If you polled a number of CIOs across a variety of data-driven organizations and asked them about the biggest obstacles that their companies currently face, it’s very likely that siloed data, teams, and systems would all rank highly on the list. Harvard Business Review notes that it is prohibitively costly to access siloed data and use it to extract data, among other initiatives. The inefficiency of this approach translates to siloed teams and systems, as well. If you work on a constantly evolving website where implementations and iterations need to be made quickly, it’s imperative that data engineers, data scientists, and IT can work together swiftly and collaboratively rather than in segmented isolation.
That’s where DataOps comes in. DataOps, which does for data what DevOps does for software, is a process-oriented methodology that acknowledges the interconnected nature of data science, data engineering and information technology operations. Large data teams employ a DataOps approach to improve the quality of data analytics and reduce the amount of time it takes to deliver data-driven insights. DataOps in the workplace involves collaboration across a variety of departments, from data engineers aggregating and curating data sets to data scientists building and publishing machine learning models.
Ultimately, a DataOps approach is a smart strategy in today’s enterprise landscape, given that companies are deploying code faster than ever before. Just three years ago, in 2014, software analytics firm New Relic found that a quarter of companies were deploying code on a monthly basis. By 2016, the paradigm had shifted greatly: 66% of companies were deploying code weekly, multiple times a week, or multiple times a day, or striving to reach that cadence. In a fast-paced environment where implementations and iterations are happening constantly, it’s important for different members of data-driven organizations to work together efficiently and expediently. A data science platform, in combination with a DataOps methodology, facilitates the process of scaling data analysis and deploying more data science models into production.
DataOps Complements Data Science Platforms
While DataOps focuses largely on people and process, it also requires an enterprise-grade platform to enable the collaboration and sharing of data and compute resources. A holistic data science platform approach, in particular, makes it easier for data scientists, IT managers, and data engineers to complete all of the necessary work before data science models are ready to be deployed to production. Here are some of the technical requirements for enterprise data science platform supporting DataOps:
Full-featured support for data science model development with built-in collaboration tools, including tools for model sharing and versioning and report building
Support for all of the popular data science languages and tools
Linear and unlimited scalability to grow with your business and use cases
Having these (among other) technical requirements in your enterprise data science platform will ensure that your team will be able to collaborate more effectively, deploy more data science models into production, and subsequently boost ROI. DataOps In The Big Picture
27% of companies will invest more than $50 million in big data by the end of 2017. In this increasingly competitive environment, it’s more important than ever that companies work swiftly, collaboratively, and efficiently to scale their data science results. A DataOps approach, paired with a data science platform, will give companies a critical advantage in the current enterprise landscape.