5 Benefits You Reap from Opting for DataOps
What is DataOps for businesses?
DataOps is gradually becoming the mainstay since it was first mentioned by Andy Palmer in 2015. It is getting great interest and much reception among those who work with data science, data analytics, data management, data usage and data value. The last point gains much traction across various industries. The reason lies on the surface: ‘value’ plays a key role, meaning money and profit while running any type of business.
“It finally feels like large organizations have embraced their “data debt” and are figuring out how to monetize their data. It helps when they realize that great analytics depend on great data.” Andy Palmer, a coiner of DataOps; a serial entrepreneur of data analytics and unification start-ups
So, the question is, why businesses are in need of actionable DataOps solutions? The advantages of DataOps can be easily derived from its definition. However, the problem is that many people, even professionals, describe DataOps in various ways as the very concept of it is quite new to the market.
Bluntly put, DataOps comes in handy while addressing data management issues, especially when it comes to handling enterprise-level datasets as well as data lakes at scale. At the core of this innovative approach lies a synergetic combination of DevOps practices tallying with data engineering expertise and data science insights. And while DevOps zeroes in on improving the software development flow, DataOps, in its turn, is designed for optimizing data analytics capabilities and robust data orchestration.
DataOps advantages for business optimization
The most essential deliverables you can leverage while integrating DataOps solutions into your business processes can be boiled down to the following five touchpoints.
1. Enhancing data analytics
DataOps implies the comprehensive combination of multifaceted analytics methods that empowers data specialists to collect, process, classify data and deliver them to the final destination, i.e. in this context, to the customer. This is a new discipline based on ML algorithms the core essence of which is to guide data on all stages of data elaboration, to receive valuable feedback from the customer in the shortest spans of time and to react to quick-changing market demands.
2. Data problem-solving capabilities
In the modern hi-tech world, it’s an established fact that the volume of data produced during one year equals the amount of data the mankind created last century. More to say, data scientists claim that now data doubles every 12–18 months. Many business enterprises encounter this phenomenon both on the local and worldwide level. DataOps is the data management methodology that helps turn raw data material into valuable information.
3. Availing new corporate opportunities
DataOps involves changes in the whole work process within the company. It helps build the new ecosystem where there are no borderlines between departments and offices but various workers, such as data engineers, developers, operators, analysts, marketing advisors and managers collaborate in real time to achieve corporate tasks and goals. The synergy of all possible specialists engaged accelerates the speed of reaction, results in better customer service and increases your company’s success.
4. Enabling the far-reaching business potential
The growing interest in AI and ML (from 70 % to 85 % in 2018) proves that many companies are no longer able to handle big data alone. They are ready to implement DataOps strategy and plan to hire DataOps specialists within the next year (73 % of respondents) in order to gain an advantage over their competitors. DataOps makes it possible as it includes necessary, technology, people, processes, services and tools to fish out precious data and benefit from it.
5. Providing long-term guidance
DataOps allows for continuous strategic data management practices. It helps negotiate the needs of business enterprises due to the multi-tenant cooperation. Data specialists organize, transform, evaluate data sources, assure data storage of high quality, provide users with fresh data and study the feedback from customers. With the implementing of ML DataOps is up to automating all possible processes and satisfying the needs of business, i. e. less time and more profit.
All in all, DataOps is a two-way street that enables full-scale interoperability between data sources and data users by streamlining data management and data analytics through automatic processes. This ensures the fast and seamless improvement of the product delivery and deployment, which proves its efficiency in data wrangling, apps customization and BI analytics.
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