By Neil Patel

Data is changing the way we do business. The amount of information available to us as business owners and that we should be processing and using to our advantage is staggering.

The amount of digital data, made and distributed, is 79 zettabytes. A zettabyte is one sextillion bytes. It’s a lot. By 2025, that number may skyrocket to 181 zettabytes.

We call it big data, but even small data is coming at us faster and faster.

It’s what they do with data that matters. It doesn’t mean much unless it’s leveraged.

Data can provide invaluable insights into everything from demographics to customer behavior, even future sales forecasting and more. It can be an unparalleled resource for you as you make decisions moving forward with your business.

Furthermore, data can come in real-time, allowing you to make on-the-fly decisions and pivots to respond to the market and capture live opportunities.

Again, none of this matters if your data is out of data or too hard to access. That’s where DataOps comes in.

What Is DataOps?

DataOps is a relatively new term that encompasses a wide range of tools to solve the problems of what to do with data coming in and how to make it pertinent to those who need it.

When you’re working with a batch of data, there are a few things that need to happen to make it relevant:

• It needs to be organized in ways that make sense: This means pulling in the relevant data and weeding out unnecessary information.
• It needs to be analyzed: How does it compare to past data or concurrent data?
• It needs to be interpreted: What do all those numbers mean for your brand? What should you do in reaction? How can you be proactive knowing this data?

All those things need to happen quickly. Then it needs to continue happening as more data comes in. The cycle needs to continue at speed.

DataOps are the architectures and software developed to do all of this at scale, in an agile, responsive manner.

How to Implement DataOps

Whether you go with a DataOps tool or build something in-house to address your needs, there are a few steps you should take to ensure smooth and effective processes.

1. Use Automated Testing

To rely on your data and the DataOps that are delivering and activating processes, you need to know you can trust the information.

Run automated tests through the programs to look for bugs and ensure that data is coming through as you expect it to. This step is about making sure the actual tools are working properly.

2. Perform Data Monitoring

In addition to automated testing, you’re going to want to conduct data monitoring. Here you will be checking in on the quality of the data being processed.

This goes back to your goals. What are you trying to measure? Use your standards for what qualifies as “good data” and check in regularly. Ensure your processes gather and analyze “good data” and not be tainted by irrelevant or inaccurate information.

These regular check-ins improve confidence …read more

Source:: Kiss Metrics Blog