Tableau Retail Analytics approach allows business people to ask and answer their own day-to-day questions, and find faster avenues to bring new ideas to the market. Retailers are blazing a path towards better insights with a new and modern approach to data self-service analytics.
This new Tableau Retail Analytics methodology, where IT manages data governance, ultimately frees IT from the antiquated reporting queue and simultaneously allows them to focus on other mission-critical projects.
Finding success in the self-service analytics requires overcoming the data silos and the entrenched, static reporting practices that plague most retailers.
The fact is, businesses can no longer thrive while working with different soiled data sets that don’t translate effectively between departments including Marketing, Merchandising, Supply Chain, Store Operations and e-Commerce.
To remain competitive, the retailers must create a road map toward an analytics approach that enables line-of-business managers to quickly grasp trends that drive the business forward. To achieve that goal, it’s important that retailers embrace the Tableau Retail Analytics strategy.
Self-Service Analytics Key Components
- Launch prototypes to drive a quick test-and-learn culture
- Create a unified data language around the training, security, and data preparation
- Use data blending to achieve a unified brand message
- Insert experience into the data analysis to drive collaborative learning
- Incorporate the visual analysis and storytelling to tap into the brain’s strengths
Self-service analytics deployments that incorporate rapid prototyping, a unified data language, connected data, visual analysis and storytelling will empower retailers to drive real business results with data.
This modern Tableau Retail Analytics approach frees the organization from the limits of traditional data management, and enables it to compete in an analytics-fueled marketplace.
Tableau manipulates the data through familiar techniques like drag and drop, part of an ongoing dialogue with the database extracts that are in active memory. Hence, the visual user interface offers a more seamless way to query the data layer.
Retailers use data to:
- See and understand the multi-channel information in a single location
- Monitor operations and track the inventory in real-time mobile dashboards
- Zoom in on the consumer segments to understand their behavior
- Spot top-performing locations and opportunities on a map
- Transform the product and ad placement from an art into a science
Tableau makes it easy for creating and sharing dashboards quickly and effectively. In matter of minutes you can connect to nearly any data source, use an intuitive interface to analyze your data visually, and then drag-and-drop to create dashboards. No programming expertise is required.
1. Launching Prototypes
To achieve a dynamic self-service analytics culture, retailers need to quickly place useful tools into the hands of every employee. But building those tools has traditionally been a lengthy, and not always successful, process.
Rapid prototyping avoids those pitfalls. In addition to IT and business users, this faster process also includes analysts, who add knowledge and business context to the conversation about tool capabilities and underlying data models.
Instead of attempting a complete list of requirements, the group members start with the business problem; then they collaborate to develop a prototype of data visualization on the spot, test it in a sand-boxed environment, and immediately discover what works and what needs tweaking.
2. Creating Unified Data Language
Another key component of a self-service analytics strategy is a unified data language that’s more than just master data management. It means the organization has come together to establish consistency across definitions, format and governance, and reach consensus on the value and importance of data.
3. Using Data Blending
Most retailers run a tangle of soiled legacy systems that were implemented for various disjointed projects. As a result, many retailers face challenges in bringing data together to support decision-making. Unfortunately, unifying data has not been a high priority simply because the value proposition isn’t clear to retail leaders.
4. Insert Experience into Data Analysis
Many industry executives believe making data actionable means producing accurate analysis and great-looking dashboards. But that is not the case. What’s often missing is context seeing the data in terms of the actions you would consider, depending on the results: shifting spending, increasing marketing or the rationalization of SKU allocation.
5. Incorporating Visual Analysis
Teams have been working with rows and columns of numbers since VisiCalc and Excel. This became the default way of working and remains entrenched. The problem is, it takes incredible effort to find patterns, trends or relationships in even a small grid of data — and it’s easy to miss something. This is holding retailers back.
Locus IT has a thorough knowledge of retail industries and the particular challenges that exist in the industry. We can help you plan and design a solution using proven tools that meet your specific needs, streamlining operations, and increasing profit. We also provide Retail Analytics services like Retail Analytics training, migration and support. For more information please contact us.