What is DataOps: A Guide to The Entire Process of Data Analysis

Data drives everything we do. For this reason, data analysis has become one of the most important elements of programming, engineering and testing across organizations. But traditional data management techniques are failing businesses by being unable to cope with hugely complex data sets.

It’s important to be able to process these sets because of their use in building large technical systems like an IBM mainframe, on which many widely-used systems are built.

Complexity in data sets is caused by its size and diversity, but also in the size, geographical and experiential diversity of data processing teams. The growth of data in industry is, paradoxically, causing chaos that is resulting in data project failure.

This is where DataOps come in, as a potential solution to data chaos and project failure.

What is DataOps?

DataOps is a set of defined practices and processes that aims to place data at the center of optimization by promoting speed, quality and collaboration in data analytics.

You can think of it as a culture or way of working, focusing on communication between different data professionals and integrating various tools and development principles into a cohesive way of processing data.

DataOps is more than just a single tool or method. It’s an approach to data processing that aims to reduce error and allow systems to manage large data sets without loss.

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For example, think of an API. What is an API? A piece of software that facilitates and defines interactions between pieces of software. When developing these, developers collect huge data sets because an API works between many different applications.

Traditional data processing methods may not be capable of storing or effectively processing such data.

Why use DataOps?

There are a few key benefits to DataOps that makes it an effective approach to data management.

• Speed. With reduced errors and large datasets processed efficiently, data teams can work faster without compromising quality.
• Reliability. Traditionally processed data has a problem with reliability, meaning data-based decisions and projects are failing at a higher rate than those done with DataOps techniques.
• Control. When a whole team is able to work on a data set with different tools without compromising the data, they have more control over the data and their ability to process and manipulate it.
• Collaboration – using collaborative tools like a data warehouse, multiple people can work on the same data set and bring their own expertise and experience to that information.

These are the benefits of DataOps, but it’s also important to understand the factors affecting traditional data processing methods. The three major components of traditional processing each have their own issue that is solved by well-implemented DataOps.

1. People – with data teams now being made up of multiple individuals, all with different responsibilities and insights, data processing can quickly become complicated.
2. Equipment – with data volume rising rapidly, processing tools quickly become outdated. You may find that your existing equipment lacks the capacity to store and process large data sets if they were implemented before such data sets were in common use.
3. Data – the data itself presents a challenge to traditional processing methods, because of the sheer volume of data now available. A diverse team using tools not made for such high volumes cannot effectively process data without loss or error.

How DataOps works

There are four principles that underpin DataOps, each of which must be properly implemented for this process to work well and enable your team to store, process and manage large data sets.

These are the four principles of DataOps, all of which take a different viewpoint on development and the ways in which information can be managed.

1. Lean – the principle of reducing the time spent on development (or, more broadly, production) and reducing response times from developers to a change in the software.
2. Agile – an approach to development using iterative development, delivering faster results to clients whilst allowing for ongoing project management and testing.
3. DevOps – the concept of software development as an ongoing project, circular and interconnected without always having a linear progression and using the interrelated skills of different roles.
4. Product Thinking – an approach to development that considers a known set of customers and the problems/pain points that they face when developing and testing new products.

About the Writer

Pohan Lin is the Senior Web Marketing and Localizations Manager at Databricks, a global Data and AI provider connecting the features of data warehouses and data lakes to create lakehouse architecture. With over 18 years of experience in web marketing, online SaaS business, and ecommerce growth. Pohan is passionate about innovation and is dedicated to communicating the significant impact data has in marketing. Pohan Lin also published articles for domains such as PingPlotter.