Context

In my post The Future of Supply chains post COVID-19, I advocated that post COVID-19 an action procurement and supply chain professionals should undertake is digitising and creating safe and secure analytics. The use and power of big data will be key in understanding your supply chain and managing risks.

Today’s businesses, especially large global enterprises have hundreds of separate applications and systems (i.e. ERP, CRM). Data crosses organisational departments or divisions and easily becomes fragmented, duplicated and most commonly out of date. When this occurs, answering even the most basic, but critical questions about any type of performance metric or KPI for a business accurately becomes a pain.

Access to data is increasing, however data is stored on many different systems and extraction has grown exponentially in complexity.

How complex can this be?

What data do I need?

How timely can I get access to it?

How can I present it in a way that is meaningful and easily understood?

Many of you have asked similar questions within your business environments, especially when presenting insights to executives and boards.

Procurement and Supply Chain Mega Trends

Lari Numminen, Chief Marketing Officer in his article “7 Megatrends for the Future of Procurement” highlights the combination of internal and external data sources as one of these Mega trends with many companies having detailed visibility on internal data, on costs, supplier contracts and so on. As more big data solutions emerge for external signals, like the weather, commodity prices, and other factors, co-founder of Sievo, Sammeli Sammalkorpi argues that a source of future competitive advantage will be the ability to combine internal and external data sources.

The EY article “Ten trends shaping the future of procurement” highlights that organizations will leverage internal and external data sources to better assess supplier risk. He stated that in the near future, most organizations will have a 360-degree view of suppliers through internal data, data from suppliers, market data and external data on suppliers’ performance (e.g., performance of suppliers with other organizations). This will not only provide historical data about supplier performance but will also enable organizations to accurately and holistically establish supplier risk profiles and to predict risk events.

Spend Matters cites Big Data and analytics as one of the 5 Mega Trends Reshaping the Supply Chain. Trend number 2 states that the chat will focus not just on analysing larger datasets but also the underlying requirements to do so — and the possibilities with Big Data approaches to procurement. (Hint: It’s anything but just expanding “spend analytics” to new areas.)

Finances online Jenny Chang published an article on the “14 Supply Chain trends for 2020: New predictions to watch out for”. The graph below illustrates clearly how data analytics has appeared as one of the priority technologies for the Supply Chain Industry.

Source: Finances online

The Link between MDM and the Supply Chain mega trend Big Data

Without…. getting answers to basic questions such as “who are our most profitable customers?”, “who are our key suppliers” and “who supplies them to create the products we receive?” “what product(s) have the best margins?” or in some cases, “how many employees do we have”? become tough to answer – or at least with any degree of accuracy.

The need for accurate, timely information is acute. As sources of data increase, managing it consistently and keeping data definitions up to date to enable all parts of a business to use the same information is a never-ending challenge.

Data analytics are one of the key Mega trends for Procurement and Supply Chain Management. COVID-19 has increased the hunger for information. Daily reports and updates on case numbers and mortality rates have been religiously watched by hundreds of millions of people globally.

Evidenced based decision making requires timely and accurate information. When managing risk in your supply chain, how many organisations truly have levels of data from every element of their high-risk suppliers?

What is Master Data Management?

Some of you may wonder what Master Data Management is? Below is a quick snapshot and definition and why MDM is important. As always please contact me if you would like any more information on this technology.

Source: Duco Consultancy

Master data management is a method used to define and manage the critical data of an organisation to provide, with data integration, a single point of reference.

Master data represents the business objects that contain the most valuable, agreed upon information shared across an organisation. It usually covers relatively static reference data and not transactional data such as sales orders, stock etc. 

The slide below highlights some of the opportunity costs of poor data management

How do you implement Master Data Management?

Arvind Joshi, PMP Director – Data Management Lead at Scotiabank in his LinkedIn article has documented a useful overview of how you implement Master Data Management

Source: Linkedin

Implementing Master Data Management must be business led. It is not simply choosing a technology solution and supplier. The key steps in implementation are data profiling, understanding what data is important to the business and what the business currently collects across all its systems and databases. Data definition and prioritisation (the data to be kept and updated) are also keys steps in implementing MDM.

