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Data operations (Dataops) originated from the word ‘DevOps’, while both DataOps and DevOps are built to induce agility in the process. DevOps is a set of practices for software development and business operation; DataOps is a process-oriented approach that is scalable, repeatable, and automated, used by analytics and data teams to improve quality and reduce the time cycle for analytics.
DevOps emerged to integrate software development and deployment for rapid delivery of quality software solutions.
Let’s look at why DataOps is necessary for data-driven enterprises:
Managing Data Volumes
Big data involves large volumes of data stored in disparate data sources and data stores. The challenge for businesses is developing a unified data architecture, to extract data volumes from data sources that may include lakes, warehouses, file-based data, and APIs.
Quality of Data
Analytics algorithms, AI, and Machine learning models are based on how good the data is. Algorithms pull data from disparate sources and with no data validation process, analytics models created will deliver inaccurate insights.
Complexities of Integration
Data Storage platforms solved the most challenging problem for businesses by collecting and storing large amounts of data. But with an increased number of data sources, integration became a significant challenge even for data storage platforms. Analytic solutions depend on the integrity of data which needs to go through constant updates and data ingestion.
Data lakes, data warehouses, On-premises storage facilities, file-based data all require a significant amount of cost to maintain their integrity. Even data policies fail, in case of big data premises where sources and schema differ.
Data-driven businesses will need to consider the governance cost, as privacy comes to the forefront. Elimination of data based on business needs(recency), situation (covid-19), and demands (User) also contribute to the cost of maintaining data sources.
Data Context and Use Cases
Data in itself doesn’t provide business value, organizations need to evaluate their operational and analytical data needs. Business use cases and data context assist in aligning the data architecture and develop a data pipeline. Organizations today fail to define use cases for data, leading to the failure of big data projects.
So what is Data Operations (DataOps)
Data is sought from —- Data warehouse, data lakes, file-based sources, APIs which need to be operationalized and analyzed to gain business value. The challenge is most data needs to be extracted from various sources, loaded onto a data pipeline, and transformed to suit the destination apps. Though loading and extraction can be interchanged in the process; most data-driven organizations have to go through the process of ELT (Extract, Load and Transform) or ETL (Extract, Transform and Load) before analytics.
“Modern DataOps is an organization-wide data management practice that controls the flow of data from source to value, with the goal of speeding up the process of deriving value from data. The outcome is scalable, repeatable, and predictable data flows for data engineers, data scientists, and business users.”
Traditional data operations focussed on schema changes i.e. transformation, with the advent of several data sources, ETL became a backbone for modern data operation. The data pipeline needs to support integration, transformation along with version control and data quality check for real-time output. Data operations become a pipeline connecting different data sources to outcomes, however as sources grow so do the pipelines.
Here is one of the statements from Ash Gupta in an article by Mckinsey on how companies are using big data and analytics, realizing data and analytics are still separated by long road and turns.
“The first change we had to make was just to make our data of higher quality. We have a lot of data, and sometimes we just weren’t using that data and we weren’t paying as much attention to its quality as we now need to. That was, one, to make sure that the data has the right lineage, that the data has the right permissible purpose to serve the customers. This, in my mind, is a journey. We made good progress and we expect to continue to make this progress across our system. The second area is working with our people and making certain that we are centralizing some aspects of our business.
We are centralizing our capabilities and we are democratizing its use. I think the other aspect is that we recognize as a team and as a company that we ourselves do not have sufficient skills, and we require collaboration across all sorts of entities outside of American Express. This collaboration comes from technology innovators, it comes from data providers, it comes from analytical companies. We need to put a full package together for our business colleagues and partners so that it’s a convincing argument that we are developing things together, that we are co-learning, and that we are building on top of each other.”
Data operation has to set the tone for an organization that depends on data for operational needs, with demand for repeatability, scalability, consolidation, and automation. DataOps assists in scaling the data delivery to all demands, encouraging collaboration between teams that were siloed previously. Loading and transformation of data that were previously resource-intensive have become automated with DataOps.
So how do you implement data operations for your Business?
According to Statista report on Worldwide data created in 2021, the amount of data created, captured, copied and consumed globally has reached 64.2 zettabytes in 2020 and is all set to reach 180 zettabytes by 2025. The next stat lays the foundation of modern data operations, only two percent of data produced and consumed in 2020 was retained in 2021. Most data created is utilized by real-time analytical solutions using scalable and reliable data ingestion and data transformation solutions.
Pre-implementation checklist for Dataops
- Benchmarks and performance analytics of data during lifecycle.
- Defined metadata and schematics for data to promote uniformity across system.
- Validate data using feedback loops to determine schema accuracy.
- Optimize for silos for data sources, the automated pipeline will assist in resource utilization.
- Improve efficiency continuously by monitoring output data value and data pipeline optimization from sources.
Nexla as a Data operation Solution
Nexla’s universal connectors make it easy for businesses that have teams demanding information from any source possible. What makes Nexla unique is it’s low/no code solution for unified data operations, enabling analysts and data scientists to generate clean data assets for their analytics system.
- The ETL/ ELT solution works on both on-premises of any business system, or SaaS based application or managed platforms, integrating governance before data operation starts.
- Nexla generates Nexsets using its metadata intelligence, making Data-as-a-product for analytical solutions. Nexsets provides all information regarding the data- data source, schema, data characteristics, and data transformation.
- Nexla is a collaboration tool hence each Nexset created can be shared by applying access controls. To provide clean data to analytics systems, Nexsets can be validated based on specified rules.
- Apply Data transformation to Nexsets to change/ check schema and data characteristics. Nexla is powered by continuous intelligence solutions hence any anomaly detection in the s data will be visible.
Automated metadata intelligence makes it easy for the specified datasets to be used in ETL, ELT, and Reverse ETL data pipelines.