Big Data Can Mean Big Problems
Businesses need to capture new types of data from new sources to explore and generate new insights that put them ahead in today’s constantly shifting markets. But how can organizations transcend traditional data storage techniques to capture and leverage that data?
The status quo presents distinct challenges:
- Storing large bodies of information creates large costs.
- Data that is trapped in silos across the business only offers partial perspectives.
- Analyzing data requires manual activities that are labor-intensive and time-consuming.
Contact Prolifics to discuss how our approach to Data Lake can help you manage vast amounts of disparate data types.
Dive into the Data Lake
A data lake is the next step forward in data storage and analytics. It draws all types of data—structured and unstructured alike—from all sorts of sources and collects it into a centralized repository in an untransformed state.
This raw data can be easily found, retrieved, and rendered fit for purpose using intelligent metadata tagging. Analytics experts can use automated tools to discover meaningful patterns, relationships, and valuable business insights that are drawn from the full body of the organization’s gathered data.
The agility of the data lake makes data capture easy. It is a technologically feasible and highly scalable way to meet today’s big data challenges at a more competitive cost than classic data warehouse approaches. With the right data lake strategy, businesses can track data lineage throughout the system and open the door to new analytics initiatives.
Suit Up with Prolifics’ Expertise
When it comes to the data lake, the true challenge isn’t in creating it—it lies in taking advantage of the possibilities the data lake creates. With vast quantities of data streaming in, it can be difficult to find meaning in the mass.
Prolifics helps position your business for success when it comes to implementing a data lake strategy. Our data management and analytics experts can help your data lake initiative:
- Data Lake Security: Keeping data secure and controlling data access
- Data Lake Governance: Assuring regulatory compliance with data governance and lineage traceability
- Data Lake Integration: Collecting structured and unstructured data from disparate sources
- Data Lake Analytics: Leveraging predictive and cognitive analytics to extract meaning from your data lake.