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Why Data Normalization Costs Consumer Brands Millions in Sales

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Manage episode 444567471 series 3514811
Alloy.ai에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Alloy.ai 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.


In this episode, we dive deep into the complexities of data management within the consumer goods industry, focusing on how brands can achieve a comprehensive view of their business by connecting data across a multitude of retail, e-commerce, and supply chain partners.

Hosted by Abby Carruthers, Product Manager at Alloy.ai, the discussion features insights from Manfred Reiche, a subject matter expert in CPG data, and Matthew Nyhus, engineering team lead at Alloy.ai. Together, they break down challenges and solutions related to data normalization—a crucial process for standardizing data from various sources to ensure consistency and comparability.

From understanding product and location data normalization to tackling the intricacies of time and metric alignment, this episode explores how brands can transform their disparate data into actionable insights that drive sales growth and operational efficiency.

In this episode, you’ll learn about:

  • Data normalization is critical for consumer brands to standardize data from various sources, such as retailers, e-commerce platforms, and supply chain partners, into a common language
  • Integrating and managing data from multiple sources involves significant technical and operational challenges, specialized systems can automatically manage these hurdles
  • Don’t shy away from the complexities of data normalization - seeking help and leveraging the expertise of others can save significant time and resources while ensuring accurate and actionable insights

Jump into the conversation:

(00:00) Introduction to Manfred and Matthew
(06:05) Using multiple retailers, integrate data sources for consumer insights
(09:55) Technology, people, and processes in master data management for product distribution
(14:47) Matching products from different sources for rich information visibility
(19:21) Consistency in managing changing product data
(21:59) Supply chain management with flexible, tailored database design
(25:24) How automation can reduce workload by 95% for all your teams
(29:45) Knowing servicing locations and translating insights for internal teams
(31:38) Distinguishing between brick-and-mortar and e-commerce sales
(34:17) Understanding net sales across channels, including returns and tax
(40:13) Backend stores metric values
(44:02) Retail data analysis pitfalls
(47:17) Being cautious with IT assumptions

  continue reading

챕터

1. Why Data Normalization Costs Consumer Brands Millions in Sales (00:00:00)

2. Data Normalization in Consumer Goods (00:00:02)

3. Master Data Management Challenges in Sales (00:10:15)

4. Handling Complex Product Matching Scenarios (00:13:43)

5. Standardizing Data Across Multiple Sources (00:26:47)

6. Data Normalization Across Time (00:37:11)

7. Modeling Edge Cases in Data (00:48:08)

16 에피소드

Artwork
icon공유
 
Manage episode 444567471 series 3514811
Alloy.ai에서 제공하는 콘텐츠입니다. 에피소드, 그래픽, 팟캐스트 설명을 포함한 모든 팟캐스트 콘텐츠는 Alloy.ai 또는 해당 팟캐스트 플랫폼 파트너가 직접 업로드하고 제공합니다. 누군가가 귀하의 허락 없이 귀하의 저작물을 사용하고 있다고 생각되는 경우 여기에 설명된 절차를 따르실 수 있습니다 https://ko.player.fm/legal.


In this episode, we dive deep into the complexities of data management within the consumer goods industry, focusing on how brands can achieve a comprehensive view of their business by connecting data across a multitude of retail, e-commerce, and supply chain partners.

Hosted by Abby Carruthers, Product Manager at Alloy.ai, the discussion features insights from Manfred Reiche, a subject matter expert in CPG data, and Matthew Nyhus, engineering team lead at Alloy.ai. Together, they break down challenges and solutions related to data normalization—a crucial process for standardizing data from various sources to ensure consistency and comparability.

From understanding product and location data normalization to tackling the intricacies of time and metric alignment, this episode explores how brands can transform their disparate data into actionable insights that drive sales growth and operational efficiency.

In this episode, you’ll learn about:

  • Data normalization is critical for consumer brands to standardize data from various sources, such as retailers, e-commerce platforms, and supply chain partners, into a common language
  • Integrating and managing data from multiple sources involves significant technical and operational challenges, specialized systems can automatically manage these hurdles
  • Don’t shy away from the complexities of data normalization - seeking help and leveraging the expertise of others can save significant time and resources while ensuring accurate and actionable insights

Jump into the conversation:

(00:00) Introduction to Manfred and Matthew
(06:05) Using multiple retailers, integrate data sources for consumer insights
(09:55) Technology, people, and processes in master data management for product distribution
(14:47) Matching products from different sources for rich information visibility
(19:21) Consistency in managing changing product data
(21:59) Supply chain management with flexible, tailored database design
(25:24) How automation can reduce workload by 95% for all your teams
(29:45) Knowing servicing locations and translating insights for internal teams
(31:38) Distinguishing between brick-and-mortar and e-commerce sales
(34:17) Understanding net sales across channels, including returns and tax
(40:13) Backend stores metric values
(44:02) Retail data analysis pitfalls
(47:17) Being cautious with IT assumptions

  continue reading

챕터

1. Why Data Normalization Costs Consumer Brands Millions in Sales (00:00:00)

2. Data Normalization in Consumer Goods (00:00:02)

3. Master Data Management Challenges in Sales (00:10:15)

4. Handling Complex Product Matching Scenarios (00:13:43)

5. Standardizing Data Across Multiple Sources (00:26:47)

6. Data Normalization Across Time (00:37:11)

7. Modeling Edge Cases in Data (00:48:08)

16 에피소드

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