Analytics as a service

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What is analytics as a service?

Every company wants to access the power of big data, but the costs of building and maintaining in house systems and solutions make it impractical and inefficient for most enterprises. Many businesses can’t justify the costs of in-house development and large data analysis teams, even when the data insight that analysis could drive could make all the difference.

That’s where analytics-as-a-service comes in. Using analytics-as-a-service, companies that can’t justify the expense of funding an in-house data team can access powerful data analysis tools. They can use cloud-based BI tools to analyze big data and drive insight, even without data specialists or expensive server space.

Analytics-as-a-service tools like Domo are designed to be as user-friendly as possible. With user-friendly analytics tools, it’s much easier for the average person to analyze data. Even those with little to no technical experience can, with a little coaching, learn how to run reports, build dashboards, and connect to data sources. This way, everyone in an organization can use data to make timely and accurate business decisions.



With these powerful tools, businesses can also perform more complex analytics, using things like pre-built machine learning applications and artificial intelligence to drive deeper insight from their data. If you do have data science experts in-house, these platforms often offer full-code solutions in common scripting languages like Python, allowing them to build their own data-science models within their BI tool.

Analytics-as-a-service tools differ from regular cloud-based BI solutions in the level of analysis they offer their customers. Some BI tools are very limited in what a user can do; these tools don’t allow users to do much more than create basic visualizations, i.e. chart and graph building. Analytics-as-a-service solutions like Domo allow for far more freedom and functionality in what users can do with the platform. Whether you need to establish new connections to your data, transform existing datasets, or build custom content and applications, choosing a flexible analytics-as-a-service platform allows you to scale beyond your initial use case.

Analytics-as-a-service tools are a valuable option for startups, small businesses, as well as enterprises looking to democratize their data to drive insight throughout the organization.

What is big data?

Many businesses have started to collect massive data sets, each with thousands, millions, or even billions of rows of data. Oftentimes these datasets contain years or even decades worth of information. The sheer size of these data sets makes it difficult or even impossible for some less powerful BI tools to handle them. Businesses with data sets like these require a powerful analytics-as-a-service solution to manage the extract, transform, and load processes for their big data.

As businesses grow, the amount of data they collect rapidly expands, and all this incoming data can quickly outgrow less robust business intelligence solutions. Analytics-as-a-service tools can keep up with growing data streams; tools such as Domo have the ability to operate at scale with speed.

The volume, variety, and velocity of big data can make it too difficult for some business intelligence tools to even handle, much less glean valuable insight from. Companies that want to use big data to drive insight will need robust analytics tools to properly analyze and evaluate what’s happening in their business.

It’s not just the largest enterprises generating and using big data. As the average person lives more of their life online, even smaller companies need to collect massive amounts of data. The challenge, then, is how those smaller companies can access the in-depth, specialized data analysis that large companies use.

Through analytics-as-a-service, any company can access the powerful analysis tools of BI software. These tools help less established businesses glean insight from their big data sets, without investing in specialized data professionals or building out expensive bespoke solutions.


What advantages does big data have?

Businesses that use big data to drive their decisions will be better equipped to compete in today’s markets. Analytics-as-a-service allows businesses to analyze and respond to data in ways that would be impossible using smaller data sets.

Big data creates transparency. In some businesses, especially ones with tight margins, it can be difficult to find trends without analyzing a massive amount of data. What may look like unrelated noise in a smaller data set might actually take the shape of an insight when more data is added.

In today’s markets, businesses need to look for even the smallest advantages to stay competitive. By using analytics-as-a-service, businesses can use powerful analysis tools to spot trends and take action quickly. Analytics-as-a-service allows businesses to maximize their insight while minimizing their cost.

Big data allows for predictive analytics. Using big data, businesses can use their current data trends to predict future ones. Datasets that contain multiple years of historical information, if properly normalized, can help businesses determine and act on seasonal variables to their business. For example, a large retailer might pull together multiple years of sales data to see how holiday’s impact MoM sales. They might also be interested in metrics like weather and temperature in order to better predict upcoming inventory demand.

With analytics-as-a-service, businesses can access predictive analysis tools without investing massive amounts of time and money for development.

Big data improves customer relationships. R&D departments can use customer trends to drive the creation of new products and solve common problems with current ones. By looking at large amounts of sales data, with the ability to refine it in dozens of different ways, companies can discover market niches that would have been invisible with traditional data solutions.

Advertising, especially online advertising, has been revolutionized by the advent of business intelligence. Now, sellers can personalize ads down to the smallest degree, only showing them to the people most likely to click on them.

Ad Personalization is possible only by pulling together massive data sets and then normalizing and validating them. A system capable of accomplishing this could take months or even years to develop in-house, or it could be set up in day’s or weeks with a scalable analytics-as-a-service solution. Using analytics-as-a-service, any company can access big data analysis capabilities that make personalized customer relationships possible.

Big data drives machine learning. The algorithms that drive machine learning need massive amounts of information to use as a starting point. Without big data to train them on, these learning models would be built on incomplete information and deliver inaccurate results.

Some modern Analytics-as-a-service tools have already done the work of training their machine learning software. Using best-in-class analytics tools, companies can use machine learning functionality to analyze data sets far quicker and more thoroughly than legacy BI tools allowed.


Why are modern analytics-as-a-service solutions better than legacy solutions?

Still today, there are companies who are reluctant to replace their legacy systems with analytics-as-a-service tools that live in the cloud. However, AaaS tools have many distinct advantages over legacy BI tools, advantages that help keep companies agile and competitive.

AaaS tools save time, money, and business resources. Not only do analytics-as-a-service tools help organizations save time and money by bypassing the need for numerous data engineers and expensive computing power, but they can also speed up processes and allow for more timely insights.

Using tools like Domo, businesses can analyze their data in ways completely impossible with legacy BI tools. Domo’s AutoML tool helps businesses improve existing machine learning models, and also allows those models to learn using new data sources. Other analytics-as-a-service tools have other machine learning tools to help drive insight.

AaaS tools help smaller businesses compete. Without the insights that powerful data analysis can bring, small businesses and startups can’t hope to compete with established players in their industry. Big data analysis is a massive advantage, and no amount of talent or hard work can completely bridge that gap.

With analytics-as-a-service, any business can access analytics tools as powerful as the ones used by major industry players. These tools help to level the playing field, helping every business operate on the same level as the biggest enterprises.

Analytics as a service—powerful analytics for everyone

With analytics-as-a-service tools like Domo, any business can leverage their data to make decisions and drive insight, without a massive investment in data infrastructure. Even for those already using BI tools, AaaS tools can provide deeper analysis, using analysis techniques that less robust tools can’t.

Domo is the best choice for small and mid-sized and enterprise companies looking for an analytics-as-a-service tool. Our analytics tools are designed to help everyone become a data expert, and our support staff is ready and willing to talk through our more complex procedures. Using Domo, any business can become a data leader.



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