This individual case study is graded. Case study assignments are designed for you to apply concepts from the module and perform analysis in the context of a real-world organization. 

This case study explores the real-world scenario of a privately held Dutch company retailer with 2,800 stores across several European countries. 

Please read the following to learn more about the details of this case study: 

The company being profiled within the case study is a privately held Dutch company retailer with 2,800 stores across several European countries. This retailer has 15 different brands that include products from toys to cookware, each brand having its own infrastructure. Each business entity is managed independently as one of 15 individual companies. They develop their processes, maintain their legacy systems, and make business decisions across finance, IT, supply chain, and general operations. 

Meeting the needs of a constantly evolving competitive environment requires global business visibility, which is a challenge for this large retailer with 15 independent brands to manage. To gain better visibility, increase business efficiencies, and lower costs, the retailer decided to develop a corporate strategy to manage data in a centralized system using a single IT department. Data centralization means that all brands will be managed within a single data warehouse and implemented brand by brand to take into account individual business processes and needs. A big challenge for this large retailer is that several systems have to be integrated, including their (15) SAPERPs, warehouse management systems, point-of-sale (POS) systems, and material master data.  

With a focus on maintaining business agility for sales and margin analysis, the retailer’s goal was to provide access to the transactional level of data originating from the roughly 50 SAP tables within each ERP system. The move to a centralized approach was especially exacerbated by the complexity and nuances across the 15 ERP instances. Work was estimated at 400 days of effort per source system to bring this data into their central warehouse. Consequently, it needed a way to justify its expenditure, develop an ongoing value proposition of its data warehouse approach, and develop a way to expedite this process. 

On the business side, the focus is on creating a centralized analytic platform with access to a global view of transactional data. Due to the seasonal nature of retail, leveraging multiple years of data is important to help identify seasonal trends, create forecasts, and develop pricing and promotions. The goal is to improve visibility and provide freedom of analytics across the supply chain, material, sales, and marketing to help this organization become more efficient in the way it does business. Consequently, the retailer selected the Teradata Database because it could handle both transactional analytics and provide advanced analytics capabilities. Its goal was to support operational analytics and flexibility by loading data without developing DMs or other logical models in advance of users asking business questions. This approach enables them to save data centrally within a Teradata Database while providing future flexibility related to data access, report, and analytics for all the brands. 

Underestimating SAP ERP complexities, the company spent the first six months cutting its teeth on a customer homegrown SAP integration. After six months with little to show, it recognized the risk and stopped the project to investigate if there were better approaches to this problem. It first met with a major SAP SI, who provided a 400-day integration estimate to load data from just the first SAP ERP. Teradata Analytics for SAP Solutions was selected because it was specifically designed to address the challenges associated with bringing data in from SAP ERP to the Teradata Database. The solution also delivers an automated approach to integrating SAP ERP data into the data warehouse and enables them to load the data required for the first brand in just five days instead of the estimated 400. The retailer spent an additional 45 days adding 25 custom (Z) tables and preparing the data for consumption. This accelerated the integration of SAP data by 800%, thereby saving 350 days of work. 

Combining a full ERP consolidation project across legacy systems creates a project with many complexities. Although Teradata Analytics for SAP Solutions provided automation for the SAP-related data management portion of the project, the trailer still encountered technical challenges because its data warehouse initiative was combined with a broader integration project. Its project was to standardize the tools and develop a framework with the first couple of brands that could be applied to the incremental rollout to the rest of the organization.

First, it needed to standardize an ETL tool and develop a new methodology and way of leveraging ETL. They used the ETL tools as an Extract Load Transform ETL tool to maintain the integrity of the granular transactional data. The retailer chose Informatica as the ETL standard and its ETL environment by using ETL tools as just a data mover and job scheduler. Second, in addition to storing the atomic transactional data, the retailer was able to leverage the Teradata platform to perform all of its business transformations in-database when moving into the reporting environment. This approach allowed them to keep a copy of the granular transactions, leverage the out-of-the-box integration provided within the Analytics for SAP data, and harness the database power to apply other transformations and analytics. Third, high data quality was imperative for them. It wanted to ensure that data could be accessed and managed in a consistent way. Material numbers highlight the importance of data governance to this retailer. Material numbers are structured differently across multiple systems and historically would have been reconciled during the load/model process. In this new architecture, it was able to easily overcome this challenge by creating unique material views in the data warehouse to harmonize the material number for reporting. 

Finally, it required an agile way to deliver data and analytics for both reports and ad hoc analytical access, which could also meet the diverse requirements of the brands. By taking advantage of Teradata’s partnership with solutions providers as MicroStrategy, the retailer was able to access the granular data stored in the data warehouse while using the BI tool to apply the relevant algorithms and leverage the flexibility designed into the data warehouse solution. 

The development of the data warehouse as a centralized data access hub was challenging at first. This was due to the requirement to develop a new framework and the overall learning curve because of the change in approach to data warehouse design. Luckily, once this framework was developed, integration using Teradata Analytics for SAP Solutions was simple and repeatable. According to the architect at the European retailer, "Teradata Analytics for SAP is a fast and flexible integrated solution, offering lower project risks, faster development, an integrated semantic model, and direct access to detailed data."

Overall, the retailer’s goal is to provide a repeatable implementation strategy across its brands to enable better business decisions, improve business efficiencies, and lower operating costs through IT centralization. Although it is still in the early phases of the project, it has already learned from the implementation of integrating its first brand into the Teradata data warehouse.

Due to the retailer’s use of Teradata Analytics for SAP Solutions, it was able to accelerate the time to value and simplify integration activities. In addition, it was able to develop some of the following takeaways to apply to the integration of their subsequent brands and to similar projects.

• Take the time for due diligence and learn what technologies/solutions exist to support implementation. In this case, the retailer was able to take advantage of Teradata Analytics for SAP Solutions, decreasing time to value and enabling it to focus on analytics as opposed to integration.
• Develop a framework to enable repeatable processes that can address the complexities of the vast amount of data and custom needs of the business.
• Keep the system design as simple as possible to ensure technology and business adoption.
• Make sure to align technical decisions with the overall vision of enabling business agility. 
• Develop a standard data governance approach to ensure data integrity that extends beyond the implementation process so that business and technical users understand how they can apply data for reports and analytics.
• Identify latency requirements to ensure that solutions—both data warehouse and integration approach–support needs. This meant ensuring that the Teradata SAP Solution also supported its operational needs.

These lessons learned, apply to the broader implementation and use of Teradata Analytics for SAP. The retailer was committed to centralizing its infrastructure and managing its brands more effectively. Consequently, it was able to take advantage of a way to automate the process and lessen time to value because of the ability to leverage a targeted solution to tie its ERP solution to its analytics.

After reading the case study, please answer the following:

1. What were the challenges faced by the large Dutch retailer?
   
2. What was the proposed multivendor solution? What were the implementation challenges?
   
3. What were the critical problems/successes?
   
4. How might you make different decisions?