Data is a critical foundation for today’s retail strategies, enabling businesses to make intelligent, analytics-driven business decisions, from refining personalized marketing efforts to improving logistics operations. The rise of e-commerce and other technological advances have made retail an increasingly data-rich environment. Yet even armed with this data, the ongoing impact of the pandemic, uncertain global economic conditions and supply chain constraints are putting pressure on retail businesses to perform and adapt quickly.
Since the start of the pandemic, the retail industry has also seen a shift from bankruptcies to takeovers, with global retail M&A volume growing 59% from 2020-2021. This increase in M&A has resulted in more dispersed data, and organizations are faced with the challenge of managing and granting access to large amounts of data spread across a newly integrated team.
The pandemic has also impacted consumer behavior, creating another challenging variable for retailers navigating uncertain market conditions. As companies like Amazon and Netflix raise the bar in terms of personalization and convenience, customers now expect and demand similar seamless and targeted experiences.
To meet these expectations, retailers must employ data strategies that enable them to better understand consumer behavior and make more informed business decisions. As organizations grow and adapt to an ever-changing environment, their data and analytics strategy must grow with them, despite the challenges associated with today’s data management.
Challenges in an increasingly data-driven environment
In 2022, global e-commerce sales are expected to reach $5 trillion, and by 2025, this number is expected to exceed $7 trillion. The expected growth of retail and e-commerce proves the importance of addressing the data management limitations that businesses are currently facing. While it has always been critical for retailers to be able to access their data quickly and easily, it has become clear that the dominant approach to data management, the data warehouse model, is not enough.
In this centralized model, it is not always clear who takes ownership of the data, resulting in a more disorganized architecture where important data and insights can be lost. Additionally, data warehouses cannot keep up with the scale and speed of change in today’s organizations, and retailers cannot afford to turn back when it comes to monetizing their data.
Changing privacy regulations pose another challenge for retailers, as consumers want more transparency around their personal information. As data protection laws become more stringent around the world, ensuring compliance with these regulations has become a time-consuming requirement that can further delay data usage and analysis.
The data mesh approach
Retailers request data for a number of reasons, including determining item return rates, cart abandonment rates, and geographic preferences. By collecting this data, retailers can not only identify problem areas, but also identify solutions that will strengthen the business and specific product areas. With the Data Mesh approach to data management, retailers can faster implement 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, richer data-driven insights and share those insights more easily. With this approach, data is treated as a product rather than a by-product of business activities, and data ownership is distributed rather than delegating responsibility to a central IT team.
By giving experts greater control over data from the beginning of the data management process, organizations are less likely to lose critical data and can bypass common bottlenecks that arise with a centralized approach. The agility of this approach benefits the entire organization, allowing more time to be spent on analysis than on data transfers or subject to the constraints imposed by a centralized IT function.
Benefits of Adopting Data Mesh in Retail
Retailers continue to seek better insights to inform customer experience strategies and strengthen relationships with key audiences. Accessing data with greater transparency and agility has become a requirement to remain competitive in the retail industry, and the data mesh approach will enable retailers to achieve more cost-effective and timely data access – and ultimately businesses in the Enable you to make faster, more insightful decisions.
Only with the popularity of online shopping and the spread of shopping across platforms and channels have the amounts of data increased further. The data mesh architecture helps organizations maintain control of their data, regardless of scale and distribution.
The pandemic has severely impacted the way 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 can avoid some of the challenges they face with data management and analysis and instead focus on realizing the potential of their data.
Andy Mott is EMEA Head of Partner Solutions Architecture and Data Mesh Lead at starburst with more than 20 years of experience in the IT industry. With a deep technical background and experience across a range of industry verticals and international geographies, Mott is an expert on considerations such as the use of analytics, analytics culture, and analytics processes through technologies such as self-service data tools, cloud, and streaming analytics. He is also an expert in data mesh, a new architectural approach.