Taming the omnichannel data monster


store owner considering an omnichannel customer data strategyWhat are the challenges we’re seeing with customers who want to achieve an omnichannel vision? Many retailers and consumer goods packaging companies struggle with the same issues as they embark upon this journey, including:

  • Data is everywhere.
  • What is the right data to use?
  • What does the data mean?
  • How do we spend less time coding the data for business needs?
  • Reports are inconsistent and tough to defend.
  • Integrating data together is an ongoing and unsuccessful process.
  • How do we create an omnichannel vision?

The first step in creating an omnichannel vision is to develop a data strategy. Your data strategy should consist of five components (explained in more detail in this white paper).

  1. Identify: Define the data and understand its meaning regardless of its structure, origin or location. A company should establish consistent data element naming and value conventions for data that will be shared. These details should be independent of how the data is stored or where it resides. The details around the data become the organization's data glossary. In addition, it's important to have a way of referencing and accessing the metadata associated with the data (definition, origin, location, etc.). If data is truly a corporate asset, a data strategy has to ensure that all of the data can be identified, located and understood.
  2. Store: Persist data in a structure and location that supports easy, shared access and processing. Data storage is a well-established function within organizations. However, most approach this with a "data creation" perspective rather than with a data sharing/usage perspective. A data strategy will ensure that any data created is available for future access without creating multiple copies for each individual or department usage.
  3. Provision: Package the data so it can be reused and shared, and provide rules and access guidelines for the data. In today’s environments it's not uncommon for data to be stored for the convenience of the application that collected, created or stored the content. But if a company’s data is truly a corporate asset, then the data must be packaged and prepared for sharing. A data strategy must address data provisioning as a standard business process.
  4. Integrate: This simply means moving and combining data that resides in disparate systems to provide a unified, consistent data view. As many IT resources know, this is easier said than done. One of the main challenges of data integration is to match data across multiple sources without having to use a unique identifier. In addition, for most companies data integration is not a centralized or critical business function.  A data strategy should put in place a discipline that uses the same rigor and methodologies across business and IT. For data to be a corporate asset, the data strategy must include integration as a core component.
  5. Govern: Governance will establish, manage and communicate information policies and mechanisms for effective data usage. The reason for establishing a strong data governance process is that once the data is decoupled from the application that created it, the rules and details of the data are known and respected by the data consumers. The role governance plays within the overall data strategy is to ensure that data is managed consistently across the company.

The strength of a data strategy is to help you identify and focus on the goals of each individual discipline area. A strategy will also help you identify an achievable and measurable set of goals that will improve data access and sharing.

Remember that a data strategy is not a single exercise – it's a process that should contain measurable long and short term goals. The power of a data strategy is that it positions you to deliver on the best possible solution as your organizational needs grow and evolve. Your data strategy is a road map and a means to address both existing and future data management and data sharing needs.

Register to watch a free webinar: Omnichannel Analytics Customer Data Strategy

About Author

Kim Kaluba

Data Management Specialist

Kim Kaluba is a member of the Product Marketing teamb covering the area of Data Management. Kim has been with SAS since 2013. She works closely with the product management and sales organizations to create and promote materials that are relevant and valuable to SAS customers. Kim's 20 years of experience in data management include sales, marketing, and enablement. Kim received her business degree in Marketing and Management from Stetson University.

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