy into. Then the MDM strategy translates that vision into a reality, but although MDM is a simple concept the simplicity masks a huge amount of complexity.
Let us explore who the stakeholders are and what their expected participation should be over the course of program development.
Without any doubt, an executive buy-in of the senior management is necessary to execute any enterprise activity in an organization. At this level the managers are motivated by their own (and team's) performance and look to demonstrate how it they have contributed to organization's growth. Transitioning to a master data environment should enable more nimbleness and agility in both ensuring the predictable behavior of existing applications and systems and rapidly developing support for new business initiatives. This core message drives senior-level engagement.
Also, adopting a strategic view to oversee the long-term value of the transition and migration should trump short-term tactical business initiatives.
The business client may derive value from improvements in data quality as a by-product of data consolidation, and future application development will be made more efficient when facilitated through a service model that supports application integration with enterprise master data services. Supporting the business client implies a number of specific actions and responsibilities, two of which are:
Capture and document the business client's data expectations and application service-level expectations and assure the client that those expectations will be monitored and met.
Understand the global picture of master object use and assess which data objects are used by the business applications and how those objects are used.
1. Executive Management
2. Business Clients
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3. Application Owners
4. Information Architects
5. Data Governance and Data Quality
6. Metadata or Master Data Analysts
7. System Developers
8. Ancillary Data Owners Like Inventory Staff, CRM , Supply Chain, etc.
Application owners are practically gatekeepers to MDM success. Any application that use the data object to be consolidated within the MDM environment will need to be modified to adjust to the use of master data. Their major concern is ensuring the predictable
behavior of the business application and may see MDM as a big risk as it involves significant transition from an underlying data asset to a potentially unproven one. Thus its important they must be socialized with the master repository to ensure that operational requirements are documented and incorporated into the model design.
The models for master data objects must accommodate the current needs of the existing applications while supporting the requirements for future business changes. The
information architects must collaborate to address both aspects of application needs and fold those needs into the data requirements process for the underlying models and the representation framework that will be employed.
The data governance and data quality staff must introduce stewardship, ownership, and management policies to monitor how an individual create access and use, modify, and retire data. This suggests that MDM needs layers of governance, whether that means incorporating metadata analysis and registration, developing "rules of engagement" for collaboration, defining data quality expectations and rules, monitoring and managing quality of data and changes to master data.
In an MDM environment, metadata incorporate the consolidated view of the data elements and their corresponding definitions, formats, sizes, structures, data domains, patterns and they provide an excellent platform for metadata analysts to actualize the value proposed by a comprehensive enterprise metadata repository.
Aspects of performance and storage change as replicated data instances are absorbed into the master data system. Again, the determination of the underlying architecture approach will impact production systems as well as new development projects and will change the way that the application framework uses the underlying data asset. System analysts and developers will need to restructure their views of systemic needs as the ability to formulate system services grows at the core level, at a level targeted at the ways that conceptual data objects are used, and at the application interface level.
One big hurdle in implementing a successful MDM strategy is that more often business stakeholders and involved ancillary teams bypass the standard protocols for data access and modification. Instead of going through the preferred channel modifications and fixes are applied to data using direct access.
Alternatively, desktop applications are employed to supplement existing applications and as a way to gather the right amount of information to complete a business process. This can be taken care of by applying proper work flow model in data governance system.
MDM IMPLEMENTATION OPTIONS IN HOUSE VS THIRD PARTY
The final step in getting started with MDM is to decide on how to implement the MDM system for the master data entity or entities that need to be managed. There are two options for implementation build your own MDM process in house or go a third party solution that can be deployed to manage master data for your organization. A number of things may influence this choice but there are only guidelines to help in making this decision.
