Treating data operations as organisational value stores fosters innovation
Over the last two decades, the data and analytics industry has witnessed remarkable progress. Initially rooted in the financial services sector, the primary focus was on executing regulatory standards and safeguarding data integrity. However, there has been exponential growth in both the scale and diversity of available data, presenting a significant opportunity. While some businesses have successfully harnessed this data through advanced analytics and artificial intelligence (AI) to gain a substantial competitive advantage, a majority of organisations have yet to experience these benefits, leaving the promise of AI and data science largely unrealised for them.
Organisations today have to strategically pivot their approach to data management. As per a recent study conducted by KPMG in India, ‘Out of approximately 75 CDOs, around 60% are seeking to adopt AI-enabled data management in their organisation, 66% have confirmed reduction in cost via improved efficiency / automation / faster time to market as their primary ROI metric. In an era where data complexity is on the rise and
businesses are continually evolving, Data Operations offers a systematic and collaborative approach that spans data ingestion, processing, storage, and analysis.
What is Data Operations?
Data Operations or Data Ops is the systematic process of organising, refining, and storing contemporary, siloed data to transform it into consumable and valuable information. This involves converting raw data into a structured and accessible format, making it easier to analyse, interpret, and utilise for various purposes.
It helps deliver high-quality, ready-to-use data that people across an organisation can easily access and apply to different business challenges. It helps them strategically leverage data to create value for customers and the business by developing new services, enhancing existing offerings, and curating insight-generating data solutions.
Leveraging technologies such as data fabric, data mesh, data lakes, and lake houses can enhance components that play crucial roles in structuring, managing, and extracting value from diverse and complex datasets, which helps organisations in the following ways.
Enable Scalable Analytics and AI: Facilitate the expansion of analytics and artificial intelligence capabilities by implementing a contemporary data architecture. This not only scales these technologies effectively but also minimises the cost and time required to gain valuable insights, thereby boosting confidence and trust in data utilised for applications, analytics, and AI. Uncover novel patterns and trends to enhance operational efficiency and introduce innovative offerings.
Enhance Data Accessibility: Simplifying data access by seamlessly integrating diverse sources, enabling unified insights from data lakes and warehouses. This approach fosters efficiency and accessibility, unlocking valuable opportunities within a modern data ecosystem. The agile strategies of data operations reduce complexity, making data more readily available for informed decision-making.
Increase Agility, Efficiency, and Scalability: Reduce data management complexity and enhance business value by adopting an agile and efficient approach. Begin with small-scale implementations and easily scale across different use cases and deployment scenarios, ensuring adaptability to varying business needs.
Accelerate Access to Credible Insights: Expedite the time required to obtain trusted insights by implementing built-in governance and metadata management, ensuring control over data privacy and security. Achieve global deployment with enterprise-wide governance solutions, providing a robust foundation for reliable and secure insights.
Quick Deployment and no Vendor Lock-in: Accelerate deployments across hybrid and multi-cloud environments. Ensure flexibility and prevent vendor lock-in by supporting all types of data and use cases.
Cost Optimization in Analytics: Optimise analytics costs by leveraging lower-cost compute and storage options. Utilise purpose-built analytics engines that dynamically scale up and down, aligning the workload with the appropriate analytic engine. This approach maximises efficiency and cost-effectiveness in analytics operations.
Business-changing impacts of Data Operations
Data operations are indispensable tools, transcending silos, and fortifying quality to empower diverse business units, facilitating swift, informed decisions. Beyond internal benefits, they offer a 360-degree view of customers, optimising processes and fostering loyalty through personalised experiences and new monetisation avenues. Crucially, data operations ensure meticulous governance, safeguarding customer trust, and positioning businesses as responsible data stewards. From a competitive standpoint, it propels organisations forward by facilitating continuous refinement based on quantified insights, positioning them as market leaders. Treating data operations as organisational value stores fosters innovation and aligns businesses for future challenges, making them resilient in the evolving data landscape.
Conclusion
Chief Data Science Officer, LenDenClub
I strongly advocate the strategic adoption of data operations for sustained success. But you can’t have a one-size-fits-all approach when it comes to data operations. It requires specific technological infrastructure, strategic processes, and, importantly, a culture that embraces adaptation. This evolution, rather than a sudden revolution, takes time.
Organisations with advanced and innovative data architecture will develop operations that address specific customer requirements, enhancing customer loyalty, engagement, and satisfaction. Over the long run, focusing on customer-centric solutions will drive business success and set them apart.
