When looking to process and store large databases to gain insights you need a fast solution. DataOps is an agile data management approach that can help your business quickly go through data and come up with useful insights.
Companies use data to update and improve their products and user experience. But they struggle when managing data. McKinsey says that companies usually spend around 80% of their analytics project time on data-related tasks. DataOps platform solutions like Meltano can greatly improve and facilitate the entire data lifecycle.
Keep reading to find what DataOps is, its benefits, and how it can help your business succeed.
What is DataOps and how does it work?
DataOps, or Data Operations, is a data management practice. It uses people, products, and processes that enable data management. It’s a method used by organizations to apply tech to automate data flow throughout their organization.
It’s a delivery system that joins and analyzes large databases. It provides predictable data flows because it tracks data. The methodology works by automating data management tasks.
DataOps gathers all steps of the data lifecycle, including reporting, aggregation, and machine learning preparation. It operates through pipelines, which are a set of tools and processes that move data from a source to a repository or warehouse.
DataOps uses technologies like AI, Machine Learning, and Agile methodology, as well as some data management tools, to reorganize data actions (processing, preparing, testing, monitoring, deploying, etc.).
It does so by monitoring the data pipeline using statistical process control. The DataOps process has five key phases:
Differences between DevOps and DataOps
It’s easy to confuse DevOps with DataOps, firstly because of the similar names. They have some similarities, but their meaning is different. DataOps was built on the concept of DevOps to improve the data analytics field.
DevOps works with software development teams and operations teams to accelerate build cycles. It breaks a project into sprints different teams can collaborate on. By extension, the most crucial elements are getting done faster.
The integration of IT ops and software development wants to improve software development in terms of speed and quality.
What Data Ops does is apply a similar approach to data analytics. It brings data engineers and analysts together to accelerate data production and maintain optimum quality.
As you can see, these methodologies were created to speed up the work cycle. Likewise, they rely on Agile systems to achieve their goals. But DevOps focuses on delivering software apps while DataOps concentrates more on data products.
Increased data volumes lead to professional challenges. The process of managing large amounts of raw data while looking to reduce errors can be strenuous. Data engineers from different departments can work together to speed up product delivery. DataOps tech is a positive step towards achieving faster results.
When teams manage data more efficiently at higher velocities, they have better results. They control the project better and simplify its steps. DataOps has some benefits, including:
- Increased productivity – by automating the data analytics pipeline, DataOps reduces manual labor. The methodology can ensure planning, coding, testing, deploying, and monitoring data without using human employees.
- Data democratization – anyone working in the company -regardless of rank – can access and use data. Thus, they can make informed decisions. DataOps helps improve all business processes.
- Analytics improvement – because data processes are automated, DataOps allows you to check and adapt your products to your market. You can easily check statistics about customer behavior, price changes, and market changes.
- Maximize data value – DataOps improves data quality and minimizes the time needed to make informed decisions. Thus, it maximizes the value your data brings to any project.
- Data security – Statista says that more than 155 million people have been victims of data breaches in the 2015 – 2020 period. Some companies still struggle with data protection after using the cloud. DataOps works on security measures meant to protect data. They include data compliance, meaning any sensitive data is safe against theft, corruption, or misuse.
Data lifecycle and ways DataOps contributes to it
Let’s talk a little about the data lifecycle and its components. We can determine four big stages that influence project effectiveness. These are the steps:
- Data generation – your business, customers, or other groups can create data. Usually, data generation comes down to:
- Data entry – when data is manually typed
- Data acquisition – when external sources generate data that your company collects
- Data capture – you collect information from any medium and convert it into a format that computers can read.
- Data processing – this second step in the data lifecycle implies converting raw data into more useful forms. That means cleaning and preparing it before conversion.
- Data storage – the third step requires the previous collection and processing of data. It implies securing and storing it. You can use the stored data immediately or in the future.
- Data management – you organize, store and maintain data from its creation until you no longer need it.
DataOps is very helpful for organizations. It allows companies and organizations with the following:
- Find and collect data
- Integrate new data automatically into pipelines
- Makes collected data available to users
- Centralizes data
- Ensures data pipeline changes automatically.
DataOps uses statistical process control to improve data quality and processing. It uses statistical techniques that monitor data to ensure meeting quality parameters. If an anomaly happens, a data analyst is alerted.
DataOps focuses on solving different issues that may arise in your company, the most important being the following:
- Quality – when working with a large volume of data, you can encounter consistency issues. DataOps improves data quality and serviceability. It measures information regarding data sources, access, and changes made to them.
- Speed – data environment can be complicated when data sources and volumes increase. Your entire operational process generates data you need to absorb and organize. DataOps implements an agile methodology meant to reduce the cycle time of analytics. As previously mentioned, it monitors and automates the entire data lifecycle. Ultimately, it enhances data flow integration and automation.
- Facilitating collaboration between users – distinct teams can work together and synchronize their results. It translates to more realistic insights and analytics.
It’s important to set healthy data practices that help your company work more effectively. Start with DataOps technologies and processes. Improve your data processing power and make it accessible to your employees.
Agile data management helps companies gain a boost in data processing power and quality. It becomes easier to focus on actionable insights and obvious data flows. Implement DataOps methodology to see improvements in your data management practices in no time.