More than 2.5 quintillion bytes of data is generated worldwide every day and that number is set to grow rapidly, according to business intelligence provider Domo. The impending increase in data traffic will come from mobile and cloud computing sources, and from developments in AI, IOT and machine learning. Insightful business decisions derived from big data can provide organisations with a 23x uplift in customer acquisition compared to those contemporaries who do not use complex data sets.
In fact, companies making insight-led business decisions from intelligent data are predicted to take $1.8 trillion annually from competitors who lack data-driven capabilities. It’s not surprising that businesses were estimated to spend$187 billionon big data and analytics in 2019 alone, finds Leftronic.
Of course, data comes in many forms and from many sources, how you use it as an organisation depends on your department and your business goals. What is certain is that the movement toward SOA and SaaS is making master data management (MDM) a critical issue. It’s also irrefutable that the data must be clean and correct from the outset in order to perform relevant and purposeful analysis. So, it’s imperative that firms build strong master data management practices into their data strategies to move forward with data analysis primed for meaningful results.
Alongside customer master data, supplier master data is one of the biggest areas where procurement departments will find the most opportunity for insight-driven strategy and decision making. But there are challenges associated with how master data can be managed for it to accurately feed the data analysis machine.
Part of the problem
Many firms are moving onto their next stage of digital transformation, but are grappling with supplier data buried in multiple system infrastructures. HR, marketing, logistics, finance, IT -- they all may be using their own versions of supplier data and maintaining individual records. In effect, they are all keeping their own version of the truth and using a narrow, departmental view as the basis for decisions.
The anomalies can stack up, especially if this scenario spans different locations, countries or entire regions. Errors and gaps in information are often duplicated across organisations which is further exacerbated when multiple ERP systems exist.
What should be a shared or common asset within an entire organisation is instead a source of inaccurate or obscured output. Master data ought to be accurate and provide useful results in spend analytics, sourcing optimisation and centralised contract management.
The fact is you can make use of the most recent AI or algorithm-based developments, but you will not reap the true benefits they can offer unless the core data on which they depend is complete, correct and rationalised. If the judgements you make as a business are based on inaccurate, out-of-date or false information, then you are creating more risk in your decisions by not having the full picture of the truth.
Risk is what you get when you make the wrong business decisions. So how do you make the right ones? How do we trust our core data so that we can take it into a meaningful business context?
Trusting your master data means moving beyond ERP …
Data has long been the bane of procurement organisations. Where do you keep your master data? Who is responsible for it? Which master data can you trust? How do you know if your forecasts, what-if scenarios, supplier measurements and spend analyses are correct when you submit them to your board?
Many organisations have struggled to get full value out of their spend analytics or optimisation investments because of lack of high-quality, granular data. Firms have embraced 25 years of ERP systems to help them manage their internal operations, but now our third-party relationships, including supplier management and contract management, need nurturing.
If we are to fulfil today’s demands for CSR, sustainability and social impact while building balanced and trusted relationships with our supplier ecosystems, we need systems that are fit-for-purpose. But in today’s landscape of often globally segregated ERP systems, bolt-on tools, hybrid collections of solutions, there is no one full picture or validation of data. So, for trustworthy supplier master data, we are starting to look beyond our ERP system towards AI-powered solutions that are more fully equipped and better designed to do that job.
... to a single source of truth
Tools that help us to master MDM are becoming more sophisticated, faster and rich in capabilities. There are now unified platforms using algorithms and machine learning to normalise the data through cleaning and standardisation, and which also leverage matching, merging and consolidating all the data from all sources. The most important feature of a good MDM system is having a high accuracy of matching data. It should be able to put all data into a common format, replace any missing values, standardise those values and remove duplicates. It should then be able to distil one valid record from the multiplicity of records which can then be pushed back into the ERP system.
The best solutions also cope with scalability. MDM doesn’t stop at creating a master data list. It is imperative that it also continue to nurture that list and scale with it appropriately as data sets grow. Investing time and money in clean, consistent master data will soon be redundant if it is not kept clean and consistent as it gets updated and expands over time.
More and more suites are offering MDM functionality because software makers realise the challenges of the buyer. But the depth and breadth of these offerings are not on an equal playing field. Those that are based on a bespoke system with home-grown capabilities, historical knowledge and experience as opposed to being acquired or bolted-on have developed organically to address today’s challenges.
