Many organizations’ data analytics efforts are hampered because their data teams are bogged down with rote work. Enterprises can streamline their analytics processes by taking advantage of automated data analytics.

What is data analytics automation?

Automated data analytics is the practice of using computer systems and processes to perform analytical tasks with little or no human intervention. Many enterprises can benefit from automating their data analytics processes. For example, a reporting pipeline that requires analysts to manually generate reports could instead automatically update an interactive dashboard.

Automation in data analytics is particularly useful when you're dealing with big data, and it can be used for a variety of tasks, such as data discovery, data preparation, data replication, and data warehouse maintenance.

Automated analytics mechanisms vary in complexity. They range from simple scripts that fit records to a pre-established data model, to full-service tools that perform exploratory data analysis, feature discovery, model selection, and statistical significance tests.

Automated data analytics can make decisions on behalf of enterprise stakeholders and create useful feedback mechanisms, such as an analytics system that regularly runs a study on data, then uses the results to automatically improve business processes while adjusting study inputs or parameters in real time.

Automation in data analytics can provide insights that might be otherwise unavailable to an enterprise. A cybersecurity firm might use a classification algorithm to categorize large swathes of web activity, then deliver information about these categories in an interactive dashboard for their clients, who are hoping to protect their own customers. Feedback and customer input to this dashboard can be automatically reverted into the classification model, improving it in real time without intervention from the team that first implemented it.

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Benefits of data analytics automation

The barriers to automation in data analytics have never been lower, and the advantages of using automation have never been greater:

  • Automation can increase the speed of analytics. A data scientist can perform analytics more quickly when an analysis requires little or no human input, and computers can efficiently complete tasks that are difficult and time-consuming for humans.
  • Automation is the key to effectively analyzing big data.
  • Automated data analytics saves an enterprise time and money. Employee time is more expensive than computing resources when it comes to data analysis, and machines can perform analytics efficiently.
  • By automating tasks that don't involve a high degree of human ingenuity or imagination, data scientists can focus on surfacing new insights to guide data-driven decision-making.

Data analytics automation benefits many members of a data team. It helps data scientists by allowing them to work on complete, high-quality, up-to-date data. And it takes basic reporting and business intelligence tasks out of the hands of analysts and engineers, freeing them to focus on more productive work, such as adding new data sources and expanding the scope of analysis. For example, a data analyst could use automated data analytics to flag variables in a dataset. Automated analytics systems can make suggestions with a final statistical model in mind, saving the scientist the time and effort required to rerun a study multiple times to evaluate different sets of manually selected and transformed data.

When to automate data analytics

Automation can enhance data analytics, but how do you know when and where to use automation? As a general rule, it's most appropriate for tasks that are rules-based, performed often, and part of a stable business process.

Automating a specific one-time study makes little sense. But automating data discovery processes in an organization that employs many data scientists, each working with varied data sources, would be more effective. Many analytical tasks are good candidates for automation:

  • Creating dashboards, and reporting in general, are ideal candidates for automation. Automated analytics systems can stream, process, and aggregate data for publishing to interactive plots and live data summaries.
  • Automation simplifies data maintenance tasks such as modifying and tuning a data warehouse. An enterprise should take advantage of the many tools that facilitate automatically integrating new data sources or migrating data from legacy systems. For example, Stitch parent Talend's suite of data integration applications allows customers to create compartmentalized data migration jobs that users can schedule and automate.
  • Automation can streamline data preparation tasks. Tools like the visual programming platform KNIME can automatically label data, train and validate models, and iterate study runs to optimize parameters.
  • An enterprise can automate data validation to detect typos, flag and impute missing values, and identify content and formats that don't match a dynamic data model. This kind of analytics automation not only streamlines data modeling processes, but also enforces adherence to models by automatically transforming data.
  • An intelligent system with access to data ingestion and replication schedules can monitor available bandwidth as well as engineering and delivery calendars. It can run batch ingestion and processing tasks at appropriate times, and tune streaming systems in real time without human intervention.

Still, though many parts of the data analytics stack can benefit from automation, human intelligence remains irreplaceable. Asking questions, validating data or statistical models, and translating numbers and graphs to actionable insight are all tasks that cannot or should not be left to machines.

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How to automate data analytics

Ready to begin automating your analytics processes? Follow this process to ensure effective implementation, prevent interruptions to existing analyses, and minimize inconvenience for data analysts and scientists.

  1. Delineate your objectives. Data analytics are often cross-functional, so many teams may need to be involved in the planning process, including marketing, operations, and human resources. Set clear goals and expectations for the automation process in advance to facilitate cooperation and understanding between teams as the process moves forward.

  2. Determine metrics for measuring the performance and utility of the automated processes. This codifies the chosen objectives and helps ensure that they're met. Metrics also provide a reference for future projects or when extending the initial automated system.

  3. Select reliable, well-supported automation tools such as R or Python's NumPy, Pandas, and SciPy packages. Development focus for these programming languages is geared toward making studies shareable among academics and analytics practitioners (as exemplified by the Jupyter project). This focus makes it easier to move code and processes between humans and improves collaboration. Many data analytics tasks can be automated with these packages in combination with other tools.

The cloud platforms that host organizations' data warehouses may provide tools for automated analytics. For example, Google Analytics includes a built-in Analytics Intelligence tool that uses machine learning to flag anomalies in time series data at the click of a button.

Not all data tools lend themselves to automation. Hadoop, for instance, is great for a variety of big data tasks, but tools in the Hadoop ecosystem require extensive human involvement and can be difficult to automate.

  1. Develop, test, iterate. Once you've prototyped an automated process, test it extensively. The automation should reduce repetitive work. An automated analytics system prone to failing or propagating errors can end up costing more time and taking more resources than a manual system.

  2. Implement the automated process and monitor its performance. Most automated data analytics systems have logging and reporting built in, so they can function with minimal oversight until failures occur or adjustments are required.

Data analytics automation, big data, and the data warehouse

Organizations dealing with big data can benefit from automating parts of their data analytics infrastructure. Data lakes are filled with unstructured information that automated processes can analyze faster than any human. Modern data warehouses have stringent data modeling and processing requirements that are also readily streamlined by automation.

Stitch provides a data pipeline for loading data to your cloud-based data warehouse or data lake. Try a free trial to easily upload data from any source directly into your automated data analytics systems.

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