An In-Depth Guide to Advanced Analytics

How to stay competitive by unlocking deep insights from a steady flow of data.

Learn how these insights can help enterprises take advantage of everything from efficiencies in operations and opportunities for automation to providing better products and services to customers. Data is the lifeblood of today's economy. The key to tapping into that lifeblood is by investing in a modern datacenter.

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Putting advanced analytics to work is not a quick and easy process.

Storing data, sorting it, finding the right projects to build out your analytics capabilities—each of these is a step in a long process.

On this page you’ll find the information you need to get started on the right path to deploying advanced analytics.

Page Contents:

Advanced Analytics: What It Is and Why It’s Important

Code on a computer screen to represent data

Any description of advanced analytics needs to begin with Big Data.

This is the catch-all term for large amounts of structure and unstructured information captured from a wide range of resources.

Most enterprises utilize this data in some form or fashion, even if they don’t label it as such. Purchase records, website clicks and page views, location and demographics information, device usage — each counts as a piece of data that can be stored and analyzed via analytics.

Traditionally, enterprises have used this information to measure past performance. Things like:

  • region icon What products sold well in particular regions
  • webpage iconWhat web pages received the most traffic
  • mobile and desktop webpage iconsWhere website traffic originated from and on what devices

This information was then utilized to make projections for the coming quarter or an entire year, etc.

As the amount of connected devices greatly proliferated, though, so did the amount of data available. And all this newly available information is radically changing how enterprises put data to use.

graph icon Predictive Analytics

In contrast to traditional business analytics, predictive analytics utilizes technologies in order to glean projections from data.

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artificial intelligence icon Artificial Intelligence (AI)

There are a number of ways enterprises are putting AI to work. Let’s look at four of them.

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machine learning icon Machine Learning (ML)

If AI is how analytics can be put to work, ML is the lessons derived from analytics.

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Use Cases for Advanced Analytics

Now that we’ve talked about what advanced analytics is and why it’s important, let’s look at three examples of it being put to use. One in finance, one in healthcare, and one in retail.

person at computer looking at financial analytics


Every minute of every day, millions of credit cards are being swiped around the world. In order to identify potential cases of fraud, credit card providers deploy ML models to help determine irregularities based on previous purchasing habits, locations of purchases, even times of day purchases are made.

Using the output of these models, credit card providers are able to know when a customer has had their information stolen before the customer does and can take preventative measures—all without slowing down the flow of transactions.

doctor with face mask holding up an object and looking at it


Redapt client Zelis is a healthcare technology company that, among other things, processes out-of-network claims between doctors and insurance providers. Given the sheer amount of claims, the company needed the ability to predict and isolate claims that were potentially fraud, waste, or abuse (FWA).

To help them with this problem, Redapt built out a framework for the company to efficiently sift through data to identify and fingerprint historic costs related to procedures. This framework was flexible enough to be modified with new information as it arrived.

By leveraging the public cloud, Zelis’ data scientists were able to run advanced analytics models to not only flag potential FWA claims, but accelerate the processing of non-fraudulent ones.

person looking through items at a store


Online personal styling service Stitch Fix connects customers to clothes by utilizing advanced analytics to develop algorithms based on popular combinations and styles.

These algorithms then intelligently style entire outfits based on customer interests, seasonality, fit, and so much more. This way, all customers have to do is simply tell the company a little about their likes and dislikes. Then, before they know it, they’ll receive outfits based on that information.

The benefits of this approach are twofold:

  • Stitch Fix is able to serve more customers than traditional, in-person styling services.
  • Customers are able to have an experience normally reserved for the more affluent.

What You Need in Order to Utilize Advanced Analytics

The first thing you need to benefit from advanced analytics is, obviously, access to data.

Chances are your enterprise already has this covered, which means you need to ensure you’re in a position to actually use that data. While there are many small steps necessary to adopting advanced analytics, we’ll focus on three large ones here: technical maturity, data storage, and data democratization.

Technical maturity

Not every enterprise is ready for advanced analytics, no matter how much data they have access to. In order to make advanced analytics effective, you need to understand your technical maturity—your capabilities, your data, and what you’re trying to achieve.

  • lightbulb icon

    Your capabilities include everything from the technical knowledge you have in-house to run advanced analytics models, to how your workflows are managed, your infrastructure, and your data storage platforms.

  • data icon

    Your data is just that: what information you have, where it’s coming from, and an understanding of what’s useful data and what’s not—and how to separate the two.

  • commercial building icon

    Finally, what you’re trying to achieve is the business case for implementing advanced analytics in the first place. Do you want to catch up to the competition? Simply sell more products? Create an environment where data can be leveraged to guide the creation of new products and services? All of the above?

Without knowing your technical maturity before moving into advanced analytics, you’re basically jumping into a deep pool of data and hoping you can learn to swim before time runs out.

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Data storage

Data needs a place to live. More importantly, it needs to be stored in a way that it can be useful and easily accessed by those who need it. Getting to the point where you can put advanced analytics to work generally involves three steps: data assessment, simplifying data, and data warehousing.

  • data assessment icon

    Data assessment provides you with a holistic look at all your data and where it’s arriving from. The process involves combing through all your current analytics workloads and data sources, examining the completeness and value of your data, and identifying which of your teams need access to specific data sets.

  • checkmark icon

    Simplifying data requires overhauling the way your data is distributed and queries are handled—scaling out vs. scaling up. This involves spreading your data in such a way that valuable information is not buried beneath a mountain of useless information.

    For many enterprises, there are very real benefits in simplifying your data via a cloud platform, including flexibility in how AI and ML models are put to work for advanced analytics. As for on-premises, Hadoop is still a go-to solution for building out scalable data processing frameworks to support large data workloads like advanced analytics.

    Regardless of the solution you go with, the goal of simplifying data is to make it possible for your data analysts and other teams to run models in a pond of data rather than an ocean.

  • data warehouse icon

    Data warehousing is where data from varied sources are stored so that advanced analytics workloads can access them. This data can arrive structured, semi-structured, or unstructured, and the warehouse is where the information is transformed and processed.

    Beyond the ability to run advanced analytics, data warehousing makes it possible for:

    • Users to easily access critical data from a wide range of sources quickly.
    • Turnaround on analysis and reporting to be greatly accelerated.
    • Security and compliance to be applied across the board.
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as well as learn everything you need to know about building a modern datacenter, download our white paper A Strategic Approach to Data Storage.

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Data democratization

The idea of data democratization is to free data from the traditional IT gatekeepers and host it in a way that anyone from analysts to executives to the marketing department of an enterprise can access it.

  • data virtualization software icon Data virtualization software, which pulls in and manipulates data regardless of data inconsistencies or file formats.
  • data federation software icon Data federation software that utilizes medatada to aggregate information into a single virtual database.
  • cloud computing icon The cloud, which allows enterprises to centrally locate its data and then partition it out into various data lakes for access

In a Nutshell …

Advanced analytics are going to be an ever-growing force for enterprises. Tools like predictive analytics, AI, and ML will drive the creation of better products and services, streamlined operations, and very real competitive advantages for companies.

Successfully putting advanced analytics to work requires a lot more than simply throwing algorithms into a mass of data, however. So before you take the plunge, you need to understand:

  • Your enterprise’s technical maturity
  • What data you have access to
  • What you’re trying to achieve

We’re here to help, so if you want to learn more about advanced analytics to start building out your own solution, get in touch with one of our experts.

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