Home Blog 5 Q’s for John Knieriemen, Vice President of Teradata Corporation

5 Q’s for John Knieriemen, Vice President of Teradata Corporation

by Sujai Shivakumar

The Center for Data Innovation spoke with John Knieriemen, vice president of Teradata Corporation, a company headquartered in San Diego that provides a data platform for enterprise analytics across multiple cloud services. Knieriemen discussed the opportunities and challenges facing retailers in using data more effectively in the post-pandemic era.

This interview has been edited.

Sujai Shivakumar: What are the most significant advantages and new uses of harnessing data for retail businesses?

John Knieriemen: Data and analytics are foundational to any retailer or consumer packaged goods (CPG) company trying to win in competitive markets today. At Teradata, we think about four key strategies: low price, frictionless, experiential, and product brand…easy to say, yet tough to deliver distinctively.

The analytics to deliver any of those four are different for each, but all require an extensive integrated analytics data foundation. A low-price strategy requires integrated, multi-dimensional cost data and purchasing and supply chain drivers. Especially due to COVID, frictionless is probably the most dynamic strategy now, with a focus around curbside and home delivery analytics for merged—digital and store—customer and inventory analytics. Experiential requires a 360-degree view of the customer experience, across all steps in the product and service journey, as well as anticipating and addressing both needs and problems. Product brand is not just about luxury, but also about product quality, assortment management across channels, and sophisticated pricing to maintain brand position.

While there are exciting new retail analytics, from technologies like computer vision and Internet of Things devices, much of the innovation is happening by applying advanced analytics: getting sharper with predictive and prescriptive models around, for example, a given scannable bar code, or SKU, at any one store, or around moving from segments to hyper-personalized offers.

Shivakumar: What challenges do retail enterprises face in realizing the full potential of data?

Knieriemen: As data volume explodes, retailers wrestle with multiple challenges. The biggest is probably data silos, which are very common since stores, online, marketing, supply chain, and call centers often each have their own tools and datamarts. Connecting, integrating, and orchestrating these silos to deliver enterprise solutions are big challenges. At Teradata, the solve we often talk about is the need to “store once, use many.” Customers demand to be known across all touchpoints; delivering that reality is complex.

Other challenges include the fact that as data piles up into billions and trillions of records, teams wrestle with how to find inexpensive data storage for granular detail across days and years, say, for seasonality analytics. Teams want to be able to compute analytics when needed, but also leverage low-cost storage for less active analytic cycles.

Another issue for some retailers is the struggle to find and retain great data scientists, who may get frustrated when they can’t readily access essential data sets for exploration. Likewise, some traditional business analysts need user-friendly interfaces to leverage powerful analytics without a PhD. All the while, analytics and data investment must drive business value to maintain support from finance and the business.

Shivakumar: How has the pandemic shifted retailers’ data strategy?

Knieriemen: The pandemic was a business blessing for grocery stores, DIY home suppliers, home goods, and companies with strong—say 40 percent plus of sales—e-commerce platforms, and delivery companies like Instacart. It was a curse for many mall-based stores with weak e-commerce presence, as well as clothing and fashion players, forcing many into bankruptcy reorganization.

Let’s look at “buy online pickup at store” via curbside (BOPAS) or delivery. Data strategy could no longer let digital clickstream and buy-flow issues be separate from store traffic and inventory. Even successful grocers struggled to give accurate inventory and pickup slot information. Or they offered irrelevant recommendations and made substitutions that aggravated customers and hurt margins.

Data strategies required orchestrating experiences with traditionally disconnected data and analytics. Forecasting models based on prior year demand for toilet paper or hand sanitizer were worthless and needed to be rebuilt with new factors. Connecting supply chain visibility farther upstream and looking at new fulfillment uses for distribution centers were prioritized analytics sets. COVID taught many painful data lessons, but also accelerated progress to better future experiences.

Shivakumar: How can data analytics, combined with AI capabilities, help CPG companies use data more effectively?

Knieriemen: AI is helping CPGs do what they have been trying to do for decades, but much better. For example, some AI forecasting—such as demand-sensing forecasting approaches—are providing exciting results. Our customers are leveraging AI for localizing assortments, tackling their massive spend on trade promotion management to get more value, and exploring direct-to-consumer business models to better understand consumer needs and upgrade personalized offers.

Let’s remember that CPGs are also typically developing, manufacturing, and distributing products, as well as working in the field to help retail partners succeed. AI is an ideal approach for testing and simulating product configurations and reactions. Predictive models can spot triggers indicating that essential equipment is close to failure or that quality issues are about to arise. Sophisticated models are used for allocation, distribution planning, and route re-working in logistics.

During COVID’s peak and even today, supply chains are battling bullwhip effects of massive consumer demand swings, subsequent supply chain swings, and the struggle to get those back in balance. AI analytics can’t prevent this but AI can help retailers respond faster and smooth some of the peaks and valleys.

Shivakumar: How are Teradata’s strategies evolving in response to changes in the retail market ecosystem?

Knieriemen: Teradata is now a cloud data analytics platform. We have numerous customers still enjoying on-premises hardware, but we are focused on helping customers migrate successfully to the cloud…or clouds! Retailers and CPGs are demanding enterprise analytics ecosystems that can deliver actionable answers and predictive intelligence.

Large enterprise customers, in particular, have different departments or portfolio companies on different clouds. That’s why we are ensuring our platform works well connecting Teradata Vantage—our flagship offering—on different clouds and connecting with other non-Teradata systems.

One question we encourage customers to ask themselves is “You want to go to the cloud…to do what?” Unfortunately, too many organizations expect that simply moving the exact same data and architecture—from on-premises to a public cloud—will save money and deliver better results. Beyond the offering of a low promotional initial rate, most companies following that strategy are disappointed to find little savings or improvement after spending extensive time and money on migration. Teradata’s strategy is to leverage a cloud migration to help our customers modernize their architecture and build a flexible and scalable data analytics platform that is easy to connect to different departments, portfolio companies, and partner solutions.

Teradata’s retail & CPG strategy has evolved to work seamlessly on public clouds, to provide easy access to open-source analytical tools and languages, to appeal both to data scientists and business analysts, and to work with a network of strong solution partners to connect easily to our Vantage platform.

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