Broadly speaking it may be best to build your own MDM process when:
There is a data store already in existence within the enterprise with a high percentage of master data already integrated
Other systems within the organization are already using this system as a 'de-facto' MDM data hub
Data is published from this system to other operational and BI systems to keep them synchronized
Frankly speaking this is an ideal case and most of the time it's advisable to go for best of breed solution. As directive guidelines it may be best to go for a third party MDM service when:
Master data is heavily fractured across many different systems
Significant numbers of process defects and customer problems arising from missing, inconsistent and erroneous master data
Problems exist with multiple conflicting master data entry systems No work flow assigned
There are many other factors that will influence the choice to build or buy including cost to build versus cost to buy and skills needed versus skills available within the enterprise. Whether MDM is done inhouse or by a 3rd party, it is essential to understand that data management systems can be developed by anyone, but the management of data itself requires great expertise and knowhow about the organizational practices, industry domain and market intelligence to get the desired ROI from such initiatives.
It is always wise to understand the state your enterprise data is in, the value it carries to business stakeholders and what they need to contribute to organizational bottom line.
When buying an MDM solution, it is important to understand the business problem(s) that an MDM solution needs to address, and that an MDM solution is selected that matches what the
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Depending on the complexity of the masters, they could be classified as small masters & key masters.
Large enterprises have thousands of line items of material, vendor or customer master data. On a daily basis, large numbers of items are created in these masters and come from a variety of locations and systems. Hence, these masters are very dynamic in nature and reflect the core of an enterprise's business. During an ERP implementation, consolidation or
up-gradation, these masters form the bulk of master data migration and hence, require adequate and comprehensive planning to ensure the success of an ERP project.
Effective and efficient migration of key masters is vital to maintain information consistency and quality across the organization. Key masters are core to an organization but are dependent on external knowledge warehouses for enhanced master data visibility and successful business process implementations. Hence, management of key masters is a very challenging activity that must be adhered to and planned for at priority.
The Material Master is the repository of the data used for a material. The Material Master is more than a single file for each material, it is where all information on a material is entered and accessed from. It is used throughout the ERP system.
The synergies driving M&A activity often are dependent on consolidating operations and inventory as well as sharing and integrating designs and leveraging use of common parts. Realizing these synergies depends on the ability to merge item master data files and to accurately report on the status of these initiatives. Failure to gain a common view of the item master data of both companies not only diminishes the synergies and drags out the integration process, but also threatens the success of the merger or acquisition itself a business event typically far more expensive than the cost of the required data maintenance.
The complex world of master data problems and ways to manage them:
A. Material MDM
What makes it important?
1. Key Masters - Material, Vendor, Customer, Product etc.
1. Merger and Acquisition Activities
2. ERP System Consolidation
3. Inventory Visibility
4. Sourcing
5. Decreased Plant and Equipment Availability
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Increasingly more organizations are consolidating their ERP instances, targeting savings and efficiencies. Business drivers for these consolidations include SOX compliance pressures, the end of SAP,R/3 version support, system harmonization across business units or
geographies, and architectural upgrades that allow companies to leverage service-oriented architectures. However, attempting consolidation before the master data is rationalized will lead to a contaminated single instance. Cleansing the data once it lands in the new system is enormously expensive and time consuming.
Warehouse management systems, ERP systems, and third-party logistics service providers manage aspects of parts and finished goods inventories. This fragmented system landscape clouds inventory visibility and leads to over purchasing, stock outs, inventory write-offs, and disruptions of manufacturing operations. Bad material data is the key culprit behind issues such as cash locked up in excess inventory, lack of inventory visibility within and across plants, low employee productivity, false stock outs and increased plant downtime. Increased inventory cost is the single largest bottom-line effect of unstructured and inaccurate material master data.
Sourcing projects and the make-versus-buy decision process in general require a view of what exists already in the approved parts lists and approved vendor lists. Bad material master data can result in supplier proliferation, part proliferation, and a failure to leverage existing contracts.
Poorly-described material items, especially MRO items, often lead to incorrect and untimely part orders. Inefficient buying of critical supplies increases the cost of maintaining
equipment and frequently results in decreased plant and equipment availability.
Disparate, Dispersed & Distributed Sources
Across Multiple Locations Across Multiple Systems Across Multiple Business Units
Unclassified Item Data
Non uniform coding standards Incongruent commodity coding Poor Item Data Visibility
Incongruent naming conventions Poor descriptions
Inconsistent item Data descriptions
Lack of Ownership (and global view) for master data definition Lack of processes (and compliance) for Master Data Creation Inconsistent System defined formats from multiple systems High cost of Master Data Maintenance (Lack of software tools)
The way forward: ROI from Material MDM
With uniform and accurate information in your business applications, you will unlock the full potential of your EAM, ERP and MDM investment.
