Organizations are becoming increasingly frustrated with the speed at which Data Science and Business Analytics teams can provide answers to their most pressing questions and the high cost incurred for only small incremental increases in revenue. Data Science and Business Analytics teams are under increasing pressure to deliver more and faster as a result, while maintaining accuracy of results and ethical standards, particularly in relation to data privacy. The root of the frustration lies in the typical data sourcing and preparation processes that Data Scientists and Business Analysts follow. These processes are manual, repeated for every question being answered or problem solved, and are centered around hand-crafted scripts for preparing and transforming data, at worst manual manipulation of data, famously resulting in data preparation steps taking 80% of the time needed to answer a question. The manual and repetitive nature of these ‘data wrangling’ processes introduce considerable risk of human error and no clear data lineage, guarantee of quality, which raises the question ‘Are you happy to bet your business on unqualified insight?’.

The Responsible Enterprise Analytics (REA) methodology tackles the shortfalls in industry-standard process by embedding technology and technique from across the Data Management (DM) domains into the analytics data preparation pipeline. The Informatica Data Engineering suite of technologies provides the data preparation technologies required to automate and centralize the expensive data preparation steps. The VIQTOR DAVIS Data Science and Business Analytics practice, armed with extensive experience implementing Informatica technologies, puts the REA methodology in action in large and complex organizations, leverage enterprise class technology like Data Catalog and technique like mastering data and implanting governance in an agile way, just enough to guarantee the answer to the question, the solution to the problem, reduce risk, reduce cost and uphold ethical standards.