Mastering Master Data in an Omni-Channel World.

Why your business needs effective master data management systems.

Don’t you hate it when people state the obvious? I do, and so I’m not going to tell you obvious things such as, how many brands are competing for your customers attention. Or that customer loyalty is more important than ever. No, you know that. 

What you may not know is that your customers might be leaving because of the way you manage data. If you did an audit of your most frequent customer service complaints, you will probably find issues like these:

  • Customer address incorrect so order not received.
  • Incorrect stock data so the customer completes a successful order but there is no stock of the required product.
  • Your automated emails are promoting products the customer already has.
  • The wrong items are captured in a sale.

These are issues that arise when your master data management (MDM) system is not working, is non-existent, or is inefficient. These systems are essential for large companies but I would argue that this is essential for SME’s to address in this present moment of ecommerce prevalence to help retain your customers, allow for flexibility, and speed up your growth.

What is master data.

The definition of master data management (MDM) takes many different forms and is influenced by industry. Before defining the definition of MDM it’s important to understand the complexity of the subject and how it touches all areas of an organisation. Gartner, one of the world’s largest information technology companies, defines MDM as:

“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.” (Gartner, 2020)

Over the years of implementing integrated solutions in an ever increasing omni-channel world, the key word in Gartner’s definition is semantic consistency. Consistency across multiple channels is the spine between how we integrate in today’s complex and multi-channel environments. This post will concentrate on just that; why and what it looks like to have a consistent master data flow between channels in an omni-channel world and why organizations need to invest in this area in order to stay flexible and ultimately competitive.

Why is master data important in today’s world.

As we see the ever increasing importance of e-commerce in the new digital world and the challenges surrounding bricks and mortar stores, it is clear that there is a changing dynamic in organizations strategies. No longer do we see a POS system connected with IT stock and payment reconciliation platforms being the only form of connected software in a retail organisation.

To be straight, simple two-way connections are now dead and there are hundreds if not thousands of tools that connect within a company’s new multi-channel world. Below are a list of some of the core tools that are integral to modern day omni-channel integrations and what these tools are used for:

IT software – the spine of an organisation managing stock, sales reconciliation ion, returns, discounts. Essentially everything that comes in and out of your business.

Ecommerce platform or CMS – this is a businesses core system that manages the website and all customer and transactional processes.

POS – Point of sales or till system within stores.

OMS – Order management system used in the warehouse facilities, could be owned warehouse or 3PL.

PIM – Product information management system that connects into your CMS and third party sellers.

DAM – Digital asset management system that holds all of your digital assets and can push to various systems. These assets include imagery or video, for example.

CRM – Customer relationship management; managing the relationship of existing or potential customers through owned customer data.

AI – Artificial Intelligence or machine learning of customer behavior to provide personalized offerings to consumers on various channels identified above.

Analytics tools – These take many forms and many of the systems above would already have integrated analytics solutions but there are third party analytics platforms that can provide a critical glimpse into the entire consumer journey.

Data Lake – is a centralised data repository that stores all of you data both clean and unclean.

There are also other tools that are becoming ever more integrated when we think about social media platforms and their drive to offer direct shopping, third party ecommerce partners like Amazon, Marketplaces such as Alibaba, own brand mobile apps and chat apps like WeChat.

All of these channels have one thing in common and that is the product information that runs through them. Product content, sales, discounts, stock, product information are just a few of the key critical informations that are driven from, to or through an integrated system architecture.

When considering industries such as fashion or electronics, many companies can have thousands of products and millions of customers flowing through many channels and product data and customer data is at the core of the success as to whether a consumer has that high-level experience. As I said, if stock is not correct you may sell something that you do not have in stock. If the product information displayed is for the wrong product, maybe the customer simply received the wrong product but a more critical issue could be incorrect legal information on the product. If the customer information is incorrect orders could get sent to the wrong address. If products are displaying the wrong discounts profit can be hit. Perhaps new items have been pushed live on one channel but have not been pushed to multiple other channels due to incorrect data in the feed.

