Automation is being applied everywhere these days – and it’s easy to see why. In the right context, automation can help alleviate the need for time-consuming and error-prone human intervention, streamlining processes and supporting efficiency. In this way, it’s no surprise that automated software and activities are being deployed across numerous different industries like financial services and retail.

However, one area where workload automation can be particularly beneficial is within data analytics. Now that the majority of companies worldwide turn to analytics to support more strategic decision-making and to enable actionable customer insights, it’s imperative that these processes help support organizational success from the top down.

Bringing workload automation into analytics can provide considerable advantages. Let’s take a look at how this works for businesses and the best situations in which to leverage automation.

Automation within analytics

As noted, automation can enable key benefits including the elimination of manual work, leading to time and resource savings as well as boosted efficiency. This process can be especially advantageous in analytics, particularly when one considers the necessary steps required to get from raw data to actionable insights.

In order for data to be useful, it must be analyzed. But before this can happen, several processes must take place, including the creation of an encompassing data repository to draw from for analysis, as well as necessary cleansing and data formatting. There are several key places within these activities wherein automation can be applied to reduce the necessary time to analyze results and ease the burden of manual work on analysts and other project stakeholders.

“Automation can create nearly limitless potential for data analysis, especially when applied within self-service platforms.”

As Dataversity contributor Arun Goyal reported, MIT researchers found that automation and the removal of human intervention in one particular instance resulted in analysis being completed in a matter of hours, as opposed to months, and with a 96 percent accuracy rate. Overall, automation can create nearly limitless potential for data analysis, especially when applied within self-service platforms.

“Depending on the technology applied, it can take only a few weeks to process, analyze and understand any amount of big data,” Goyal wrote. “In all these regards, automation has added benefits like reducing the operational costs, improving operational efficiency, enhanced self-service modules, and increased the scalability of big data technologies.”

data automationAutomation software’s data-parsing accuracy is far superior to that of manual data analysis.

Which workloads and processes should be automated?

All that being said, there are certain, specific workloads and processes which are ideal for automation:

Data cataloging

Before analysis can begin, the necessary data sources must be gathered and consolidated in a single location. As many organizations have multiple different sites where data lives, including databases, storage systems, applications and other areas, creating a single and unified data catalog is an incredibly important first step for analysis.

This is an ideal place to leverage workload automation. An advanced data catalog will include workload automation to help streamline the creation of the catalog, including the ability to catalog data sources of different types and support automatic object indexing. This helps ensure that no data source is overlooked and the resulting catalog represents a complete resource for data sources.

Data preparation

Once data has been gathered into a catalog, it must be further cleansed, formatted and prepared to support accuracy in analysis. This is another key place where workload automation can represent a significant advantage.

An innovative data preparation solution will enable workload automation for:

  • The parsing of semi-structured and unstructured data sources.
  • Overall data cleansing and formatting to ensure sources included in analysis can be accurately compared to one another
  • Other critical preparation capabilities, including processes such as data enrichment, normalizing, transforming, filtering and formatting

Industry-leading analytics workload automation

In order to achieve this level of workload automation within your analytics processes, advanced software tools are required. Self-service data analytics can especially benefit from workload automation, including the tools encompassed by the Unifi Data Platform.

Specifically, the Unifi Data Catalog and Data Preparation capabilities can offer essential workload automation for key processes like gathering data sources and preparing them for analysis.

Learn more about the workload automation advantages supported by Unifi, and connect with our analysis experts for a free trial today.