How Retailers Can Optimize These 5 High-Demand Cloud Data Workloads

Everyone’s been talking about Big Data in retail since the days of targeted direct mail. But recent advancements in AI/ML, automation, and cloud-based platforms have made it easier for retailers to utilize complex decision-making models. 

And therein lies the challenge: complex models require large datasets. And large datasets equal higher data management, processing, and querying costs—not to mention potential performance lags. 

Thankfully, there are solutions that can help you accommodate these larger workloads without incurring the wrath of your CFO or users. Let’s take a look at five high-demand data workloads and autonomous data cloud optimization tools that help you better manage them. 

Why 5 high-demand data workloads increase costs and slow down performance

As a whole, the retail & CPG space is getting smarter and more data-driven in their decisioning. This is largely due to growing use of the following analytical models, all of which create powerful insights that are essential to strategic planning and GTM tactics.

However, they’re also very data-hungry and, as a result, can lead to higher compute costs and performance lags. Let’s look at each one to see why this is the case. 

1. Demand forecasting

Demand forecasting is a critical component of effective inventory optimization, supply chain efficiency, and overall strategic planning. However, success in this area requires use of and access to large datasets, for a number of reasons:

• Data complexity and granularity, as demand forecasting has to take into account a wide range of variables to create fine-grained, accurate projections
• Higher quantities of data enable demand forecasting models to separate true trends and patterns from outliers or noise, improving model performance and reducing fluctuations
• Real-world demand patterns can shift significantly in response to various factors, including seasonal changes, economic conditions, weather patterns, or an inherent irregularity to demand

However, the more data you’re storing in your cloud data platform, the more work your cloud platform has to do. This not only increases compute costs, but can also slow application performance. Worst case scenario, it does both. 

2. Supply chain analytics

Whether we’re talking about shipment tracking, stock levels, or supplier performance, the ability to analyze all aspects of your supply chain is groundbreaking. But it also requires you to pull in large amounts of data from hundreds (if not thousands) of sources: 

• Numerous interconnected processes from sourcing to delivery
• Granular insights from individual products, stores (both online and brick-and-mortar) and timeframes
• Complex variables like historical sales, short- and long-term trends

If your data cloud runs on a pay-as-you-go model (like Snowflake or Databricks), your costs will get out of control really quickly. What’s more, querying data from a large number of sources can increase latency, thus slowing insights on the back end. 

3. Customer 360 & personalization

Gone are the days where personalization is a merge tag that pulls data from your CRM into your marketing and sales emails. Personalization now requires models that analyze omnichannel data to create a holistic customer profile (i.e. Customer 360). 

Now, this is well worth the investment: 80% of retail customers are more likely to make a purchase when they experience some kind of personalization. At the same time, it can drive up your data cloud costs:

• Large data volumes 
• Need for sophisticated and complex data segmentation
• Use of complex SQL functions to integrate and transform data—advanced joins, aggregation and grouping, window functions, and more

What’s more, because you need to provide personalized customer experiences at the point of engagement, automated queries of large datasets with undue latency could create a poor experience on the front end. 

4. Dynamic pricing

Dynamic pricing is essential to maximizing revenue, improving market competitiveness, and remaining adaptive amid a tumultuous economy. But optimizing prices requires a great deal of data, including:

• Historical sales, prices, units sold, conversions, and buyer information
• Current inventory levels and remaining supply
• Manufacturing, sourcing, and production costs
• Competitor pricing, market demand trends, and other macroeconomic indicators
• Customer behavior data, including segmentation, behavior, intent, and preferences

We’re not just dealing with a large quantity of data. These datasets are always shifting and require constant updating. This can significantly increase your compute costs and can result in performance lags. 

5. Competitive intelligence

Finally, there’s the need to always keep an eye on the competition. Just as you need a 360-degree view of customers, you need a similar window into your competitors, their behaviors, market gaps, etc. That way, you can make informed, data-driven decisions in your positioning, marketing messaging, sales campaigns, pricing, product development, and more. 

The more data your competitive intelligence models have, the better they’ll be at identifying the signals you should respond to—and the noise you should ignore. But this requires a diverse set of data:  

• Competitor pricing across different channels (e-commerce, omnichannel, physical stores, etc.)
• Historical pricing trends & promotional campaigns
• Product details—features, categories, availability
• Private label vs. national brand prices
• Market trends, industry benchmarks, and emerging trends that could impact consumer preferences
• Customer sentiment analysis (especially comparing competitors to your own brand) 
• Competitor financial performance metrics like revenue growth or profitability

Analyzing all these datasets requires extensive querying, table joining, data transformation, and other activities that sap your compute power. 

How to use AI to tackle these workloads in Snowflake

So we’ve established that some of the most impactful data workloads in modern retail, let’s talk about our solution. Keebo’s unified AI platform is built to optimize both costs and performance efficiency. 

Here are the three tactics we use to improve the efficiency of your advanced data models & capabilities. (Note: Since Keebo is a Snowflake optimization engine, that’s where we’ll keep our focus for now.) 

Autonomous warehouse optimization

Keebo autonomously optimizes warehouse sizes, clustering, and caching to provision the exact resources needed for a given workload in real time. Our platform operates 24/7 and learns and adapts to changes in demand—even when your data team is asleep.

At the same time, we offer fine-grained control over these optimizations, so you can dictate how aggressive you want your optimizations to be. 

FinOps & observability

When you’re managing complex workloads with many interconnected datasets like the ones listed above, getting full observability into performance and spend can be tricky. You may be able to identify broad trends, but getting the information you need to fix the problem is a bear. 

Keebo fixes this issue with 360-degree observability into spend, query, warehouse, storage, and data health. Our workload intelligence tool handles complex workloads and scales seamlessly to create transparent, measurable, and actionable insights—no “black boxes.”

Resource & query efficiency

If you have a complex web of applications that are querying your database and adding new data to it, application logic and performance considerations become impossible to untangle. With Keebo query routing, we can dynamically direct queries to the optimal warehouse based on size, load, and query characteristics—and not their point of origin. 

This enables you to keep lightweight queries from running on expensive warehouses (and vice versa), as well as improve load balancing to create homogenous workloads.

See Keebo in action with a personalized demo and free 2-week trial!