If your idea of automation is robots replacing humans, think again.
While some may argue that automation is about implementing a system to streamline repetitive tasks and eliminate human labor, the reality is much more nuanced — and hopeful. More than simply automating tasks that humans are already doing, automation — done intelligently — elevates and amplifies humans, helping them do more strategic work while making their lives easier.
With automation, data workers can answer bigger questions, seek out more transformative business outcomes, and ease the burden of those time-consuming, manual tasks that eat up precious minutes every day.
In this blog, we explore what automated analytics is, including how you can — and why you should — use it in your day-to-day work, whether you’re a data analyst, data scientist, or business user.
“Organizations that invest in integrated automation platforms that span analytics, data science, AI, and process automation will extend the reach of their transformation initiatives and build a sustainable competitive advantage.”
— John Santaferraro, Research Director, Analytics, BI, & Data Science, EMA
What is automated analytics?
Picture all those time-consuming tasks that monopolize your workday and keep your deadlines just out of arm’s reach — tasks like wrangling, parsing, cleansing, and formatting data. All of those manual, repetitive tasks are ripe for automation.
Automated analytics is the use of software and AI — typically machine learning (ML) algorithms or generative AI — to automate end-to-end analytics. Similar to business process automation (BPA), automated analytics connects disparate systems (e.g., your data warehouse, analytics platform, and dashboarding tool) to create a unified and end-to-end analytics workflow.
Automated analytics solutions can automate any piece of the analytics lifecycle (or all of it), including collecting data, preparing and blending data, analyzing data, building reports, constructing models, and even generating email summaries and PowerPoint decks for stakeholders.
Types of automated analytics include:
Automated machine learning (AutoML): AutoML platforms help data workers quickly deploy models through the use of low-code, no-code solutions. These solutions can automate every step of the model-building process, including defining a business problem, selecting the right features, writing the code for that model, and even fine-tuning it.
Generative AI: Many automated analytics solutions today integrate generative AI capabilities into their tools, helping data workers further automate steps of the analytics lifecycle. For example, generative AI features in Alteryx can help you with governance and documentation by generating summaries of an analytics workflow’s purpose, inputs, outputs, and key logic steps. These features can also help you quickly generate slide decks or emails to share insights with key stakeholders.
Analytics automation: Analytics automation platforms like Alteryx can automate end-to-end analytic processes, including data ingestion, prep & blend, transformation, and reporting. With a visual, intuitive interface, you can build analytic workflows once and then automate them forever. These solutions can connect and unify different solutions, like an RPA system, which can automatically modify a file or data set at the end of an analytic workflow.
Business intelligence: Business intelligence refers to the strategies and technologies used to analyze data and share those insights via data visualizations and dashboards. Automated analytics can automate the steps of business intelligence, automatically surfacing hidden insights in your data and even generating dashboards to help key stakeholders always have the information they need to make informed decisions.
Not only do the various forms of automated analytics help data workers with some of the most time-consuming tasks in their work days, but automated analytics also lowers the barrier to entry for more technical forms of data analytics and data science — such as building predictive models — helping data workers increase the scope of what’s possible.
“Analytics professionals are critical thinkers, creative problem solvers, and active listeners that help build solutions for end users. That being said, just like many other professions, there are many tasks in the analytics lifecycle that can be repetitive or mundane that still require human oversight. With the dawn of generative AI, there’s a huge opportunity to automate these tasks so that human operators can spend more time on strategic, innately human activities like picking up new projects, spending more time understanding the business, or generally providing more value to their organizations.”
— Peter Martinez, Senior Product Marketing Manager, Alteryx
Why you should automate your analytics
From faster insights to reducing errors, here are several reasons you should use automated analytics.
Reduce manual work
Manual data preparation and cleansing in spreadsheets has been a thorn in data workers’ side for years, with one study showing the typical analyst devotes two hours per day exclusively to data preparation (or roughly 500 hours a year).
Automated analytics can do that same work in seconds, saving data workers hours, just like Bank al Etihad, which reduced its data processing time by 80%.
Find insights faster
Most enterprise analytic workflows use millions of rows of data. Tabular worksheets simply can’t keep up in the age of big data. Automated analytics also help you bypass the constraints of spreadsheets by pulling all your data into an analytics automation solution built for massive volumes of data. Not only can you offload processing onto a server for faster analysis, but you can also automate multiple parts of the analytics lifecycle, saving time at every turn and freeing up your day for more strategic activities — like helping business users define and overcome challenges and finding more ways to save the business money.
