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How To
March 23, 2021
A short guide to automated data analytics
Learn about automated data analytics and your next steps.

Is repetitive and menial work impeding your data scientists, analysts, and engineers from delivering their best work? Consider automating your data analytics to free their hands from routine tasks.

What is automated analytics?

Automated analytics is the use of computer systems to deliver analytical products with little or no human activity.

By automating analytics, you build systems that automate either a part of or the entire data pipeline, which brings a data product to life - from automating business intelligence dashboards to data-driven self-governing machine learning models.

What are some examples of automated analytics?

Any part of the data pipeline can be automated:

  1. Data collection. Before you can analyze data, you have to have data. But getting the data can be time-consuming. From finding all the dispersed Excel files to building a script that extracts data from 3rd Party Apps, collecting data needed for analysis can take a lot of time. By automating data collection, you can speed up the turnaround time needed to deliver a data analysis. You simply use tools to extract the data from various data sources automatically and set them on a schedule to keep your incoming data fresh.
  2. Dashboards. Think about what it takes to build a dashboard that tracks company KPIs. You need to collect the relevant data, analyze it to extract metrics (with Excel, Python, R, whatever your language of choice is), and then visualize the analysis on a dashboard where the decision-makers can see it. Running the entire pipeline end-to-end can be time-consuming. Automating scripts that analyze the data, or using tools like Looker or Metabase which automatically visualize data, can chip away some precious times when constructing dashboards. 
  3. Business intelligence. Building business intelligence excellence takes more than just dashboards. You have to look at different metrics and their breakdowns by important business units to gather new insights. Are monthly orders increasing in the EMEA region, but not in APAC? Automate the construction of the different breakdowns by automating data preparation. Build cubes that aggregate data broken down by various dimensions within your warehouse. For example, the total number of orders (aggregation) by geographical region (dimension). Or average order size (aggregation) by customer segment (dimension). Then just look at the cubes to draw insights from the automatically made analysis. 
  4. Machine learning models and big data automation. Machine learning specialists build statistical models which can outperform humans at many tasks. For example, a machine learning model is much better at predicting which ads will get clicks than humans are. But building a click prediction model once will not cut it. Consumers’ tastes are changing and what they shop for online changes alongside. Instead of data scientists and machine learning engineers re-building models every few weeks, automate model construction and selection. By using automation, you can build different models by selecting different parameters based on the different data combinations. Then automatically select the model with the best click prediction score and deploy it to production. Automation is not reserved for building models. Banks and financial institutions use state-of-the-art anomaly detection algorithms that look for signals, which indicate fraudulent transactions. Once the signal reaches a certain threshold, the models themselves trigger an account inspection by sending alerts to human inspectors.

The possibilities for automating data analysis are endless, so why should you do it?

What are the benefits of automated analytics?

There are four main advantages to automating your analytics:

  1. Accelerate analytics. The average turnaround time from request to analytic delivery can be shortened by automating (part of) the data pipeline needed to create the analytic report.
  2. Save time and money. By automating (part of) the analytics pipelines, you save engineers’, scientists’, and analysts’ hours of work. That also means you pay less for rota tasks since they are taken over by a computer system.
  3. Liberate time for creative work. Your data experts do not need to spend time tweaking pipelines manually, so they have more time to tackle the hard business problems and come up with creative ideas for revenue-generating work. 
  4. Improve processes and systems. Running analytics manually often involves complex processes. From who your analysts need to talk to, keeping in mind all the exceptions needed when cleaning the data before analysis, and multiple coordination meetings to pass the data alongside different departments and employees. By automating analytics you skip the parts, which are prone to human error. And once you find an error in the automated processes, you only need to correct it once. With automation, you build future-proof processes and systems. 

When to automate data analytics?

Not every analytics task is ripe for automation. Before you start automating, check that:

  1. The candidate task has real value. By automating the task you could either solve a business problem (delayed insights cause lost opportunity), impact the bottom line (the time savings translate to relevant cost savings), or offer the potential for business growth (the automated analytics discovers new sources of revenue or cost-savings). 
  2. The candidate task is repetitive. If you only build a custom report once, there is no need to automate it. It is often through repetition that we discover which parts of the process can be more easily automated. For example, producing the same KPI dashboards 3 weeks in a row turns on the light bulb over our head, that the dashboards use the same data sources, so automating data extraction can help us speed up business intelligence reporting. 
  3. Automation saves time or reduces errors. If building an automated system would cost us more than running the system manually, automating data analytics would be wasted time. The same applies to high-error processes, where the human agent can outperform patter-discovering machine learning algorithms. 
  4. You are prepared to iteratively improve. No automated system is made perfect on the first try. You need to be prepared to iteratively improve your automated data analytics if you want to succeed. This has two consequences: (1) you need to adopt a growth mindset, where you consider how to improve the system, (2) you need to set up a system of criteria and monitoring to assess if the system performs well. 

How to automate data analytics?

The implementation of data analytics depends on which level of automation you are looking at:

  1. Partial automation. Automation supports existing behavior but removes parts of the manual work. For example, your data team would write scripts, which speed up parts of their work.
  2. End-to-end production. Automation is set up end-to-end, and computer systems produce data products for human decision-makers to inspect and act on. For example, automation produces KPI dashboards or alerts of fraudulent behavior without an employee touching anything. 
  3. Full automation. Automation takes business decisions in near real-time without any human intervention. For example, an AI algorithm decides whether the signal in the data is good enough to automatically buy or sell assets. 

The more you move towards full automation, the more the value of automation shifts from saving hours to providing independent impacts on the business bottom line.

But how do you start automating your data analytics?

  1. Identify candidate analytical tasks. Use the four checks we presented above: the task has business value, is repetitive, saves time, reduces errors, and you can iteratively improve on it.
  2. Set up expectations by formalizing criteria for success. In the early stages, automation should serve as a way to solidify processes, and save time. But be clear on what you expect. Start small, by automating one data pipeline.
  3. Use devoted platforms and tools to speed up automation. Your engineers can write all the SQL procedures themselves and Python scripts to automate code, but relying on specialized tools and platforms saves you time when building your automated pipelines. 
  4. Repeat and evaluate. As you automate part of the data analytics processes and products, evaluate them against the success criteria set up before. If successful, automate more.

Keboola is an end-to-end data operations platform, which provides tools that help you automate your data analytics:

  1. Automatically extract data from multiple data sources - from 3rd Party Apps like Facebook ads to data warehouses like Amazon Redshift. 
  2. Automate your data cleaning and transformations to save precious time while keeping data clean and validated.
  3. Use machine learning tools to construct state-of-the-art AI models.
  4. Integrate Keboola with dashboarding software to always have the latest reports as soon as new data becomes available.
  5. Schedule end-to-end pipelines to run on their own with Keboola’s Orchestrators.

Try it for yourself. Keboola offers a no-questions-asked, always-free tier, so you can play around and tap into the potential of automating your data analytics. 

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