DRAFT Master Data Management (MDM)

The governance, organisation and management of accurate and consistent information accessible to everyone across the organisation

Master Data Management (MDM) is a process and technology-driven set of principles where the business and IT parts of an enterprise work in conjunction to ensure that the total volume of its data is uniform, accurate, semantically consistent and has a chain of accountability among key sales, marketing and operational personnel.  Start with Data’s MDM services are not solely focused on MDM technology, because without governance and processes, even the best MDM platform will fail. 

With Start with Data’s three-pronged approach – MDM consultancy to shape your vision, implementation of a MDM solution, business and technology support for you throughout the transformation process – our strategic and technology expertise offer you a laser-like focus on ensuring your enterprise is future-ready.

What is Master Data?

Master Data is a single source of common data  used across an organisation, often in multiple disconnected systems, which covers a number of different domains. The primary domains are;

Customer data 

personal information, order history, permitted data.

Location data

geographical data (for example, GPS-located services).

Product data

attributes, specifications, product metadata.

Product data

attributes, specifications, product metadata.

Business benefits of an MDM strategy

To have real value, a master data management strategy must not only focus on the technology but also engage with key variables;

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Data Quality Dimensions

Data quality meets the following six dimensions:

Completeness

how much of a data set is populated rather than left blank

Timeliness

how useful or relevant the data is based on its age

Validity

ensuring the data is a true reflection of the type of information you originally recorded

Uniqueness

whether a data entry is duplicated, uniqueness is guaranteed by one recorded version of a data entry

Accuracy

defining whether the information you hold is correct or not (not the same as validity, which is a measure of the correctness of the type of data)

Consistency

how far you can safely compare data across different storage areas, data sets and media and whether they have been recorded in the same way

Data quality meets the following six dimensions:

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Common data quality issues

Data can be degraded, corrupted and rendered unusable in a variety of ways;

Inaccurate data may contain incorrect personal details such as email addresses, phone numbers. It may also have missing information about product dimensions and measurements leading to problems with the size of delivery vehicles being used. These inaccuracies can be down to something as simple as a field inadvertently left blank or misspellings.

Non-compliant data doesn’t comply with regulatory or legal requirements.

Uncontrolled data becomes polluted if it is left without constant monitoring.

Unsecured data with no security protocols can be open to access and attack by hackers.

Static data is never updated, causing it to become obsolete. 

Dormant: data left in a repository loses its value – unshared, unused and essentially useless.

Understanding what data quality looks for your organisation is an important step in overcoming data quality issues. Overcoming data quality issues is possible through better product data management processes, data governance and technology. 

Data Quality Management

As information sources rapidly increase and legal and regulatory compliance becomes more complex, organisations need to access, ingest and reuse data from widely differing origins in a consistent and reliable way. Best practice for data quality management means taking a proactive approach, rather than a reactive approach. This involves checking and measuring the degree of data quality before it enters a company’s core systems.

Data Quality Maturity Model

Using the 5-stage data quality maturity model helps to evaluate the degree of data quality maturity which exists in a given organisation. A scale from worst to best practices would identifies;

  • A purely reactive approach – no plan, no standards, essentially firefighting.
  • Siloed management – each department or area do what they like without reference to the larger picture.
  • Coordination – there is a common acceptance and understanding of data problems, and steps are being taken to remedy them.
  • Governed procedures and policies – there is a framework of stewardship, and policies in place.
  • Proactive data quality management – procedures, policies and AI-driven functionalities are in place to act on data quality problems before they occur

Data governance

Best practices in data quality management usually refer to a unified platform under a data governance framework. This means a set of initiatives which are also applied to PIM projects:

By using unified procedures for monitoring and scoring data quality directly within a governance framework, organizations can implement quality controls to ensure end-to-end data quality from source to destination.

Data Quality Tools

Data quality tools are designed to enhance the accuracy, completeness, relevance and consistency of a company’s data. Most tools fall into four general categories:

  • Data Cleansing (or scrubbing)
  • Data Auditing
  • Data Migration
  • Data integration

 

Some will focus on one category, but as data analytics technology matures, cross-functional solutions are becoming more common. Data quality informs many cross-functional elements of an organisation including master data management and meta data management, and the best tools for your task are determined by your case’s specific needs. you choose a data quality solution, you will need to understand which of these areas you need to focus on. 

Of the many software providers, most offer specific capacities;

– data integrity and data cleansing tools

– drag-and-drop graphical interface

– near real-time synchronisation of data

– merging of duplicate records

– deduplicating import files

– measurement of performance against internal or external metrics and standards

 

With advances in AI capabilities, based on semantic technology data quality tools make use of semantic discovery and built-in pattern recognition and can automate repeatable tasks on a scheduled basis.

 

Data Quality Consulting Services

Our Data Quality consultancy services help clients overcome data quality issues through better product data management processes, data governance and technology.  Our three phase Product Data Quality Assessment process will identify poor quality data, estimate the impact on the business, and to advise on actions to improve your data quality.

Three Phase Product Data Quality Assessment

A targeted data quality assessment to guide your organistaion on a prioritised roadmap for process, data governance and technology improvements for product information management

Discovery

Phase 1

Evaluation

Phase 2

Play Back

Phase 3

We pride ourselves on our flexibility and adaptability to clients’ requirements and it may be the case that your organisation will need to access certain parts of our Data Quality consultancy services, rather than the full wraparound offering. No problem! Our mission is to serve your needs with expertise so that the outcome aligns totally with your brief.

Find out more

If you would like to find out more about how product data management, PIM and MDM can create value for your business, we’d love to hear from you – Ben Adams, CEO Start with Data

Case Study

“Start with Data are helping transform product data management, laying scalable technology and data governance foundations”