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Analytics

Part 1: How machine learning, AI and automation could break the BI adoption barrier

Can we fix the plague in analytics with AI? Every Business Intelligence (BI) and analytics vendor is integrating a form of artificial intelligence (AI), machine learning algorithm (ML), and natural language generation (NLG) into their products. 'Augmented analytics', is the hot new topic and full of hype right now, but can it fix the fundamental flaw that has plagued BI tools for decades - adoption?

Why going global was the best thing we did

I often get asked why Yellowfin decided to go global. While the Australian market is relatively large, it’s also quite risk averse, which makes it a challenging market to sell into. While VC-backed software vendors have the benefit of selling to others within their VC family, as a bootstrapped startup we had to forge legitimate markets outside of Australia. We knew that we had to spread our wings to grow and expanding overseas quickly gave us the opportunity to sell faster.

Don't blame your people for not being data-driven, blame your technology

Recently, I read why companies are failing in their efforts to become data-driven in the Harvard Business Review. It said that 72% of Chief Data Officers believe their organization doesn't have a data culture and 92.5% of them blame their people. But I think they’re wrong. The real issue is that people aren’t using the tools that their CDOs have bought for them. That means the problem isn’t with the users, it’s with the technology they’ve been given.

Making Sense of the 2019 Gartner Magic Quadrant for Data Quality Tools

By now, you know that data is the lifeblood of digital transformation. But the true digital leaders have taken a step beyond by starting to understand the need to preserve this lifeblood with people, process and tools. That’s why data quality is so important in its ability to take control of the health of your data assets from diagnostic to treatment and monitoring with whistleblowers.