Retail Use Cases: Solving Data Challenges in Privacy, QA and Analytics

The acceleration of AI in retail and the growing demand for real-time, personalized data underscore the importance of this information to retailers – nearly 9 in 10 organizations state that real-time data is crucial in achieving business objectives. But consumers are increasingly wary as they become more educated about privacy issues related to data collection and hear stories around data security risks. Additionally, regulations have become more complex, leaving companies scrambling to keep up.

Read more about these common challenges in the below scenario, and take a look at a solution that helps companies meet regulatory requirements, maximize business intelligence, and reassure customers that their data is safe.

Use Case: Complying with Data Privacy Laws without Losing Access to Retail Insights

Scenario:

Megan is responsible for a team that generates sales reports and analytics for the large retailer where she works. The analytics her team generates are critical to the decision-makers at Megan’s company, who need to understand which products are selling well and which are not, the regions where those products are selling, the demographics of the store's customers, and the popularity of different products the stores carry.

Challenge:

The company must also address new regulations and increased consumer awareness of data privacy. Governments and regulators in various jurisdictions have introduced additional rules about how companies can use customer data, giving customers more control of the data that retailers like Megan’s company collect. News stories and shifting trends have led to customers of Megan’s company being far more sophisticated about data collection and willing to demand privacy.

Need:

Megan needs a way to access all of the important data she needs to generate reports, without violating the various data privacy regulations and laws, and without potentially alienating an increasingly sophisticated customer base who are aware of and expect data privacy. CTA: S.i. Systems helps retail teams like Megan’s unlock analytics insights with synthetic data while staying fully compliant and privacy-focused.

Use Case: Generating Quality Test Data for E-Commerce Load Testing

For organizations where heavy website traffic is a major contributor to success, understanding how it performs under a high-stress load is critical to ensuring reasonable response times and meeting customer demands. However, as real-world data becomes increasingly difficult and expensive to obtain due to privacy concerns and regulatory requirements, tech and quality teams require a more effective approach to building large datasets for testing performance and load.

Synthetic data usage is expected to accelerate in the coming years – in part due to this challenge. This scenario outlines the role it can play in testing and offers a solution for generating high-quality synthetic data.

Scenario:

Norm is the head of Quality Assurance at a large retail company with a public e-commerce website that generates millions of dollars in sales daily. Because his company is well-known, the website receives and handles a large volume of traffic and transactions. As a result, when the engineers at Norm’s company work on the website, it’s critical that Norm test their work, including the volume and load.

Challenge:

The performance and load testing that he needs to conduct require a large dataset to mimic the real-world conditions of the website. He can’t have access to the production data and hand-crafting quality data for testing takes a lot of time and isn’t a scalable solution for the performance testing that Norm needs to run.

Need:

Norm needs a way of generating high-quality test data that he can use to properly load test changes to the website.