here is more data and more WAYS to store and use it than ever before. While this data holds business opportunity, corporate data landscapes are growing increasingly complex, and it is getting harder and costlier for organizations to not only understand the data that they have, but to work across all the different systems that need to use it, and apply end-to-end governance, to capture the maximum value.

Key Pain Points:

• Data is kept in silos (files, HADOOP, Data Warehouses, etc.) across the enterprise. Users can’t access and work with the data they need across the silos where it’s stored. In particular, it is complex, time consuming, and costly to connect Big Data with enterprise data and business processes to gain insight and value from it.
• End-to-end data governance required across complex landscapes: The need to manage and govern data across a landscape is well understood. Ensuring data lineage and impact analysis of changes, managing security and privacy requirements, etc. are all critical aspects of a trusted enterprise landscape. With the increased complexity of enterprise landscapes, which can now include Hadoop data lakes, EDWs, Cloud storage, enterprise apps, etc., the ability to appropriately provide effective governance is more difficult. Without end-to-end governance across all data sources, organizations cannot trust and RELY on the data’s accuracy, creating risk for anyone using analytics or operational applications that use the data.
• Big Data technologies lack enterprise readiness: Businesses generally cannot solve the complexity of their landscape simply by storing all their data in a Hadoop data lake. Hadoop solutions, while powerful, often do not have the extent of governance and security measures that enterprises require. Data lakes often have limited governance for Big Data initiatives, LITTLE automation to schedule processing in the landscape, fragmented monitoring and tracing capabilities of individual technologies, and lack common security and access management.
• Currently available tools require high effort to productize data scenarios across the enterprise: Many integration tools today are point to point, require highly trained resources to execute, and are highly manual. This makes it CHALLENGING to rapidly connect and implement desired data outcomes.
• Specialized skill sets are often needed to implement, scale and create value out of Big Data initiatives. These specialized resources are often difficult to find and difficult to retain.

here is more data and more ways to store and use it than ever before. While this data holds business opportunity, corporate data landscapes are growing increasingly complex, and it is getting harder and costlier for organizations to not only understand the data that they have, but to work across all the different systems that need to use it, and apply end-to-end governance, to capture the maximum value.

Key Pain Points: