Although some argue that automation is about implementing a system to facilitate repetitive tasks and eliminate human labor, the reality is much more subtle and promising. More than automating the jobs people are already doing, automation — when done wisely — elevates and empowers people, helping them do more strategic things while making their lives easier.
Thanks to automation, data workers can answer bigger questions, seek more transformative business results, and lighten the burden of time-consuming, manual tasks that consume precious minutes every day.
In this blog, whether you're a data analyst or a data scientist, we explore what analytical automation is, including how you can use analytics automation in your day-to-day work and why you should.
What is Analytical Automation?
Visualize all those time-consuming tasks that occupy your workday, such as organizing, parsing, cleaning, and formatting data. All these manual, repetitive tasks are quite suitable for automation.
Analytical automation is the use of software and artificial intelligence—typically machine learning (ML) algorithms or productive artificial intelligence—to automate end-to-end analytics. Similar to business process automation (BPA), analytical automation connects disparate systems (e.g., your data warehouse and analytics platform) to create a unified and end-to-end analytical workflow.
Analytics automation solutions can automate any part of the analytics lifecycle, including data collection, preparing and collating data, analyzing data, generating reports, building models, and even creating email summaries for stakeholders.
Types of automated analytics include:
Automated machine learning (AutoML): AutoML platforms help data workers deploy models quickly using low-code, no-code solutions. These solutions can automate every step of the model building process, including identifying a business problem, selecting the right attributes, and writing code for that model.
Productive Artificial Intelligence: Today, many analytical automation solutions integrate productive AI capabilities into their tools, helping data workers further automate the steps of the analytical lifecycle. For example, the productive AI capabilities in Alteryx can help you with governance and documentation by creating summaries of the purpose of an analytics workflow, inputs, outputs, and key logic steps. These features can also help you quickly create presentations or emails to share insights with key stakeholders.
Analytical automation: Analytical automation platforms such as Alteryx can automate end-to-end analytical processes, including data retrieval, preparation and collation, conversion, and reporting. With a visual, intuitive interface, you can create analytical workflows once and then automate them forever. These solutions can connect different solutions together, such as an RPA system that can automatically modify a file or data set at the end of an analytics workflow.
Business intelligence: Business intelligence refers to the strategies and technologies used to analyze data and share these insights through data visualizations and dashboards. Automated analytics can automate business intelligence steps, automatically uncover hidden insights in your data, and even create dashboards to help key stakeholders always have the information they need to make informed decisions.
Why automate your analytics
From faster insights to reducing errors, here are a few reasons why you should use automated analysis;
Reduce manual work
Manual data preparation and cleaning in spreadsheets has been a nuisance for data workers for years; according to one study, a typical analyst devotes two hours a day to just preparing data. Analytical automation does the same job in seconds, reducing data processing time by 80%.
Discover insights faster
Millions of rows of data are used in most enterprise analytics workflows. Tabular worksheets can't keep up with the age of big data. Analytical automation can not only transfer processing to a server for faster analysis, but can also save time each time by automating multiple parts of the analytical lifecycle.
Avoid mistakes
Working in spreadsheets involves complex calculations with large volumes of data that take place in small cells. Analytical automation solutions like Alteryx have a visual interface where you can see exactly what's happening to your data at every step of your workflow, helping you avoid costly errors. In addition, manual tasks such as copying and pasting and writing values manually are more prone to error. Automation can facilitate these tasks to reduce the risk of typos.
Stand out in your career
When you find real-time insights, save your business money, and avoid mistakes, it's inevitable that you'll be noticed. Analytical automation solutions can help you get ahead in your career by helping you achieve more than ever before.
Automating your analytics: getting started
Automated analytics may look different depending on your organization's needs, but here are a few steps we recommend for getting started:
- Identify your business problem
First, determine what you are trying to solve with an automated analytics solution. Are you trying to automate preparation and consolidation or quickly replicate machine learning models? By understanding your goal, you can choose the type of automated analytics solution that is right for your team.
- Choose the right solution
Some automated analytics solutions cover end-to-end analytics and numerous use cases. Others offer much more specific solutions, such as optimizing a marketing campaign or forecasting revenue. Once you know what business problem you're trying to solve, look for a solution that meets that problem and is enterprise-grade, that is, robust security features and easily integrates with your existing infrastructure, data, and applications. This will allow you to quickly start working without unexpected glitches.
- Collect the appropriate data
Once you put a solution into practice, make sure you collect the data you want to analyze (such as CRM, ERP, financial systems, social media accounts, web analytics) Then create your analytics workflow and watch the process run fully automatically.
Analytical Automation Examples
Here are a few examples where analytics automation is at work;
Demand estimation
Retailers need to know at all times how much stock they need to order and when they need to order. Demand forecasting can eliminate estimates by modeling the delivery of products from the supplier to the shelf.
Analytical automation can help you build demand forecasting models in three quick steps.
- Data Link: Automatically pull historical sales data and supply chain capacity data.
- Prepare and Blend: Blend and format data to enter a model.
- Create a predictive model: Create a model (low-code or no-code) that predicts expected future sales.
Scenario analysis in financial forecasts
FP&A professionals often refer to the Data Sheet feature in spreadsheets for a significant portion of their financial modeling. For example, they perform scenario analysis by creating tables with the appropriate interest rate and maturity range, then identifying data table entries and generating results. While this allows them to explore a wide range of scenarios, it is known to be a time-consuming and error-prone approach. With an analytical automation solution, you can copy the DataTable function of spreadsheets to achieve greater accuracy with much less work.
With analytical automation platforms such as Alteryx, you can;
- You can save time and reduce errors
- You can create data tables for multiple scenarios and easily change parameters after the first test.
End of month closing automation
Closer to the close of each month, staff accountants begin the reconciliation process to ensure that all financial transactions for the month are recorded. This may include sending overlooked invoices, eliminating discrepancies in inventory, comparing budgets with expenditures, analyzing results, and preparing reports.
With analytical automation platforms such as Alteryx, you can;
- Reduce the effort and time required to upload multiple source files together, associate them with each other, and create multiple views of monthly data.
- Automate your entire workflow to eliminate human error in planning, selecting, filtering, merging, and formatting reports into data sets that accountants can use or delegate for additional changes.
- Evaluate accountants' time for higher value tasks with routine calculations performed automatically every month.
Get started with analytics automation today
Whether you're automating data preparation and consolidation, or rapidly building machine learning models, analytics automation expands the boundaries of what's possible for data workers everywhere. With Alteryx AI Platform for Enterprise Analytics, you can use self-service analytics to automate the entire analytics lifecycle.
Access data from anywhere, anytime; prepare and analyze that data in seconds; build predictive models with or without code, and create dashboards and email summaries to quickly share insights with stakeholders — all to uncover the insights your business needs faster and easier than ever before. Are you ready to start analytics automation? You can contact us to find out more.
İlginizi Çekebilecek Diğer İçeriklerimiz
Veri analisti (Data Analyst), verileri toplayan, analiz eden ve bu verilerden anlamlı içgörüler çıkararak işletmelere stratejik kararlar almalarında yardımcı olan bir profesyoneldir.
Makine Öğrenimi Mühendisi (Machine Learning Engineer), veri analizi ve yapay zeka algoritmalarıyla çalışan, makinelerin öğrenmesini ve veri odaklı kararlar almasını sağlayan sistemleri geliştiren bir profesyoneldir. Bu mühendisler, istatistik, programlama ve veri bilimi becerilerini kullanarak, iş süreçlerini otomatikleştiren ve optimize eden çözümler oluşturur.
