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How Managed Analytics Services Is Key to Getting Insights from Data Quickly

By Kyle Clubb | Posted on March 8, 2021 | Posted in AI/ML, Data & Analytics

As the availability and usage of data has exploded in recent years, many enterprises have struggled to fully leverage the amount of information they now have at their disposal.

It’s one thing to invest in building out your data science capabilities, but it’s quite another to infuse data into every decision you make in every department. Sales, supply chain management, operations—for a lot of companies, getting these and other wings of their organization adept with putting data to work is an uphill battle.

Enter Managed Analytics Services (MAS)

Designed to help enterprises build out their analytics capabilities—not just on the data science level but throughout the entire operation— MAS consist of an array of practices that can quickly be adapted to an organization’s unique needs.

These practices include things like traditional business intelligence reports and the development of advanced analytics models that power artificial intelligence and machine learning (AI and ML), to technologies like voice analysis to improve customer service, and models to gauge brand sentiment on social media.

Kyle Clubb, Redapt’s Director of Advanced Analytics, gives an example of how that’s applied in the video below.

HubSpot Video


In many ways, MAS are akin to a SaaS model, where instead of making a bulk investment up front, enterprises develop a lasting relationship with their MAS provider to guide the direction of all their analytics initiatives. 

This has two benefits:

  1. Because the MAS provider has built a relationship with the organization, they are better able to adapt quickly—and even anticipate—that organization’s needs.
  2. Organizations partnering with  MAS providers are able to spend much less time building out their own analytics capabilities and models and more time focusing on their own products and services.

MAS in action

For an example of what MAS can do for an enterprise, let’s take a look at Redapt’s work with the healthcare technology company Zelis.

A core part of the company’s business involved processing out-of-network claims between doctors and insurance providers. Flagging potential cases of fraud, waste, and abuse was a major challenge—especially given the fact that there were millions of claims from hundreds of different providers to sift through.


Rather than invest in the capabilities on its own, Zelis partnered with Redapt on a process for predicting and isolating potentially fraudulent claims. The result of this process was:

  • A framework for efficiently skimming through data to identify and fingerprint historic costs related to procedures
  • A flexible rules engine for analytics models that could be modified with new information derived from a constant stream of data
  • AI/ML environments for data scientists to run models efficiently

As a result of these and other implementations from  Redapt’s MAS team, Zelis is now able to continually improve its algorithms to stay ahead of new fraud trends and accelerate processing of non-fraudulent claims—all while having a single pane of glass for data model outcomes based on hundreds of different providers.

Getting started with MAS

Whether your organization is just now focusing on building out its analytics capabilities or you’re already leveraging data science and advanced analytics in your day-to-day operations, MAS can provide you with something every enterprise needs: more time dedicated to developing great products and services.

To start getting more out of your data without greatly expanding your roster of talent, contact us today. Our team of experts is ready to help you quickly build or increase your ability to get more from your data.

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