Evaluating data as part of your tech stack

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(Image credit: Pixabay)

In the ever-evolving age of e-commerce, retailers must stay on top of the latest trends in data and technologies to create best-of-breed data ecosystems for the most optimal outcomes. By understanding first-party data – information that a company collects and owns – companies are better equipped to help improve the customer experience (CX) while optimizing customer retention and increasing loyalty.

Retailers have historically used data to make buying and operational decisions and improve efficiencies. While that is still true today, data provides retailers with many more opportunities, such as enhancing and personalizing the shopping experience, advancing customer acquisition and retention efforts and much more. For retailers to ensure they are maximizing their data use, they must first optimize their approach to data platforms and architecture to support their data analytics.

Data and decision-making go hand-in-hand

It’s been proven time and time again that companies that use data to make business decisions provide better experiences to their customers and ultimately become market leaders in their space. Companies should focus on several practices that enable advanced decision-making when developing strong data science and analytics programs.

The first is utilizing a reliable data platform, by taking advantage of the many advancements in technology and cloud computing over the past few years. By moving to the cloud, companies can open up the possibility for unlimited expansion. At the same time, there has been an explosion of both commercial and open source data tools and platforms capitalizing on the limitless scalability of the cloud. It could be very tempting to go after the newest tool on the market or, alternatively, get overwhelmed with the abundance of choices and ultimately decide not to modernize your approach. When making a decision about a data platform, it’s important to remember that the purpose of any platform is not only to support immediate business needs but also to be able to grow with the business, adapting to its changes. Reliable data platforms are necessary to ensure that the data being collected is accurate, complete, timely, understandable and accessible. Data tools need to empower users to understand and interpret data quickly; otherwise, it is not actionable.

Veronika Durgin

Veronika Durgin is VP of Data at Saks.com.

Why retailers should use their data

It’s important to recognize that data is a critical business asset. Data is the backbone that supports not only operations but also the end-to-end customer experience. The more we understand our customers, the better experience we can serve them. In order to translate this benefit to the customer experience, companies need to be able to extract value from data and quickly turn it into insights.

Retail’s future data applications

Data volume, velocity and variety grow at an exponential rate. Any data ecosystem needs to be able to handle data ranging from structured to semi-structured like JSON to unstructured in the form of documents and images. Modern data tools make it easier for data teams to move data to centralized data lakes, but that is not enough to make data actionable. Automated pattern-based approaches to ingestion, modeling and code deployment are required to ensure that it does not take long to get from analyzing data to gleaning actionable insights and therefore value.

In a world where data is the most valuable currency, many retailers are falling behind by not taking advantage of modern data technologies and cloud computing. Recognizing the importance of data begins with establishing data literacy, modernizing technology and improving time-to-value. You’re only as good as the data you’re looking at. Working in luxury fashion, trends come and go, but investing in data will be worth the time and money every time.

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Veronika Durgin is VP of Data at Saks.com. Veronika Durgin is responsible for the data strategy, from driving enterprise digital transformation and governing enterprise data, to enabling data efficiency and supporting analytics and reporting of the full customer shopping journey.

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