Darren Cooper

Synthetic data is test data that makes business operations run smoothly; if they are automated with AI or machine learning (ML), master data management is critical to be sure decisions are unbiased.

Data generates data which in turn generates more data. How do we know if what is being produced is fit for purpose? What if a bot, designed to help us to make an informed investment decision or simply provide the best answer to our customer services question, gets it wrong?

Obviously, testing all different corners of solution sets is important. As AI takes a more dominant role in automating decision processes, it is essential to make sure MLOps (maching learning operations), enabled by master data management, are working from high-quality data that is explainable (XAI), trustworthy and free from bias.

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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

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Multidomain master data management gives personalization strategies a better start in life

Customer experience starts with understanding what customers want. Multidomain master data management (Multidomain MDM) is a key discipline to help in the successful development of a personalization strategy. Multidomain master data management can help you build a 360° view of the customer that is needed to fuel personalization and hyper-personalization strategies with the right insights.

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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.

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Spending too much on big data? Try small data and master data management first: Together, small data and master data management reveal new insights into your business

Over the past few years working in data governance, I have encountered many projects aiming to re-write decision making processes using ”big data.” Data lakes, predictive analytics, AI and a cacophony of other acronyms have for many people veiled the complexity of big data, which pledged to deliver the four golden eggs of data insight; 1) better decision making, 2) happier customers, 3) freedom from risk and 4) identification of new opportunities.

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Trusting the data you use has never been more important in the life sciences industry

As supply chains undergo ever greater pressure to perform faster and more efficiently, business leaders need to become more data-driven in their decision making to identify areas for cost efficiency improvement, new business opportunity and transparent compliance management. However, the most business critical of data, including product, supplier and customer data, is impeding the data-driven objective. This data is lacking in quality, coherence and availability due largely to fragmented and inconsistent data management processes.

You just cannot be data-driven if you don’t trust the data you use.

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Master data management enables telecom providers and CSPs to gain control of their data and leverage it for new business opportunities.

Telecom operators, telecom equipment manufacturers and digital communications service providers (CSPs) are looking into a large untapped business potential with a 5G, edge-connected world allowing to address new types of subscribers in new ways. The telecom industry, challenged by competition and legislation, has a unique opportunity to leverage new technologies, such as AI, and develop new business models, renew their offerings and become more efficient.

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Financial services institutions, such as banks and insurance companies, rely on transparency of their customer master data and product master data to capitalise on a number of industry trends

Master data management plays a critical role in ensuring data transparency. Master data management therefore enables you to leverage the customer and product master data – that you already have – to address and drive revenue from these current trends in the financial services industry.

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Discover the seven business benefits of transparent product information management (PIM) in the pharmaceutical manufacturing industry

The business of pharmaceutical manufacturing is undergoing a data evolution. Business leaders need to understand how mastery of their data is essential to achieve leadership in a highly competitive market. The importance of the digital supply chain is growing. Manufacturers are building new processes that interconnect the back office with the front office and even directly to the customer’s operations. The pharmaceutical industry is becoming more digitally enabled and data intensive. Building a digital supply chain requires harvesting the raw materials that constitute it: Data.

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Get the eight business benefits of transparent product information management in the medical device manufacturing industry - It's not all about compliance with medical device regulations

The business of manufacturing medical devices is undergoing a data evolution. Business leaders need to understand how mastery of their data is essential to achieve leadership in a highly competitive market.

The pertinence and prevalence of the digital supply chain is growing. Manufacturers are building new processes that interconnect the back office with the front office and even directly to the customer’s operations. The industry is becoming more digitally enabled and data intensive.

Building a digital supply chain requires harvesting the raw materials that constitute it: Data.

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