Understand the data for omnichannel

1

Male store owner tries to understand the data for omnichannelIn my last blog post, I discussed the value of establishing a data strategy for omnichannel that's supported by data governance. In this post, I'll cover what’s next in the omnichannel data journey.

Once your data strategy is in place, you'll want to identify what data is needed to build a "golden record" or single view of the customer. This step is more difficult than it sounds, because the data you need most likely lives in many places and systems across the enterprise. Identifying where this data is and which is the right data to use is not a task that should be taken lightly.

From my experience, I've found that most organizations have some type of data models of their various systems and what they contain. Though that type of diagram is helpful, it won’t be enough to enable you to determine what is the right data to use. For example, customer information probably resides in CRM systems, ERP systems, marketing systems and applications, while e-commerce data resides in other places. So, how do you determine what data is best to use when you want to build that golden, single view of the customer?

Where to start

One very good place to start is with the data strategy you've put in place. In addition, conducting a data assessment on your systems using data profiling will help you understand the condition of your data. So you'll be able to answer questions like these:

  • How complete is the data?
  • How unique is the data?
  • How recent and frequent are the data elements?
  • How does the data join to identity data relationships across systems and data types?
  • What is the cardinally of the data?
  • What are the actual data values?
  • What anomalies exist in the data?
  • How much of it is null?
  • How much of it is repeat values (duplicates)?
  • How much of the data is bad or completely useless?

Auditing the data

The next question is: Who conducts these audits? From the data governance program, you should have already identified the data owners and stewards of the data. The task of auditing should fall to the data stewards, working with both IT and the business data owners. But make note: The data stewards need a very good profiling tool with visualization and lineage capabilities to conduct these assessments quickly and effectively. Writing code or exploring data in Excel spreadsheets is not the answer for an effective, informative data assessment.

Once the data assessment has been completed, the data stewards, IT and data owners should call a data governance council meeting to discuss what they've learned about the data. This is where visualization and lineage tools come into play. The visualization tools will help the council see the state of the data while the lineage tools will help them understand data usage across the organization. This insight is imperative, because the goal of this exercise is to improve the customer experience – not break systems that are important to the customer engagement process.

In my next post, I'll address what happens after the assessment and how to incorporate data standards powered by governance into the omnichannel data journey. Taking this approach will provide an accurate, reliable and comprehensive "single view" of your customer.

Download an e-book about data governance and MDM
Share

About Author

Kim Kaluba

Data Management Specialist

Kim Kaluba has more than 20 years of successful partnering with companies to identify and resolve data management issues. This involves providing guidance and solutions for data quality, data governance, master data management, data federation, personally identifiable information protection, business intelligence, data visualization and analytics.

1 Comment

Leave A Reply

Back to Top