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