With data at the core of AI and profit optimization, data is on every retailer’s mind; specifically: where to get it, how to process it, who should manage it and what’s the best way to govern it. Columbus Consulting has been featuring data all month long in our content series and continues to partner with our clients to enhance their data practices. While the topics around data are vast, our data management expert, Dave Wargo, has outlined some of the most pressing issues and questions he is most often asked.
- WHAT ARE THE TOP 3 CHALLENGES YOU SEE RETAILERS FACING WITH THEIR DATA TODAY?
- Lack of Business Ownership. Many organizations feel that the IT team is responsible for data. While IT is responsible for securing data, architecting it, integrating it, and readying it for use – amongst other responsibilities – the business owns the data standards. Without clear guidance and direction on what the data must do and the rules for ensuring the data meets conditions for intended use cases, there will be data gaps.
- Lack of an ongoing Governance Model. One time data fixes are not sustainable. On “week 2,” old practices resume, new data records are created incorrectly, and updates to existing data break rules. An ongoing governance operating model with owners, processes, and standards must be set as a base to ensure data integrity to meet desired standards.
- Lack of a solution to manage the data. Cleaning up data in a solution that lacks the controls and process gates for data management, yields the same “Week 2” results as above. Too often, retailers assume that existing legacy solutions can consume and maintain desired data standards. Typically, that is not the case. Lack of exploration in data management solutions (e.g., PIM/MDMs) can be a shortsighted approach with inevitable rework to continually clean data, possibly in a manual fashion.
- HOW IS AI MOTIVATING/CHANGING THE WAY RETAILERS THINK ABOUT DATA?
- Regardless of the AI use case opportunities, foundational data to train the models must be part of the equation. Retailers must think about their core data as a critical enabler for AI. If core data domains (e.g., product, location, and customer) lack incomplete or inconsistent data, the models cannot learn as effectively and efficiently as with clean data. Retailers must approach AI objectives as they would with any other strategic plan: What is my intended goal? Does my data support that intended goal sufficiently? How do I ensure that my data can support my AI use case?
- Retailers can also think about how AI can support cleaning and standardizing data. “AI to support AI.” Opportunities to automap products to desired hierarchy levels or with attribute values can support accurate data standards. Additional AI models can look for anomaly detection (e.g., the sofa with a 9” length that should have been 91”) and enable automated notifications for review and remedy.
- WHAT STEPS DO YOU RECOMMEND RETAILERS TAKING NOW TO ENHANCE THEIR DATA PRACTICES?
- Educate leadership on the value reliable data brings to the organization. Create a strawman value model based on current efforts that are limited by unusable data (focusing on sales, margin, loss, compliance, and customer impacts).
- Assign a key data steward to form the ground base for a data governance operating model. It can start small with one use case to prove out the value and how it fits into the organization and culture.
- Complete a data strategy assessment. By exposing the data model, data creation or ingestion processes, and more, an organization can understand gaps and create an end-to-end roadmap for enabling their data.
Don’t let data get in the way of retail transformation. The time is now to review your data quality and practices and develop a roadmap for continuous improvement. Data enables retailers to deliver end-to-end visibility to the supply chain. Data enables best-in-class customer experiences. Data enables never out of stock practices and higher full price selling. Data powers ML/AI. Data is profit. Don’t know where to start? Columbus Consulting can help. Contact us for a data assessment. https://www.columbusconsulting.com/contact-us/
Find out more about our data and analytics practices here.
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.
