Data is changing how we do business. As business owners, the amount of information we have available and how to use it to our advantage is astounding.
The total amount of digital data that can be made and distributed is 79 zettabytes. One sextillion bytes is a zettabyte. That's quite a lot. This number could rise to 181 zettabytes by 2025.
It's called big data. But even small data is getting faster and more frequently.
It is what they do with the data that matters. Without being leveraged, it doesn't matter much.
Data can give you invaluable insight into everything, from customer behavior to demographics, and even future sales forecasting. Data can provide invaluable insight that will help you make better business decisions.
Data can also be available in real-time. This allows you to quickly make decisions and pivots to react to market changes and take advantage of live opportunities.
It doesn't matter if your data isn't in the right place or difficult to access. DataOps is here to help.
What is DataOps?
DataOps, a relatively new term, encompasses many tools that can be used to address the problem of how data is being received and made relevant to those who require it.
There are some things you need to do when working with large amounts of data.
- It must be organized in a way that makes sense. This includes pulling in relevant data and eliminating unnecessary information.
- It must be analysed: How does it compare with past data and concurrent data?
- This information must be understood: What does it mean for your brand? What can you do to react? What can you do to be proactive with this data?
All of these things must happen quickly. It must continue to happen as new data is collected. This cycle must continue at a rapid pace.
DataOps is the software and architectures that enable you to accomplish all this in a flexible, responsive way.
You can use a DataOps tool to help you address your needs or you can build it in-house. There are steps that you should follow to ensure smooth and efficient processes.
1. Use automated testing
You must be able to trust your data and DataOps who deliver and activate processes in order to rely on them.
Automated tests can be run through the programs to check for bugs and to ensure data is getting through as expected. This is where you verify that the tools actually work.
2. 2. Perform data monitoring
Data monitoring is an additional tool that you will need to use in addition to automated testing. This is where you can check the quality of data being processed.
This is how you get to your goals. What are you trying measure? You should set standards about what constitutes “good data”, and make sure you check in on them regularly. You must ensure that your processes are able to gather and analyze “good information” without being influenced by inaccurate or irrelevant information.
Regular check-ins increase confidence in the system.
3. Multi-environment Work
DataOps should be conducted in different environments, just like DevOps. These are places where you can test and experiment with your DataOps. These environments will be used for data analysis, testing, and going live.
These can be kept separate to allow you to create new workflows and ideas in a staging environment, before you move to a live environment. This helps to prevent data from being distorted by bugs or bad development. They can be solved in a earlier environment.
Your team can also work together in the initial stages of development, idea testing and bug testing before you go live. You can have multiple ideas simultaneously without having to work in different streams.
DataOps' fundamental goal is to be agile. Your code will stay simple and streamlined if it is containerized. Containerizing is the act of packaging code in simple, reusable pieces so it can be used on multiple platforms and languages.
This allows it to be reused, tweaked and rerun for other projects. You can update and launch new products quickly as you improve your data operations.
Regression testing is crucial as you move forward with DataOps. You'll need to test for new problems and fix old ones with each update or new operation. Regression testing is a process that runs through a program to verify it is still functioning properly after the changes have been made. You can always go back to the original version if you find any bugs, then make sure it runs properly before you introduce the new update.
5 DataOps Tools Examples
Many programs and tools are being created to support DataOps's approach to data analysis and processing. Your goals, data volume, and integration requirements will determine which software you choose. You may not need some of the software options.
To determine if this is the right product for you, make sure to read the details and compare it with other tools.
All of these promises a certain ease and accessibility, but they all start from a position of general knowledge and confidence in data software and API integration. For support, you might want to contact your web development team. These software developers offer consultations and in-house support that could help you get your DataOps up and running.
Fraxses promises to assist brands that have lots of data but need help integrating it in a way that works for them.
A retail brand that was receiving lots of data from customers, but not able to integrate it with their own data on one platform or dashboard, is shown in a video on their homepage.
These solutions are offered by Fraxses in the agile format required by DataOps. The tool is an example:
- It doesn't depend on one language, but can be written in any language you require
- is decentralized
- Is it low code or not code?
