CIOs discuss how data teams can be empowered

CIOs discuss how data teams can be empowered

Enterprise leaders point out that for tackling unprecedented issues like a global pandemic and the need for rapid innovation, enterprises need robust and collaborative data teams

CIOs point out that data is required at a high priority in the current scenario to solve major issues. The world has witnessed first-hand the initiatives taken up by data teams as they stepped up in response to COVID-19, and effectively mobilized entire communities. For ensuring better utilization of data, organizations need strong and collaborative data departments. Doing so will prove beneficial for all industries and not only the IT domain alone.

CIOs believe that it’s high time to stop segregating data scientists and data engineers in different departments. They say that the convergence of data departments will be similar to the one witnessed between Development and operations teams and DevOps with software development.

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Full-stack engineering teams are an even better example of this trend. Enterprise leaders acknowledge that data teams have ample opportunity to make a significant impact, but the success factor will be relying on simplifying efforts and boosting them in the right manner.

Acknowledging the restrictions of siloed data teams

CIOs say that it is vital that they acknowledge the negative effect of siloed activities on efficient operations. It’s necessary to break down silos between the data teams and expose more space for collaboration, which improves efficiency.

Leaders believe that siloed work on data departments contributes to internal friction and decreases tech effectiveness. Enterprises don’t always report business gains from highly invested AI solutions. Siloed data departments could further affect the successful harvesting of the real power of AI. In an iterative model development process, siloed teams could cause friction in the operations, which effectively slows down the development of AI solutions and tools.

Proper alterations to data pipelines can also take a prolonged time if they need support from multiple disjointed departments. Cross-functional collaboration between teams is needed for agreement on data metrics or definitions and good quality analytics for AI projects that need a blend of different datasets.

These silos need to be broken down, allow collaboration between data science and data engineering teams, and create new data team architecture.

Allow more room for new roles

CIOs think that data teams will be more vertically targeted on business issues, and more hybrid roles will be observed. These updated roles include ML engineers that can manage an AI application from data analysis, preparation to production or data engineers or scientists who have full-stack data experience.

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CIOs believe that enterprises need to consider investing in more experienced talent roles to encourage collaboration over silos. They should invest in Chief Analytics or Data Officers who are capable of coordinating such projects from the initial level to the completion level.

They acknowledge that a unified team structure is not the proper solution for all situations. This method of convergence has been observed in the computing domain in various scenarios. One good example would be the combination of cloud services with DevOps tooling and frameworks that have helped the full-stack web application development process.

CIOs need to select a team structure that allows them to iterate on business issues and serve value rapidly. Data professionals will be required to suggest measures that allow the leaders to deliver value to the enterprises.

Translation of a broader business value

Leaders agree that no organizational data policy is complete without the inputs from business leaders across all enterprise levels and interests. Apart from the suggested new roles within the data departments, CIOs suggest the role of a “data translator.”

This person will be responsible for bridging the gap between data scientists and front-line managers. Such translators are vital to streamlining complex data for executive stakeholders and training employees on the true power of data.