Imposing a Collaborative Data Operations Workflow in a Company

Data Operations, or DataOps, is considered as an agile and processes-oriented methodology for developing and delivering data analytics. By streamlining, developing, and maintaining data design of applications based on data and data analytics, data operations managers aim to seek and improve the way data is being managed and how products are created, as well as to coordinate these improvements with goals of the business.

DataOps Managers then bring together Development Operations (DevOps) teams with data engineers and data scientists in making sure that companies are provided with tools, processes, and other organizational structures in supporting the data-focused enterprise. The challenge, however, is how a manager can bring forth a collaborative environment between DataOps and DevOps.

What DataOps Entails

As a software development methodology, DataOps brings continuous delivery to the systems development life cycle by making sure that the development team and operations team are well incorporated into one harmonious single unit that is responsible for a product or service.

A data operations manager piggybacks on that concept by adding other data specialists––data analysts, data developers, data engineers, and data scientists––to focus on the collaborative development of data flows and to ensure continuous use of data across the organisation.

With this, no group within any modern business or enterprise faces a higher level of individual complexity than this data organisation team. By using various tools, aligning to mission-critical deliverables, and interacting with nearly all other groups in an organisation (from the marketing team, to the accounting team, up to the CEO), the data organisation team’s role is incredibly challenging as it requires both top-notch skills in leading and managing.

Despite their level of expertise, most if not all data professionals are routinely dealing with project failures, slipping schedules, busted budgets as well as other seemingly minuscule errors that it has become increasingly hard to get things done in data organisations.

Data Operations principles and methodology

Similar to DevOps, DataOps takes most of its principles from agile methodology. This approach values and ensures continuous delivery of analytic insights with the primary goal of satisfying the customer. DataOps teams also put a lot of value in data analytics that actually work as measured by the performance and efficiency of data analytics by the amount of insights they bring to the table. With these insights, DataOps teams embrace change and constantly seek understanding of the evolving needs of the customers.

Data operations teams also find a way to bring balance to data tools, data codes, and data environments from start to finish, resulting in high-quality reports. They view analytic pipelines as analogous so as to lean manufacturing lines that regularly reflect on the feedback that are provided by customers, team members, and operational statistics.

How to manage complexity with a data operations team

Mapping out all these communication and task coordination patterns in a real organisation is perhaps the most common problem with DataOps. Imagine having to manage groups and keep them on track while sticking to a certain budget. These are some of the real life challenges that DataOps managers face on a daily basis. This internal organisational complexity can destroy a shareholder’s value or prevent an enterprise from meeting its objectives.

This challenge is nothing new to us at ADEC Solutions UK. In fact, we have been instrumental in dealing and tackling these challenges using a set of solid methodology that helps your DataOps capabilities spring to life from the whiteboard into your data center with the shortest amount of time possible.

We believe that utilizing DataOps is the best and surest way in managing all the intricacies and complexities of data and analytics-development pipelines by learning how to manage and govern workflows, coordinate tasks and define data roles effectively. The ability to create and cultivate teamwork from a sea of chaos makes it easier to get things done.

Let our in-house data experts help you navigate these complexities by booking a consultation at your earliest convenience.