5 Biases of Business un-Intelligence

To effectively engage in data-driven decision making we must confront the five biases of business un-intelligence . Until the day when future BI technologies can help mitigate cognitive bias, big decisions call for a collaborative effort that harnesses data and intuition.

The top reason that the highest performing organisations had for implementing BI, according to the 2014 annual Business Intelligence Gleansight Benchmark report, is executive level demand for data-driven decisions (92%). Yet unreasonable biases can impede both analysis and intuition, especially when we're making sense of big data, or tapping into the power of business intelligence.

Fighting failure with transparency

A review of how transparency in 2014 may help address some of the causes of significant failure and incidents of fraud in 2013.

In the Anticipate Change community we dig around in emerging issues relevant to those adopting business intelligence. This post is a summary of some of the lessons learned in 2013, which we hope leads to better decision making in 2014. The focus is on failures and fraud that we noticed around the world, emphasising the need for transparency about those individuals and organisations holding power over the public.

Buy locally, my dear Watson

Investigating the potential of an artificial intelligence that could assist midsized organisations in making smarter purchases locally.

In the pursuit to save money, some of those responsible for procurement and purchasing have hopped on the bandwagon of offshoring and taken advantage of the globalisation of suppliers, neglecting their local economies. Consumers have been making similar choices, turning away from local purchasing toward buying online. What some may deem progress, others experience as draining the life out of their local High Street.

To achieve a mix of local and global, a clearer understanding of product/service's supply chain must be available, both for consumers, and those involved in business. Imagine the day when Watson, the cognitive computer that defeated humans at the quiz show 'Jeopardy' in 2011, develops a heart. Who knows, with IBM's recently announced business unit, The Watson Group, along with its $1 billion initial investment, anything may be possible in the years ahead.

Decisions in the Age of Abundant Data

A summary of bite-sized insights and views about decision making from fresh articles around the web.

Over the last several months at Cause Analytics we have been scouting the web to bring you some helpful articles from experts in business intelligence. Today, our team will summarise several insights we have collected in our Google+ community in order to save you time and help your organisation with decision making.

You probably know of the debate between speedy decisions based on intuition versus slower decisions that draw upon evidence and rational deliberation. It's a complex debate because both sides have a role to play in various contexts, but in the 'Age of Abundant Data', business intelligence can aid you on your quest for faster results. As analytics and algorithms increasingly offer practical predictions, executives and managers will be able to act upon these predictions to communicate decisions reflexively, and perhaps, intuitively.

Learning to Anticipate Change

Learning to anticipate change is a challenge in complex environments. Business intelligence and predictive analytics can help make sense of the signals.

We have all heard, "A smooth sea never made a skilful sailor". Well, for an analyst, a complex sea of data makes the skilful means of sensing evermore necessary. An analyst also needs a crew to help anticipate and navigate the changes that may arrive. These changes arise in three types of systems: simple, complicated, and complex.