One trusted and long-standing provider, GEP, acknowledges that the best solution is one that has never changed platforms, retains a stable base and a strong data model. As Rese Cleaver, Rese has a accountSenior Manager, PMG at GEP Worldwide, says:
“With the growing volume of data that organisations are dealing with, it just isn’t feasibly possible to continue to rely on the workforce to wrangle and reconcile it all. To get a valid source of truth, this has to be delegated to an AI-enabled system with proven history of reliable data models that produce demonstrative results.”
Conclusion
In a world of hybrid ecosystems including ERP, P2P and CRM, what data can you trust? There simply cannot be multiple master data files. And today, how do we make sure that whichever master data model we use is scalable for the future as volumes of transactional data expand? These issues have been weighing procurement down for a long time. Clean and accurate master data is the key to providing trusted information on a global scale. Procurement can enable those daily micro decisions that inform the larger macro business picture on which corporate decisions are made.
From negotiating to contracting, from onboarding to renewing and updating, the entire supplier-related process is carried out with little regard for what is already in the ERP system. Who else is managing this if not procurement? The onus is on them to make that data a safe source, so that whether you are the CFO, CMO or CIO, it is usable and trustworthy. It’s a growing challenge requiring a holistic MDM strategy, the tools that can support it and a procurement team that can make it happen.
Disclaimer: this Brand Studio article was written in conjunction with GEP
FAQs
Why do master data projects fail? ›
In fact many of the problems seem to be tied to the typical reasons any IT project can fail: Underestimating the work. Not enough resources. Trying to do too much at once (including scope creep)
How can master data management be improved? ›- Gathering as much information as possible. ...
- Checking the quality of information. ...
- Creating a common metadata layer. ...
- Organizing data. ...
- Improving access to data. ...
- Improving cybersecurity. ...
- Raising employee awareness. ...
- Providing adequate training.
Master data management (MDM) is a technology-enabled discipline in which business and IT work together to ensure the uniformity, accuracy, stewardship, semantic consistency and accountability of the enterprise's official shared master data assets.
What are master data management best practices? ›- #1 Always include more master data or multi-domain.
- #2 Make data governance an integral part.
- #3 Build your MDM to drive business goals.
- #4 Organize master data for simplicity and scalability.
- #5 Make master data your data foundation.
- #6 IT is no more the Data custodians.
Master data management (MDM) is the practice of identifying, cleaning, storing, and – most importantly – governing this data. MDM best practices vary, from merging all master data into a single location to managing data assets for easy access from a single service or application.
Why do data governance programs fail? ›Lack of support from management. Lack of communication and change management. Unclear goals. Focus on tools, not processes.
Why MDM is needed? ›Master data management represents the perfect single source of truth to support business processes. Since many master data systems offer easy-to-use (mobile) applications, employees can access the latest and high-quality master data whenever needed to support their operations.
How can you reduce the maintenance effort for customer master data? ›Significant reduction in maintenance effort. Quick deployment for new applications. Centrally managed distribution of data ensuring consistency.
Is MDM a good career? ›Not only is this an exciting opportunity, but it is also a career choice that offers long-term stability.
What are the main roles and responsibilities of master data team? ›Assists in the application and implementation procedures of data standards and guidelines on data ownership, coding structures, and data replication to ensure access to and integrity of data sets. Conducts data cleaning to rid the system of old, unused data, or duplicate data for better management and quicker access.
Who is responsible for master data? ›
When organizations implement an MDM process, they generally appoint three people to oversee it. Data owner: The data owner has the ultimate responsibility for master data. They oversee the business rules and ensure that everyone is following them.
What are master data principles? ›MDM principles
Once established, MDM ensures the consistency and quality of a company's data assets, including product data, asset data, and customer data, by making this data available to end users and other applications.
- Identify sources of master data. ...
- Identify the producers and consumers of the master data. ...
- Collect and analyze metadata for your master data. ...
- Appoint data stewards. ...
- Implement a data governance program and data governance council. ...
- Develop the master data model. ...
- Choose a toolset.
Yes, MDM is more than data governance and data governance is more than MDM. But the MDM program should be considered as an implementation of both practices. And as always, stay calm and allow your data governance program to prosper.
What is Master Data Management example? ›Customer information—such as names, phone numbers, and addresses—is an excellent example of master data. This data is less volatile but occasionally needs to be updated when a customer moves or changes their name.
