Insights > Blog

When Does Managed Analytics Make Sense?

By Kyle Clubb | Posted on November 18, 2020 | Posted in Cloud Adoption, Featured

Pulling data shouldn’t be back-breaking work—weekend yard work already takes care of that. 

You know you need this critical business data for your sales and marketing team to understand customer trends and identify new revenue stream opportunities but sometimes analytics can be more pain than gain. 

So, what can you do to ease the strain of the process and optimize your workflow? It may be time to think about whether you want to outsource your analytics with managed analytics services—rather than keep it in-house.

What are managed analytics?

Before we get any further, let’s look at what managed analytics are and when they make sense. 

Essentially, managed analytics is automating the process of gathering data and turning it into insights for your company. Whether you need to pull key performance indicators for revenue growth or track influenced customers for the marketing team, often the data is living in silos that are accessed by different people across different locations and departments. 


In other words, the information is hard to find and time-consuming to dig up. The process is slow, and only certain people in your organization have access to the data. 

As you may know, that’s just the first step—then, you have to analyze and pull the insights from these disparate sources to provide meaningful business intelligence to your team. There’s got to a better way to connect, share data, innovate, and learn together. 

Say goodbye to data silos

If you’re nodding your head at this point and relating to these challenges, managed analytics can help you make sense of the data mess. 

An end-to-end technology partner such as Redapt can help you find the right solutions to streamline your data analytics. With a cloud solution such as Microsoft Azure, you can reach all of your critical data in one library that you can access anytime—from anywhere. Redapt can do the heavy lifting for you, too, by architecting, designing, developing, and implementing a modernized data platform that is suited to your unique business needs. 


Build or buy—which costs more?

While it seems like managed analytics could be costly, it may actually cost you less than hiring a full-time data analyst or engineer.

If your company can’t afford a full analytics team, managed analytics may make smart fiscal sense. In fact, employing an internal team may cost you millions of dollars every year, whereas managed analytics may range between $10,000-$20,000 or so per month, depending on your needs. 

Also, think about whether it would be more cost-effective to have a smaller, stable spend you can use to get the value you need. Speaking of value, managed analytics is focused on the overall business value; the data insights that align with your company’s strategic initiatives (think increased conversions) are invaluable. 

To determine if a managed analytics solution is right for your company, ask yourself a few questions. These will help you narrow down whether or not outsourcing managed analytics makes sense for you now—or even down the road. Cost is obviously at the top of the list of questions, as is efficiency. 


Here are a few questions to get you started :

  • How much money do I have to allocate to a managed data solution? How much will a full-time employee cost me? How about a full-time team?
  • How efficient is my current system? How much time am I spending pulling from myriad sources? What are the pain points of my current system? Am I getting the data that allows me to gain insights quickly and easily?
  • If my current analytics solution is working today, is it flexible enough to still be effective in the future? Are there business intelligence or machine learning capabilities that I want to enact someday? 

Once you’ve answered those questions, you may have a better idea of which data solution is right for your company. 

What could be missing?

If you don’t choose to go the cloud route, it could take months or years to hire the right people to build your team. And without access to your critical data analytics, you could even be missing out on what your competitors are seeing and using to learn and grow.

Let’s think about it this way: If one month you need access to data engineering for one issue and then next month you need access to data science resources for different projects, you’d need to hire internal teams of data scientists and engineers for each of these needs. 

That’s going to cost you a lot more money and resources—which could even be wasted if you don’t always utilize them. Instead, what if you could pull from a shared team whenever you need it? And, what if you could gain key business intelligence in a matter of weeks instead of months?

Considering a cloud approach


Here’s an idea of a plan to advance your data operations with a Microsoft Azure cloud solution, provided through a partner like Redapt. Here’s an idea about how the Redapt team can help you throughout the process:

1. Analyze your workloads

The first step is to ensure you’re deploying the right product for your needs by examining your current workloads and solutions. 

2. Optimize the process

Second, the team will pull the data into an organized library, where different people can concurrently read it (at any time, from anywhere). Next, the Redapt team will make sure all of the information is going through the testing process and that any reports and data have been cleaned. At this point, Redapt can also identify and remove blockers, leverage frameworks, and move and load data into data lakes to give you a modernized DataOps solution.

3. Utilize clean data to manage and track costs, processes, and efficiencies  

Finally, you can now enjoy a modern data platform that you can grow and learn from, and scale up or down as needed.

When it comes to the benefits, better business intelligence is at the top of the list, along with the aforementioned scalability, flexibility, and cost savings. With an Azure cloud solution, you’ll also be better equipped to utilize machine learning and data science to advance your analytics. 

In fact, one Redapt client, Zelis, implemented a big data framework based on Azure that helped the company identify potential fraud in its claims data. By building this cloud-based platform, the healthcare company can now stay in front of trends and catch fraud early. 


Learn more about data analytics and when managed analytics makes sense by contacting us today!