The cost benefits of clean MRO data in your business application are significant. With clean, structured MRO item descriptions, you can:
Identify duplicates and obsolete items Reduce inventory and procurement costs Reduce plant/equipment downtime Enable strategic procurement efforts
Inventory and stores savings Maintenance material costs are related to the frequency and size of the repairs made to the company's equipment. The total number of parts, in addition to the stores policies, purchasing policies and overall inventory management practices contribute to the overall maintenance materials costs. Since little attention is paid to maintenance materials in some companies, inventories may be higher than necessary by 20 to 30%.
Good inventory control enables companies to lower the value of the inventory and continue to maintain a service level of at least 95%. This enables the maintenance department to be responsive to the operations group, while increasing the maintenance department's own personal productivity. Successful computerized maintenance management system users have averaged 19% lower material costs and an overall 18% reduction in total inventory.
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B. Vendor Master Data management
What makes it important?
Roadblocks in a comfortable Vendor MDM Ride
Without question, it is an integral part of the procurement and accounts payable control environments. A well maintained vendor master file helps prevent failure of system controls, process inefficiencies and inaccurate management reporting. Failure of system controls can result in duplicate and erroneous payments, missed earned discounts, uncashed checks, unapplied credits, tax reporting errors and fraud.
The Vendor Master in ERP holds details about each vendor used by the customer. The Vendor Master has two distinct sections. These are discussed in some detail in the following
subsections.
The general data is, as the name suggests, general information about the vendor that can be entered into the system by the group identified to create vendor records. The basic date entered at this level includes name, search terms, address, telephone, and fax. After this data is entered, further information can be added to the Vendor Master record by Accounting and Purchasing.
The accounting data is the financial data that is entered at the company code level. This data includes tax information, bank details, reconciliation account, payment terms, payment methods, and dunning information. The transaction used in the purchasing data is entered for the vendor at a purchasing organizational level. The data entered is relevant for one
purchasing organization and may be different between purchasing organizations. The data entered includes control data required in purchasing, partner functions, purchasing default fields, and Invoice Verification indicators.
High volumes of data and multiple languages
Incomplete data (original vendor name, address, parent-child hierarchy, etc) Duplication (rationalization of vendor base by cutting down on multiple suppliers) Payment & taxation related concerns
General Data Accounting Data
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What makes it important?
Roadblocks in a comfortable Customer MDM Ride:
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The way forward: ROI from Vendor MDM
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Avoidance of duplicate vendor payments Improved purchasing/negotiation power Complied credit and discount globally
Reduced price discrepancies within the group Mitigate risk exposure to the supplier
Better pricing through consolidation of global vendors
Improved access to vendors give them a better rate realization
It ensures that all relevant departments within the organization have real-time access to the most current and complete view of customer information. Historically, the effective
implementation of customer data integration has been a complex challenge because customer data is spread across applications, resulting in inconsistencies and duplications among these competing sources of data.
To improve the operations and reporting of marketing, customer relationship and retention teams, moreover facilitating actionable information for the finance and compliance
departments - recognizing, resolving, and relating customer master data, which is shared across business processes and applications is very necessary.
Inability to make relevant cross-sell offers Customer defection due to poor service Inefficient and ineffective compliance process
Controlled marketing expenditures per customer Reduced credit losses
Higher A/R turnover rate
Revenue increase due to cross selling/up selling. Consolidated view of customers
The way forward: ROI from Vendor MDM
C. Customer master data management
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Better customer categorization
Roadblocks in a comfortable Product MDM Ride:
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The way forward: ROI from Product MDM
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It is focused on centrally managing information about products, with a focus on the data required to market and sell the products through one or more distribution channels. A central set of product data can be used to feed consistent, accurate and up-to-date information to multiple output media such as web sites, print catalogs, ERP systems, and electronic data feeds to trading partners.
