Automated data integration can help you jumpstart your machine learning efforts. Learn about the modern data science stack. It’s an oft-cited factoid that data scientists spend only 20% of their time doing actual data science work, while the rest of their time is spent on doing what is often delightfully referred to as “data munging” — that is, obtaining, cleaning, and preparing data for analysis.
Timely, accurate and trusted data has never been more important than it is now during this pandemic. Since late summer, many areas across the UK have had more stringent restrictions imposed to reflect the growing number of cases. Similarly, Test and Trace uses information on who we’ve been in contact with to provide guidance for when we should self-isolate, which in turn helps us personally manage the risk to those around us.
For decades, the analytics/BI community has suffered from low user adoption (~30%). Dashboards and fancy visualizations have only proven to be the starting point of the analytics journey, not the endpoint. We are living in a world that demands far greater agility than a fixed layer of information or KPI can provide. Poor user adoption is the result when the analytics system fails to support the users full journey: from data – to insight – to action.
When it comes to cloud, being an early adopter does not necessarily put you ahead of the game. I know of companies that have been perpetually “doing cloud” for 10 years, but very few that have “done cloud” in a way that democratises and makes data accessible, with minimal pain points. Cloud is an enabler. It makes it easier to collect, analyse, and disseminate information.
The COVID-19 pandemic has shone a bright light on the need for flexible and engaging leadership. Most of the recent leadership-themed articles focus on how to bridge the remote-only workplace relationship gap, the importance of increased engagement and empathy, and the need to provide technology that empowers and provides a more equitable work/life balance.
