Business analytics intelligence prediction number one: After the hype, the rise of explainable AI
AI promises to enhance human understanding by automating decision-making. But, as organisations rely on AI and machine learning for data-driven decision-making, we’re seeing a rise in human hesitation about the trustworthiness of model-driven recommendations. Many machine learning applications don’t offer a transparent way to see the algorithms or logic behind decisions and recommendations. This need for transparency will drive growth of explainable AI in 2019. If you can question humans, why not have the same option with machine learning when making decisions?
Business leaders will put greater pressure on data science teams to use models that are more explainable and reveal how models are constructed. AI has to be trusted to make the strongest impact, and the generated conclusions must be intelligible, simple and dynamically answer questions to help humans understand their data.
Explainable AI : The margins of accountability
Business analytics intelligence prediction number two: Natural language humanises data analytics
Natural language processing (NLP) helps computers understand the meaning of human language. BI vendors will incorporate natural language into their platforms, offering a natural language interface to visualisations. At the same time, natural language is evolving to support analytical conversation—defined as a human having a conversation with the system about their data. The system leverages context within the conversation to understand the user’s intent behind a query and further the dialogue, creating a more natural, conversational experience. That means when a person has a follow-up question of their data, they don’t have to rephrase the question to dig deeper or clarify an ambiguity. Natural language will be a paradigm shift in how people ask questions of their data. When people can interact with a visualisation as they would a person, it allows more people of all skill sets to ask deeper questions of their data. As natural language evolves within the BI industry, it will break down barriers to analytics adoption and help transform workplaces into data-driven, self-service operations.
Business analytics intelligence prediction number three: Actionable analytics put data into context
Data workers need to access their data and take action—all in the same workflow. In 2019, expect more organisations to use data analytics exactly where it’s needed and not in isolation. Organisations will truly reap the benefits of how BI platform vendors are offering capabilities like mobile analytics, embedded analytics, dashboard extensions, and APIs. Embedded analytics puts data and insights where people are already working so they don’t have to navigate to another application or shared server, while dashboard extensions bring access to other systems right into the dashboard. And mobile analytics put data directly into the hands of people in the field. These advancements are equally powerful as they meet the needs of different business teams and verticals by empowering new audiences with on-demand data in context.
Organisations across the globe lack data analytics maturity, says study
Business analytics intelligence prediction number four: Enterprises get smarter about analytics
Business intelligence initiatives often have a well-defined start and end date and it’s not uncommon for them to be considered “complete” after they are rolled out to users. But merely providing access to business intelligence solutions isn’t the same as adoption. Chief data officers, primarily, are re-evaluating how BI adoption plays a part in a strategic shift towards modernisation, because true value isn’t measured by the solution you deploy, but how your workforce uses the solution to impact the business. The assumption that everyone is getting value out of a BI platform just because they have access to it can actually be an inhibitor to real progress with analytics.
As these internal communities on-board workers onto a BI platform, organisations can start to delegate analytical responsibilities and create new user champions. This will ultimately reduce the heavy lifting for maintenance and reporting, traditionally reserved for IT. More internal champions will start to emerge, acting as subject matter experts who socialise best practices and align people on data definitions. Inevitably, all of these movements will lead to more people using and getting value out of BI software. And most importantly, your workforce will become more efficient and your organisation more competitive.
Business analytics intelligence prediction number five: Accelerated cloud data migration fuels modern BI adoption
When modernising your data strategy, you must think about where data is stored. For many companies, this means considering moving data to the cloud because of added flexibility and scalability at a lower total cost of ownership. The cloud also makes it easier to capture and integrate different data types. Moving to the cloud increases agility and it recasts the possibilities around what you could do with BI and analytics. The concept of modernisation naturally follows. The concept of data gravity suggests that services and applications are pulled in the direction of where the data resides. So as data moves to the cloud at an accelerated rate, analytics will naturally follow. This is causing leaders to shift from traditional to modern BI, assessing whether or not their chosen BI platform will support a move to full-cloud analytics. Not every company is ready for this move, but many are experimenting with hybrid solutions to take advantage of diverse data sources and the benefits of the cloud.
James Eiloart is SVP of EMEA at Tableau
