What is DataOps? Ultimate Guide on Definition, Principles, & Benefits 101
Effectively handling the exponentially growing data is a huge challenge in front of every business today. To gain insights, business teams need data in real-time for making strategic decisions. In this fast-paced environment, requirements change rapidly which often creates delays. To remedy this, businesses are now implementing the DataOps technique to develop a more collaborative ecosystem between IT and business teams.
Table of Contents
Employing DataOps allows the Data teams to work independently. Using the Agile approach and Lean Manufacturing concepts, you can build a more efficient system that maintains higher data quality, enhanced speed, better governance, and more reliable response to errors.
In this article, you will learn about the DataOps methodology used across organizations globally.
Table of Contents
- What is DataOps?
- What led to the rise of DataOps?
- How does DataOps work?
- DataOps Capabilities in Data Pipeline Tools
- What does a DataOps Organization look like?
- Key Factors to Assess Your DataOps
- Best Practices for DataOps
- What does the future hold for DataOps?
What is DataOps?
DataOps(Data Operations) is a Data Management methodology used across an organization focused on enhancing the communication, integration, and data flow automation among both data administrators and consumers. The DataOps technologies directly promote reducing the time it takes to build a data pipeline, increase the output of analytical datasets, generate high-quality datasets, and achieve reliable and predictable data delivery. Aiming to eliminate silos between IT operations and software development teams, DataOps allows business teams to work flexibly & effectively with data engineers, data scientists, and analysts.
Key Benefits of DataOps
After understanding the DataOps definition, you can employ the DataOps Strategy for your Data Management to enjoy the following benefits:
- Improved Speed: Based on the Agile approach, DataOps promotes collaboration, analyst self-reliance, and component reuse. All of this combined with advanced innovation & experimentation allows you to reduce data science application cycle time and provide real-time insights to customers with increased velocity.
- Higher Data Quality: Concentrating on enhancing data cleansing, data usability, data completeness, and transparency, DataOps provides high data quality and very low error rates.
- Data Governance: With clearer & transparent results, and secure & close monitoring of how data is distributed across the organization, you get better data governance.
- Reliability: This unified data management strategy ensures a more reliable & seamless data flow along with predictable response times in the case when a request is made or an error is identified.
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What led to the rise of DataOps?
As a business grows, companies want to ensure that they also scale their teams timely economically, and efficiently. The DataOps strategy is an effective way to promote productivity, efficiency, and innovation in any organization. You can consider the following factors to account for the popularity of DataOps in enterprises globally:
1. Technology Overload
To gain insights from your raw data for making strategic business decisions, you need tot to transform the data into an analysis-ready form. As a result, all data collected from the customers need to undergo a combination of transformations through a data pipeline. Critical aspects such as Data profiling, cleanup, conversion, and storage in a secure location encourage an improved quality, integrity, and relevance of your data. All of this plays an essential role in complying with data protection regulations and policies.
To carry out each of the above processes, you may use a variety of tools, from data cataloging tools and data profiling tools to analysis and reporting tools. As a result, the technology is overloaded.
2. Diverse Mandates and Roles
Today, an organization consists of data teams that continuously design, deploy and monitor data pipelines. These teams include data engineers, data scientists, data analysts, etc. Owning a diverse set of experts coming together to build a more effective data management environment, you may find the following combination of professionals in a company:
- Data engineers work on data preparation and transformation.
- Data scientists are concerned about getting the most relevant & accurate data for their algorithms.
- Analysts are responsible for creating daily / weekly reports and visualizations.
- IT maintains data access logs to ensure data quality, security, and integrity.
- Business managers are interested in keeping a tab on their business performance and the pain points.
3. Massive Volumes of Sophisticated Data
Today companies are dealing with huge volumes of data that are increasing daily at an alarming rate. The Definitive Data Operations report recorded that an average enterprise deals with more than 5000 datasets along with a front-end data consumer to data engineer ratio from 5 to 30. This also includes integration with over 10 plus third-party partners. Enterprises process different formats of data at different times, and data inhomogeneities show no sign of slowdown. The case for large enterprises is especially complex as they work with tens of thousands of data sources and formats. For instance, you can consider CRM data, financial transactions, online reviews & comments, customer details including sensitive information, etc.
