If you’ve ever walked several dogs at once, you understand the challenge of a complex data environment. Each dog moves at a slightly different pace, pulls you in a different direction, and has its own needs and environmental stressors.

Data products provide a similar challenge. Between different data models, formats, and other software eccentricities, even a skilled team of data engineers and architects can struggle to make tools play nicely with one another.

Now, contrast the image of the neighborhood dog walker getting yanked down the street with a musher driving a team of sled dogs. What’s the difference?

Not only are the sled dogs trained to work with one another, the sled itself provides a harness that yokes them together in an organized and productive fashion. In this analogy, the sled represents a Unified Data and Analytics Platform (UDAP).

As noted in the first post in this series, UDAPs emerged in the last decade as the result of feature consolidation in the business intelligence (BI) space. Rather than focusing on doing just one thing well, UDAPs provide end-to-end functionality across the data and analytics stacks. They connect to sources, model and transform data, and provide the tools to visualize and report insights.

Not only do UDAPs provide the individual functions needed in a data workflow, they supply the infrastructure that yokes the tools together in an efficient way. Most UDAPs are also extensible, meaning companies can add the third-party, best-of-breed tools they already own into the structure of the UDAP.

If buying a point tool is like buying a really fast husky, buying a UDAP is like buying a team of dogs and the sled to go with it. Individually, the stand-out husky may beat any of the dogs on the team, but the synergy between the team of dogs allows them to work more efficiently and go farther than the fastest dog could on its own.

And the beauty of an extensible UDAP is that you can actually clip the husky into the sled’s harness so it will cooperate with the other dogs. This structure along with extensibility are what make UDAPs a great fit for data-mature companies with complex data environments.

Large enterprises have been in the analytics game for a long time. As a result, their data landscapes are often bogged down by a myriad of legacy tools from each prior generation of data solutions.

Mergers and acquisitions compound this natural tendency to collect products over time by creating parallel landscapes derived from each company’s pre-existing environments. These fractured landscapes result in complex data workflows that are hard to build, difficult to troubleshoot, and slow to deliver.

A UDAP helps in two major ways:

• Centralization. First and foremost, UDAPs are highly connective. Some, such as Domo, come with hundreds of pre-built connectors. Many also have data visualization capabilities. This allows the UDAP to integrate and analyze data from all systems of record and storage within a single platform without always moving or copying the data. UDAPs also centralize functionality. Rather than passing data from tool to tool, a UDAP provides features for each step in a typical data workflow, so the data remains in one place, increasing the efficiency of the system.
• Simplification. Using a UDAP doesn’t mean throwing out all the tools you’ve invested time and resources on. It just enables organizations to pick and choose their point tools more strategically. The extensibility of most UDAPs allows you to integrate the tools your team likes into the centralized workflow. At the same time, by providing basic functionality for the major steps in the BI lifecycle, UDAPs empower you to pare down your landscape. You can keep point tools where they provide added benefit and replace them with the UDAP’s native solution where they don’t.

A UDAP in action

To see what this all looks like in practice, let’s imagine Company X. Company X is a large regional retail chain centered in the Southeast. It recently closed a deal to buy Company Y, a similar retail chain with operations in the Midwest. Both companies have mature analytics environments with separate and different systems of record, databases, ETL tools, data warehouses, and BI tools.

Post-acquisition, Company X is still essentially two companies from a data perspective. Its pipelines for each part of the business run in parallel, and it needs a way to analyze combined data. Company X decides to buy a UDAP to solve this problem. In the short term, it uses the UDAP to connect to both former Company Y and Company X data sources.

This means it has a combined flow of data for analysis. The UDAP also links together tools from both landscapes, enabling Company Y analysts and Company X analysts to share the platform. In turn, this reduces data silos across the combined business. Company X analysts benefit from Company Y analysts’ knowledge of Company Y’s data and vice versa.

Over time, Company X evaluates which point tools best meet its needs and which it can do without. It gradually eliminates the superfluous tools, using the functionality of the UDAP as a fall back to keep its pipelines running. In the end, Company X is left with a single, integrated data and analytics environment.

Conclusion

UDAPs aren’t just for companies new to analytics. Data-mature enterprises with complex data environments benefit as well. Particularly for BI, UDAPs help harness the power of existing tools while centralizing workflows and enabling organizations to simplify their data landscapes.

To learn all about UDAPs including all the ways they can benefit companies dealing with overly complex analytics environments, join me and Domo’s vice president of data curiosity, Ben Schein, on April 21, when we host a webinar titled Modern BI for All: Why Invest in a UDAP. To register for the event, click here.