No matter how you slice it, data rules the commerce world.  Brands and merchants that take command of enterprise data can beat the competition through improved understanding of all aspects of their business, from sourcing and merchandising to marketing and loyalty.

Using insights that come from a well-managed enterprise data operation, commerce companies can:

  • Create a 360° view of customers and Improve segmentation
  • Source and merchandise with better accuracy
  • Spend marketing dollars more efficiently
  • Grow average order value and customer lifetime value
  • Better target customers across channels
  • Move faster to identify new trends and opportunities
  • Improve the input used to make decisions by tech and finance teams

On the other hand, when data is not optimized across the enterprise, companies often face these challenges:

  • Keeping reported data in sync with source data
  • Transforming data into usable forms
  • Turning learnings from data into new and best practices
  • Publishing metadata and training users
  • Collecting, storing, and integrating internal and external data
  • Understanding marketing technology needs
First things first

It is critical to comprehensively understand your current and future data needs. Create a grid that shows:

  • Types of data you currently collect
  • How the data is currently collected
  • How the data is reported to stakeholders (e.g., daily via dashboards, weekly via spreadsheets)
  • What business purposes the data is used for
  • Wish list of data you would like to collect

Data warehouse or data lake?

Data warehouses typically provide reports for a pre-determined suite of metrics that are considered table stakes in most businesses, including daily/weekly/monthly sales and profit margins, inventory-vs-demand, and marketing data by channel.

Data lakes, on the other hand, are less structured repositories of data, allowing business leaders to ask questions on the fly (e.g., did today’s sale on blue jeans perform better with customers who recently purchased sweaters or with those who purchased tee-shirts?). By allowing companies to identify queries on the fly, data lakes can build on data warehouse reports to identify emerging trends and assess new opportunities.

While data warehouse functionality is typically meant to be used out of the box or with limited assistance from data scientists, data lakes often require ongoing programming from skilled in-house data scientists.

Is change management needed to bring true data utilization to an organization?

Yes! Many companies currently handle data in silos. To create the most efficiency and usability, all stakeholders need to be on the same page about what data is collected and how it will be used. Organizations also need to be aware of interdependencies between departments or business functions.

Change management is often required to help organizations set up new processes for collecting, analyzing and sharing data.

Last but not least: Convincing the C-suite

It often helps to give an example of how you plan to use data to solve a current business problem. It may also help to share our lists at the top of this post about what happens to an organization when data is or isn’t optimized.

FitForCommerce has put together 5 tips around enterprise data for you, depending on your current level.

Click on the button to get your tips.

Beginners are still using spreadsheets to collect or report data, with few or no automated systems. Data collected is basic or gives a limited view of the customer.

Intermediates have an automated data system and find it hard or expensive to create new types of reports or data formulas that will give them more insights into the consumer.

Advanced consumers of data have an established data warehouse and data lake capabilities, and are using artificial intelligence to find data points that determine a 360° view of the customer.

Get in touch with us to learn how to leverage enterprise data for your organization.