How to Pinpoint Where Your Organization Wins (and Loses) with Data
Innovation is driven by the ease and agility of working with data. Increasing ROI for the business requires a strategic understanding of — and the ability to clearly identify — where and how organizations win with data. It’s the only way to drive a strategy to execute at a high level, with speed and scale, and spread that success to other parts of the organization. Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy.
A sea of complexity
For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives. Layering technology on the overall data architecture introduces more complexity. Today, data architecture challenges and integration complexity impact the speed of innovation, data quality, data security, data governance, and just about anything important around generating value from data. For most organizations, if this complexity isn’t addressed, business outcomes will be diluted.
Increasing data volumes and velocity can reduce the speed that teams make additions or changes to the analytical data structures at data integration points — where data is correlated from multiple different sources into high-value business assets. For real-time decision-making use cases, these can be in a memory or database cache. For data warehouses, it can be a wide column analytical table.
Many companies reach a point where the rate of complexity exceeds the ability of data engineers and architects to support the data change management speed required for the business. Business analysts and data scientists put less trust in the data as data, process, and model drift increases across the different technology teams at integration points. The technical debt keeps increasing and everything around working with data gets harder. The cloud doesn’t necessarily solve this complexity — it’s a data problem, not an on-premise versus cloud problem.
Reducing complexity is particularly important as building new customer experiences; gaining 360-degree views of customers; and decisioning for mobile apps, IoT, and augmented reality are all accelerating the movement of real-time data to the center of data management and cloud strategy — and impacting the bottom line. New research has found that 71% of organizations link revenue growth to real-time data (continuous data in motion, like data from clickstreams and intelligent IoT devices or social media).
Waves of change
There are waves of change rippling across data architectures to help harness and leverage data for real results. Over 80% of new data is unstructured, which has helped to bring NoSQL databases to the forefront of database strategy. The increasing popularity of the data mesh concept highlights the fact that lines of business need to be more empowered with data. Data fabrics are picking up momentum to improve analytics across different analytical platforms. All this change requires technology leadership to refocus vision and strategy. The place to start is by looking at real-time data, as this is becoming the central data pipeline for an enterprise data ecosystem.
