Retail is the third largest industry in the United States by revenue and number of businesses. Retail activities include wholesaling goods in bulk to consumer-facing organizations, retailing bulk-purchased goods to consumers and direct-to-consumer, just-in-time provisioning. The same organization might engage in one or several of these activities. In all cases, retail businesses must contend with complex supply chains and inventories, as well as distribution channels ranging from brick-and-mortar locations to ecommerce operations. All the while, retailers compete in a low-margin, volume-based market. This creates several data-related needs for retailers:
- They must keep inventories lean and supply chain management as efficient as possible. One example is the ability to access fresh data on the status of purchases and deliveries in order to fulfill orders on time. Another example is the ability to forecast sales and inventory needs.
- They must be able to build a complete picture of the buyer’s journey and optimize their responses to customer needs. This includes the ability to curate personalized shopping experiences, as well as to calculate customer lifetime value for revenue forecasting and customer acquisition.
- They need to create and automate an omnichannel customer experience. As customers leave digital footprints across multiple applications and operational systems, retailers must be able to communicate with customers across all relevant media and channels (e.g. email, text, chat, messaging apps, etc.) throughout the entire customer lifecycle, updating customers on the status of purchases and shipments, following up after fulfillment and so on.
In short, retailers have an abiding interest in the ability to manage and analyze data, making digital transformation a key consideration for retailers.
Data centralization and digital transformation for retail
For an organization to effectively use data, it must first centralize its data by moving it from a variety of sources and making it accessible in one place. The immediate goal supported by data centralization is the ability to combine records from disparate data sources into data models. Once data is centralized, data teams will use business intelligence platforms to translate the data models into visualizations and dashboards. Visualizations and dashboards support the discovery of insights to support business decisions.
Data centralization is essential to building a comprehensive, 360-degree view of a retail business’s operations, customers and products. Full visibility and understanding of retail operations results in the ability to optimize internal processes. Likewise, understanding the customer lifecycle means a better ability to engage and support customers. Understanding how storefronts and product lines perform (and don’t) is essential to improving the core business and profitability of an organization.
Data centralization is the first part of a digital transformation progression. It enables data democratization as well as the building of data solutions. Retail organizations are inherently ripe for data democratization. They often consist of a large number of more-or-less independently managed storefronts (and an ecommerce platform or two), all of which benefit from real-time visibility into local conditions and the ability to respond intelligently to them. This is to say nothing of decentralizing the corporate organization on the basis of functional roles, enabling different departments to make decisions in response to their specific needs.
Infrastructure modernization, or the integration of new tools and processes to provide new or improved capabilities, is an ongoing process that accompanies all of these efforts.
Data centralization combined with good data governance protocols enables data democratization, devolving decision-making to department heads, franchisees and individual contributors. This ensures a more agile, responsive and innovative organization.
Data centralization also enables organizations to build data solutions, i.e. turn data into productive assets or otherwise monetize it. Data solutions can be thought of in three ways:
- Enterprise pipeline management involves management of data assets at a massive scale and complexity, requiring programmatic control. Large organizations can have data move in a dizzying range of directions for both analytical and operational purposes. For large-scale, decentralized retail operations, programmatic management of data pipelines is a critical capability; a retail corporation shouldn’t be asking individual franchisees, for instance, to intensively manage their own data operations.
- Analytics products are derived from raw data and can range from dashboards and reports all the way to productionized artificial intelligence/machine learning models. In retail, a concrete example of an analytics product is personalized recommendations for customers built off of machine learning models that mine data for observable patterns of preferences and behaviors.
- Data sharing systems make real-time data available both internally to business units within an organization and externally to customers and other third parties. Retailers interact with many third parties and may have various reasons to share data with vendors, customers and other entities.
Infrastructure modernization includes any instance in which an organization changes the tools, technologies and platforms it relies on for data operations. It can take place along several, non-mutually exclusive dimensions:
- An organization may move from on-premise operational systems, pipelines and destinations to the cloud for greater flexibility and speed along with reduced maintenance.
- Data teams may switch a data pipeline from ETL to a more modular, flexible ELT-based architecture.
- Different destinations differ by cost structure, performance and other attributes, so an organization may migrate from one type of destination to another.
- In the pursuit of greater organization agility, a data team might upgrade its data movement capabilities from intermittent, batch updating to real-time or streaming.
Fundamentally, modernizing infrastructure is about improving the capabilities of your data organization to serve the goal of improving data centralization, data democratization and data solutions.
A retail roadmap for digital transformation
Data centralization, infrastructure modernization, data democratization and building data solutions are all essential elements of a cohesive strategy for digital transformation. This process fundamentally starts with data centralization, specifically with automated data movement characterized by the following three pillars:
- Automation – The processes and technologies used to move data must minimize the use of engineering time. Labor is the costliest asset for nearly all organizations. Automation further enables organizational agility and faster turnaround for all data-related executions, enabling organizations to assemble the data needed to support decisions in a matter of days rather than months or quarters.
- Reliability – In a similar vein, data movement must involve a minimum amount of maintenance and downtime. Data pipelines must not be disrupted by schema changes or failed syncs.
- Scalability – Data operations must be able to seamlessly grow in scale. Keys to scalability include the ability to accommodate a huge range of data types and sources, govern the usage of data across departments and programmatically manage administrative tasks associated with data movement.