If you’re looking to unlock new avenues of efficiency and provide better products and services to customers, data analytics is your answer.
The challenge you may face, however, is the ability to properly manage your data in a way that those benefits—efficiencies, better products and services—can be found.
Changing how your enterprise manages its data can be scary. Entrenched workflows, comfort levels with storage solutions, legacy applications—these are the types of things that routinely stand in the way when it comes to enterprises like yours adopting a new data analytics management solution.
To help bypass your roadblocks to gaining real insight from analytics, here are three fairly recent examples we’ve seen of enterprises doing data management right.
1. Panera Bread
Click here to watch a detailed presentation on how Panera Bread bolstered its data analytics capabilities in partnership with BlueData software and Dell.
Boiled down, the company transitioned from a traditional data warehousing solution to a PaaS framework. This allowed them to spin up a cluster, ingest and manipulate data, and then send the findings to a reporting dashboard.
One of the major benefits of this transition was the ability for Panera Bread to best use its resources. Instead of data scientists working in silos on high-end gear, the new PaaS framework made it possible to spin up clusters from a massive pool of data for specific purposes and then destroy those clusters once the work was finished.
For companies such as Panera Bread, this efficiency often translates into flexibility, since resources can be allocated for front-end transactions during busy periods (say, lunchtime) and then easily shifted to analytics and reporting in off-peak hours.
As highlighted in this brief video clip from Dell EMC World 2017, the credit card giant was able to utilize Hadoop and Machine Learning (ML) in order to apply rules to millions of transactions in milliseconds.
The goal of this shift in data analytics management was to accelerate the speed of transactions while also implementing fraud detection.
This was done via a combination of supervised and unsupervised ML to create a streaming workflow where data (the transaction) is processed as it comes in. As a result, Mastercard was able to pivot to real-time feedback on transactions. Or, as it’s described in the video clip, the company is now better able to prevent fraud upfront rather than chasing it.
This final example comes from our work with insurance broker QuoteWizard. You can read our case study about the company here.
In a nutshell, they were using an outdated data storage infrastructure that relied on SQL Server databases—and their database performance wasn’t keeping pace with the growth of the business.
In order to better manage its data, QuoteWizard partnered with us to build a data warehouse capable of handling more than four billion records, as well as a platform that allowed data refreshes to be done every hour rather than daily.
As a result of these and other changes, QuoteWizard is now able to utilize their new framework and modernized reporting tools to make better business decisions at an accelerated rate. The ripple effect of this acceleration is their ability to remain efficient and nimble as they pursue new markets.
For more information on data management and data warehousing, download our free whitepaper, Navigating the Flood: Building Value by Reducing Data Complexity and Properly Managing Your Data.
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