I cannot stress enough the importance of developing a data governance model and appointing a data steward and a governance council. It is pointless spending $Millions on technology if you do not maintain data quality.

Data quality management is as important as the data itself and if not maintained to the highest standards it could end up being out of date. Poor data can cost you millions as highlighted in the slide earlier.

Once you have completed the key steps above and the governance process is defined and architected then the data must be extracted from your existing systems cleaned and then loaded onto the solution chosen by your organisation.

Extracting the data from all the sources throughout your technology environment can be complex; however there are tools that can assist to help transform the data  to your requirements and  load into the system that you will maintain as your single source of truth.

Technology Providers

There are many technology solutions today to capture, present & report information on your important data across multiple domains and systems within an organisation. Often many large organisations have just grown organically. Acquisition has also added to the complexity of the technology environment. Access to data in complex IT topologies has challenged many experts.

For reference here is a list of some of the top technology suppliers of MDM solutions (forgive me if I have left some of you out);

  • Ataccama
  • Profisee
  • Talend Master Data Management
  • Orchestra Networks
  • SAS
  • SAP
  • Stibo Systems
  • Tibco
  • IBM
  • Agility Multichannel
  • Omni-Gen
  • Riversand
  • Oracle
  • VisionWare
  • Informatica 360
  • Enterworks

Gartner magic quadrant has more up to date information on each supplier that contributes to its publication. Please note that these are often not exhaustive. Add this tool to your set of tool when evaluating what is suitable for your organisation.

Supplier selection should be based on business led consultation and alignment with needs and expectations. There are some big differences and maturity between each solution. Understanding your current and future state requirements is paramount when making a technology choice.

Summary

All organisations will need to continually Improve their data insights to effectively manage their data.  Peter Drucker is often quoted as saying “if you can’t measure it, you can’t manage it.” But it is more than just measuring today!

Big Data is used to create competitive advantage, alongside measuring, forecasting and risk management. The insights created are the fundamental elements of governance, leadership, strategy and operational intent.

MDM platforms draw information from multiple domains and departments and singles out the core data that administrators have determined is most relevant to the organisation. Users can then implement that data as they see fit, keep records of data history, and make projections based on findings.

The link with Data Analytics and Supply Chain Management be it risk, demand, supply and its importance in decision making should be obvious. Post COVID-19 the need for Procurement and Supply Chain professionals to collate the information and provide strategic and operational insight is essential for understanding our failings during the crisis.

Master Data Management is one of the silver bullets! (note: one of the silver bullets)

There are multiple solutions and getting it right first time is attributed to business led delivery. Use professionals to help you on this complex journey.

Data quality pro have a great “Beginners Guide to Master Data Management (MDM)” there is a link in my references below.

If you would like further information on how to deliver MDM and capabilities required Duco Consultancy ( https://www.ducoconsultancy.com ) are experts in delivery of the Technology solutions for MDM.  Help with business cases and any information please send me an email at Mike@ducoconsultancy.com or mike.blanchard@xtra.co.nz

References

 <https://mikeblanchardcom.wordpress.com/wp-admin/post.php?post=178&action=edit&block-editor=1&frame-nonce=6c015c591d&origin=https%3A%2F%2Fwordpress.com&environment-id=production&support_user&_support_token>

  <https://profisee.com/master-data-management-what-why-how-who/>

  <https://sievo.com/blog/7-megatrends-for-the-future-of-procurement>

 <https://www.ey.com/en_gl/advisory/ten-trends-shaping-the-future-of-procurement>

<https://spendmatters.com/2016/03/14/5-mega-trends-reshaping-the-supply-chain/>  

<https://financesonline.com/supply-chain-trends/>

  <https://www.linkedin.com/pulse/master-data-management-mdm-framework-arvind-joshi-pmp/>

<https://www.dataqualitypro.com/blog/beginners-guide-to-mdm-master-data-management>

  <https://www.g2.com/categories/master-data-management-mdm>

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