Business Intelligence

What Are Automated Analytics, and What Do They Mean for the Future of BI?

Nov 16, 2022

Automated analytics can be a game changer for your business, but only when set up properly for success. Learn how automated analytics can work for you in this article.

Andrew ConradSr Content Writer
What Are Automated Analytics, and What Do They Mean for the Future of BI?

What we'll cover

As a business leader who may be struggling to move away from manual and siloed methods of analytics, automation is your friend. Automated analytics—which can help save time, prepare data, and generate insights without adding staff—aren’t just a luxury for trendsetting businesses, they’re a necessity for any business that wants to stay competitive over the next decade.

In fact, Gartner predicts that by 2025 data stories will be the most widespread way of consuming analytics, and 75% of those stories will be automatically generated using augmented analytics techniques [1].

In this article, we’ll use Gartner research to dig into automated analytics and share tips on how you can quickly adopt automated analytics to boost your business intelligence efforts.

What are automated analytics?

Automated analytics are a form of advanced analytics that use emerging technology, such as machine learning, artificial intelligence, and natural language processing, to assist human data scientists and analysts with tedious tasks like data gathering and preparation.

You may be wondering about the difference between analytics, augmented analytics, and advanced analytics. Check out the graphic below for a quick overview of the similarities and differences between these three concepts.

advancedanalytics graph

The 3 phases of automated analytics maturity [2]

What are the challenges of implementing automated analytics, and how can you overcome them?

The biggest challenges in automating analytics come down to capacity and budget. As we discussed above, an automated analytics system without guardrails can essentially pull limitless information, causing valuable insights to get buried beneath irrelevant noise.

Your automated analytics system also requires constant oversight once it’s up and running to ensure that your data sources are clean and the insights being presented are still relevant to organizational needs.

Here are a few examples

Challenge: Our organization is still getting up to speed with traditional analytics.

  • Solution: Continue upskilling and supporting your analytics team until they’re better equipped to take on more advanced analytic techniques and technology.

Challenge: We don’t have the capacity to oversee large scale analytics automation.

  • Solution: Since analytics automation without oversight can cause more problems than it solves, we recommend starting slowly. Take small automation steps, like automating data preparation for a single data set, and then expand as capacity allows.

Challenge: We don’t have the budget for enterprise-level analytics automation software.

  • Solution: Automation isn’t an all-or-nothing proposition, and your existing BI software may already include some automation features. Ask your BI software vendor about processes that you might be able to automate now and how artificial intelligence can assist with analytics tasks, for example, automatically pulling fresh data into your dashboards.

In the next section, we’ll help you figure out when the time is right to automate.

When should you automate your analytics?

When deciding whether your team is ready for automated analytics, you should ask two key questions:

  1. Is your team capable of standing up an automated analytics system?

  2. And, does your team need automated analytics?

The easiest way to get an answer to the first question is to determine how proficient your team is in advanced analytics. Are they already doing things like using big data software to pull and prepare data on a large scale? Are they using predictive analytics? Have they set up interactive dashboards? If the answer to these questions is yes, then your team should be ready to employ automated analytics.

The second question is equally important. While automated analytics are very powerful, setting them up can take a lot of time and resources. If your current approach to analytics is giving you all the information your organization needs, automation may not be necessary. Ask team leaders around your organization if they’re satisfied with the quality and frequency of the data insights they’re receiving. If they can give compelling examples of reports that they could benefit from on a regular basis, you should strongly consider moving forward with automated analytics adoption.

What are the benefits of automated analytics?

We’ve already looked at many of the benefits of automated analytics. To summarize, automated analytics can save businesses significant time, and boost the effectiveness of your existing analytics team. Here’s how:

  • Real-time reports. Traditional analytics require manual reporting, meaning that any report you see is at least a few hours old. Automated data pipelining and reporting means that the information being shared is as up-to-date as possible.

  • More data. With traditional analytics, the amount of data that can be prepared and analyzed is limited by the capacity of human data scientists. When these processes are automated, massive pools of data can be analyzed around the clock via AI and machine learning.

  • Higher value projects for analytics teams. When data scientists are freed up from gathering, cleaning, and preparing massive amounts of data, they have more time to analyze that data, investigate new queries, and present their findings to decision makers.