Can MDM see browsing history? ›Can MDM see browser history? MDM can't see your browser history. Like we mentioned earlier, MDM is basically management software. Your organization can install additional invasive tools but can't monitor your Chrome or Safari history using a tool like Jamf.
What are the five core functions of Master Data Management? ›Master Data Management Essentials
Cleansing and Correction of Erroneous Data. Data Quality Monitoring and Reporting. Business Taxonomy and Hierarchy Management. Concept Standardization (e.g. Address)
Also found under Settings -> General -> Device Management. Android tells you exactly what information MDM collects from your phone and exactly what restrictions have been placed on it.
What are the four phases in data quality? ›Let me explain further: The Informatica Cloud Data Quality Methodology consists of four key stages: Discover, Define Rules, Apply Rules, and Monitor.
Why do some quality initiatives fail? ›CONCLUSIONS. Many quality improvement interventions fail because of breakdowns in the implementation process. Surgeons play key leadership roles in driving quality initiatives in their respective operating rooms and organizations.
How can data governance be successful? ›
The three critical aspects of building an effective data governance strategy are the people, processes, and technology. With an effective strategy, not only can you ensure that your organization remains compliant, but you can also add value to your overall business strategy.
What is master data in SAP in simple words? ›Master data management is the process of creating and maintaining a single master record – or single source of truth – for each person, place, and thing in a business.
How does MDM prevent cyber attacks? ›Implementing a Multi-Factor Authentication can prevent a majority of hacking breaches. Most MDM solutions also offer a device encryption feature that further decreases the chances of cyber-attacks. If the managed device were to be stolen, you can track down its location or in the worst-case scenario wipe the device.
What are the components of customer master data? ›Customer master records consist of Company Code Data, General Data, and Sales Area Data.
What can the customer master vary by? ›The data for one customer can differ for each sales area. This data is only relevant to Sales and Distribution. If you edit a Customer Master record, you must enter the Customer number and the Sales Area to access screens containing Sales and Distribution data.
Why is price list essential in setting up the customer master data? ›You can use price list types to apply conditions during pricing or to generate statistics. In the customer master record, enter one of the values predefined for your system. The system proposes the value automatically during sales order processing. You can change the value manually in the sales document header.
What are the 5 types of master data? ›The most commonly found categories of master data are parties (individuals and organisations, and their roles, such as customers, suppliers, employees), products, financial structures (such as ledgers and cost centers) and locational concepts.
Where is master data stored? ›Master data can be stored using a central repository, sourced from single or multiple systems, or referenced centrally through an index. However, when it is being used by several groups, master data can be distributed and stored redundantly in a variety of applications across an organization.
What is the difference between transaction data and master data? ›Transactional data relates to the transactions of the organization and includes data that is captured, for example, when a product is sold or purchased. Master data is referred to in different transactions, and examples are customer, product, or supplier data.
Is MDM easy to learn? ›Easy and quick to learn – When compared to other tools in the market in the same domain, Informatica MDM is relatively easy and quick to learn.
Is SAP MDG in demand? ›
While it is a well-known fact that SAP professionals are always in high demand irrespective of the industry and economic situation, the employment opportunities for SAP MDG practitioners are huge with organizations vying for the best talent in the industry, offering the highest salaries possible in the domain.
Is SAP MDG is good module? ›SAP MDG is ideally suited for the SAP ERP system of SAP Business Suite. This combination boosts maintenance strategies, significantly improves the quality of basic data and ensures compliance with legal requirements.
What does an MDM administrator do? ›MDM Administrator – These are the experts at configuring the MDM platform itself, from data modeling, business rules, to the front-end user experience. At larger organizations, these resources are often dedicated to MDM on a full-time basis.
What does master data analyst do? ›A master data analyst collects, processes, and performs statistical analyses on a large dataset. They discover how data can be used to answer questions and solve problems. With the development of computers and an ever-increasing move toward technological intertwinement, data analysis has evolved.
What is the role of data manager? ›A data manager is responsible for developing, overseeing, organizing, storing, and analyzing data and data systems. A data manager ensures that all of this is always done with the utmost security and confidentiality, and in a timely manner.
Is MDM a data warehouse? ›Master Data Management is only applied to entities and not transactional data, while a data warehouse includes data that are both transactional and non-transactional in nature. The easiest way to think about this is that MDM only affects data that exists in dimensional tables and not in Fact Table.
How can Master Data Management be improved? ›- Gathering as much information as possible. ...
