The ability to collect, analyze and use data to make strategic business decisions has always been the key to success for most companies. According to a recent report by McKinsey Global Institute, data-driven businesses are 23 times more likely to attract new customers, six times more likely to retain customers, and 19 times more likely to become profitable and attain long-term success. [1] That said, the ability to effectively manage data has never been so critical.

Over the past few years, the total amount of data captured, created, copied, and consumed has rapidly increased. A report by Statista shows that global data creation is expected to hit more than 181 zettabytes by 2025, jumping from 64.2 zettabytes in 2020. [2] For this reason, it has become increasingly difficult for businesses to manage data effectively within the set time frame. This is where data operations (DataOps) come in.

Read on to learn more about DataOps and how you can integrate it into your business. You’ll also learn about top DataOps platforms that you can choose from.

What is DataOps?

Introduced by Lenny Liebmann, DataOps is a set of best practices and solutions that combine agile software frameworks and process-oriented data management methods to enhance data quality, and automate and boost collaboration among data professionals. [3] It does all this while stimulating a culture of continuous improvement in data analytics.

Source: snowflake.com

DataOps intends to simplify data management by breaking down large volumes of data and gathering fragmented information in a well-structured manner that is fast, reliable, and scalable. The main purpose of DataOps is to boost customer satisfaction and maximize the business value of data.
To achieve this, DataOps focuses on the following key areas:

Collaboration

With the ever-rising demand for faster data analysis, it’s now common for various data professionals to work on the same project simultaneously. For this to succeed, a proper and more robust means of collaboration is needed. Effective team collaboration among these professionals will lead to more data-driven business culture and better decision-making.

That said, an effective collaboration framework should be able to track changes in real time, quickly resolve conflicts, compare notes, restore different software versions, and develop necessary features simultaneously. This framework should also provide well-structured means of communication among all team members.

Data quality

Good data quality is important for the success of every data-driven business. DataOps improves data quality by implementing effective data validation, data detection, and data cleansing techniques. [4]

By improving data quality, businesses can make better decisions that improve their bottom lines and help them attain their long-term goals.

Automation

Automation is necessary for the successful implementation of a DataOps strategy. It streamlines all the daily demands of managing huge data volumes, freeing data professionals to develop better data analytics in a more efficient and effective manner.

Automating the processes of collecting, analyzing, and interpreting data minimizes errors and allows for faster turnaround times.

What is a DataOps platform?

A DataOps platform is basically an integrated solution used by data professionals to process data and secure environments for sharing data. This platform is where data coming from different sources is compiled, analyzed, controlled, and delivered to respective users and data applications.

It orchestrates people, processes, and technology to develop trusted data pipelines for all users. In other words, it acts as a command center for all DataOps processes.

A reliable dataOps platform does not only process data used for privacy, security, and compliance but also customer data, technical operations data, and every other data a business receives or generates from its daily operations. They also provide the much-needed flexibility to support a variety of existing data management tools. This way, companies can manage their entire workflows and related processes to ensure that data-driven decisions are made at all times.

Additionally, companies can use DataOps platforms to gain on-demand insights for making successful business decisions. And data professionals within an organization can use them to reduce data storage infrastructure costs and increase their productivity.

On the other hand, software development and testing teams can use DataOps platforms to decrease development cycles and minimize errors during app development processes.

Factors used to assess a DataOps platform

There are two factors you can use to assess performance and determine the best DataOps platform depending on your needs. These factors include:

Scalability

Scalability refers to the capacity of a DataOps platform to handle increasing volumes of data, the number of users, and general operational complexity. A highly scalable DataOps platform is fit to process large volumes of data in almost an instant. The scalability of a DataOps platform gives you a glimpse of its data management skills and speed in critical times.

For instance, if your company deals with online advertising data frequently, you need a DataOps platform that can process up to terabytes worth of data in a day. On the other hand, if you’re dealing with retail product data, a DataOps platform that can effectively manage several gigabytes of data in a day will serve you well.

Even though the term ‘large capacity’ is relative, having a DataOps tool that can properly manage the maximum amount of data is important.

In addition to data volume, a reliable DataOps platform needs to be able to accommodate an increasing number of users. The number of people working in your organization will likely increase with time. Therefore, you need a tool that will accommodate everyone, including business analysts, data professionals, technical support teams, implementation teams, and partnership teams.

Repeatability

Repeatability refers to the program or system’s capacity to automate or repeat tasks. The best DataOps platform should provide optimal data repeatability despite different data types, sources, and formats. This includes data on Application Programming Interfaces (APIs), FTP servers, and even file-sharing services.

Since new data is added daily, the DataOps platform needs to constantly deploy better data pipelines. Platforms capable of supporting data pipeline duplication, flow pauses, editing tasks, and activations are the most suitable for managing large volumes of data.

The DataOps platform should also be capable of moving, developing, and testing data pipelines quickly before they get to a production environment where monitoring is required.

Besides that, they should provide real-time alerts and recommendations where necessary. For instance, if, for whatever reason, a program error pops up, a DataOps platform should be able to detect the discrepancies in the data in real time.