Key BI software features to enable automated analytics

Now that you've discovered a little more about what automated analytics can offer you and your business, the next step is to explore the tools and features that can enable automation. This technology is typically delivered through modern business intelligence software. Here are a few of the key BI software features to look out for:

  • Machine learning and AI. Programmed into the backend of your business intelligence software as algorithms, this technology is essentially the “magic” behind analytics automation.

  • Data mining and preparation. One of the biggest time-saving advantages of automated analytics is the ability to gather, process, and clean massive amounts of raw data around the clock.

  • Interactive dashboards. Automated analytics are very powerful, but interactive dashboards are the essential medium for translating data into understandable insights that users can explore and act upon.


An interactive dashboard in Phocas analytics software (Source)

Want to learn more about interactive dashboards? Check out our guide here.

How to get started with automated analytics

Gartner recommends five essential practices for getting started with automated analytics:

  1. Make sure that it’s the right time for your organization to adopt automated analytics. Do you already have the staff in place to oversee automated analytics adoption? Has your BI team already shown the ability to use advanced analytics applications like AI-assisted data preparation, natural language processing tools, and interactive dashboards? If the answer to these questions is no, then you should focus on those areas before attempting to further automate.

  2. Incorporate machine learning, reporting and alerting as a first step to automated analytics. Machine learning is the key technology behind real-time data and near real-time data (allowing data to be pulled automatically from a data source), and this connection can in turn be used to power reporting that can automatically alert users when certain thresholds are hit (for example, revenue reaching a daily goal).

  3. Share these near real-time reports across organizational and geographic boundaries. Data insights are almost useless until they’re delivered to the decision makers that can take the appropriate action based on them. Automation can be used to produce reports, but some human oversight is also needed to break down information silos and make sure that those reports are being distributed wherever they can be helpful. By using the right data visualization techniques, you can ensure that these insights will be as impactful as possible.

  4. Tweak the system to make sure that the most essential data is being pulled and presented at the right time. Getting your automated analytics up and running can be a powerful feeling, but eventually you’ll need to be judicious about the flow of information to cut down on noise. In the same way that untapped plumbing can quickly flood a home, unrestricted data flow can flood your organization with information, rendering it unhelpful. Make a decision model to decide which reports are automated and when.

  5. Remember to build guardrails into both automated and traditional analytics. You’d never consider allowing your analytics team to operate independently of the rest of your organization without any kind of oversight, but it can be easy to allow your automated analytics to fall into a “set it and forget it” situation. While automation is doing lots of heavy lifting for you behind the scenes, you should still regularly audit your system to make sure that your data sources are clean, the right type of data is being pulled, and it’s answering the right questions.

Combine people and technology to maximize your automated analytics effectiveness

In this article, we learned more about automated analytics and how they can help boost the effectiveness of your business intelligence team. In summary, here are the key takeaways:

Key takeaways

  • Before attempting to launch an automated analytics program, double-check to make sure that your analytics team has already displayed proficiency in advanced analytics.

  • Start by setting up automated reports and sharing them with relevant decision-makers throughout your organization.

  • Tweak the system to ensure that only useful information is being automatically pulled, and audit the system regularly to make sure that the data is still clean.

There’s no such thing as a low-tech workaround to automated analytics adoption. You need the right business intelligence software in place to take advantage of this transformational technology. The good news is that we have you covered with our business intelligence software directory. It features a buyers guide to help you answer any lingering questions you have about BI software, as well as our Category Leaders, which highlights 15 top options based on thousands of verified user reviews.


GetApp’s Category Leaders in Business Intelligence for 2022 (Source)

You can also browse our regularly updated business intelligence blog to continue learning about how analytics can help you grow your business. Here are a few recent articles to start with:

Note: The applications mentioned in this article are examples to show a feature in context and are not intended as endorsements or recommendations. They have been obtained from sources believed to be reliable at the time of publication.

About the author

Andrew Conrad

Sr Content Writer
Andrew Conrad is a senior content writer at GetApp, covering business intelligence, retail, and construction, among other markets.

As a seven-time award winner in the Maryland, Delaware, D.C. and Suburban Newspapers of America editorial contests, Andrew’s work has been featured in the Baltimore Sun and PSFK. He lives in Austin with his wife, son, and their rescue dog, Piper.
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