What is your ultimate goal? I’d suggest it’s to get your product into the hand of the consumer and in this omni-channel or multi channel world, product data is at the core of this. You can market your brand or products or give a great price but if there is discrepancies or simply incorrect product information it will inevitably impact the customer experience and their opinion of your brand.

What a consistent data flow could look like.

In simple terms a consistent data flow would be the transformation of clean data from system to system without any errors or mistakes and in the simplest way possible. Obviously the above statement is not always possible, hence this post! I stated the obvious, I know. However, the statement still holds true in terms of what companies should be targeting, so the best way to define a consistent flow is to break the statement up into achievable KPIs:

Clean Data – Within any organisation, SME or multi national, clean data is a definition and process issue. The question a start-up must ask is what does my master data set up need to look like in order to grow my omni-channel offering? A multi-national’s question needs to be how can my existing data support our existing or future omni-channel strategy? Both of these questions lead to the same answer:

  1. What is my data definition? – Consistency and definition is a means to define what is in and out of your data set and how it feeds each channel. Definition is then a subset to how data is enriched and should not deviate otherwise. The implementation of a clearly defined data template can aid in the management of consistency.
  2. What is my data creation and enrichment process? – An avoidance of redundancy and push for consistent data workflows between channels. For example, product data could be fed into multiple channels in an omni-channel set up, managed by different colleagues and leading to errors. A single platform as one source of truth to enrich these channels is a clean and clutter-free approach.

System to System The integration of system-to-system is integral to the success of mapping between different data points. Technical integration can take many forms but there is always a way that makes more sense than the others, depending on the systems and data flowing between. When considering a large organisation with multiple stakeholders, the system integration can support simpler management and less errors. Integrations mainly take two forms; API connection, which is a direct connection, or file transfer (FTP, SFTP) that shares a flat file such as a CSV. Each of these connections are very different in terms of management and process.

What is important for organizations is to consider the best approach that serves the day to day management of the data flowing between the systems. In my experience, an FTP integration carries many more risks than an API, however an API takes a lot longer to implement and is often a lot more costly.

Errors and mistakes – The avoidance of errors and mistakes is the outcome of the proper implementation of the above two KPIs. Defining the data and process that is integrated through a thorough system connection enables stakeholders to have a clearly defined process for data enrichment and ultimately the push to its various systems. It is simple:

Clean data + Clean system-to-system integration = avoidance of errors and mistakes.

Simplest way possible – There is a lot of talk on the subject of big data, and rightly so, but the word ‘big’ does not need to mean complex. When we talk about the integration of an omni-channel architecture it does not make it any more complex than a simple system-to-system data integration. The bricks, that be the data, simply move left, right or up and down between each platform. Data mapping or data flow is a long-term management task that can be as complex or as simple as the approach for stakeholders to manage. The fundamental way to have a successful integration is to address it in the simplest way possible:

If I have a data set that needs to sit in platforms a, b and c. All an organisation has to ask itself in order to keep it simple is the following:

  1. Clean the data – is my data clean and clearly defined for the key stakeholders?
  2. System-to-system connection – is the connection between system-to-system enabling my data to flow without the avoidance of errors and mistakes?
  3. Process – is the process clearly mapped out with predefined templates and system definition?
  4. Owners – are there clear owners for the data enrichment in order to keep a streamlined approach?

So, what should you do now.

As we see ecommerce take an even bigger share in the consumer goods market we will see inter-connected, omni-channel strategies at the forefront of successful company strategies. There is no doubt that if you want to be dynamic in this ever-changing fast-paced industry you must have your flow of data, of all kinds, correctly mapped out and connected. The best way to look at this is to keep it simple and follow these four keys steps:

  1. Clean the data.
  2. Invest in the CORRECT integration for the long-term.
  3. Define the data enrichment and mapping process.
  4. Create system or product owners.

Thank you for reading! If you have any comments or questions, hit me with them in a comment below.

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