How to drive productivity with automated data analytics – Part 2

In our last post, we looked at how automated data analytics adds value to senior-level decision making. But how can you implement it in your organization? Let’s take a look at the steps to automation in more detail.

Five steps to automating data analytics

1. Create objectives

Before bringing automation into your data analytics, determine your objectives. Maybe you want to work on trend recognition or perhaps your goal is to generate an AI algorithm that makes decisions based on different data signals. Whatever your goal, make sure you communicate it with the rest of your team – even those who aren’t involved in data analytics directly.

2. Set expectations

Create your own ‘criteria for success,’ and set expectations for your team, complete with milestones and timelines. This will help keep you on track toward your goals and ensure that everyone knows what’s expected of them.

As part of this, you’ll need to determine your performance metrics. If, for example, your objective is to collect more data on customers you’ll probably want to track how many customer profiles your automated system generates.

3. Build foundational data

As almost three-fifths (58%) of B2B marketers will agree, the most important thing needed to amplify the success of an automation tool is the quality of the data you feed into it. Therefore, before you can start, you need to build a layer of what we call foundational data work from.

4. Partner with a leading data analytics automation provider

Now it’s time to research data automation providers to determine which kind of software will best suit your business needs. While there are standalone tools available, they often require extra manual input. If you’re looking for a fully integrated data analytics tool with versatile capabilities, consider a trusted cloud-hosted data analytics automation platform such as Switchboard.

5. Adopt a test-and-learn mindset

Now that you’ve implemented data analytics automation software into your system, it’s key that you keep developing and testing out its capabilities. Every time you automate something new, test it and monitor how well it’s performing. When researching automation providers (see step 3), be sure to choose a solution with a dedicated Customer Success team on hand to help you make the most of new data and integrations.

READ MORE: Data Analytics Use Cases

Which parts of the data pipeline should you automate?

Remember, your data engineering team is a valuable resource, so start by evaluating how you can help free up their time. Tasks that are repetitive or rules-based will benefit the most from automation:

• Data preparation: Data collection, data discovery, and data structuring are all examples of data preparation that can be sped up and streamlined with the help of automation.

• Data maintenance: Simple everyday maintenance tasks – i.e., index reviews, backups, and data cleansing – can also be automated to save time.

• Big data: Since big data often involves sifting through large volumes of information and carrying out ‘big data’ testing, automation can enable you to leverage data at scale – something that just wouldn’t be possible to do manually.

Your business teams are also key drivers of the organization, so make sure they have access to the right data when they need it.

• Reporting: Reporting activities, such as accessing dashboards and running ad hoc reports, comprise a lot of minor manual tasks (i.e., streaming, collecting, and filtering data) that can easily be simplified with automation.

While automation can be incredibly beneficial, it shouldn’t replace human intelligence. Continue to give your data engineering team the autonomy to ask questions, experiment, and use their skills to advance your organization. Meanwhile, give your business teams access to the dashboards they need – when they need them – using automation.