WHY DOES DATAOPS FOR DATA SCIENCE PROJECTS MATTER?
Today organizations are carrying out more and more data projects that promise great opportunities to drive agility and competence. But they are facing a growing pressure to extract meaningful insights from data. Most of them realize the potential of data science to deliver business value, even some are already investing heavily in data science programs. There is no wonder that the landscape of data is growing rapidly and processing and analyzing that data requires a vital approach. This is where data scientists step in performing data visualization, data mining, and information management.
As most companies view a significant return from data science investments, most data science implementations are high-cost IT projects. Meanwhile, they often not generate value for businesses. Therefore, experts are now talking about DataOps, a new and independent approach to delivering data science value at scale. DataOps arises from the need to productionalize a rapidly increasing number of analytics projects and then to manage their lifecycles.
With the introduction of DataOps, data scientists and data engineers can work together and can bring a level of collaboration and communication to generate actionable insight for a business.
Significantly, DataOps is driven by data lifecycles and insights. It basically applies the DevOps process to data pipelines, using automation and Agile methodology to cut the time spent fixing issues in pipelines as well as get data science models into production quicker. Despite this, both are carrying distinct features and capabilities. While DevOps is the collaborative process between two technical teams, DataOps simplifies collaboration between data analysts, engineers, and data scientists, among others within an organization who use data. This essentially makes DataOps a much more multifaceted process than DevOps.
DataOps for Data Science Success in an Enterprise
Translating structured or unstructured data into business and operational insights, and subsequently incorporating them into a data monetization value chain is a very complex task. Even data analysis by companies doesn’t produce much value for them. According to Gartner, 80 percent of analytics is likely to not deliver business outcomes through 2020, and only 20 percent of data insights will deliver business outcomes through 2022.
In this regard, DataOps emerges as an agile way of developing, deploying and operating data-intensive applications, helping in fostering a data factory mindset. This is also orchestrating, monitoring and managing the data pipeline in an automated way for everyone handling data.
For a majority of organizations, DataOps currently is slowly becoming a crucial practice to endure in an evolving digital world, where coping with real-time business intelligence is necessary to gain a competitive edge over peers. Instability of data, rapidly evolving technology landscape, and increasing demand of the Agile business ecosystem are few reasons surging the need of DataOps.
IBM DataOps, for instance, enables agile data collaboration to accelerate speed and scale of operations and analytics throughout the data lifecycle. This also assists in creating a business-ready analytics foundation by offering market-leading technology that works together with AI-powered automation, infused governance, data protection, and a robust knowledge catalog to operationalize relentless, high-quality data across the business.
Comprehensively, applying DataOps practices in all data activities, from data management and integration to data engineering and data security, enterprises can simplify the process of Data Science across an organizational level.