- Can be democraticated
Fraxses is a mesh or fabric that you can use to cover your existing data structures and platforms in order to connect and pull together the information you need.
RightData defines DataOps (DevOps + Analytics) as DataOps. They provide brands with DevOps-level support for their data management and analytics, but they also have the DataOps constraints which include:
- An agile approach
- Continuous delivery of data
- Sprints or quick releases are possible.
RightData integrates with DevOps to help you manage data and analytics in your business. They promise to keep up with the monitoring and testing part of the cycle once you have developed your system. This ensures that your DataOps can continue to move forward, and work seamlessly and quickly.
RightData is also focused on customer privacy, security, and that is a crucial component of DataOps. Data breaches can immediately stop your DataOps continuous processing, and slow down the entire system. To move forward with confidence, it is important to maintain security.
For more information about RightData DataOps, companies can contact RightData directly to request a demo or a quote.
MLflow is Machine Learning flow. It's a cloud-based platform that allows you to run DataOps.
It is an open-source platform that works in any language and with any code. MLflow is available for use by one user or an entire organization with many users.
It was designed to address the issue of too many data analytics tools making a DataOps cycle difficult to navigate with agility and consistency. DataOps is based on seamless reproduction. This allows for quick sprints and not long waiting periods to crunch data while it becomes irrelevant.
MLflow is a community solution that allows brands to collaborate to create and share better products.
You might be interested in MLflow if you are into this type of tinkering.
K2View provides all DataOps solutions for a brand under one roof. You don't need to worry about how you integrate this or if your DIY DataOps fabric covers all bases.
Its basic premise is straightforward. It promises an all-in-one DataOps platform that will give you all the benefits, including:
- A single dashboard that allows you to view and manage all information at any time.
- Full, detailed information about any product, customer or location, as well as demographics, is available. This data is current and relevant and not lagging.
- Continuous delivery of data
- A flexible and adaptable framework that responds to new data
- Security support
The integrations ensure that everyone in your company has access to the relevant data, from sales to marketing, to management, to the floor.
K2View can provide a quote or a Proof of Concept. You can also contact them for a free estimate.
Tengu, another DataOps platform that you can use as a brand owner, is also available. Tengu is also low-code or non-existent, and can be used as an easy-to-use, ready-to-go option for anyone looking to get started with DataOps solutions. You can use it in the cloud to support remote teams, spread out teams, or at a single location if you need something more secure.
Tengu does not want a lack in knowledge to be a limitation. Tengu is built around self service so users can access the features they need. You can also set it up with very little technical experience.
They are also proud to say that they offer more than the technology they provide. They offer consulting services to their customers on how they can make their data more useful and which systems will best help.
Tengu is available for consultation by those who are interested.
Commonly Asked Questions about DataOps
What is DataOps?
DataOps is a type of agile and continuous methodology, for the managing and interpreting of data for a company. With this approach, brands can process their data faster and more pertinent to their needs.
DataOps: Why is it Important?
DataOps works at scale to crunch data quickly and more efficiently, in repeatable sprints, so companies have access to the information they need in real-time, in a single location, across departments.
What is DataOps Marketing?
You can continuously gather data from customers, their experiences, the products people are buying, and more to make real-time decisions about how to reach more of your target audience.
What are DataOps tools?
DataOps tools integrate into your existing data collection software to process and deliver data information in a primary platform or dashboard. Examples include FraXses, RightData, MLflow, K2View, and Tengu.
DataOps Guide: Conclusion
Data is crucial to our sales and marketing cycles. There are many great data analysis tools, but sometimes you need the information faster. Speed comes with the need to be efficient, accurate, and secure. DataOps provides the solution in flexible and agile environments. DataOps continuously drips in reliable data that your brand can use for better sales processes, customer needs, and to reach your goals with greater efficiency.
What DataOps tool will you first try?
By: Neil Patel
Title: DataOps Solutions: Software, Tools, and Alternatives
Sourced From: neilpatel.com/blog/dataops/
Published Date: Wed, 15 Sep 2021 13:00:00 +0000