An Introduction to DataOps: Factors, Principles & Advantages

There were days when data operations were quite hard to maintain within the organization. For example, consider if the CDO, Chief data officer leaves an organization, it is expected that the knowledge and context of the organization also leaves with him. But the advent of DataOps has made the information accessible to all the people working with the organization.

 

Not only accessibility, DataOps has also increased the transparency of data operations. There is no such black box or hidden magic in the game. DataOps made the business developer trust data developers without any fear or cost.

 

In this blog, we will study about DataOps. DataOps is said to be a collection of technical practices, workflows and cultural norms. DataOps help in rapid innovation and experimentation within the data operation. Scroll this blog to the end to read more about it.

  

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What is DataOps?

 

DataOps expanded as data operations. It is an emerging discipline that brings data teams such as data engineers and data scientists together. It provides the data team the tools, processes, and organizational structure to build the data-focused enterprise. DataOps is a collection of technical patterns and workflow.

 

It is an agile, process-oriented methodology that is used for developing and delivering analytics. It is employed for the improvement of data products and helps in activating business value across all technology tiers from infrastructure to experience.

 

It is a collaborative data management practice. It focuses on improving the communication, automation, and integration of data flow between data managers and data consumers across the organization. The major goals of DataOps are to deliver faster value of the product by creating predictable change and delivery of data, data models and other related artifacts.

 

DataOps employs technology to automate the deployment, management and planning of data delivery with accurate levels of governance. DataOps utilize the metadata to enhance the utilization of data in a dynamic work environment.

 

The important aim of DataOps is to rationalize the planning, improvement and maintenance of raw data and data analytics. It is done to manage data and to formulate products. The coordination of these data is essential to improve the goals of the business.

 

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Background of DataOps

 

As mentioned above DataOps is a short form used for data operations. The history of DataOps is not much old. For the first time DataOps was introduced in 2019 by Lenny Liebman in his blog post on the IBM Big Data and analytics. Though, the term gained popularity by Andy Palmer and Steph Locke. Gartner included the term DataOps in its dictionary in 2019.

 

Earlier, DataOps was defined as a practice without any standards or frameworks. But, now a number of technology providers have started using the term and implementing it in their data analytics field. 

 

The term widely covers concepts like significant ecosystem development, increased keyword research, open-source projects, surveys and publications related to data science and management.

 

Benefits of DataOps

 

Here are the advantages by which DataOps ease the work of data leaders:

 

1. True data democratization: DataOps help in managing data. It provides universal access to data for everyone who works within the organization.

 

2. Quick insight time required: DataOps provide everyone equal and fair visibility of data. Thus, they can get time insights and implement the same for organization.

 

3. Strong control: DataOps set a standard data set and policies for the government. It creates standard data creation, usage and deletion policies. Thus, helping a fair central data governance.

 

 

Factors affecting a DataOps Tool

 

There are two factors that define and rate the working of your DataOps. These two vectors are scalability and repeatability. Once you understand these factors, you can employ the DataOps better for your organization.

 

1. Scalability

 

Scalability is the measure by which a DataOps system calculates the growth in volume of data, number of data users and operational complexity. Higher the scalability of DataOps, higher will be its ability to manage high volume of data and processes in real-time. Scalability of DataOps is also a proof to its data management skills in critical times.

 

And, not only with data, DataOps also need to scale with the people working on it. In general, the number of people working with an organization grows with time. DataOps hence should be scalable and understandable to the team including business analysts, data analysts and technical support team.

 

2. Repeatability

 

Repeatability is the second vector affecting the working of DataOps. It is defined as the measure by which a system or program can automate and repeat tasks. Repeatability of data is accompanied by the tasks such as extracting, transforming, and loading of data. Sophisticated DataOps are able to maintain repeatability of data despite its types and formats.

 

DataOps should be capable enough to move data quickly. It should provide alerts and potential recommendations. If a program error can be stuck for a number of reasons, a DataOps should be capable enough to detect those bottlenecks in data. Repeatability is the ultimate key to process data.

 

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Principles of DataOps

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Principles of DataOps

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Now comes the question related to the working of DataOps. DataOps is based on three principles, including Agile, DevOps, and Lean Manufacturing. DataOps employ these principles for the better management of data teams and processes.

