By now, retail IT leaders are aware that they will need to migrate SAP installations to the Cloud, if they haven’t done so already. Originally, the deadline from SAP for ending support for the ECC version of the SAP software was 2025, but the date has been extended to 2027. And it’s not likely that SAP will extend ECC support any further.
So, while SAP users are planning to or currently are making the move to the Cloud, it’s critical for them to also determine how to structure their retail analytics landscape to take advantage of the advanced capabilities offered in the Cloud.
In the past, retailers relied on legacy data warehouse/platforms for analyzing business data to make business decisions. But retailers operate on small margins, and in today’s digital age, business data isn’t enough. Consumers have multiple options, and their buying patterns have changed drastically. Retailers can leverage a wealth of external data on regional trends, consumer behaviors, buying patterns, weather, etc., to run and operate their business decision-making more accurately and efficiently.
Today’s standard is to use computer systems that gather internal and external data, and then help retail leaders to make data-driven decisions that relate to pricing, merchandising, inventory, marketing, and store operations.
The problem arises when the data analytics environment is on a legacy system that can’t provide all the capabilities offered by the Cloud. Retail leaders need to update those systems to stay competitive.
Limitations of Using Legacy Data Analytics Platforms
Typically, a legacy data analytics environment has a number of limitations. For example,
- The legacy system may be limited to analyzing structured data coming from SAP. This often results in long lead times for getting the information because of how the legacy system operates.
- Analyzing unstructured data is important, and many legacy analytics systems don’t offer that feature, but it’s especially valuable in the retail environment. Retailers have a large amount of structured data to analyze, but they also need to analyze unstructured data such as customer reviews, pictures, social media posts, and hashtags.
- Legacy systems are often difficult to scale. It’s necessary to acquire new systems when demand increases, and that is a costly undertaking. In addition, licensing costs grow substantially.
- Legacy systems are not cost-effective anymore. Some systems require in-memory processing which is not suitable to host and analyze large data sets. Further changes require long lead times due to the inherent design of the legacy systems.
Benefits of Using the Cloud’s Data Analytics Capability
In today’s retail environment, the most competitive businesses are supercharging their data analytics capability. Taking advantage of Cloud offerings is an excellent way to do that. It provides capabilities such as the following:
- Customer sentiment analysis is the process of gathering and analyzing how customers feel about your product, service, and brand, which can only be accomplished by analyzing unstructured data.
- Demand forecasting is critical for a retailer that needs to predict the number of products they’ll need in specific timeframes and in different regions or locations to satisfy their customers.
- Basket analysis is important to understand your customers’ buying patterns. For example, knowing which products are usually purchased at the same time would allow a retailer to create planograms that encourage more purchases of related products.
The beauty of data analytics in the Cloud is that it allows you to do various types of analyses like those above in a streamlined and cost-effective manner.
- Scalability is key: In the Cloud, you can easily increase resources when demand increases without capital expenditures. If you experience seasonal fluctuations, you can just as easily reduce the resources you consume without a penalty.
- Cloud analytics are typically structured as real-time or near real-time activities: Decision-makers don’t need to wait weeks for reports. They’ll have the information they need when they need it.
- The scalability and flexibility of Cloud analytics systems gives businesses a clear path to innovation.
- In the world of AI, Cloud provides access to innovative data and AI ecosystems and services to build new products and services faster to compete in the market.
Planning is Key
If you’re going to put your data analytics platform in the Cloud, appropriate planning is key to ensuring that you get the right one. Here are just some of the questions you’ll need to answer.
- Do you have a Data and AI strategy aligned with your business goals?
- How much structured and unstructured data do you want to analyze?
- How can you access external data sources?
- Have you defined Data and AI Governance strategy ?
- What technology investments have already been made?
- What strategic decisions have already been made concerning Cloud or Multi-Cloud usage?
Next, identify the use cases you’ll pursue. Examples include:
- Developing detailed customer segmentation using cross-channel data that can be used for more targeted promotions and streamlined campaigns.
- Managing inventory by analyzing purchasing patterns and forecasting future trends.
- Optimizing prices by analyzing competitor’s pricing data, sales data, sentiment data from social media, and more.
- Optimizing your supply chain by analyzing shipping times, supplier availability, and inventory levels to help cut down on delivery times.
Retail markets are getting more competitive every day. With the right data gathering and analytics platforms, retailers can understand their customers better, manage inventory more easily, optimize pricing, and create more effective marketing campaigns.
If you’re a retailer currently running in the Cloud, or planning to run SAP in the Cloud, you have decisions to make about your data analytics platform. You can give your business a competitive advantage by ensuring that you’re leveraging the scalable and flexible data analytics that are available in the Cloud. If you have questions about next steps, feel free to contact the experts at Lemongrass to discover the answers.
Learn more about JD Group and how they sought to improve its peak-period planning for warehouse efficiency. Recognizing it would need data-driven insights to realize this objective, the company kicked off a project to create a data lake house for their SAP and non-SAP data.
Recommendations:
Legacy Data Platform Migration Service / Business Warehouse Migration Service / Business Warehouse to Cloud Datawarehouse Migration Service