This astronomical amount of complex raw data can’t directly answer your strategic questions such as the location of the next business location, what services your customer expects, upcoming market demands, etc.
How does DataOps work?
To devise a more flexible and effective data management plan, DataOps based its working on the principles of the following aspects:
1. Agile Methodology
DataOps at the core emphasizes collaboration and innovation. The Agile techniques when applied in DataOps provide an environment that reduces the friction between the IT and business teams. The Agile methodology implements the concept of sprints where the data team releases new or updated analytics in small intervals. Innovations occur so rapidly that teams can continually reassess their priorities and adapt more easily to changing needs based on continuous user feedback.
This technique is more beneficial in cases where the requirements change rapidly. You can also significantly reduce the time you spend searching for the relevant data and deploying your data science model into production. This allows the IT teams to quickly change and adapt to the speed of the business teams. This also promotes more transparency as now the business teams are also aware of the work carried out by the data teams.
2. Lean Manufacturing
Consider how the manufacturing plant works. Just like a data pipeline, the raw material goes through a series of manufacturing operations till it is transformed into a finished product. Applying the Lean Manufacturing approach minimizes waste and improves efficiency without sacrificing product quality. For instance, you will often observe that apart from building the data pipelines, Data Engineers are constantly engaged in taking the models into production and troubleshooting the pipeline issues. Applying a lean manufacturing technique here can significantly save you a lot of time.
Methods such as Statistical Process Control (SPC) check and monitors the operational characteristics of the data and data pipelines to ensure that statistical variances are acceptable. With the SPC in place, the data flowing through the operating system is verified at each step of the data analysis pipeline. In case there is a discrepancy, the data analysis team is the first to get the error notification through automatic alerts.
DevOps is a strategy in software development that uses automation to accelerate the build life cycle. DevOps focuses on continuous software delivery by leveraging on-demand IT resources and automating code integration, testing, and deployment. This integration of software development (“dev”) and IT operations (“ops”) reduces deployment time, and time to market minimizes errors and solves problems.
Taking inspiration from the principles of DevOps, Data teams can collaborate better and deploy faster. Boosting self-reliance & instead of depending on the engineering or the IT Teams, Data Ops allows you to independently deploy models and perform analysis quickly.
DataOps Capabilities in Data Pipeline Tools
To achieve a seamless data flow throughout the organization, the data platforms must support some key features that facilitate the DataOps process. You can narrow down to the following 5 major areas of improvement while employing the DataOps strategy:
- Speed: To quickly understand and perform the data jobs, you can make the whole process user-friendly via Code-free data pipeline definitions, Code Reusability, Cooperation, Self-service UX & easy production.
- Output: For improving the performance, you need to incorporate functionalities such as flexible delivery & consumption, scalable execution engine, Performance optimization, and scalable governance.
- Quality: To maintain a standard for high-quality data you need data quality features supported by ML, data quality analysis, data usability, data integrity, and end-to-end granular data lineage.
- Governance: Ensuring the data protection policies and compliances, you need a complete and detailed catalog and metadata, enterprise-level security, end-to-end granular data lineage, and a detailed audit.
- Reliability: For a better response time to handle errors you can implement systems for automated operations, data retention and archiving, end-to-end granular data lineage, data pipeline monitoring, fine-grained logging, Change revision & error notifications.
What does a DataOps Organization look like?
To understand the working of a DataOps Organisation, you can consider an example of a company that uses DataKitchen.
- Tens or hundreds of data sources are integrated into the data lake, passing complex combinations of transformations, and are sent by the user in an Analytics chart and graphs for visualizing the data. Automatic Test (Statistical Process Control) inspects the data entered in the system, and the entry, output, and business logic of each transformation step.