Avoid errors
Working in spreadsheets involves complex calculations with huge volumes of data happening in small cells. Analytics automation solutions like Alteryx have a visual interface where you can see exactly what’s happening to your data at each step of your workflow, helping you avoid costly errors. In addition, manual work like copying & pasting and manually typing in values is more prone to error. Automation can streamline these tasks to reduce the risk of typos.
Collaboration
Most automated analytics solutions are either cloud-based or hybrid, meaning you can easily collaborate with team members on analytic processes and workflows. Not only that, but automated analytics solutions also lower the barrier to entry for machine learning and predictive modeling, creating a way for data scientists and analysts to work together that simply wasn’t possible before.
Accelerate your career
When you’re finding real-time insights, saving the business money, and avoiding mistakes, you’re bound to get noticed. Analytics automation solutions can help you accelerate your career by helping you accomplish more than before.
Automating your analytics: getting started
Automated analytics can look different depending on your organization’s needs, but here are a few steps we recommend to get started:
Define your business problem
First, determine what you’re trying to solve with an automated analytics solution. Are you trying to automate prep & blend or quickly iterate on machine learning models? By understanding your goal, you can choose the type of automated analytics solution that’s right for your team.
Select the right solution
Some automated analytics solutions cover end-to-end analytics and a myriad of use cases. Others are more like oyster knives — they have a very specific function and purpose, like optimizing a marketing campaign or forecasting revenue. Once you know which business problem you’re trying to solve, search for a solution that meets that problem AND is enterprise-grade, meaning it has robust security features and will easily integrate with your existing infrastructure, data, and applications. This will ensure you can get up and running quickly without any unexpected hiccups.
Gather the appropriate data
Once you have a solution in place, it’s now a matter of putting your automated analytics to work. Make sure you’re collecting the data you want to analyze — whether that’s from your CRM, ERP, financial systems, social media accounts, web analytics, etc. — then build your analytics workflow and watch as the process runs fully automated.
Execute then optimize
Automating prep & blend today doesn’t mean you can’t delve into predictive modeling tomorrow. Over time, you’ll find areas for incremental improvements. Automation is a force multiplier, helping you do more than you could without it. The more you can automate, the more room you have for strategic, value-added activities.
Examples of automated analytics
Here are several examples of analytics automation in action.
Demand forecasting
Retailers always need to know how much stock they should order and when they should order it — but often, improving on-shelf availability and avoiding stockouts is left up to guesswork. Demand forecasting can eliminate the guesswork by modeling the delivery of products all the way from supplier to shelf. Analytics automation can help you build demand forecasting models in three quick steps.
- Data Connection: Automatically pull in historical sales data and supply chain capability data.
- Prep and Blend: Blend and format data to input into a model
- Build a predictive model: Create a model (with or without code) that predicts expected future sales.
What-if analysis in financial forecasting
FP&A professionals often turn to the Data Table feature in spreadsheets for a major portion of their financial modeling. For example, they perform what-if analysis by making tables with the proper range of interest rates and terms, then specifying the data table inputs and generating results. While this allows them to explore a wide range of scenarios, it’s well-known to be a time-consuming and error-prone approach. With an analytics automation solution, you can replicate the Data Table function of spreadsheets for greater accuracy with far less work.
With analytics automation platforms like Alteryx, you can:
- Save time and reduce errors
- Construct data tables for multiple scenarios and easily change parameters after initial testing
- Hard-code calculations for any scenario while maintaining the flexibility to change them anytime
Month-end close automation
Near the close of each month, staff accountants begin the reconciliation process to make sure the month’s financial transactions have all been recorded. This might include posting missed invoices, resolving discrepancies in inventory, comparing budgets with expenditures, analyzing the results, and preparing reports.
With analytics automation platforms like Alteryx, you can:
- Reduce the labor required to load and map multiple source files together and generate multiple views into the monthly data.
- Automate your entire workflow to eliminate human error in scheduling, selecting, filtering, joining, and formatting reports into data sets that accountants can use or hand off for additional modification.
- Free up accountants’ time for higher-value tasks with routine calculations automatically performed each month.
Get started with analytics automation today
Whether you’re automating prep & blend or rapidly building machine learning models, analytics automation is changing what’s possible for data workers everywhere. With the Alteryx AI Platform for Enterprise Analytics, you can use self-service analytics to automate the entire analytics lifecycle. Access data anywhere, anytime; prepare and analyze that data in seconds; build predictive models with or without code; and generate dashboards and email summaries to quickly share insights with stakeholders — all to uncover the insights your business needs faster and easier than ever.
Ready to get started with analytics automation? Check out “How to Master Analytics and Automation for a Future-Ready Organization” to learn more.