How CPG companies can adapt to the New Normal
Manufacturers of consumer-packaged goods (CPG) are finding themselves in the midst of a historic transformation.
The COVID experience and the heightened environmental awareness of consumers and lawmakers are changing the game for CPGs. You can add diversity and multiple ideological standpoints to the mix.
At the heart of these impactful trends lies an unsettling unpredictability that has been coined “The New Normal”.
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.
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.Read More
An integrated approach to data management is critical for CPGs to take on supply chain shocks, rising prices and whatever comes next.
“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
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.
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.
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
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:
- Too much at once (or too little)
- Lack of support from management
- Lack of communication and change management
- Unclear goals
- Focus on tools, not processes