When it comes to data governance, the first considerations should be the company, its organizational structure, processes and the responsibilities that need to be defined before the right software can be deployed.

This is mirrored in the five most common reasons of failing data governance initiatives:

1. Too much at once (or too little)
2. Lack of support from management
3. Lack of communication and change management
4. Unclear goals
5. Focus on tools, not processes

Data governance is an organization-level program

Why data governance

Data governance refers to management or control of data through overarching rules, such as data policies, guidelines and the creation of specific structures. The goal of structured data management is to ensure the availability, usability, integrity, security and confidentiality of information.

Learn more about master data governance

Data governance is nothing new. However, it’s still a challenge to many companies of which some are reluctant to engage in what they believe is a tedious data clean-up and to accept the associated costs.

But here's why they shouldn't wait any longer:

• Bad data costs money. According to Gartner, “poor data quality costs organizations an average $12.9 million. Apart from the immediate impact on revenue, over the long term, poor quality data increases the complexity of data ecosystems and leads to poor decision making.”
• On the other hand, high-quality data can enhance your analytics, improve operational efficiency and lead to new business opportunities and higher customer satisfaction.
• The volume of data generated by businesses has risen significantly in recent years. According to the International Data Corporation (IDC), "The amount of digital data created over the next five years will be greater than twice the amount of data created since the advent of digital storage. The question is: How much of it should be stored?"
• Data has become critical to our society and our personal lives. Almost 90% of all data should be subject to a certain amount of security – but less than half is secured properly. What’s more, this data continues to become increasingly important. IDC estimates that, by 2025, almost 20% of global data will be critical to daily life, and almost 10% hypercritical, meaning it will have direct and immediate impact on the health and wellbeing of users.
• Legal requirements, such as the EU’s General Data Protection Regulation (GDPR), governs how companies capture, store and use the personal data of EU citizens. Any business that fails to comply with the GDPR may incur penalties, with potential fines of up to 20 million euros or 4% of a company’s annual global revenue.
• Risk mitigation: The issue of data protection is becoming a differentiation factor and is therefore becoming increasingly crucial to businesses. Any negative publicity in this context has a direct impact on a company’s customer base and turnover.

So, obviously, your data governance initiative can be very impactful and is definitely much needed. Despite this, many data governance programs fail at the first attempt (as many as 90%, according to Gartner). Why is this?

Get the infographic: Ten Useful Steps to Master Data Governance

Data silos are a huge problem for your ability to collaborate, operate efficiently and deliver excellent customer experiences. However, multidomain master data management (MDM) can bring your data silos together and create new zones of insight.

This article will explore the relationship between product and supplier master data domains.

Different master data domains, such as product data or supplier data, are typically stored and managed in separate systems by different departments using proprietary data policies. From this practice, that can seem logical from a business point-of-view, data silos emerge and tend to develop indigenous governance rules. Such data silos are problematic when you want to share data across departments. Fortunately, your master data domains can have relationships and commonalities that make joint data governance relevant.

Bringing data domains together can reveal new insights into certain parts of the business and deliver new levels of data transparency that can help power your business to the next level of delivering superior customer experiences. We will use the product data domain as a foundational start point when exploring domain relationships due to its heterogenous application across multiple industries.

To make money from data requires insightful data.

Data monetization is generally understood as the “process of using data to obtain quantifiable economic benefit” (Gartner Glossary, Data Monetization). Three typical ways to achieve this, include:

• Using data to make better decisions and thus enhance your business performance
• Sharing data with business partners for mutual benefit
• Selling data as a product or service

Whichever way you choose, data monetization gets more profitable if you can provide context to the data and make it insightful. The more insight your data provides, the more valuable it is. Take as an example a person about whom you know the job title. That is basic master data. Adding to that information the insight that the person has recently changed jobs, can open new business opportunities or guide your marketing efforts to be more targeted. Perhaps the job change entails a salary increase, a change of location, new business connections, etc.

In this article, you will find some data monetization examples and use cases and learn how master data management, specifically multidomain master data management can help provide data insight.

In short, master data management (MDM) can help build insights by adding context to information and creating 360° views of customers and businesses. Multidomain MDM is characterized by common governance of different data domains, thus revealing enhanced insights at the intersections of those domains.

• Learn more about Multidomain MDM

Grocery is one of the retail sub channels that is poised to gain most from digital transformation.

The COVID-19 pandemic has permanently changed how we shop for groceries. Online grocers and fresh food delivery services are experiencing a rise in demand that can be hard to manage for those who are lagging digitally. Consumers expect expediency, transparency, and a seamless digital experience.

Your MDM roadmap should be embedded in an overall MDM strategy methodology.

There's no one-size-fits all. A suggested methodology for master data management (MDM) strategy and data governance initiatives typically include the following phases:

1. Education
2. Readiness assessment
3. Strategic roadmap
4. MDM software selection
5. Business case

High-quality master data and transparent processes are necessary to protect and grow your business. Master data governance helps you achieve it.

Transparency and high-quality master data are essential to everything related to your business: revenue growth, operational efficiency, risk management and compliance, analytics and digital transformation; and your ability to control costs and be agile.

The following discusses what master data governance is, why you need it, and how to implement it.

Not all “multidomain” master data management (MDM) solutions are the same. Here are the differences and why you need to know.

When selecting solutions to help support your master data management and data governance ambitions, one of the many aspects you are likely to evaluate will be support for “multidomain” MDM. Your initial inclination is almost certain to be selecting a solution that can “do-it-all.” In many cases, this will be despite the fact you currently have a business use case for only one data domain; customer or product data, for example. A single solution that could do more in the future would, of course, be desirable, to make more efficient use of available resources.

A primary growth vector on the mind of many CIOs today are mergers and acquisitions. And many of these CIOs are in charge of managing a host of internal systems that share data. Establishing a single source of truth that can unite these systems in near real-time, so that operations can run seamlessly across old and new business units alike, is a challenge all of these CIOs face.

Make sure MDM includes matching and linking, business rules, governance, data privacy, enrichment and consent management

If you’re evaluating master data management (MDM) solutions, what are the basic features it should offer and why? More importantly, how will each of these elements come together to benefit you and your organization? Following are six master data management tools and capabilities you should look for before selecting and investing in a solution for your organization.

In times of global, business and economic uncertainty – when data points are changing on an hourly basis and supply chain responsiveness and flexibility are more critical than ever – being able to trust your company’s data is paramount.