Stibo Systems Blog

Data management during mergers are a challenge to CIOs and IT leaders. They have to manage a doubled IT infrastructure and merge product data into a single product classification system.

Merging product data is important to reap the benefits of a business merger, including brand consistency, better customer experiences and operational efficiency.

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Using DaaS to make master data easily accessible to high-volume, data-consuming applications can drive significant competitive advantage.  

If you’re like most companies, you have several digital enablement projects underway to improve operational agility or enhance the customer experience in some way. For these initiatives to be a success, real-time access to business-critical master data is essential.  

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An integrated approach to data management is critical for CPGs to take on supply chain shocks, rising prices and whatever comes next.

Fact:

“In the past three months, more than 80% of consumers bought a different brand than their usual – lower prices (65%) and out-of-stock products (51%) were their primary motivating factors.”

- Insider Intelligence, September 2021

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Migration to S/4HANA from several disparate ERP systems entails great risk and can take years, requiring many different tools, enormous human resources and large budgets. Master data management (MDM) can not only mitigate that risk but actually allows you to thrive and reap benefits while preparing your data for S/4HANA.

During your S/4HANA migration journey, your organization needs to “keep the lights on” with both your legacy ERPs and your new S/4HANA environment until full deployment. MDM enables this with minimal or even in some cases a reduction in resources.

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Synthetic data is test data that makes business operations run smoothly; if they are automated with AI or machine learning (ML), master data management (MDM) 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 MDM, are working from high-quality data that is explainable (XAI), trustworthy and free from bias.

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With NRF 2022 complete, one thing is clear: change continues to be the new normal for the retail industry.

As industry leaders trekked back to New York to attend NRF 2022, attendees couldn’t help but notice that while things looked similar, much had changed. Since the last in-person NRF in 2020, while the world and various aspects of the global economy have shut down at various points, the retail industry kept going at an accelerated pace.

Photo credit: National Retail Federation

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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

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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.

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We are living in a world where retailers are transforming their business models towards a paradise of service and personalization to gain competitive advantage with less loyal customers. It is a world where customers can order a product online and have it at home in less than 30 minutes. Tonnes of plastic and cardboard waste generated by this delivery whirlwind are a result of these forces. According to the World Bank, the apparel segment makes up 10% of humanity’s carbon emissions. Fast fashion has increased clothes “consumption” by making fashion accessible to the masses and increasing the demand for new garments to an unsustainable extreme. It takes about 7,500 liters of water to produce a single pair of jeans, which is equivalent to one person’s drinking water for seven years. Impressive figures when we look at the various jeans in our wardrobes.

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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

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

 

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