Data exhaust - logs of usage, traffic, interactions, transactions, search trends - is at such a granular level that before what was considered data exhaust is now considered data gold to help advise in micro-reactions, trends, and guidance to influence the smallest of interactive, search, algorithm, and model signals. This is Big Data.
Former ‘data exhaust’ and metadata is now fueling new data flow constructs to maintain the fidelity. We help determine new patterns and solutions to feed this knowledge-first trend.
Clients wish to move to platforms to integrate traditional MIS and data warehouse with new data pipelining architectures to feed their data-driven ambitions.
- Analytics Integration (Develop)
- Data Analytics Solutions Architecture (Lake, Warehouse, Graph, Real-time) (Design/Dev)
- Use Case Analysis and Portfolio Planning (Strategy)
- Data Hub and Lake Operations
Top 5 Challenges Big Data Face
- Big Data's first challenge is most organizations have traditional relational database and warehouse architecture approaches which need to revisit the vision, roadmap, and investment plans for the path to transform to support this new scale.
- Big Data is such a large investment that assessing the Portfolio Management needs to identify and prioritize the knowledge asks prior to investing in the data supply needs, existing data integration and readiness gaps.
- Big Data architectures require some data literacy, outreach, and sponsorship alignment prior to such an investment.
- Big Data with high velocity and geospatial require additional integration considerations.
- Big Data is intimidating to the level of data and information overload. It requires thoughtful consideration to increase the density to be of value for analytics.