- Checking the quality of information. ...
- Creating a common metadata layer. ...
- Organizing data. ...
- Improving access to data. ...
- Improving cybersecurity. ...
- Raising employee awareness. ...
- Providing adequate training.
The IT department is typically responsible for implementing a data management system. This is usually overseen by a CDO or the lead on the project. However, a company may also choose to outsource the data management implementation process.
What is the difference between MDM and MDG? ›MDM offers robust data integration and distribution capabilities across SAP and non-SAP platforms. MDG is an add-on component of SAP and it can share the same SAP-ERP server providing centralized data. It has limited integration capabilities with non-SAP systems.
What are master data management best practices? ›- #1 Always include more master data or multi-domain.
- #2 Make data governance an integral part.
- #3 Build your MDM to drive business goals.
- #4 Organize master data for simplicity and scalability.
- #5 Make master data your data foundation.
- #6 IT is no more the Data custodians.
What are MDM implementation styles? ›
There are four master data management (MDM) implementation styles, and their different characteristics suit different organizational needs. These include consolidation, registry, centralized and, ultimately, coexistence.
What is MDM in ETL? ›Master Data Management (MDM) and Data Integration or Extract, Transform, Load tools (DI or ETL) are all part of a complete Enterprise Information Management (EIM) architecture. But there's some level of confusion or disagreement about how exactly they relate to one another.
What are the challenges of mobile device management? ›- Managing Heterogeneous Environments. The era of Windows-dominant work environments is over. ...
- Security and Compliance. ...
- Mobile Application Management. ...
- Network Access Control. ...
- User Experience and Preferences. ...
- Bring Your Own Device (BYOD) ...
- Lost or Stolen Devices.
- Guaranteed data quality:
- Eradicates slow business process:
- Promotes business agility:
- Avoids duplication and increases data accuracy:
- Reduces security risk and ensures better data compliance:
Master Data Management (MDM) is the technology, tools and processes that ensure master data is coordinated across the enterprise. MDM provides a unified master data service that provides accurate, consistent and complete master data across the enterprise and to business partners.
Which of the following is an important challenge in the area of data integration and consideration? ›8 Most Important Challenges of Data Integration
Understanding the behavior of source and target systems. Logically mapping heterogeneous data structure between source and target systems. Processing the source data to fit into the target system based on business needs or rulles.
- Security. Introducing more devices and endpoints into your business means increasing the ways hackers can potentially exploit those endpoints and devices. ...
- Network access control. ...
- Migration. ...
- User experience. ...
- BYOD.
- Lock and wipe. ...
- Determine device mobilization. ...
- Recognize the limitations of MDM. ...
- Align MDM with overall IT management strategy. ...
- Remember MDM can be extended.
- Extends Existing Data Governance Program and Tools.
- Cleansing and Correction of Erroneous Data.
- Data Quality Monitoring and Reporting.
- Business Taxonomy and Hierarchy Management.
- Concept Standardization (e.g. Address)
- Deduplication, Matching and Unique Keying.
MDM principles
Once established, MDM ensures the consistency and quality of a company's data assets, including product data, asset data, and customer data, by making this data available to end users and other applications.
Who is responsible for master data? ›
When organizations implement an MDM process, they generally appoint three people to oversee it. Data owner: The data owner has the ultimate responsibility for master data. They oversee the business rules and ensure that everyone is following them.
Is MDM a good career? ›Not only is this an exciting opportunity, but it is also a career choice that offers long-term stability.
What are the main roles and responsibilities of master data team? ›Assists in the application and implementation procedures of data standards and guidelines on data ownership, coding structures, and data replication to ensure access to and integrity of data sets. Conducts data cleaning to rid the system of old, unused data, or duplicate data for better management and quicker access.
How do you overcome integration problems? ›- Find the simplest solution that works. ...
- Automate as much as possible. ...
- Build it so that you'll know when something fails. ...
- Opt for multiple, smaller integrations over large, more complex ones. ...
- Choose system integration software that allows you to maintain or change easily.
There are three issues to consider during data integration: Schema Integration, Redundancy Detection, and resolution of data value conflicts.
How can data integration problems be prevented? ›- You have disparate data formats and sources. ...
- Your data isn't available where it needs to be. ...
- You have low-quality or outdated data. ...
- You're using the wrong integration software for your needs. ...
- You have too much data. ...
- Clean up your data. ...
- Introduce clear processes for data management.