The top business intelligence trends for 2019
The new year is underway, and the search for the ‘next big thing’ to help increase a business’s
chance of standing out against the competition in 2019 is paramount.
|According to Gartner’s IT Glossary, Business Intelligence (B.I) is often defined as “an
umbrella term that includes the applications, infrastructure and tools, and best practices that
enable access to and analysis of information to improve and optimize decisions and
B.I models are essentially data-driven ‘Decision Support Systems’(DSS) that are used with the
intention of better supporting a business. With B.I, visual data discovery, and business analytics
solutions are continuing to evolve, which in turn is enabling non-technical users to interact with
more types of data.
As a result, SavoyStewart.co.uk pulled highlights from the report ‘2019 Business
Intelligence Trends’ by Tableau, to reveal the major trends that will be shaping Business
Intelligence in 2019.
1. The rise of explainable A. I
The need for transparency has led to the growth of explainable Artificial Intelligence (A.I) - the
practice of understanding and presenting transparent views into machine learning models.
Explainable A.I enables users to ask follow up questions as to why a model recommends
something and what it would say if the inputs were different. According to Gartner Research,
85% of CIOs will be piloting A.I programmes to buy and build outsource efforts by 2020.
Companies have embraced the value of A.I but for it to make a disruptive impact it must be
trusted, presenting valid conclusions that will help humans better understand their data.
2. Natural language humanises your data
Natural Language Processing (NLP) amalgamates computer sciences and linguistics to allow
computers to better comprehend the meaning behind human language. With the natural
language generation market estimated to grow to $825.3 million by 2023, B.I vendors are
offering a natural language interface to visualisations.
This enables users to interact with their data. Understanding user context creates a more natural
dialogue, which can lead to analytical conversations between the user and the system about their
3. Actionable analytics put data in context
B.I platforms are evolving to place data where people want to take subsequent action. For
example, data workers need their data and actions in the same place, rather than performing an
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analysis and actioning it elsewhere. B.I platforms are closing this gap by merging with core
business operations, workflows and processes such as analytics and dashboard extensions.
Actionable analytics are utilising the decision-making processes for both technical and non-
technical roles, allowing data workers to analyse their data and take an action after finding an
insight in the same place.
4. Codes of ethics catch up to data
In light of GDPR regulations in the EU, leaders have begun assessing the future of ethical data
practices. The topic of data privacy has risen in prominence and consumers are more conscious
than ever about sharing their personal data.
This in turn is affecting how businesses approach data monetisation, as well as collection of data
and the sharing of sensitive information. As data continues to proliferate in every area of
business, companies are starting to evaluate how to best ensure compliance, as well as their own
internal moral code of ethics.
5. Accelerated cloud data migration fuels modern B.I adoption
Data gravity is resulting in more businesses moving to the cloud at an increased rate, driving
businesses to re-think their data strategy. Essentially this means services and applications are
pulled in the direction of where the data can be found.
The cloud makes it easier for companies to capture and integrate different types of data. As more
companies are highlighting the benefits of moving to the cloud, data gravity is also pushing
analytical processes to the cloud.
6. Data story telling is the new language of corporations
Data story telling will continue to permeate workplace culture and shape how organisations use
data to engage, inform and test ideas. Often defined as using data visualisation as a language to
convey information in a way that is actionable and easy to understand, data story telling is
essentially sharing the steps they took to discover the insights of data.
As organisations create cultures of analytics, data story telling methods are more about nurturing
a conversation about the data and not so much about arguing for a singular conclusion.
7. Enterprises get smarter about analytics adoption
Chief data officers are beginning to re-evaluate how B.I adoption plays a part in a strategy
towards modernisation within their businesses. The report defines its true value as not “measured
by a solution you deploy, but how your workplace uses the solution to impact business.”
Instead of adoption, leaders are focusing on whether data and analytics are changing the way that
decisions are mad, through the evaluation of programs that encourage engagement like internal
communities, to ensure they benefit the most from their B.I systems.
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