5 Innovative Ways to Integrate MDM Capabilities with Business Processes

Master data management (MDM) refers to the discipline of harmonizing information tech and business or organizational needs by ensuring the uniformity, accuracy, stewardship, semantic consistency, and accountability of an enterprise’s shared master data assets.

Master data can be any data that requires consistency and interpretability by different interests within an organization. MDM capabilities include centralized databases, business glossaries that define types of data, and any tools or documentation that govern the collection, storage, and use of data.

MDM capabilities are therefore bigger than simple control over the data itself. They’re also about managing the various business processes that rely on data, as well as those that create new data or retire old data.

As control over data and how it’s used is increasingly imagined as a corporate specialization or subfield of general management, it’s important not to lose sight of the ways that MDM is still situated within the remit of traditional management functions and business oversight.

Although good MDM is not just important for businesses, and should be implemented by organizations from schools to hospitals too, this article will hone in on its consequences for businesses. These five new approaches to MDM are focused on integrating data-based technologies with general business processes.

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1. Use Master Data for Business Process Mapping

When thinking about how efficient MDM can be used to optimize business processes, it’s useful to consider three different types of processes: those that use master data, those that change existing master data, and those that generate new master data.

To enable colleagues from different departments to perform at their best, they must understand their place in MDM data flows. This, along with using the best apps for work, can streamline several business processes and ensure teams don’t step on each other’s toes.

In the digital age, we increasingly see business processes as having the sole purpose of enriching master data. For example, data analytics functions that transform customer and potential customer data into useful information for sales and data-driven marketing teams are now central to any e-commerce business.

These new data functions require clean and updated data to create value in business processes, hence the importance of “master” data that can be translated across divergent business needs. With such a schema in place, employees working in data analysis must have a firm grasp of who uses the product of their labor and to what end. Master data is of no use if it can’t be interpreted by the various parties that need to use it.

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2. Train Management Staff in Data Science

A hundred years ago, business leaders were educated in industrial fields such as design, agriculture, or engineering. In the latter half of the twentieth century, it became the norm that company directors had backgrounds in economics and finance, and we saw the rise of business studies as a discipline in and of itself. In the twenty-first century, it’s become common for managers with an in-depth understanding of information theory and data science to rise to the top.

As MDM becomes an increasingly indispensable component of today’s business practice, it’s more important than ever for management-level employees to have at least a basic understanding of data science.

Enriching the data-scientific knowledge base of your company’s management should be one of your primary HR team objectives. This can be done through training programs or by implementing a proactive hiring policy that recruits staff with a background in AI, data, automated testing, or information science.

Reflecting the growing importance of digital technologies in all aspects of business and commerce, understanding your organization’s operations on a technical level can no longer be entirely relegated to specialized computer scientists and systems engineers.

Although it’s tantamount that management-level employees have a grip on their business’s MDM practice, the new data-based skill sets should complement rather than replace traditional management qualities. The ability to inspire confidence and cultivate strong interpersonal relationships is still pivotal to strong leadership but can be enhanced by a good understanding of the role of data.

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3. Make Data Available to Remote Workers

As remote working emerges as an important, and in many ways preferred, employment paradigm, the question of how to synthesize MDM capabilities with a more distributed workforce is of pressing concern.

A key first step is implementing the correct remote work tech stack for your business. Effective use of a video conference meeting, for example, can facilitate seamless sharing of master data among employees, helping to maximize the usefulness of your MDM program.

For sensitive data, many organizations are put off from allowing staff to work remotely, given the risk that data protection could be compromised. To ameliorate this problem, consider software tools like a virtual desktop system or one of these alternatives to Grasshopper phone system options, which prioritize system security.

The ability to generate usable anonymized data can be a valuable asset in your MDM suite. Rather than allowing data protection legislation, such as the EU’s GDPR, to inhibit the growth of data-centric business processes, many companies are using encryption and anonymization to add value to their sensitive data and enable it to be used for a greater number of applications.

The security advantages inherent in anonymized data make it more accessible to remote workers and the various mobility solutions your business relies on, which might not necessarily permit traditional cybersecurity options.

With it, you can increase the possibilities for managing remote teams without sacrificing the confidentiality of your customer’s personal information. And, because anonymized data is by nature less useful to malicious attackers, it’s less likely that sharing it between decentralized devices will lead to data breaches.

4. Allow Data Discovery Tools to Complement Enterprise Reporting

Advances in data discovery tools and techniques represent some of the greatest changes to traditional business intelligence in the last decade.

Data discovery tools have evolved to benefit users who need more agility than traditional enterprise reporting allows for. After all, even with today’s communication toolkit, which includes instantaneous calling, messaging, and clever apps to facilitate collaboration, like these alternatives for Basecamp, sometimes it’s better to automate the transferal of information.

Software offerings from the likes of Domo, Qlik, and Tableau provide fast reporting and can intelligently combine different data sources. By streamlining the collection, sorting, and visualization of data, such solutions are powerful tools for MDM and can provide valuable insights at the touch of a button.

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In the digital sphere, software-driven data discovery can massively enhance your enterprise SEO efforts by drawing on the power of Big Data. The sheer volume of search and website data that the new tools can sift through gives them a huge probabilistic advantage when it comes to spotting patterns. The potential for increasing your web visibility is massively increased when you are working with well-organized master data.

Nonetheless, data discovery tools should not be seen as a one-for-one replacement of enterprise reporting. Other modalities of data compilation still have their place. Understanding when to use each and how they work together should be the central pillar of your business’s analytics strategy.

5. Use Machine Learning to Optimize Sales

MDM can also be useful in the order-to-cash process and can be applied to various sales problematics. In these instances specifically, machine learning is a technology that is proving to be a very useful application of master data.

Machine learning generally describes any algorithmic process that automatically self-improves through experience and by the use of data. Machine learning and the AI models that power it have applications for various business processes, from supply chain management to advertising and beyond.

For businesses operating an omnichannel retail strategy that generates large volumes of data, being on top of MDM can help to facilitate more profitable outcomes thanks to AI and machine learning.

If you think machine learning could be applied to your business and procurement process needs, remember that a ready set of reliable data is essential for training models. A coordinated MDM program benefits such initiatives by prioritizing the collection, storage, and organization of data.

Your CRM system will be an important data mediator here, and making sure that historical sales data from any different platforms your company uses are coordinated should be one of the main sales engagement functions of your MDM plan.

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When defining data taxonomies and hierarchies, it pays to keep in mind the way the artificial neural networks that drive today’s machine learning read and use the data we provide them. If it is well-prepared, it can be possible to feed historical data into a machine learning model that can then suggest or even automatically initiate the next step in the sales cycle.

Master Data for Data Mastery

If there is a single fundamental lesson for the new data economy, it’s that data-driven business processes are only as good as the data that fuels them. Implementing an MDM initiative is the single most effective way to ensure all your analytic and optimization efforts aren’t for nothing.

By taking a holistic perspective that considers collection, storage, security, legal, and social responsibilities together, MDM has the potential to transform your business practices across the board.