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Three Analytics Success Stories

  • John Morrell
  • January 20, 2020

Shifting to a data-driven model is the key to remaining competitive and unlocking growth in your business. Successful data strategies at the companies mentioned in these three analytics success stories address challenges like data management, security and deriving valuable insights from the right data sets.

Data projects are as unique as the organizations that develop them. Studying how businesses create value from data sets can teach you some practical lessons for developing your own data-driven strategy. Here are three success stories of organizations that made a difference with data.

Big data drives the UPS supply chain

UPS is a company that successfully leveraged new technologies to keep up with the increasing demand for fast delivery linked to the e-commerce boom. The delivery service currently handles 19 million packages in a typical day and relies on big data to optimize its operations.

UPS has built a modern data-driven supply chain that collects data from multiple sources. ORION, or On-Road Integrated Optimization and Navigation, is one of the elements of that supply chain. The ORION algorithm processes data from customers and vehicles to optimize delivery routes in real time as new data becomes available about traffic jams or weather events.

ORION is part of a larger system that interacts with more than one billion data points in a typical day. Thanks to that sophisticated system, UPS can move its vehicles to areas with high anticipated demand, reduce emissions by optimizing delivery routes and guarantee fast delivery times.

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How Starbucks won the personalization game

Customer experience is now a key differentiator that influences consumers’ decisions and is a common use case for analytics success stories. How does a chain with more than 15,000 locations in the US offer a consistent experience across its locations and deliver the feel of a small coffee shop?

The answer is big data. Starbucks gathers data through its app and rewards program. The chain launched its Digital Flywheel Program in 2017 to make personalized drink and snack recommendations.

The program makes relevant recommendations based on a customer’s past orders, but it also looks at the time of day, day of the week, weather and other relevant factors.

Starbucks goes beyond recommendations by using big data to deliver special offers that feel more relevant, and the chain has bridged the gap between technology and human connection by making personalized information about each customer available to its baristas.

Using data involving traffic patterns and demographics, the Starbucks app and rewards program also support growth by contributing to decisions about locations for new stores.

Shell’s spare parts inventory management strategy

Part failure is one of the challenges of managing oil drilling machines. The Shell analytics success story has helped the company develop an effective approach to reduce costs and save time on repairs.

Shell uses data from its different vendors to predict when more than 3,000 different parts used on oil drilling machines are likely to fail. The data is used to manage Shell’s spare parts inventory.

Data informs which parts should be kept in stock and where the parts should be. This strategy has helped Shell save millions on inventory analysis and reduce the need to move parts. It also reduces repair times by ensuring that the right parts are available in the right locations.

How will your analytics success story be written?

Data and analytics are unlocking unprecedented value for organizations that invest in projects with a clear scope, goal, and plan.  Investment is important in all three phases of analytics: people, projects, and the right software.  How will your analytics success stories be shaped?

Even with today’s self-service BI tools, it still takes analysts on average a week to answer new ad-hoc questions that come from the business – a rate too slow in our modern, face-paced digital economy.  Answers need to be discovered in minutes and with details that can help shape and optimize the execution of new business strategies.

Data Transformation Plays a Key Role

In each of these three stories, data transformation played a critical role.  Successful data transformation programs ensured that complex and diverse datasets were able to be blended and shaped into analytics-ready form to be easily consumed by downstream analytics teams.

Datameer SaaS Data Transformation is the industry’s first collaborative, multi-persona data transformation platform integrated into Snowflake.  The multi-persona UI, with no-code, low-code, and code (SQL) tools, brings together your entire team – data engineers, analytics engineers, analysts, and data scientists – on a single platform to collaboratively transform and model data.  Catalog-like data documentation and knowledge sharing facilitate trust in the data and crowd-sourced data governance.  Direct integration into Snowflake keeps data secure and lowers costs by leveraging Snowflake’s scalable compute and storage.

Learn more about our innovative SaaS data transformation solution by scheduling a personalized demo today!

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