Data quality

Tackling machine learning enemy #1, poor data quality –  an interview with Sahar Changuel, PhD

Data quality is a business problem, as well as a tech problem. It is the biggest enemy of data-driven business and machine learning. Bad quality data can block or render a data project or machine learning use case unusable and thus a waste of money, human resources and time. Tackling data quality needs to be a targeted, systemic and ongoing, rather than a huge, one time cathartic event.

Tech debt sloth breeds a culture of sloppy operations – An interview with Daniele Marmiroli, PhD

Tech debt is often unavoidable in most early stage startups. Not fixing the tech debt as a company gets traction and scales is more of a problem than the original creation of the tech debt. Turning a blind eye to tech debt has implications beyond the stack and creates an unstructured and sloppy culture.

Data governance starts at both the C-Suite and metadata level of your organization- An interview with Laurent Dresse

Facilitating the interface between IT and business is data governance, which is filled with opportunity. There is no specific career path into data governance, but the ability to understand metadata and contextualize organizational insights to executives holds much opportunity.