Make data more accessible
While the growth in data from mobile, social and IoT sources far outpaces traditional transactional data, the unexpected gems of insight come from marrying these diverse sources of information on customers, processes, or operations. This requires managing both the scale and distribution of sources to ensure data accessibility.
Traditionally, enterprises used data warehouses to integrate data for analytics. Data warehouses allow data to be cleansed and organized for rapid querying by analytic engines which works well for transactional data. However, with the explosion in unstructured and semi-structured data, various solutions have emerged that are more scalable and cost effective. The most notable example is data lakes. This option can cost-effectively store large amounts of data in the native format and is particularly suited for exploration.
Data lakes should not be considered a replacement for data warehouses.
Each has its own merits and should be carefully evaluated relative to the intended workload.
In fact, many businesses choose to have both data warehouses and data lakes in their environment. The data warehouse provides a clear and reliable view of time-based trends in business-critical data, already pre-aggregated and pre-integrated, and the data lake helps independently generate insights from new data sources.
Regardless of the data store, we need to consider the dispersion across systems and business units, both on premises and in clouds. These data silos can limit access to data and slow speed to insight.
A flexible and scalable data management architecture can help with accessing data across these siloes, wherever they reside.
Whether it’s on-premises or in a cloud, the right hybrid data management architecture will enable data consumers to rapidly access, integrate, and query the wealth of data, in a governed environment, to uncover meaningful and impactful insights. This speed and agility can deliver competitive advantage.