By Dave Wargo, Associate Partner, Columbus Consulting
Enabling analytics and insights, AI and Machine Learning, Planning and Operations, and more.
For years, retailers have been racing to absorb and manage data from every area of their businesses. The birth of ecommerce provided first party data and direct access to not only transactional, but behavioral data as well (e.g.,.what customers search for, what they add to their basket, how often they shop, where they shop, what colors they prefer, what they say about us on social media, etc.). Layer in loyalty programs, third party data, syndicated product information, vendor inputs and financial data, and now we have a near limitless universe of information at retailers’ disposal.
Today, retailers are realizing that nearly three decades of fast and furious data creation and collection have left them with petabytes of information that can create more confusion than clarity. Even with cloud migrations, data science initiatives, chief data officers, and multiple databases, retailers are still asking the question of: “do I have good, reliable data, and, if so, how can it support my business objectives?”
As retailers spin up new initiatives across core functions – ERP transformations, customer experience improvements, merchandise planning and allocation programs, enhanced reporting and dashboarding, and others – these programs can hit a wall in the following areas:
- They do not have clean data to support new solution operations optimally – e.g., missing product attributes to enable assortment or pricing strategies
- They struggle to make sense of the transactional (e.g. POS/IoT) and operational data (e.g. promotions) embedded in these efforts, since they do not have reliable base master data to which this data can be aggregated and compared
How does a retailer understand what percentage of their returns are in ‘slim fit’ if the products are missing this attribution? Or whether chicken sandwiches sell better with a $ off, % off, or as a buy 1 / get 1? The dual purpose of master data elements to enable core operations and as core insight aggregators makes these data assets critical for success.
Indeed, master data management (MDM) is not a new retail initiative, but with high customer expectations, privacy laws, evolving carbon footprint tracking regulations, and artificial intelligence, the need to hone in on master data has never been more urgent. That’s where the Big 6 comes in.
While overall data management for a retailer can be a massive undertaking, value can be gained focusing on any or all of these key mission-critical data domains:
- Product
- Vendor
- Location
- Financial
- Customer
- Employee
These six domains are the backbone for running a business. They can be approached either holistically or in a more targeted fashion based on business initiatives and use cases. Depending on timing and budget, a retailer needs to assess whether they should take a broad overhaul (the former) or a pain point approach (the latter) to enhance and elevate their MDM. Either way, retailers will improve and gain ongoing competitive advantage by keeping their foundational data accurate. Improvements here open up a world of opportunities for reducing margin leak and improving all aspects of operations — foundation driving transformation.
While most retailers recognize the importance of MDM, many are hesitant to address the next phase in their data strategic plan. Why? Knowing where to start can be the biggest hindrance. MDM initiatives can be accompanied by high investments, longer-term value returns, and require talent resources and company-wide discipline. Current or new platforms, systems, and processes need to not only allow for data cleansing, ingestion and migration, but must support a higher level of accuracy to be deemed business-ready and useful for machine learning, standard analytics, business operations, and more. And the business needs to get involved as data owners; MDM initiatives must be a partnership with technology team members. Overall, a strong roadmap and strategy paired with shorter term, targeted efforts can show value and kickstart the MDM initiatives.
Whether broad or targeted, MDM programs typically involve several key components for success:
- Data Assessment and Strategy to set use cases, maturity, and build a roadmap
- Data Model Management & rebuild to set business-ready data models
- Data Model Transformation to migrate to the target data model
- Data Management Solution Exploration & Execution to house and manage the data model and assigned records
And are paired with:
- Data Governance Models to oversee go forward standards
- Enterprise Data Architecture Frameworks to enforce oversight and manage data pipeline operations
- Change Management & Communication
- Specialized Services to eliminate silos and centralize core MDM functions
It’s quite a bit to digest! Don’t go it alone. Revisiting your MDM strategy requires a use-case driven approach. Cleaning master data to have clean master data will not rally the team. Knowing your business insight and operational needs is the first step. Grocers, quick service restaurants (QSRs), fashion, convenience, electronics, home furnishings and other retail businesses all need to be assessed and governed by different criteria and attributes. Once you understand your objectives and uses for data, you can then drill down into the detailed domains.
