Stibo Systems Blog

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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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

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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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Creating value and driving growth for CPG brands in 2022 starts with optimizing the value of data

The CPG industry experiences significant change every year, but for manufacturers of consumer packaged goods, 2020 was one for the record books. At this time last year, industry experts already predicted big changes were coming. Then came the global pandemic, causing the market to shift at an even faster pace. For brands that had taken steps to anticipate and prep for evolving trends and opportunities, the journey was tough but navigable. For others, there were difficult, unexpected challenges.

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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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Four ways of enhancing product information management instead of returns management – or how to drop from 20% to 0.5% return rate

Returns have been a pervasive problem for online retailers since online shopping began, and it’s only going to become more challenging with the growth in online sales. Right now, we can foresee a perfect storm building with the approaching holiday season’s shopping spike amid the COVID-19 crisis and the encouragement to minimize in-person activities. As a result, January 2023 may be the largest month in absolute volume for returns ever seen by retailers, and January 2nd may be the height of that returns storm.

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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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What the foundation of your Master Data Management (MDM) system is built on will depend on the implementation style you agree gives you the best chance of project success. And this will largely be determined by your business situation when it comes to data management.

There are a few different implementation styles to choose from with the main differences being whether you control your data from a central hub or synchronise the hub with your existing data sources.

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Manufacturing companies can benefit greatly from mastering their vendor data to capture the significant potential for better decision making and quicker time-to-market.

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