With data such a valuable commodity today, organisations are looking to business intelligence (BI) solutions to elevate and accelerate their data-driven decisions.
Mining raw data is no longer enough. BI solutions have evolved, transforming analytics into dynamic, real-time visualisations to inform, develop and fine-tune business strategy.
According to research by BetterBuys, the main objectives for BI software adoption are: to make better decisions, improve operational efficiencies, boost revenues, and gain competitive advantage.
Companies which invest in BI software see the benefits through more satisfied employees, happier customers, more sales, and increased profit.
Here are four key trends that are driving the analytics space over the coming year:
According to Gartner, by 2020 more than 40% of data science tasks will be performed by machines. This transition to automation is already well under way – from data preparation to delivery. Vendors are keen to build an augmented analytics story, regardless of the depth of the story.
This desire to automate everything is being driven in part by the ongoing skills shortage in analytics. There is a huge demand for analytics, but there are not enough people with the skills to deliver. Businesses recognise the need to do more with less and have realised the best way to speed up the process is via automation.
Businesses need to convey information to decision-makers in a way that is actionable and easy to understand. Known as data storytelling, this is a vital part of analytics; it starts a conversation around data and places the audience at the centre.
Organisations need their data to be interpreted and contextualised and are creating specialised roles to do this. In 2019 we will see storytelling solutions that tell long-form narratives with data. And we all have a role to play. Those working with the data need to be aware of how the audience reaches a conclusion from the data visualisation, while audiences must be willing to be informed.
3. Natural language and conversations
BI uses natural language processing (NLP) interfaces to help users interact with data naturally and ask questions in the same way they would when speaking with friends and colleagues. By leveraging context within the conversation, the system can recognise the user’s intent behind a question and use it to further the dialogue and create a more natural conversation experience.
This move to more natural language represents a shift in how people ask questions of data. As we start interacting with a visualisation as we would with a person, analytics becomes accessible to more people than just data scientists. As natural language matures in BI, the user will be limited only by the scope of questions he or she asks, not their analytics skillset.
4. Mobile BI
Now bear with us on this one. Mobile BI has been dubbed the one to watch in the past, but those predictions came to nothing. The reason previous mobile BI solutions failed was because they tended to replicate the desktop experience.
That was seven years ago, this time round mobile BI will be very different. Back then, users weren’t content with a desktop experience transferred onto mobile – and that will always be the case. People want the ability to consume information on different devices in different ways and will be looking for a sophisticated mobile BI experience.
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