The increasing number of channels for product data (e.g., web sites, print catalogs, electronic data feeds) emphasized the need for product data management, as information kept by businesses is frequently scattered throughout disparate departments and held by certain employees or systems instead of being available centrally. Product data often exists in ERP systems, R&D PLM systems, spread sheets and personal databases. Data are saved in various different formats or are only available in hardcopy form. Information is utilized in varying environments and contexts such as for detailed product descriptions with pricing info in product catalogs or for size and weight data for calculating freight costs in a logistics department. Product MDM in this example represents a solution for centralized, media-independent data maintenance for providing purchasing, production and communications data for repeated use on/in multiple IT systems, languages, output media and publications. It also provides a solution for efficient data collection, management, refinement and output.
wide array of products
frequently changing product characteristics
non-uniform IT infrastructure (potentially resulting from merger activity) successful online business
customer pressure to offer electronic ordering
Track and manage all changes to product related data Accelerate return on investment with easy setup; Spend less time organizing and tracking design data; Improve productivity through reuse of product design data;
D. Product Master Data Management
What makes it important?
2. Small Masters - Chart of Accounts Master, Currency Master, etc.
During data migration, an enterprise view of financial chart of accounts, cost centers and legal entities with a unified aim to govern on-going financial management and consolidation is an imminent activity. Chart of accounts, currency and other small masters should be migrated based on consistent definitions of financial and reporting structures across general ledger systems, financial consolidation, planning and budgeting systems to capitalize on the advanced business process improvisation features offered by the new ERP software. Although these masters are small in terms of enterprise data volume, their impact on the planned ROI from the ERP project is holistic and substantial in terms of projected enterprise needs and requirements from such an investment. Hence, adequate planning and expertise is required to facilitate such a migration.
To implement MDM effectively, it is vital to get off on the right foot. Before beginning an MDM initiative, take account of the nature of your business. We advise starting small, achieving short-term wins that will gain acceptance of and support for MDM, and then spreading the initiative across the enterprise.
Start with a subset of items or services for example, one parts family or commodity group and once the process and technology is proven, expand the number of item categories. Service companies may want to tackle customer master data first, since customer data often has structure and is well-understood. On the other hand, manufacturers probably should look at materials master data as a key part of manufacturing processes.
Next, build a business case that includes costs of errors. Our extensive benchmarking research on MDM shows that most organizations have not evaluated the financial consequences of operational errors, such as incorrect deliveries or invoicing mistakes. Such errors often can be linked directly to problems with product or item master data. This activity not only helps in constructing business case for MDM but also gives an idea about the real cost of inconsistent Master Data.
Creating accurate master data and managing it consistently are challenges that no enterprise can afford to ignore. When your business runs on information, it needs a solid foundation of data it can rely on.
Success in Implementing MDM
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About Verdantis
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Verdantis, Inc. is an independent company focused entirely on master data quality, master data
management and master data governance solutions for G1000 organizations.
Verdantis is the first to offer Master Data Management services and solutions that bring real ROI
and Business Value by focusing on the business use and application of organizational Master data.
Verdantis uniquely offers end-to-end automated ERP MDM solutions driven by our suite of
Artificial Intelligence (AI) based solutions and business roles and rules, easily configured to fit
enterprise requirements for classification, enrichment, screens, fields, security, attachments,
workflow approvals, languages and more.
Verdantis Harmony services prepare legacy data to become master data in its true sense
assuring a de-duplicated, consolidated, classified, validated and standardized data set in the
output formats needed for uploading into client's ERP and EAM systems and Verdantis Integrity
On-going MDM suite. Verdantis Integrity a bolt-on ERP suite of easy-to-use On-going Master Data
Governance repositories and processing solutions for on-boarding new enterprise asset master
information and maintaining current data for Items, Suppliers, Customers, Products and Financial
information. Leading global companies have chosen Verdantis solutions for the following
reasons:
End-to-end automated processes to harmonize & enrich historical
master data
Ability to ensure both semantic and structural ongoing data integrity and
quality
In-depth industry and data specific domain expertise with a robust
project methodology
Ability to handle huge volumes of cryptic and complex data in multiple
languages
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