Data is a critical foundation in today’s retail strategies, enabling organizations to make smart analytics-based business decisions, from refining personalized marketing efforts to improving logistics operations. The rise of ecommerce and other technological advances have made the retail sector an increasingly data-rich environment. However, even armed with this data, the continued impacts of the pandemic, uncertain global economic conditions and supply chain constraints are putting retail businesses under pressure to perform and adapt quickly.
Since the start of the pandemic, the retail industry has also experienced a shift from bankruptcies to buyouts, with the global retail M&A volume increasing 59% from 2020 to 2021. This increase in M&A has created more dispersed data and companies are challenged with the task of managing and granting access to a large volume of data spread across a newly integrated team.
The pandemic has also affected consumer behavior, creating another challenging variable for retailers as they navigate uncertain market conditions. With companies like Amazon and Netflix raising the bar in terms of personalization and convenience, customers now expect and require similar seamless and targeted experiences.
In order to meet these expectations, retailers must deploy data strategies that enable them to better understand consumer behavior and make more informed business decisions. As companies grow and adapt to an ever-changing environment, they need their data and analytics strategy to grow alongside them, despite the challenges surrounding data management today.
Challenges in an Increasingly Data-Driven Environment
In 2022, global ecommerce sales are expected to reach $5 trillion, and by 2025 that number is expected to be over $7 trillion. The anticipated growth of retail and ecommerce proves how important it is to address the data management limitations businesses are currently facing. While it has always been critical for retailers to have fast and easy access to their data, it has become clear that the dominant approach to data management, the data warehouse model, is not sufficient.
In this centralized model, it’s not always clear who takes ownership over the data, which leads to a more disorganized architecture where key data and insights can be lost. Furthermore, data warehouses cannot keep up with the scale and speed of change in today’s organizations, and retailers cannot afford setbacks when it comes to monetizing their data.
Changing data privacy regulations create yet another challenge for retailers as consumers look for more transparency regarding their personal information. As data privacy laws become increasingly more stringent across the globe, ensuring compliance with these regulations has become a time-consuming requirement that can further delay data consumption and analysis.
The Data Mesh Approach
Retailers query data for a number of reasons, including determining item return rates, cart abandonment rates and geographical preferences. By collecting this data, retailers can not only identify problem areas but also determine solutions that will strengthen the business and specific product areas. With the Data Mesh approach to data management, retailers can more rapidly deploy data strategies that help them better understand their customers and make valuable business decisions.
The Data Mesh architecture is a decentralized approach to data management that can help retailers gain faster and greater data-driven insights and more readily share these insights. In this approach, data is treated as a product instead of a byproduct of business activities, and data ownership is distributed, rather than having the responsibility fall on one centralized IT team.
By giving the experts greater control over the data from the beginning of the data management process, businesses will be less likely to lose key data and will be able to bypass common bottlenecks that occur in a centralized approach. The agility of this approach is beneficial for the overall business and will allow for more time to be spent on the analysis, rather than data transfers or depending on the constraint imposed by a centralized IT function.
Benefits to Data Mesh Adoption in Retail
Retailers continue to look for greater insights to inform customer experience strategies and to strengthen relationships with key audiences. Accessing data with more transparency and agility has become a requirement for remaining competitive in the retail industry, and the Data Mesh approach will empower retailers to achieve more cost-effective, timely data access — ultimately empowering businesses to make faster, more insightful decisions.
Data volumes have only continued to increase with the popularity of online shopping and the distribution of shopping across platforms and channels. The Data Mesh architecture helps companies maintain control over their data, no matter the scale and distribution.
The pandemic has greatly affected how consumers shop, and to remain competitive retailers must be able to adapt quickly and implement innovative data-driven strategies. With the Data Mesh architecture, retailers will be able to avoid some of the challenges they are facing with data management and analysis and instead focus on leveraging the power of their data.
Andy Mott is EMEA Head of Partner Solutions Architecture and Data Mesh Lead at Starburst with more than 20 years in the IT industry. With a deep technical background and experience across a number of industry verticals and international geographies, Mott is an expert in considering how an organization’s use of analytics, analytical culture and analytical processes can be optimized through technologies such as self-service data tools, cloud, and streaming analytics. He is also an expert on Data Mesh, an emerging architectural approach.