The Finworks Data Platform automates the entire data pipeline enabling DataOps to provide extremely high data quality.
Data delivery revolutionised
We live in a time when the word "data" is ubiquitously used in contemporary narratives.
In 2018 research, Gartner defines Data Ops as “a collaborative data management practice focused on improving the communication, integration, and automation of data flows between data management and consumers across an organisation."
Organisational transformation and management can be driven by balancing the components of people, process, and technology. The role of DataOps is to use these three components to deliver quality, validated data to operations and applications within the data ecosystem. DataOps utilises process and workflow automation to improve and facilitate communication and coordination within a team and between departments or business entities. Data delivery should fit both the requirements of the data manager for control, transparency and auditability and those of the business user for analytics-ready, real-time data, to maximise value. With properly governed data, businesses can have robust and secure data to progress up the data analytics maturity model.
Using new tools and methodologies DataOps removes barriers to high levels of quality data and therefore can deliver data-rich outcomes with greatly improved cycle times. Data Ops provides a solution to today's current data management challenges and is an attempt to enhance collaboration between data creators and consumers in three ways.
DataOps is all about automation around data processing. To automate all the operations, workflows are either scheduled or triggered by events. Essentially, the main purpose is to automatically pull data and then process the analytics on top of it without the need for any manual processing. The main role of DataOps is to pull data from various diverse sources, process the data, and apply analytics with limited development and involvement from the IT team.
Data Discovery automatically completes analysis of the data and creates metadata. Everything is confirmed in a step where the user can review the results and edit the definitions created by the system. The user can describe or define the data in human-readable format files such as JSON. Workflow templates support self-service data onboarding. Data is pulled by time schedule or data can be pushed via automation. Users can flexibly change file definition and all the changes to the data can be applied in real time to the live data and so the benefits of the new or changed data are achieved extremely quickly.
It is important to emphasise that Data Ops utilises additional Agile methodologies to accelerate the development of analytics in alignment with business objectives. The principles of DataOps stipulate that there should be no need for IT or a project to adjust elements within the data pipeline. The system lets users easily and directly change file locations without the help of an IT department, or a project development to adjust the format of data. Any changes to the raw data could be applied in real-time and the user can start benefitting from this new refined data. Therefore, there is no need for a development team to get data and put it in a form for users to use the data. Without DataOps the data pipeline is not flexible, quick, or agile, but it needs planning and resources with impacts on budget and time.
How can DataOps impact organisations?
To achieve data-driven transformation, an agile approach should be applied across the entire data supply chain, which includes infrastructure, processes, and the people involved. By integrating all these elements, Data Ops helps you to accelerate cycle times and improve performance. There is no doubt that Data Ops has the potential to completely transform the way your organisation delivers and consumes data, which will result in an array of operational benefits. Some of the ways in which it could make a significant difference in your business include:
Streamlining analytic processes
Achieving data-driven transformation requires agility and real-time insights. By automating manual tasks, not only reduces the analytics cycle time - it also frees up resources for higher-level analytical tasks.
The Data Ops solution streamlines data science and business analyst collaboration - and encourages discrete business units to share data analysis results. Data Ops is different from traditional task forces that focus on specific issues, all business users get valuable data when they need it, on a consumable and governed basis.
Analytical insights do not need to be isolated from the day-to-day operations of the business. Instead, they can be shared with a broad set of line-of-business users, each with their own expertise.
Governance of data delivery
This modern, scalable approach gives users timely access to data while layering in quality assurance and role-based responsibilities. The right data gets to the right people at the right time.
To summarise, DataOps enables:
· Rapid innovation and agility in delivering data insights
· Extremely high data quality and very low error rates
· Collaboration across data ecosystems (people, technology, and processes or environments)
· Transparent analysis and reporting of outcomes
Building a trustworthy Data Management Platform
For Finworks, being agile is a part of our ethos as we strive to do all things related to the client "With the client, For the client." The Finworks Data Platform includes functionality essential for DataOps to manage and orchestrate the data pipeline within organisations. The workflows for validation, cleansing, and monitoring of incoming data and managing the central repository of metadata. Data errors are prevented from entering the data analytics stage and processing errors are caught mid-pipeline before corrupting analytics. With the Finworks Data Platform, inefficient manual effort previously devoted to operating, verifying, and fixing the data pipeline is redeployed to higher value-add activities.
Finworks has always taken a data-driven approach to solve challenges and improve decision-making. Finworks provides data integration and analytics solutions that help organisations gain valuable insights and act based on data.
Our expertise lies in assisting businesses to implement data analytics solutions so that they can gain a deeper understanding of their customers, explore new approaches to their business, uncover new data perspectives, and maintain a balance within the organisation. Enquire now.
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