The necessity to store and process high volumes of data to gain insights are complicating data management in organizations. According to a survey by McKinsey, companies spend 80% of their time in analytics projects on tasks such as data preparation. Gaining the agility to boost the speed of data processing and increasing the quality of data to derive actionable insights is the focus of many businesses. This focus creates a need for an agile data management approach such as DataOps.
What is DataOps?
DataOps (Data Operations) is an automated and process-oriented data management practice. It tracks the lifecycle of data end-to-end, providing business users with predictable data flows. DataOps accelerate the data analytics cycle by automating data management tasks.
What is the data lifecycle?
- Data generation: Data can be created by your business, your customers, or other parties. Data can be generated in three different ways:
- Data entry: Manual entry of new data
- Data capture: It is the process of collecting information from any document and converting it into a data format that computers can understand.
- Data acquisition: Process of collecting data generated by external sources.
- Data processing: Process of cleaning, preparing and converting raw data into a more usable form.
- Data storage: Once data has been collected and processed, it must be protected and stored for future use.
- Data management: Process of organizing, storing and maintaining data from the day it is created until the day it is no longer used.
How DataOps contribute to the data lifecycle?
DataOps enables organizations to:
- Identify and collect data from all data sources.
- Automatically integrate new data into data pipelines, and makes data collected from various sources available to all users.
- Centralize data and eliminate data silos.
- Automate changes to data pipeline.
In order to increase data quality and improve data processing, DataOps uses statistical process control (SPC). SPC uses statistical techniques to monitor the data and the data pipeline to ensure that the overall quality of the pipeline is within proper range. It alerts data analyst in case of an anomaly.
What problems is DataOps focused on solving?
- Speed: As data volumes and the number of data sources increase, data environments become more complicated. Each touchpoint in an operational process generates new data. Companies need to find a fast way to ingest and organize data. DataOps is an agile approach that aims to reduce cycle time of data analytics. DataOps monitors and automates data life cycle. It improves the integration and automation of data flow between users in the organization.
- Quality: Large volumes of data can cause data inconsistency problems. DataOps is designed to improve usability and quality of data. To ensure data completeness and transparency, DataOps provides information about the source of data, who access to data, how they changed it, etc.
- Reduction of the manual effort: DataOps automate the entire data lifecycle from data preparation to reporting and increase the agility of all data processes.
- Enabling collaboration: DataOps enables collaboration between different teams and allows them to work synchronously. This leads to more accurate analytics and better insights.
How DataOps differs from DevOps, MLOps and AIOps?
DataOps and MLOps can be considered as applying DevOps practices to data analysis and machine learning model building.
- Continuous development of software, development done by engineers/technically skilled persons.
- Reduction in the development lifecycle.
- MLOps:MLOps is a set of practices to standardize and streamline the process of construction and deployment of machine learning systems. DataOps encompasses MLOps. MLOps involves:
- Model training/creation of machine learning pipeline to automate the retraining of existing models
- Monitoring of model performance in production
- Pipeline automation
- Model deployment – integration of the trained and validated model into production workflows as a prediction service.
- AIOps, on the other hand, is the integration of Artificial Intelligence (AI) into IT operations, including event correlation, anomaly detection, and causality determination. It overcomes challenges such as processing increasing amounts of data or finding root-cause identification. It supports DataOps by enabling AI-powered recommendations.
Recommendations / Next Steps
In addition to applying DataOps technologies, processes and people also need to be considered for better data operations. For example, it is important to set up new data governance practices that are compatible with DataOps. The human factor is also crucial. Teams need to update and expand their skills.
If you have other questions about DataOps and which solutions to choose, we can help:
Next to Read
Your email address will not be published. All fields are required.