Today’s data-driven companies have a choice to make – where do we store our data? As the move to the cloud continues to be a driving factor, the choice becomes either the data warehouse (Snowflake et al) or the data lake (AWS S3 et al). There are pro’s and con’s for each approach. While the data warehouse will give you strong data management with analytics, they don’t do well with semi-structured and unstructured data with tightly coupled storage and compute, not to mention expensive vendor lock-in. On the other hand, data lakes allow you to store all kinds of data and are extremely affordable, but they’re only meant for storage and by themselves provide no direct value to an organization.
Data privacy regulation is bigger than just GDPR. Other countries and jurisdictions are enacting their own versions of the data privacy regulation, each with subtle nuances - such as the California Consumer Privacy Act (CCPA), Lei Geral de Proteção de Dados (LGPD), and more - and that’s on top of existing privacy regulations. Moreover, consumers increasingly expect more protection for their sensitive information. A recent IBM-Harris poll of 10,000 individuals revealed that 75% of consumers won’t buy from companies they don’t trust no matter how great their product or service.
PrecisionProfile – a bioinformatics technology company – focuses on enabling oncologists and research scientists to rapidly analyze genomic profiles and create personalized treatment plans for cancer patients. Like most organizations, PrecisionProfile struggled with the most time-consuming part of every analytics exercise - combing, cleaning, and shaping data into actionable information. With self-service data preparation, they were able to design and develop a platform to accelerate data pipelines, enabling scientists to spend more time analyzing data and formulating how they can leverage it to save lives. View this webcast to learn: Empower researchers and oncologists to spend a fraction of their time restructuring data. Reduced cycle time of a genomic clinical study from 1-3 months to 2-8 hours.
Learn how a modern data warehouse increases data accessibility and provides seamless data analytics for Petabyte scale data.