Data is changing the way we do business. The amount of information available to us as business owners that we need to process and use to our advantage is staggering.
The amount of digital data, made and distributed, is 79 zettabytes. A zettabyte is one sixty million bytes. It is very. By 2025, that number could rise to 181 zettabytes.
We call it big data, but even small data comes to us faster and faster.
This is what they do with data, which is important. It does not mean much unless it is used.
Data can provide invaluable insights on everything from demographics to customer behavior, even future sales forecasts and more. This can be an excellent resource for you when making decisions with your business.
Furthermore, data can come in real time, allowing you to make immediate decisions and turn around to respond to the market and take advantage of live opportunities.
Again, none of this matters if your data is unfounded or too difficult to access. This is where DataOps comes in.
What is DataOps?
DataOps is a relatively new term that contains a wide range of tools to solve the problems of what to do with data entry and how it is relevant to those who need it.
When working with a bundle 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 retrieving the relevant data and removing unnecessary information.
- It needs to be analyzed: How does it compare to previous data or simultaneous data?
- This should be interpreted: What do all the numbers mean for your brand? What should you do in response? How can you be proactive by knowing this data?
All of these things need to happen quickly. Then it has to keep happening as more data comes in. The cycle must continue quickly.
DataOps is the architecture and software developed to do it all on a scale, in a deft, responsive way.
How to implement DataOps
Whether you are using a DataOps tool or building something in-house to meet your needs, there are a few steps you need to take to ensure smooth and effective processes.
1. Use automatic testing
To trust your data and the DataOps that deliver and activate processes, you need to know that you can trust the information.
Perform automated tests through the programs to look for errors and ensure that data passes as you expect. This step deals with the certainty of the actual tools.
2. Perform data monitoring
In addition to automated testing, you want to perform data monitoring. Here you look at the quality of the data being processed.
It goes back to your goals. What are you trying to measure? Use your standards for what qualifies as ‘good data’ and report regularly. Make sure your processes collect and analyze ‘good data’ and are not tainted by irrelevant or inaccurate information.
These regular entries improve trust in the system.
3. Work in multiple environments
Just like in DevOps, DataOps must occur in different environments or spaces. Consider these levels where you can experiment and test your DataOps. You need environments for developing DataOps, for testing and analysis, and for going live.
Keeping it separate gives you the freedom to develop new workflows or ideas in a stage setting before moving on to a live one. This prevents your data from becoming skewed by poor development or errors. You can work it out in an earlier environment.
It also enables your team to work simultaneously in the early stages of development and idea testing through bug testing, all before you begin. Your team can also work on different ideas at the same time without crossing streams or returning, which can potentially confuse each other’s projects.
4. Containerize code
A fundamental goal of DataOps is to stay agile. By keeping your code, keep it streamlined and simple. Containerization means packaging in simple, reusable pieces of code so that it can be used on platforms or languages.
It also means that it can be redefined or slightly adjusted and repeated for another project. It keeps the whole operation agile, so you can act quickly with updates and new launches while customizing your data operations.
5. Perform regression testing
As you move forward with DataOps, regression testing is critical. With each new update and new operation you use, you want to ensure that new issues are not introduced and that old issues are not re-introduced. With regression testing, a program is executed through its spaces to ensure that it is still working properly with the new changes. If bugs occur, you can revert to the previous version, make sure it works properly, and bring the update back to development before re-launching it.
5 DataOps Tool examples
As DataOps evolves, many applications and tools are being developed to support this approach to data analysis and processing. The software you follow will depend on your goals, the amount of data you are dealing with and other tasks or tools you need to integrate. Some of the options listed here may be larger than you need.
Before you buy, read about the features offered and how it works with tools you already use to determine if this is the right option for you.
You should know that all of this promises a degree of convenience and accessibility, but it starts with a place of general knowledge and confidence with data software and API integration. Maybe you want to reach out to your web development team for support. Some software developers listed here also offer internal support and consultations that can help you get your DataOps up and running.
Fraxse promises to help brands that have access to a lot of data but need help integrating the data in a way that actually works for them.
