We’ve seen unprecedented change over the past few years in the retail landscape and in consumer buying behavior, driven both by the COVID-19 pandemic and by retailers’ digital transformation efforts. A recent report by McKinsey notes that “…in a matter of 90 days we have vaulted forward 10 years in consumer and business digital adoption.”1 And there is no sign that the speed of change is slowing — if anything, it is accelerating.
Besides grappling with changing consumer behavior and their digital transformation initiatives, retailers must continue to look for opportunities for growth even as they face tougher competition, disruptions to supply chains and ever-more stringent regulatory requirements.
To boost revenues, reduce costs and comply with regulations in this demanding environment, retailers need to:
• Anticipate customer behavior and preferences
• Deepen their insight into which channels perform best for which customer segments and which products
• Increase customer engagement and loyalty
• Ensure the data they have on customers is accurate and protected
• Deliver a successful digital transformation
At the heart of all this is data. However, to be of value, the data needs to be fit for business purpose.
Although retailers are rich in data, many struggle with fragmentation and data that’s locked away in various enterprise applications. These application silos create multiple versions of the data with varying standards which makes it difficult for the business — sales, marketing, customer service and operations — to gain a complete and trustworthy view of its business-critical data, such as customer data or master data.
Data that is not cleansed, standardized and verified impacts the business in a number of ways, such as:
• Inability to identify customers and provide personalized offers across channels
• Slow response to identify additional or alternative suppliers
• Lack of trust in the results from AI/ML initiatives
• Delays in new product launches
• Ineffective pricing and promotion initiatives
• Non-compliance with data privacy regulations
• Delays and cost overruns with digital transformation strategies
“66% of retailers say inaccurate inventory data creates buy online/pick up in-store (BOPIS) inconsistency.”2
“Between 12% and 21% of survey respondents said they switched to brands that sent them relevant messages or promotions in their preferred channel.”3
As consumers become increasingly concerned about their privacy, a wave of data protection laws now also impacts how companies collect, store, share and manage their sensitive data. Two examples of these data protection laws are the General Data Protection Regulation (GDPR) legislation, with its global implications for every company that does business in the European Union or with citizens of the EU, along with the California Consumer Privacy Act (CCPA) which secures new privacy rights for California consumers.
For many regulations, compliance is largely a data-driven discipline. But in large retailers, data environments have become much more complex, with data locked in many silos, and data quality and governance applied inconsistently.
Data protection regulations often come down to three criteria: One is knowing where all your sensitive data lives and where it is used. The second is your ability to prove that you’re using it legally. And the third is whether you can prove that you’re securing it.
The metrics of data protection success are clear: you want to avoid fines and damage to your organization’s reputation. Without data quality, achieving these metrics becomes much, much harder. For example: if a data field or column is poorly labeled or its metadata doesn’t conform to business rules, you won’t see that it contains personally identifiable information (PII) — and you won’t secure it.
The most common challenge retailers face with customer, product, inventory and sales data is not its quantity or timelessness — it is the quality of that data. The reality of your data warehouse is that you can’t just keep dumping information into it, hoping it will sort itself out. If you want to leverage your data for research, analysis building and training AI algorithms, the data has to be of sufficient quality. Without high-quality data feeding your analytics applications, any decisions around identifying customer trends, pricing, promotions, assortment management or inventory optimization will be flawed.
That’s why data quality is so critical to analytics and AI/ML. Bringing together insights across different data sources — structured and unstructured, static and streaming, each with its own data schema — puts a high demand on the quality of the data being combined. Data quality problems that inhibit analytics in a single silo become exponentially worse as you combine that data with more and more data sources.
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3Retail reimagined: The new era for customer experience, McKinsey, 2020