Importance of Data Integration for Building a Data-Centric Organisation

Data integration is the cornerstone of building a data-driven organisation and businesses should keep developing upon it to reap the rewards of being a true data-centric business.

FREMONT, CA:Data integration is the foundation for building a data-driven organisation. However, after a proper understanding, companies should keep developing it to reap the rewards of being a true data-centric business. Data integration should be taken further to leap establishing data architecture to weaving data integration throughout the culture, teams, and processes.

Companies use insight engines to track and curate trends. They use these insights to inspire innovative products and aid in product development. The insight engine is powered by artificial intelligence and machine learning. Companies still need to take valuable data and use their brains to extract analyses related to specific aspects. Many organisations empower their people to understand data trends like the market, geography, or demographics and will have a better handle on how to optimise these trends from a historical perspective, the present state, and the future of the trends.

A major challenge for clients is that they realise the benefits of integrating data culturally but often need help with a familiar roadblock, such as the skill set. As the skillsets of teams change, organisations frequently run into problems aligning their teams with their data integration needs. To remain competitive, they should learn new competencies and skills like data engineering, networking and security, and user experience (UX).

The management, integration, virtualisation, and distribution of comprehensive data in the cloud are the competencies that product leaders and engineers should master in the current digital transformation phase. Moreover, these skill sets will be quickly increased and amplified by the application of model-based services over the coming years, based on the state of AI and ML progress. This indicates that teams will no longer need to do their forecasting. They can leverage a model to operate it for businesses, but only if they have the necessary skills and quality data to feed it.

The rise of DataOps is driving the emphasis on skill sets. Industry-wide, there is a maturation of data operations that are propelling the delivery of data and the acceleration of analytics that businesses can effectively use. The discipline of DataOps focuses on communication, collaboration, integration, automation, and measurement of cooperation between data engineers, data scientists, and other data professionals.

Analyst 2.0 is a cross-functional generalist who shows an aptitude to pick up technical skills. Companies also have product leaders who are curious about solving problems, good at learning, and care deeply about their departments. Product leadership is important in product ownership, but the attributes of product leadership are vital to making the role successful. Analyst 2.0 and product leadership roles can be held by the same person or by multiple team members. However, the key is that they need to play different roles. Furthermore, they must have the ultimate tool in their toolbox of skills, such as communication. It is inevitable that team members, from technologists to engineers, should be able to share information to drive data throughout the culture of an organisation.