How Automated Data Analytics Can Improve Your Data Team's Productivity

Not sure If data automation Is worth the effort? Learn more about automation, the methods, the tools, and more.

How Automated Data Analytics Can Improve Your Data Team's Productivity

Every business has data. Data on customers, services, products, processes, hardware performance, finances, operations, staff, etc.

Decisions that were once driven by an individual's past experiences, or a team's tribal knowledge, is now driven by hard data (or should be!). Because of this transition, the volume of data has proliferated exponentially. This has made superstars of data professions such as data engineers, data scientists, and data analysts.

Whether you realize it or not, you are likely sitting on mountains of valuable data. What can you do about it? Automate the processing and analysis of your valuable data!

the technology of data engineering, data science, and data analytics

What Are Automated Analytics?

Automating analytics is the process of involving machines or computers to handle analytical tasks instead of solely relying on people. Sometimes this is a simple process of running basic scripts that will change the structure of a data table to fit a pre-determined data model. This will, of course, give you specific reports that are useful.

After the scripts are written, automation can help analyze data faster and more powerfully than the typical exploratory and iterative process. Regardless of the use case and outcome, automation takes the person out of the task and has a computer do the work, usually faster and more accurately.

How Automation Is Changing How We Work

Data and analytical automation has impacted how we work on a daily basis. Historically, we had to deal with limited feedback on a business process, and we often had to wait on this analysis. Today, with automation, the analysis happens in near-realtime, providing feedback even while the business process is happening. This allows course correction to happen, resulting in a better final outcome, and ultimately a more profitable business.

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Why You Should Automate Your Data Processes

There are several reasons why a business would want to look at automating its data analytics, data pipeline, and data engineering. First, and perhaps most importantly, it leads to a productivity gain for the company. What employees once had to do themselves can now be relegated to a computer, can be done faster, and can generally be done more accurately. Employee time is more expensive than computer time, so money is saved with automation. Additionally, boring and mundane tasks can and should be automated. This generally leads to humans being able to accomplish more complex, important, and insightful tasks and opportunities. You may even be able to increase job satisfaction by offloading the boring stuff.

Overall, data automation can lead to new products, new services, tweaks to existing products to make them less costly or make them more useful, and an array of other advantages. Ultimately, this will help any company become more competitive in the rapidly changing digital landscapes.

Team creating a data strategy

Improve Productivity Through Data Automation

As mentioned previously, one of the reasons to automate a business's data processes is to improve productivity. With reams of data to sift through, there are a variety of mundane tasks like entry, organization, storage, movement, and validation. These tasks can and should be automated so that computers can do them. Computers can do this work faster and more efficiently than people can, and this frees up the employees to focus on more important tasks, such as interpreting the automated data and developing new courses of action based on this automated data. These employees are now utilizing their time better for the business.

When Should You Automate Data Analytics?

You might be tempted to say "right away" to this question, but not all data are created equal. If you have a single analytical project that is reliant on properly modeled data, there may not be enough justification to put in the time to create automation. However, when you have recurring processes, such as creating data models that power dashboards for groups of employees and need to be updated daily, weekly, monthly, or quarterly, those are great candidates for automation of the data pipeline and analysis. Yet even here, it is possible to go too far with automation. When that happens, you may be missing new insights because the data is getting filtered out for an older use case. The key is knowing just how far to take data automation and then turning that over to human intelligence.

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Big Data Automation

Automating big data is a challenge itself, but there are some processes that lend themselves to making this more manageable. Because big data often means volumes of data to sift through, complex data models, and permutations and interactions within the data set, automation can provide a serious advantage. As automation processes begin to compound, new ideas emerge for the business to pursue. But, the processes related to automating big data can actually be the biggest challenge. If you can work through these challenges, the benefits will be rewarding. Payoffs include things like reduced costs, enhanced customer satisfaction, and improved decision-making, which are often worth the investment.

Testing Automation and Data Analysis

Where automation of data can really shine is with testing, especially when that testing is then coupled with data analysis of the test results to provide feedback on those results. There is only so much time that employees can deal with testing, so if they can automate at least some of those tests, that will free up time to either do more testing or better analyze the results of the run tests. As the time horizon on test results gets shorter, the feedback from the test data analysis can affect future testing and even draw out test scenarios that are flaky and won't lead to anything useful. As automation is added, things get thrown out to further enhance the testing process.

Tools For Automating Data Analysis

When you are looking for data analytics automation tools to automate your data analysis, data pipeline, engineering, or other data processes, Zuar has the perfect solution to help. If you are like most businesses, your data isn't in a nice neat package, but trapped behind software APIs, spread over different databases, and stored in flat files. Without having all of your data in the same place for a single source of truth, your analysis is incomplete and you aren't getting the full picture. Zuar Runner is a solution that helps get your data cleaned, transformed, and compiled into a central location, all while automating the entire process to give you a complete picture of your business.

Mitto modeling and automating data from desperate systems

Not only does Runner automate the flow of all of your data into a single destination for analytics, but you can enhance and transform the data by way of adding contextual information. This additional contextual information helps with modeling and flow while providing more ways to understand the data for analysis.

As a business with lots of data, you need to be able to dig into that data to confirm the things you already know while also learning things you don't. Automating your data analysis is the best way to be able to sift through mountains of data quickly and accurately to get to the important take-aways that inform your business decisions. And the faster you can do that, the faster you can adjust your business to capitalize on what your customers want, creating the ultimate competitive advantage.

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