The product or item data domain is business critical for all retailers. QSRs need to understand selling by flavor profile, beverage size and type (carbonated/non-carbonated), al la carte vs. bundles, and common customizations. By contrast, fashion retailers need to focus more on core vs. seasonal, brand, color, size, fit, and material. Convenience businesses need tight standards around buying vs. selling packs to maintain inventory accuracy and replenishment. A product domain with customer-centric hierarchies and conditional attributes sets this foundation.
For brick-and-mortar retailers, more careful attention should be applied to location master data as over 80% of overall retail sales are still done in this channel. The operating cost of real estate today further warrants the need to understand individual store profitability. And stores need to be customer ready for service and optimal product selection. MDM can organize such attributes as:
- Square footage and department space allocation
- Drive through or BOPAC location details
- Design package and renovation dates
- Fixture type, count, and unit capacity
- Operating data: hours, employees counts and schedules
- Utilities, WIFI carrier
- Customer counts
- GPS: Weather/location flag
- Tourist/Urban/Rural definition
- Language density
- Population trends
- Adjacent retailers
All of this enables insights on conversion rates, top performing styles by location or cluster, staffing efficiencies, and more.
What’s on the horizon for MDM? Focused development in the MDM space is the innovation around data augmentation and intelligent solutions that automatically aggregate, deduplicate, clean data, and correct errors, before the data is processed.
Another key development in MDM is the focus on capturing product sustainability attributes. New data fields like carbon footprints, factory dying practices, distance between production points, fabric sources, packaging details, and other inputs are commanding the attention of CIOs and CMOs (chief merchandising officers) across the industry. With potential regulations on the horizon in capturing this data across the end-to-end supply chain, including subcontractors and components from suppliers, the need for standards is increasing.
A third critical trend in MDM is the ability to enable artificial intelligence and machine learning capabilities. Legacy systems/platforms could not predict the elevated need to accommodate such advanced capabilities. AI alone can make the case to revisit MDM standards and solutions. The ability to process data faster, extract significant data and pivot to predictive and exception-based solutions to business questions and scenario plans requires clean, consistent and well-defined master data.
Modern day retailers have advanced capabilities and the ability to capture more refined insights than ever before. The flip side of this, however, is that such a level of business management requires a more sophisticated, modern approach to harnessing and leveraging data. Having a holistic understanding of master data, breaking down the internal silos (departments, divisions, countries and even brand concepts), and organizing/standardizing data models fit for business use are the new core requirements for transformation. Building a strong foundation of master data disciplines and organizational ownership is the cornerstone for retail innovation.
ABOUT DAVE WARGO
Dave Wargo is an associate partner at Columbus Consulting. He is a data-led functional architect, bridging the gap between business and technology to empower data as an asset. He applies tactical retail use-case driven approaches to create processes, standards, and disciplines to enable AL/ML and harden analytics, insights, operations, and optimal customer experiences. Dave is experienced in luxury, quick service restaurants, core retail, convenience, and curated museum merchandising. He has held both retail leadership and consulting delivery roles in data standardization, process overhaul and improvement, retail change implementation, product development, product assortment planning, brand planning, demand planning, and buying.
ABOUT COLUMBUS CONSULTING
Columbus Consulting delivers solutions that drive true value and have been transforming the retail and CPG industries for over two decades. We are a retail consulting company of industry experts. Our approach is simple, if you do it, we do it. We are more than consultants; we are experienced practitioners who actually sat in our clients’ seats. We understand the challenges, know what questions to ask and deliver the right solutions. Columbus offers a unique, consumer-centric approach with an end-to-end perspective that bridges functional & organization silos from strategy to execution. Our specialties include: unified commerce, merchandising & category management, planning & inventory management, sourcing & supply chain, data & analytics, accounting, finance & operations, people & organization and information technology. Let us know how we can help you. To learn more, visit COLUMBUSCONSULTING.COM
