So, What is DataOps?
Data Operations or simply DataOps can be defined as a process-oriented methodology for data management. It is focused on improving the collaboration, integration, and automation of data flows among the teams to deliver value faster. That means bringing together data engineers, data scientists, and other data professionals to facilitate the tools and processes to support data-driven enterprise decisions.
With DataOps, teams across the organization have the authority to make decisions with data. Put simply, they have access to the data, which is well-managed and consistent at every touchpoint. For example, your finance department knows where to find revenue margins by sales channel. They can see how it is calculated and assure that they’re using the same numbers as data scientists are using.
Michele Goetz, VP at Forrester, defines DataOps as, “the powerful capability to create data products and put data into action for business outcomes across all technology tiers, right from infrastructure to experience level”.
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DataOps takes inspiration from Agile, DevOps, and Lean Manufacturing methodologies. And it has the same objective to achieve better data management between data teams, processes, and people.
A 451 Research outlined that the DataOps trend is accelerating across the enterprises, with almost 100% of respondents say they are currently planning or pursuing initiatives to deliver more agile and automated data management.
A good way to understand DataOps is to think of these principles.
Agile Project Management and Data Ops
This approach promotes the completion of work in short increments. It involves semi-autonomous teams that shift quickly as businesses learn new things & experience priorities change. In DataOps, the approach enables data teams to work with Big Data and drive quick insights for decision-making. They can significantly reduce the time they spend finding the right data. IT can change and adapt at the speed at which your business operates.
It breaks down the siloes between development and operations teams and brings collaborative culture to fasten development cycles. DevOps uses version control systems and code repositories for parallel development and code reuse. DataOps work on the same principles of DevOps to collaborate better and deploy data models into production faster. For example, your data scientists depend on the IT or engineering team to deploy their models- right from data analysis to deploying ML and DL based algorithms. It eliminates all sorts of dependencies and shortens the deployment times.
Like manufacturing takes place in pipelines – raw materials pass through various manufacturing work points to finally get into the production stage. The main objective of this technique is to ensure minimal waste and greater efficiency without compromising product quality. DataOps teams work on the same principle by building pipelines (like ETL) to turn data into rich visualization reports.
Let’s say that today your data engineers spend time working on models (that data scientists worked upon) to get in into the production, building pipelines and fixing issues. With DataOps, that time can be reduced drastically.
DataOps for Governance
DataOps for governance is about improving the ability to keep up with the regulatory requirements alongside establishing the data governance rules that can foster analytics initiatives.
By combining the principles of DevOps, Agile and Lean Manufacturing, DataOps allows the continuous design of data products and continuous governance for establishing an Information Governance framework, a practice, and standards for information management organization wide.
451 Research Survey revealed that 70% of business participants said that they are using governance initiatives to fuel organizational analytics objectives. The survey also highlighted that one of the key focus areas for enterprise DataOps’ spending was Data Governance.
As data accessibility is dependent on local, regional, corporate, government, and other industry regulations, automation can take the lead in masking the data to ensure compliance with data governance and other security requirements.
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DataOps aims to provide better business results by reducing the data analytics time along with elevating the data quality. Organizations are adopting DataOps to overcome the challenges faced during the accessibility of data and delivering analytics solutions.
According to a study conducted by Delphix, 86% of business leaders are planning to invest in DataOps strategies and platforms and 92% expected that this methodology brings positive business results.
DataOps for sure drives more agility and directs businesses towards advanced processes.
Here are some business benefits of DataOps:
DataOps works on DevOps & agile methodologies that bring collaboration between the teams and uplift the efficiency of the workforce. With the help of testing and automation mechanisms into the analytics pipeline, the organization can focus on strategic tasks instead of spending time on repetitive tasks of maintaining spreadsheets. DataOps helps build data-first businesses that make businesses stand ahead and face the ever-evolving challenges in the data-driven world.
Obtain Data with Better Quality
According to Experian Data Quality report, 96% of CDOs believe that business professionals need access to data more than ever before. With the help of automated and iterative processes, data chiefs can take control of the code checks and project rollouts. Plus, the automation can reduce the human error getting distributed across multiple servers and affecting the network. With DataOps, delivery time can be accelerated along with better agility to keep up with the market needs. As the competition is increasing at a cracking pace, DataOps will help organizations to get to the market quickly by improving the quality and speed of data processing. This, in turn, makes businesses better prepared to launch new features and products before their rivals hit the market.
Get Faster Access to Business Intelligence
With better data quality, businesses can harness the benefits of actionable business intelligence. They get insights into customer behavior patterns, market trends & volatility through automation ingestion, processing, and analytics of data. All these predictions-based results support faster planning, quick decision-making, and increased efficiency with reduced errors.
A whitepaper on Customer Engagement Journey in Pharma stated that leading life sciences companies are prioritizing and deploying DataOps to get into the patient-centric data to increase engagement with health care providers and optimize the data for the next actionable results. To achieve more personalization at scale, they are equipping themselves with automation like AI and Machine Learning to build tools for patients to take control of their health and enable caregivers to leverage the data and create meaningful relationships and enable intelligent decision-making.
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A Bigger Picture of Dataflow
DataOps help businesses look at the information in a single dashboard. The aggregated view of the entire data across the organization helps business professionals to analyze the macro trends such as acceptance rates of the features and solutions. In addition, they can get the data based on customer behavior patterns, purchasing habits, and location, providing a clear and bigger picture to segment and analyze the information further.
For example, a large pharmaceutical organization, invested in customer data operations strategy to develop a unified view of the customer. The major benefits of centralizing the management of customer information are to drive mass customer insights and decision-making. This, in turn, will help the company outline better customer journey strategy & planning, customer segmentation & profiling, and customer data management & governance.
Possible New Avenues of Growth
In the data analytics and operations milieu, excellent opportunities are waiting for those professionals who are ready to learn DataOps implementation. They can become the next leaders to run the teams and set standards for data practices. In addition, a forward-thinking company that eliminates redundant business tasks can reduce the employees’ churn rate.
The Future of DataOps
Data doesn’t belong to data scientists and analysts, or IT. Data belongs to everyone in the business to drive the analytics and operations forward. As organizations evaluate the value that DataOps unlocks across every tier, making the process automated & scalable can help ensure that the enterprises can achieve higher agility, scalability, and efficiency.
In the coming time, DataOps will become the mainstream for data and analytics teams. With new approaches, a new generation of data scientists and data engineers will evolve, and thus, will take the business to the next level.
Besides, the growth of data volumes will continue & restrict the automation of data ingestion. Hence, Artificial Intelligence and Machine Learning algorithms/models will be developed to do the heavy and compliant lifting that users cannot perform on their own.
At Icreon, we help organizations to build DataOps strategies that support a larger goal of bringing teams on the same page to use data more effectively. Drop us a line to make your DataOps implementation faster.