 

1. Agile

 

Agile is defined as a methodology that is an iterative project management principle for software projects. Agile methodology helps the IT teams and software teams to release new software within a few hours. Usually, release of a software takes more than months, but Agile eases the speed of this process without compromising the quality of the software.

 

Data teams or software teams use Agile by fusing its applications with big data and drive quick decision-making. For consideration, suppose your data team takes a month to deliver a decision, this time period automatically counts the days required for the release of a project and thus, delaying your business operations.

 

The delay also creates a huge gap between your IT and business teams. Agile and datops can definitely help your business in reducing the time required to find the right data. Thus, increasing the overall speed of data operations.

 

2. DevOps

 

DevOps is expanded as the development operations. The methodology links the gap between development and operation teams within the organizations. It assists in fasting up the software development process. It also makes the process more easier and collaborative.

 

Data teams employ DevOps to collaborate better projects and deploy them faster. Data engineer takes data models from exploratory to data analysis. It deploys machine learning algorithms. It helps the models to perform itself and analyze the program quickly.

 

People sometimes resemble DataOps and DevOps as the same. While DataOps is a whole process, DevOps is just a principle attested to the process. DevOps is basically a technical process while DataOps is a technical as well as business term. The challenges of DataOps are more complex in nature as compared to the limitations faced by DevOps.

 

 

3. Lean Manufacturing

 

Lean manufacturing is defined as the manufacturing that is done in pipelines. When raw materials flow through various manufacturing workstations to be transformed into finished goods via a pipeline structure. The basic advantage of lean manufacturing is that it ensures minimal wastage and greater efficiency of the product without compromising the original quality of the product.

 

Data teams employ pipeline structure to transform raw data into insightful visualizations. A data engineer while releasing a software utilizes most of time in utilizing models into production, building pipelines, and fixing the same.

 

Lean manufacturing significantly reduces this time requirement. Thus, DataOps uses three methodologies named Agile, DevOps, and Lean Manufacturing for the better data operations and business operations. These processes appear easy while reading, but in actual they are a series of complex and technical events.

 

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Why is DataOps Essential for Organizations?

 

For businesses and organizations, it is essential to employ DataOps to ensure accessibilities to their data teams, to ease data operations, to scale up productivity, innovation and efficiency in the businesses. 

 

No matter how much an organization spends in data and analytics initiatives, they will still struggle to get the ultimate value of it unless they invest in DataOps methodologies.

 

Another reason for which employing DataOps is essential in the organization is the growing number of consumers of data within an organization. This increase in population makes it essential for the organizations to manage and expertise its projects. 

 

The data teams and software leaders are also expected to deliver value to the business with data. Here are the following a few reasons showing how DataOps is essential to ensure the productivity of all the teams and process management.

 

1. To manage massive volumes of the complex data

 

With the advent of big data, all businesses despite of its scale and size are burdened up with a huge amount of data. Each business works on a pile of raw data coming from different sources in different formats. And this picture is more complex in bigger organizations. 

 

DataOps help in managing the complex data pile. Some of the data may include information related to financial transactions, CRM data, online review, comments, and customer information. Managing data and targeting specifically is the biggest advantage of DataOps.

 

2. Technology Overload

 

DataOps is filled with the latest technologies and innovation. Thus, making it a prior choice for the organization. To solve your business query, it is essential to have all data in a format that is most suitable for your organization and make the analysis easier. Data undergoes a series of transformations before reaching its actual destination.

 

The DataOps help in profiling, cleaning and transformation of data. Rather than using different data cataloging and data profiling tools for different actions like analytics and reporting. The organizations prefer to use a single methodology of DataOps to serve all the important purposes.

 

3. Diverse roles of DataOps

 

The diverse role of DataOps is popularizing its name in business management. DataOps assist the organization in following significant ways:

 

1. DataOps help the data engineers to emphasize on data preparation and transformation.

 

2. DataOps help the data scientist to gather the right algorithm for data operations.

 

3. DataOps helps the analysts to build better weekly reports and visualizations.

 

4. DataOps helps the IT team to maintain the data access protocols.

 

5. It guarantees data quality, data security and integrity.

 

6. DataOps help in business management.

 

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As the volume and variety of data is increasing everyday in the same. It is essential to use advanced tools and processes to extract the insight. Datops is capable of bringing this revolution in the field. It helps the teams to create scalable, repeatable and predictable data flow despite data variety and data format.