In a video preview on their homepage, a retailer got very good data, but it did not have a way to obtain and integrate data directly from their customers that they could integrate in real time on a single platform or dashboard.
Fraxses offers these kinds of solutions in the agile format that DataOps requires. For example, the tool:
- does not rely on a single language, but can be written in anything you need
- is decentralized
- is a low code or no code
- can be democratic
Fraxses describes itself as a mesh or fabric that you can lay on your existing data structures and platforms to gather and connect the information you need.
RightData describes DataOps as DevOps plus analysis. They offer branded DevOps support for their analytics and data management, with the limitations of DataOps, which include:
- a nimble approach
- continuous delivery of data
- a fast release time or sprints
RightData is a DevOps integration to support data and analytics management in your brand. Their promise is that they can keep up with the testing and monitoring of the cycle after you have developed a system. It lets your DataOps roll forward and works seamlessly and quickly.
RightData also focuses on privacy and security of customers, which is an important component of DataOps. Data Violations can immediately stop your ongoing processing of DataOps and clog the entire system. Maintaining security is the key to trust.
Businesses that want to learn more about working with the RightData DataOps tool can contact them directly for a demo and quote.
MLflow stands for Machine Learning Flow and it is a cloud-based platform on which you can manage DataOps.
It is an open source platform that can work in any language or with any coding. MLflow can be used by a single user or an entire enterprise with many users.
It was created to solve the problem too many data analysis tools, making it too difficult to move through a DataOps cycle with agility and continuity. DataOps relies on seamless reproduction to move forward in a fast sprint, not marathons of time to wait for data to crunch while becoming irrelevant.
MLflow provides a community solution that brands are welcome to try, develop and work together to make it better.
If you like this kind of tampering, you might want to check out MLflow.
K2View brings all the DataOps solutions needed for a brand under one roof, so you do not have to think about integrating this and that, or your DIY DataOps fabric covering all the basics.
Its premise is simple. They promise an all-in-one DataOps solution that offers you all the benefits, including:
- a single dashboard to monitor and consume all the information you need, whenever you need it
- complete, in-depth information on any product, customer, location or area, demographic and more data that is current and relevant, rather than slowing down or getting old
- continuous delivery of data
- an adaptable and flexible framework that responds to the incoming data
- security support
The different integrations also ensure that everyone at your business who needs access to the data gets the interpolated and real-time information, from marketing to points of sale, from management to the floor.
You can contact K2View for a quote and you can also watch a Proof of Concept for free fortnight.
Tengu is another DataOps platform available to you as a trademark owner. Tengu also promises to have low or no code, an accessible option on the shelf for anyone who wants to start working with a DataOps solution. It can be used in the cloud for remote or distributed teams or directly in a single physical location if you want something safer.
Because the lack of knowledge is not a limiting factor, Tengu is built around self-service, allowing users to access the features you need, and you can set it up with little technical experience.
They also boast that they are more than just the technology they deliver. They support their clients with advice on how they can make better use of their data and what systems will help them do so.
Those interested in Tengu can contact them directly to find out more about the price levels of Tengu and various consulting services.
Frequently asked questions about DataOps
What is DataOps?
DataOps is a kind of agile and continuous methodology for managing and interpreting data for a business. With this approach, brands can process their data faster and more relevantly to their needs.
Why is DataOps important?
DataOps works on a large scale to grow data faster and more efficiently in repeatable sprints, so that businesses have access to the information they need, in real time, in a single place, in different sections.
How do you use DataOps in marketing?
You can constantly collect data from customers, their experiences, the products people buy and more to make real-time decisions about how to reach more of your target audience.
What are DataOps tools?
DataOps tools can be integrated into your existing data collection software to process and deliver data information on a primary platform or dashboard. Examples include FraXses, RightData, MLflow, K2View and Tengu.
DataOps Guide: Conclusion
Data is critical to our sales and marketing cycles. While there are very excellent data analysis software options, sometimes you need the information faster. With speed comes the need for efficiency, accuracy and security. DataOps is the answer, in flexible and agile environments, and constantly drips reliable data that can build your brand better sales processes, respond to the needs and wants of customers, and achieve your goals with more efficiency.
Which DataOps tool are you going to try first?
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