Every digital organization that is functional today is data-driven. Or at least they generate some data that facilitates decision-making.
With that context in place, we can define DataOps as a set of organizational practices and principles that aim to improve the speed, quality, and reliability of data-driven processes.
The building blocks of DataOps
DataOps is a culmination of people, technology, and processes that will help deliver the right data to the right person, when they need it, to do their work in a better fashion. That said, DataOps is made up of three build blocks:
The end-users across the org who belong to all departments and functions who could be creating and analyzing data for making data-driven decisions.
The tech stack that enables end users to carry out data operations at scale. Includes basic tools like spreadsheets to cloud-based tools that help data in various ways.
The predefined workflows of how data will be created, gathered, compiled, used, and secured by the organization.
Let us now take a look at how DataOps can be a superpower for any department, function, or end-user.
The tangible benefits of DataOps
The definition of DataOps might have made you ask: “Why should your business care about Data Operations?”
The answer lies in the several benefits that DataOps provides:
- Reducing the time taken to deliver data-driven projects. For example personalization in an eCommerce store
- Reduce errors in business decisions. Example: Choosing the region where marketing efforts need to be ramped up.
- Ensure clean data accessibility and availability across the organization. Example: Accounts department getting access to manufacturing log.
- Set up a well-equipped data infrastructure that will drive long-term growth. Example: Readymade templates or dashboards for each department or function to take real-time data-driven decisions.
Across industries and organizations, DataOps can help organizations work faster, boost employee productivity, and also achieve tangle cost reduction.
The four major types of DataOps
DataOps is not a single function like accounting, marketing, or sales. It is an umbrella term used to address a set of data-centric operations. Broadly they can be classified into four:
1. Data cleaning
Data cleaning refers to the process of eliminating incorrect values, corrupted fields, etc. in a dataset to make it ideal for analysis. Data cleaning also involves fixing inconsistencies in the dataset like inconsistent formatting, duplicate records, empty fields, etc. Data cleaning is recommended if the dataset is a result of data combined from multiple sources or if the system that delivers the data output is known to provide ‘dirty data’.
2. Data transformation
Data transformation is the next step to data cleaning where the master data is converted or transformed into a usable format, mostly a file format, or in a specific form of presentation (like a calendar timeline, charts indicating metrics, numbers showing ratio, etc.) that will be used in data-driven decision-making.
3. Data integration:
Data integration refers to the unification of data from disparate sources to create a single source of truth. For instance, in an organization, data from sales, marketing, finance, and human resources, could be integrated to create a single view of data to be used for customer service, forecasting, reporting, analysis, etc.
4. Data governance
Data governance defines how all the previous activities of cleaning, transformation, and integration is carried out. It lays down intra-organizational guidelines on how data should be sourced, extracted, stored, accessed, transformed, or even shared with internal and external parties.
Popular tools used for data operations
In the past, DataOps was possible with traditional systems like spreadsheets and basic programming software. Today, due to the volume, variety, and velocity with which data is created, it is no longer possible to use traditional tools for DataOps. In fact, why would an organization rely on redundant tools when bespoke tools for specific DataOps functions are available.
The most popular tools used for data operations can be classified into:
Databases and SQL
Database tools help organize raw data in databases that have well-defined structure and organization. They make it easier for various end-users to narrow down on granular data. SQL is a programming language used for creating, modifying, and querying databases. The most popular database and SQL tools include
- Oracle Database
- Microsoft SQL Server
Data visualization tools
Raw data usually takes the form of text or numerals. For a user who juggles large volumes of data and wants to crystallize a piece of specific information from a large dataset, data visualization tools are necessary. They help in converting text or numerical data into charts, graphs, maps, and other visual representations of data.
Popular data visualization tools include
- Microsoft Excel
- Google Charts
- Google Data Studio
Data integration tools
Data integration tools make it possible to integrate data from disparate sources in a single location so that data is cohesive and ready to consume.
Some examples of data integration tools include:
- ETL (Extract, Transform, Load) tools: Talend, Pentaho, and Informatica.
- Data synchronization tools: DBSync and Talend Data Integration.
- Data quality tools: Informatica Data Quality and Talend Data Quality.
- Data transformation tools: Talend Data Transformation and Apache Beam.
- Data lake integration tools: Talend Big Data and AWS Glue.
- Data mesh tools: Dremio and StreamSets Data Collector.
Data governance tools
Data governance tools aid in controlling who has access to what data and how. It is used for protecting sensitive data, or to keep the master data from being tampered with during the day-to-day activities. The most common types and examples of data governance tools include:
- Data catalogs: Alation, Collibra, and Informatica MDM.
- Data quality tools: Talend, Informatica Data Quality, and SAP Data Quality.
- Data lineage tools: Collibra, Informatica MDM, and Talend.
- Data governance frameworks: Data Governance Institute’s Data Governance Framework, the Open Group’s Data Governance Maturity Model, and the Data Governance Council’s Data Governance Framework.
What makes DataOps work?
