What is data literacy and why is it important

Everybody that works with data, and that makes decision based on data, needs to have an understanding of the underlying, fundamental data management competencies – such as data governance, data quality and data modelling – that allow them to assess whether data is fit for purpose.

Streaming analytics is key to leveraging value from M2M and IoT data

The constant and relentless nature of ‘always-on’ data generation also makes real time analytics even more important. Much of this data is only relevant now, in the moment, and once it is historical it no longer has worth. It must be analysed immediately and then deleted, otherwise no value can be gained.

5 essential characteristics for a great data analyst

A few week’s back I wrote about the data translator – a role on the data science team that bridges the gap between the data scientist and the business. That got me thinking – what are the characteristics (that may be less obvious) that make for a good data analyst? 1. Intellectual curiosity and focus…

Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models

Poor data quality is the single biggest contributor to the poor performance of customer risk-rating models. Incorrect know-your-customer (KYC) information, missing information on company suppliers, and erroneous business descriptions impair the effectiveness of screening tools and needlessly raise the workload of investigation teams. In many institutions, over half the cases reviewed have been labeled high risk simply due to poor data quality – McKinsey