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Looking for the perfect data automation platform? Five tips for data analysts

Posted on April 9, 2020 in Industry Insights by Conversionomics

Hey, data analysts in quarantine: do you find yourselves with extra time now, while WFH, to take on projects that you’ve been putting off? For those who have that opportunity, we suggest checking out a data automation platform or two. 

Automating your data pipeline gives you the time to analyze, instead of spending it cleaning and preparing your data. If you’re one of the 80% of data analysts who use spreadsheets to aggregate and work with data, you’re painfully aware of what we’re talking about. So, try out the data automation tools you’ve been hearing about and flip the balance! It could be a low-risk, high-payoff project that helps you save hours of drudgery every week!

Are you ready to explore the wonderful world of data automation? Below are some tips on finding the tool most suitable for your needs, skills, and budget.

Besides the comparison tools and review sites, the most useful thing you can do is hands-on, free trials. Since you’re already here, you can start with Conversionomics, our own data automation platform. We built it for our analysts, now a team of 30+, who just want to spend their working hours doing what they do best: data analysis (more about us here). 

Using Conversionomics, our analysts burn through 180 TB/mo of data processed, containing hundreds of millions of rows of data. All this data supports 300+ dashboards and just as many requests for ad hoc analysis. This is just one example of the various factors to consider when evaluating data automation tools for your organization.

Here are a few more tips on what to look for in data automation tools:

Tip #1: Get more data processing capacity than you think you need.

The amount of data generated, stored and processed continues to grow every year. It is safe to assume that your organization’s appetite for data will keep growing, too. Make sure that the data automation tool you pick can support it in the long run. Data tools rarely tell you that you can keep only 60 days of your historical data, for example. Some other ways data automation tools impose limits on the data you can work with include:

  • Number of accounts, connections, or users
  • Total number of rows 
  • Amount of total data MB/GB/TBs
  • Restricted use of features 
  • Limits on file sizes
  • Restrictions on historic data uploads
  • Support options availability, etc…

So, check out the small print under those pricing plans and ask the tough questions of the helpful sales people. You need to ensure that your pick can meet your needs today but also grow as your data operations grow. And we all know, our data needs will only continue to grow.

Tip #2: Check the refresh rates for your data.

How often do you need to report your data – every day? weekly? monthly? Most tools can easily handle fetching automated data updates at these frequencies. But if your stakeholders want “real-time” reporting or you get requests for ad hoc analysis randomly throughout the day, you’d need to refresh your data every hour or every few minutes. Consider a system that allows unlimited manual data pulls and/or the ability to receive streaming data.

Tip #3: Know where your data is coming from.

This is where those connections we mentioned earlier matter. A typical analytics team connects to 3-5 sources per project, on average. Depending on the project or the team’s specialization, the sources can range from finance and operations apps, like Oracle or Excel, to marketing platforms like Google Ads or Facebook, with countless variations in between. The connection types can vary, too – from APIs that connect automatically to file exports that can be ingested via email, to .csv files that you have to download and upload manually. Some tools may charge you extra for the API connections, some may have limits on the number of API calls you can make. Learn what options there are for the sources that matter to you and how well the available connections fit in your workflow.

Tip #4: Know where your data goes.

Do you already have a data warehouse where most of your data is stored? Then make sure the automation tool you’re considering can store the processed data there easily and cost-effectively. If you need a data warehouse, you’d want to look for a system that offers one already built-in. Platforms with an integrated data warehouse are easier to set up and operate, but may pose other issues, such as compatibility with existing systems or reluctance to adopt new ones (especially if you’re sharing data with other parts of your organization).

Tip #5: Know how you work with your data.

Do you prefer to write your own SQL expressions or do you rely on click/drag/drop templates? Are you always racing to meet deadlines and need a practical solution with lots of presets? If you are a DIY learner that needs detailed documentation, forums or other support options, does your pick offer those? Keep in mind how you and/or your team operate on a daily basis and make sure the apps you are considering will not require too much effort, time or mental gymnastics to learn.

The winning tool in your selection process should match your work style and skill level but not constrain you as you grow as an analyst or as your team and data needs grow. That’s a lot to ask of a data automation platform, but it should be possible to find it in the deep sea of data tools out there. Good luck in your quest for the perfect data automation tool!

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