Data wrangling — the process of transforming raw data into a more desired format by removing errors, restructuring, and combining data sets — is an essential part of analyzing aggregated data. You need to organize data from disparate sources to prepare it for analysis. After all, the insights you derive from data analysis are only as good as the aggregated data itself. 

Data wrangling helps maintain quality and enables better decision-making in your organization. And yet, the way many enterprises handle data wrangling is killing their productivity. These are some of the key issues that arise from the typical methods for wrangling data: 

No Scalability

Trying to manually wrangle a few small data sets is a manageable task for most data professionals. When you’re dealing with minimal amounts of data, you may not find any problems with your organization’s current strategy. Your data teams will go through data from each of the data sources and unify that data into one cohesive, usable structure. 

Often, the problems with this strategy appear when you want to scale your data analysis. If your decision is to gather more data for analysis and incorporate additional data sources, there’s no good way to scale this manual process. You would either need to add to your data team or expect your existing data professionals to devote more time to wrangling the additional data — cutting into the other work they do. 

This issue would arise every time you decided to use more data within your organization. Say you decide to implement new applications, for example. Since each application typically has its own data format, your team would need to wrangle all this data to prepare it for communication with your other systems. Manual wrangling simply isn’t a scalable solution. 

A Drain on Analytics Teams

As things stand, data analytics teams often need to devote a considerable amount of their time — up to 80 percent — to data wrangling. That leaves less time for actual data analysis, cutting into the number of valuable insights that analytics teams can provide to decision-makers within their organizations. It’s not like they can simply forgo wrangling the data in favor of analyzing it — the wrangling is a mandatory prerequisite for conducting meaningful analysis.  

So manual data wrangling acts as a drain on your data team’s time and energy. Decision-makers within your company might not be satisfied with the team’s data analysis when team members have such little time to devote to it. You’re also arguably wasting your data analysts’ time and paying handsomely for that time since they can earn $200,000 to $300,000 per year. Think about how much more productive your data team could be if your data professionals could devote their time to data analysis rather than acting as “data janitors.” 

Data Wrangling Isn’t What Analysts Want to Be Doing

Data analysts are often quite honest about the fact that wrangling data is not their preferred activity out of all the tasks they complete at work. It’s tedious, monotonous work that’s more like a chore than an intellectually enriching task like actually analyzing data — the reason companies hire them and the job they want to do. 

So what happens when you have a group of overqualified data analysts wasting the bulk of their time on a task they don’t enjoy? You get burnt-out, frustrated team members who aren’t making as big a contribution to your organization as they could be otherwise. In some cases, you may even struggle with employee retention on your data team which can be costly all on its own. Replacing talented data analysts isn’t easy. 

Improving Your Data Wrangling with Automation

Your enterprise can’t just give up on wrangling data altogether, but it also doesn’t make any sense to keep damaging your data team’s productivity by making them responsible for the task. So what should you do if you want to get the most out of data analysis and your data team? Turn to automation.

Businesses across industries are finding ways to use automation to reduce the time their employees spend on time-consuming routine tasks. Using automation to more efficiently and effectively wrangle your data is the next logical step. Organizations that don’t automate the data wrangling process are missing out on a valuable opportunity to free up their data teams for more impactful work. 

Some of the benefits your enterprise may enjoy from automating this process include:

• Save time: If the members of your data team don’t have to manually wrangle all of your organization’s data, they will have much more time to devote to data analysis.
• Better morale: When your data professionals can spend their time working on tasks they actually enjoy and want to complete, you’ll have better employee morale which is good for the whole organization. 
• Save money: Most automated tools for wrangling data will indeed require a financial investment. However, that investment likely offers a great return in terms of the money your business will save by not having to pay highly qualified professionals to complete a relatively simple task. 
• Fewer mistakes: Even top data professionals make mistakes while manually wrangling data. Automated wrangling tools lead to fewer errors which means you receive more reliable insights from your data. 

Key to Note

There are a variety of automation tools available to help with wrangling data. These tools typically apply machine learning and artificial intelligence (AI) to eliminate bad data from data sets and convert the remaining good data into the format you need for data analysis. Live Earth is one of the solutions that can change the way your organization deals with data — from data wrangling to comprehensive analysis of aggregated data. Live Earth’s all-in-one platform leverages AI technology to unearth valuable insights from both internal and external data sets in real-time. The platform aggregates all the data and offers better business intelligence, optimizing the way your business handles data analysis. Contact us today to see the platform in action.