If bots could learn, they would require less up-front effort in RPA deployment. Thanks to advances in applied artificial intelligence (AI) and machine learning algorithms that have the ability to detect patterns and make predictions and recommendations, bots do not have to receive precise programming instructions to adapt to changes in business processes. Bots will be able to be used to automate a far wider range of business processes than is currently possible, which could drive demand for the technology.
The key to achieving BI success by making it accessible to everyone starts with generating insights, then operationalising those insights and being able to place a monetary value on the benefits gained. The goal is to turn data into actionable insights with real business outcomes. However, there are several common mistakes organisations make when rolling out BI and analytics projects that result in their investments ending up as shelfware: unused, forgotten and representing missed opportunities.
Analytics projects fail not because the solution doesn't work, but because the business fails to realise value from its investment, or the technology is not used at all. The cost of this failure is enormous. The first step towards having analytics take its rightful place in the organisation is for data to be regarded as an asset, on par with every other asset owned by the business. There are seven key factors that can mean the difference between an analytics project succeeding, or adding to the high statistic of big data project failures.