How to Monetize Data Assets
The volume, variety, velocity and volume – the so-called “4 V’s” of big data – threaten to overwhelm many companies. In fact, more data has been created in the past two years than in all of previous human history.
The key to generating value – the fifth and most important “V” of all – from your digital data is not simply capturing and storing it, but making the right data accessible so the right users can answer critical business questions and fulfill pressing needs.
Fortune 1000 companies that increase data accessibility by 10% typically see more than $65 million in additional income.
To manage the challenges posed by all this digital information, companies must embrace proven and emerging data management technologies and processes. No longer are traditional on-premise data warehousing platforms such as Oracle, Teradata and SQL Server the only ways to effectively manage data across the enterprise. Instead, a mix of cloud-based and on-premise technologies – such as the Hadoop stack and NoSQL environments – now work alongside traditional relational database management systems (RDBMS) and enterprise data warehouses (EDW).
The key for organizations to monetize data assets is determining and architecting the right set of tools and environments that allow for data to be accessed across their ecosystems to drive business insights and differentiated consumer experiences.
Big Data & EDW Services from Infinitive
Infinitive’s team of big data and data management pros knows how to help companies from a range of industries drive big value from big data investments. Leveraging 20+ years of experience and proven best practices, our team helps clients master all facets of data management, from data architecture and strategy, to program management, data integration and data governance.
Our specific services include:
- Big data architecture & infrastructure: strategic planning, information design and implementation of data within the Hadoop technology stack
- Relational database architecture & infrastructure: strategic planning, design and development of relational enterprise data warehouses, data marts (centralized and federated), and operational data stores using technologies such as Oracle, Teradata and SQL Server
- Non-Relational database architecture & infrastructure: strategic planning and implementation of non-relational databases using technologies such as Dynamo, Cassandra and MongoDB
- Data movement & integration: development and management of batch and real-time extract, transform and load (ETL) process both into and from data management platforms
- Data management, quality & governance: data definitions and standards, QA monitoring, data profiling, and stewardship