- Notifications such as Status, Warning Messages, and Error Warning are sent to the data teams in real-time. Data errors never enter the data analysis pipelines, and processing errors are identified in the mid-pipeline, thereby preventing any data corruption. Implementing these strategies ensures a higher Data Quality and Operation Time KPP (key performance parameters). This also reduces additional work by 99% or more caused due to errors.
- The workspace has an automated and tuned pipeline that can be run by the scheduler on a case-by-case basis. In many cases, creating a new analysis requires developing an incremental derivation of existing components and pipelines rather than “creating from scratch.”
- The workspace is also tightly coupled with version control, so all source files and artifacts needed for operations are essentially centralized, versioned, and protected. With minimal key regeneration and manual steps, data scientists can collaborate and proceed with analysis for production deployments. As you can observe, DataOps workflow automation improves and promote communication and coordination between groups within teams and data organizations.
Key Factors to Assess Your DataOps
To apply the DataOps methodology in your firm, you can assess its usability by the following 2 factors:
Complex DataOps systems can provide optimal repeatability despite heterogeneous data types and sources including flat files on FTP servers, APIs, and file-sharing services. Because new data flows are continuously being added, DataOps needs to constantly deploy new pipelines. A system that supports pipeline duplication, editing, flow pauses, and activations provide the highest reproducibility. As the number of data flows increases, orchestration becomes more complex and important. DataOps is responsible for managing data flow interdependencies and must provide a structured path to ensure that modifications done to upstream processes do not affect downstream.
The system needs to be able to develop and test before moving the pipeline to a production environment where monitoring is important. DataOps are most reproducible when you can intelligently evaluate your source connections and effortlessly consolidate and transform your data. The Data Ops platform, which enables enterprise-wide collaboration transformation, can bring the power of reproducibility to more people.
While evaluating a DataOps system based on scalability, you can refer to its ability to handle the increase in the amount of data, the number of data consumers, and operational complexity.
A highly scalable data operations infrastructure can process large amounts of data in near real-time. For instance, looking at online advertising technology, massive amounts of data can be measured in terabytes (or petabytes) per hour. Looking at retail product data, volume can be expressed in gigabytes per day.
The definition of “large-capacity” is relative, but the ability to properly handle the maximum amount of data is important. Business analysts, data analysts, technical support teams, implementation teams, partnership teams, and others all have a good understanding of data but may not have programming skills. Equipping them with tools is important for Data Ops scalability. Data engineers can focus on the most complex issues if they can handle 80% of their use cases with the right tools.
This has an organization-wide impact on the data. Appropriate tools, processes, and personnel as an integral part of the DataOps strategy can create a power-enhancing effect.
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Best Practices for DataOps
When implementing DataOps in your organization, you can adhere to the following best practices:
- You can record and maintain progress benchmarks and performance parameters at every stage of the data lifecycle.
- It is a good practice to design semantic rules for data and metadata from the start.
- You can add feedback loops to check and verify the data.
- Employing data science solutions and business intelligence data platforms for automating mundane tasks can improve efficiency and reduce errors.
- You can optimize processes for handling bottlenecks and data silos. This includes software automation for growth, evolution, and expandability.
- It is recommended to use a disposable environment that mimics the actual production environment for your experiments.
- Build a DataOps team with different technical skills and backgrounds.
- You can think of DataOps as a lean manufacturing method by focuses on continuous efficiency improvements.
What does the future hold for DataOps?
As the amount, speed, and diversity of data grow, new solutions and methods are needed to gain insights. IDC predicts that the amount of data produced will increase to 163 zettabytes by 2025, 36% of which is structured data. Today’s platforms, techniques, and organizational structures are not equipped to process this significant increase in data entry and the expected increase in value from its output. Organizations need scalable, reproducible, and predictable data flows as more employees need access to this data to do their jobs. This is where DataOps can be of great advantage to companies to prevent data heartache in the future.
In this article, you have learned in detail about the DataOps methodology. As the number of data sources grows, it becomes a challenging task to effectively manage data without creating any bottlenecks. A strong and flexible data management strategy is required that allows scalability and repeatability. DataOps is a collaborative Agile technique that promotes an effective and continuous data flow between the business and IT teams.
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