The best DataOps platforms in 2023

There are so many DataOps platforms in the market today. So, how do you get started? What should you look for? Which DataOps tool will benefit your company the most based on your specific needs?

Here is a curated list of the best DataOps platforms in 2023:

DataKitchen

DataKitchen is a great DataOps tool that allows for easy automation and coordination of people, workflows, tools, and environments of your company’s data analytics. It handles a variety of tasks, including the orchestration of data pipelines, deployment, monitoring, automated testing, development, and many others.

Source: datakitchen.io

With great features like parameterized testing and parallel execution, DataKitchen is the ideal DataOps platform for time-saving on high-volume data processing.

K2View

K2View is basically an all-in-one DataOps tool that houses all the DataOps tools and systems a company needs under one roof. This way, you’ll never have to think about integrating the platform with other tools.

K2View comes with a single dashboard monitor where you can access all the information you need whenever you need it. It compiles customer data from different sources, enriches it, and stores it in a patented Micro-Databases, one for every user.

Tengu

Tengu is a low-coda DataOps orchestration tool ideally designed for both data experts and non-data experts. By making the data available and useful at the right moment, Tengu enables businesses to become data-driven and maximize the value of their data.

It helps data professionals like data engineers, data scientists, and data analysts speed up the data-to-insights cycle and navigate the complexity of a data-driven company.

Unravel

Unravel simplifies data processing on everything, including GCP, Azure, AWS, or even your data center. This helps optimize performance, automate troubleshooting, and minimize operational costs. With Unravel, you can easily monitor, manage and improve your data pipelines on-site and in the cloud, driving better performance in the applications that propel your business.

Source: unraveldata.com

This tool provides much-needed insights into KPIs and the overall cost of cloud migration. The Unravel Application Performance Monitoring (APM) uses these insights to make predictive recommendations.

Zaloni Arena

Zaloni Arena is a DataOps platform that uses master data sets and detailed visualizations to improve accuracy and reliability. With the help of its masking and tokenization features, the platform also offers high-end data security services.

Zaloni Arena comes with flexible connectors that make it easier to automate data integrations from various sources on the go. On this platform, users can profile their data to improve data quality and ensure the continuous availability of error-free data.

Superb

SuperbAI’s machine learning (ML) data platform enables Artificial Intelligence (AI) teams to build better using fewer resources and in less time. The SuperbAI Suite is basically an enterprise SaaS platform meant to help ML engineers, data annotators, researchers, and product teams develop efficient training data workflows while saving money and time.

Delphix

Delphix is undoubtedly one of the best DataOps tools you can find and is renowned for providing a great data platform that accelerates digital transformation for data-driven companies worldwide. This platform supports all kinds of systems, including ERP applications, mainframes, Kubernetes containers, and even Oracle databases.

Delphix also offers data compliance automation services for privacy regulations and a wide variety of data operations that make CD/CI workflows possible. [5]

Census

Census is an industry leader in operational analytics with reverse ETL (extract, transform lead), which offers organizations a secure base for their warehouse data to be used in daily applications. [6]

It integrates well with existing DataOps tools allowing everyone to access the data without the need for IT personnel or custom scripts. This explains why many companies use Census- it’s dependable, secure, and effective.

Datafold

Datafold is a DataOps platform that helps users keep track of data flows so that they can detect and prevent data discrepancies in good time. The platform’s Data Diff feature makes it possible to test ETL code and highlight available changes as well as their impact on the produced data. Datafold can integrate seamlessly with other DataOps tools, which explains its popularity among data professionals.

StreamSets

StreamSets allows highly skilled data professionals and visual ETL developers to collaborate on vital data engineering tasks. With its extensive features and intend-driven design, this DataOps tool helps you build data pipelines within the shortest time possible.

In fact, you can build data pipelines of your choice within minutes and deploy them to provide high-quality data for real-time analytics.

Final thoughts

With the help of a DataOps platform, it’s easier for data-and development-focused teams to work together to develop effective DataOps practices for the company’s betterment. When data is managed effectively, businesses accelerate incident responses, solve problems faster, maximize productivity, and generate more profits.

Additionally, the data pipelines will be managed, maintained, and optimized in a way that helps the organizations stay competitive in today’s fast-paced business environment.

References

[1] Mckinsey.com. How Customer Analytics Boosts Corporate Performance URL: https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance. Accessed April 4, 2023
[2] Statistica.com. Worldwide Data Created. URL: https://www.statista.com/statistics/871513/worldwide-data-created/. Accessed April 4, 2023
[3] Medium.com. Successful DataOps Framework for Your Business. URL: https://balavaithyalingam.medium.com/successful-dataops-framework-for-your-business-67531709a764. Accessed April 4, 2023
[4] Solarwinds.com. Improving Data Quality Through DataOps. URL: https://orangematter.solarwinds.com/2022/01/05/improving-data-quality-through-dataops/. Accessed April 4, 2023
[5] GitHub.com. URL: https://resources.github.com/ci-cd/. Accessed April 7, 2023
[6] Talented.com. Reverse ETL. URL: https://www.talend.com/resources/reverse-etl/. Accessed April 7, 2023

The post Top 10 DataOps platforms in 2023 appeared first on Addepto.