DataOps is not a tool that can be bought off the shelf or a service that can be subscribed to. Instead, it is akin to a function that needs to be set up from the ground up. In addition to the building blocks we saw earlier, it also has several core components that delivers value for the organization.
- Collaboration and communication: DataOps relies on strong collaboration and communication across different teams and functions, including data scientists, developers, and operations staff.
- Automation: DataOps relies on automation to streamline and accelerate data-related processes, such as data ingestion, transformation, and analysis.
- Data quality and governance: Ensuring the quality and integrity of data is critical for making informed decisions. DataOps includes processes and tools for verifying and validating data, as well as for tracking and correcting any errors or inconsistencies.
- Monitoring and feedback: DataOps includes monitoring and feedback systems to track the performance and quality of data-driven processes, and to identify and address any issues or bottlenecks.
- Continuous improvement: DataOps is an iterative process, with a focus on continuous improvement and optimization. This includes regularly reviewing and updating processes and tools to ensure they are effective and efficient.
Best practices that squeeze maximum value out of DataOps
A typical digital enterprise will have several data users ranging from the Chief Digital/Data/Technology Officer to data scientists, analysts, engineers, etc. Further, there could be stakeholders from other departments and functions who could be using data for carrying out their roles and responsibilities. To enable these stakeholders to squeeze out maximum value from DataOps it is necessary to follow certain best practices, such as:
- Ensuring data quality
Low-quality data, that is dirty data carrying duplicates, errors, inconsistent fields, etc. can hamper DataOps. It is mandatory to conduct data cleansing activities including data cataloging and metadata management to improve data quality.
Improving data quality ensures that stakeholders can trust that their data-driven decisions are indeed accurate. Further, when the organization becomes AI-ready, it becomes easier to train AI systems with existing datasets.
- Maintaining data security and privacy
One of the primary objectives of DataOps is to ensure that the right data is available to the right user in the right format when needed. However, at the same time, it is also necessary to protect the data from security and privacy threats.
Data security and privacy policies should be put in place to ensure that users have role-based access to data that is relevant to their requirements.
- Implementing data governance policies
Data governance is critical to DataOps because they ensure that the data helps ensures that the data being used and managed is accurate, secure, and compliant with applicable statutes. These pointers help in implementing data governance policies:
- Assigning data governance roles and responsibilities to individual users
- Setting standards and guidelines for data access, storage, and security
- Implementing data quality controls, data cleaning, and data integration procedures
- Timely review and updation of policies to keep them abreast with latest developments
Use cases and real-world examples of Dataops
Here are a few real-world examples of how organizations can use DataOps to improve their operational efficiencies and customer experience.
- A healthcare organization can use DataOps to automate the deployment of data pipelines that extract, transform, and load data from electronic medical record systems. The pipelines are continuously tested and monitored to ensure the quality and accuracy of the data.
- A retail company can use DataOps to establish processes and tools for data governance and compliance. This includes implementing data validation rules, tracking data origin, and creating dashboards to monitor data quality metrics.
- A financial services firm can use DataOps to streamline the development and deployment of data-driven applications. This includes using continuous integration and delivery (CI/CD) practices to automate the build, test, and deployment of data pipelines, and using collaboration platforms to facilitate communication between data management and data processing teams.
- A transportation company uses DataOps to optimize the performance and scalability of its data infrastructure, including its data lakes and data warehouses. This includes using tools to monitor and tune the performance of these systems, as well as automating the provisioning and scaling of resources.
The future of DataOps
Some potential future developments in DataOps include:
DataOps will gain pace with automation
DataOps is likely to continue to focus on automating various aspects of data management and processing, including the deployment and testing of data pipelines, the provisioning and scaling of data infrastructure, and the monitoring and optimization of data quality.
DataOps will get smarter with artificial intelligence (AI) and machine learning (ML)
DataOps may increasingly incorporate AI and ML technologies to improve the efficiency and effectiveness of data management and processing. For example, AI and ML could be used to automate tasks such as data cleansing and transformation or to optimize the performance of data pipelines.
There will be an increased focus on data privacy and security
As data becomes increasingly valuable and sensitive, DataOps may place a greater emphasis on ensuring the privacy and security of data. This could include implementing stronger data governance and compliance practices, as well as using encryption and other security measures to protect data in transit and at rest.
DataOps adoption will multiply
As organizations continue to recognize the benefits of DataOps, more and more companies will likely adopt these principles and practices. This could lead to the development of more specialized tools and services for implementing DataOps, as well as the creation of new roles and job titles related to data management and processing.
Conclusion: Augment your organizational efficiency with DataOps
DataOps in a nutshell is everything that your organization does with data. It has risen in popularity in recent years when data creation and management have exploded in volumes. Even in the years to come, there will be a significant increase in data creation which would inflate the demand for DataOps.
Although largely automated, DataOps required significant technology infusion. The right tech stack consisting of data cleaning, transformation, integration, and governance is required to make DataOps work. Process improvements are required to ensure that all stakeholders are in alignment with how they will create and manage data. Undoubtedly, DataOps will continue to evolve and become increasingly important in the future as organizations rely more on data-driven decision-making and as the volume and complexity of data continue to grow.