Build holistic Enterprise Data Management solutions over Data Lakes for enterprise collaboration

As the data continues to become multi-dimensional, storage and data management becomes highly imperative for enterprise-wide collaboration. As a result, Data Lakes are fast becoming the most accepted solution for Enterprise Data Management.

Data Lake solutions are preferred over data warehouses when enterprises have complex operations and incur high costs for maintaining structured, semi-structured, and unstructured data, that is multi-structured data.

Data lakes usually store the data in the as-is form and don’t create a schema before data capture. This arrangement is conducive to data democratization and reduces the overdependence on data science teams.

These solutions are used to conduct data discovery exercises by using the stored multi-structured data for exploring and extrapolating towards predictive and prescriptive analytics.

Strong analysis of this stored multi-structured data conducted in a collaborative manner allows discovering the key variables that offer better performance.

Data Lakes do away with a siloed architecture and offer a comprehensive enterprise data solution. This arrangement facilitates pattern identification among the data sets and data points held within.

As Data Lakes are primarily used for enterprise-wide collaboration, you need to follow certain key points towards building a strong foundation for enterprise data management:

• Self-service analytics: Navigate the enterprise towards self-service, insights-driven culture, and enterprise collaboration thus ensuring proper utilization of the data lake investment.
• Metadata: Use metadata for each and every multi-structured digital asset for faster search and retrieval and steering clear from generating a data swamp.
• Learning culture: Build an organizational culture of learning and institutionalize the right skill sets to avoid falling into the trap of over-dependence on programmers.
•  Governance & monitoring: Constantly monitor the data sets that get built up over time and delete the ones that are not used for over two years.

As global firms reorient themselves towards using unstructured and multi-structured data, Data Lakes solutions are becoming the obvious choices over data warehouses. However, you have to consciously avoid creating a data swamp by focusing on self-service analytics, metadata tagging, building a learning culture, along constant monitoring of the data sets.