Why Data Science Needs DataOps

We’re still hopeful that the digital transformation will provide the insights businesses need from big data. As a data scientist, you’re probably aware of the growing pressure from companies to extract meaningful insights from data and find the stories needed for impact.

No matter how in-demand data science is in the employment numbers, equal pressure is rising for data scientists to deliver business value and no wonder. We’re approaching the age where data science and AI draw a line in the sand for which companies remain competitive and which ones collapse.

One answer to this pressure is the rise of DataOps. Let’s take a look at what it is and how it could provide a path for data scientists to give businesses what they’ve been after.

See also: DataOps: The Antidote for Congested Data Pipelines

Isn’t DataOps just DevOps?

DataOps is a result of the advances in DevOps, but applying DevOps principles to data won’t get you DataOps. The DevOps cycle is an infinite loop between the planning and creating stages of software development and considers the unique abilities of software developers and engineers.

DataOps is a much bigger operation. Where DevOps brought together the development teams and the operations side, DataOps seeks to marry every part of a business into one pipeline. It democratizes data and ensures no one process grows in complexity beyond the capability of the data science team.

There are three parts to DataOps according to Data Kitchen:

• DevOps: The iteration cycle for products must tie to the data insights to produce real business impacts.
• Agile: Companies that respond in real-time to customer needs will be the ones to survive the digital transformation.
• Lean manufacturing: The data pipeline resembles a physical factory. Raw data must flow into one end of the pipeline and produce systematized insights at the endpoint. This flow drives business operations.

The pipeline ensures that everyone has access to data, systems in place for producing a usable form of data, and tying data directly to goals the business has.

What Does DataOps Look Like?

The DataOps pipeline uses two intersecting pieces to produce continuous insight. The first pipeline handles the cleaning and management of raw data, producing valuable insight into the question’s businesses need answered before proceeding with any new initiative. This is the value pipeline.

The innovation pipeline introduces new ideas from DevOps into this stream of business value to find solutions for new products and services. When the two intersect, the iteration cycle produces the newest solutions.

The Benefits of DataOps

One of the most prominent adjustments data scientists make when moving into the business world is the structure of producing data insights. In school or research, using cutting edge, complex models was the way to go, but business is only concerned with the impact.

The complexity of data can get out of hand quickly. Data scientists working in business can expect to spend around 80% of their time doing just data prep tasks – finding data, cleaning it, labeling it, and other mundane tasks. This increases if established businesses also have a considerable backlog of legacy data to maintain.

The value pipeline streamlines this initial process to help data scientists find the data they need to produce insights in the first place. This type of continuous intelligence helps businesses pivot with customer needs and ensures data is a true team effort.

DataOps reduces pressure on any single data scientist or team. It provides scalable solutions for business growth and ensures a sustainable process, not just one that’s cutting edge.

It also measures analytics in real terms; successful analytics are ones that deliver real impacts. Even more critical, it productionalizes the data process, offering businesses the chance to manage an ever-increasing number of products and their life cycle management.

That’s right. DataOps is an answer to those legacy systems that plague new members of a data science team.

Obstacles to DataOps

There are a few roadblocks to implementing DataOps where your business stands now.

• Lack of Visibility: More data leads to clearer insights, but if you have no idea where your data is, how it’s stored, and how it’s been used in the past, you’re in a bind. Find out about your data and put systems in place for its governance.
• Unrealistic expectations for pipelines: A pipeline is an automation tool, but it can still get complicated. Data scientists must have an operationalization understanding to set up pipelines that work. Project creep can derail a pipeline with unnecessary steps and activities that don’t align with a business’s goals.
• Inadequate monitoring: Addressing the root cause of issues and standardizing success measurements can make or break a pipeline. DataOps relies on effective monitoring with clear and attainable goals.

AI-powered data pipelines are picking up some of the slack, but DataOps requires an integrated approach from all business stakeholders to implement.

DataOps: The Path to Digital Transformation

In the early days of development, businesses experienced some growing pains between the development/test iterations and production rollout. Now, companies are experiencing the same growing pains with data science.

DataOps prevents data projects from overpromising while underperforming. The pipelines ensure that data science projects are developed with business impact in mind first and delivered in a way that management can understand.

In fact, DataOps brings a level of communication to the table not seen with silos. Traditionally, data scientists developed projects on one side while development engineers put these projects into production on the other. Problems bounce around from side to side with no end in sight, and the sides frequently use different metrics to measure success.

DataOps brings these sides together along with users on the end and decision-makers at C-suite level. Managers now have the chance to make data freely available for the entire company to use, i.e., the dream of the “data-driven” business. With the right infrastructure in place for data scientists to do their high-level work and provide those insights, companies could finally cross the hurdle of digital transformation.