What is the Future of Business Intelligence?
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The Business Intelligence industry has come a long way since its inception. From its infancy in the 19th century where data-driven decisions replaced hunches, to on-premises databases and challenging IT reporting projects that only specialists could translate in the 20th century, to the current self-service models we know and use today, business intelligence technology, applications, and trends are only becoming more and more accessible.
That is exactly what the next generation of business intelligence industry trends promises to be: accessible, approachable, customizable, and conversational. The future of Business Intelligence is proactive - information will come to you before you know to ask for it, prompting you to ask questions you never thought to ask, and revealing insights we never knew we needed. Complex IT projects and clunky, outdated dashboards will be replaced by automated, beautiful data storytelling visualizations.
The future of BI promises intelligent, unbiased, fair, transparent, and accountable Artificial Intelligence and Machine Learning that we can trust to illuminate the potential our data holds without doing harm. The next generation of BI will empower every person throughout an organization to truly understand and harness the power of data to make better business decisions in an intelligent, ethical way. Read on to see what that looks like.
Self-service BI is great. Automated BI is even better. The future of business intelligence will see greater levels of automation with augmented analytics. Machine learning combined with Natural Language Processing (NLP) promises to automate the business analysis processes typically performed by data scientists and specialists.
With augmented data analytics, Artificial Intelligence will become a standard in analytics and BI processes, providing users with a powerful, streamlined workflow that improves data discovery, data preparation, data ingestion, BI platform interactions, and our overall understanding of correlations in data.
The processes of cleaning up data and integrating data from different sources, tasks once relegated to humans, will be taken over by AI components with greater frequency. The bulk of analysts’ time is spent preparing data and performing data quality checks, so allowing AI to manage these tasks frees up an enormous amount of time for analysts to actually analyze the data leading to better business decisions.
Augmented analytics dashboards for business intelligence provide a platform for data storytelling that is actionable, automated, embeddable, flexible, contextual, visually stunning, and easy for the average user to interface with.
The next generation of BI systems will be able to handle any amount of real-time data, from any source, and understand the difference between datasets, how they interact, and how best to query them. Advanced machine learning algorithms are capable of data visualization that reveals hidden relationships in enormous amounts of data without bias, providing users with insights on questions they hadn’t even thought to ask.
Natural Language Processing
The future of BI tools will feel like having a chat with a virtual assistant. Intelligent augmented analytics BI systems will provide a conversational approach to all your business data. Data and analytics researchers at Ventana Research predict that a third of organizations will deploy conversational experiences, such as chat bots, as a standard BI system capability by 2022.
NLP components are able to read and understand the data once it is ingested, draw and present conclusions to the user, who can then query the system by asking questions in plain English via voice or chat interface.
BI systems will be like an always-on, immersive, virtual intelligent assistant, accessible from a wide array of devices, that can walk you through your data, explain conclusions, and help you derive greater insights, faster and easier than ever. Automated alerts instantly update you on any changes before you have to ask, so that you can respond in real-time. The practice of passively receiving information is known as data proactivity.
The goal is to transform numbers into narrative, data into stories, in a language that anyone can understand, so that anyone in the business hierarchy can make better business decisions.
Sharing is caring. Business intelligence future trends will include more of a social aspect. Business insights are only as useful as our ability to quickly share and act on them. Future BI platforms will have capabilities similar to those found on social media networks, such as stories, tagging, notes, instant notifications, and sharing insights and data visualizations with anyone on any device within the organization.
This aspect of BI systems will foster collaboration and encourage ordinary users (not just data scientists) to adopt analytic technologies. The more business users you have on board actually using analytics technologies and understanding their output, the better your business decisions will be.
Emerging trends in business intelligence implementation include powerful cognition engines. Data sets are expanding rapidly with no signs of slowing, and data cognition engines promise to help manage the data deluge. Cognition engines are not brand new, per say, however the use of these engines for business intelligence purposes is only set to grow as big data expands.
Cognition engines can compress terabytes of data into a model that takes up less than five megabytes per terabyte, which will be an invaluable attribute as data sets continue to climb into the terabyte territory.
Cognition Engines are a collection of machine learning algorithms that analyzes transaction-based performance data across application topologies. These engines attempt to mimic the human brain, with built-in context, instant analytical responses to queries, early detection of anomalies and issues, fast root cause detection and resolution, interactive visual data exploration, and relevant and precise alerts, i.e. Machine Learning will understand your business so well based on historical data, that it will know what’s normal for your organization, and know when to bother you or not.
As Artificial Intelligence becomes a mainstay of our daily lives, we are no longer content to simply accept the results of the black boxes that are Machine Learning models. We want to inspect the decision making process and understand how these models work, how they make decisions, and why. This is the premise of Auditable AI, or Explainable AI.
Explainable AI refers to the methods and processes that help humans better understand the expected impact of a ML model, and help categorize a ML model’s prediction accuracy, fairness, outcomes, potential biases, and transparency. Humans are responsible for retracing the steps an algorithm took to arrive at a specific output, so that we can explain, question, and even challenge the outcome.
In order to build trust in stakeholders, especially in high-risk arenas like Finance and Healthcare, it is crucial to ensure that AI-driven business decisions are accountable, transparent, and trustworthy. Business leaders and decision makers are being held accountable for the behavior of their ML models and are expected to have answers if the results are biased and unfair.
Explainable AI also helps ensure compliance with company policies, and government regulations, which often require detailed information regarding the logic involved in automated decision-making tools.
An important component of proactive BI solutions is data security. Proactive threat detection will become a standard BI system capability, enabling users to identify pre-incident indicators, assess risk, and mitigate potential threats before they harm your organization. The larger and more complex a data landscape is, the more points and opportunities there for attacks.
The next generation of BI systems will incorporate threat intelligence software that uses the same calibre of augmented analytics, delivering critical threat intelligence before you know to ask for it. Authorized team members will be able to access critical intelligence, anytime and anywhere, with automated, instant notifications for real-time responses. Business threat intelligence and predictive analytics will help anticipate both inside and outside threats before any damage or loss occurs
Composability is a system design principle that deals with the inter-relationships of components. Composable data and analytics uses components from multiple data, analytics, and AI solutions to create an agile, consumer-focused experience using existing analytics assets. This flexible, user-friendly environment makes it easy for users to connect data insights to business actions.
Analytics capabilities are becoming more modular, and composable data and analytics initiatives are introducing new ways of packaging data as part of a service or product, enabling businesses to craft the building blocks to create a tailored analytics experience. In some cases microservices or cloud-based tools, rather than clunky monolithic applications, add agility to help cope with increasingly complex and unpredictable business needs, help users to derive greater business value faster.
What’s the end result?
The future of BI is inclusive and analytical. The success of an organization will depend on greater adoption of these technologies by the average user. Every member of an organization will be able to apply analytics to any business decision and make data-driven decisions.
No longer will only specialists be tasked with hunting down relevant information. The information will come to us, and average users will be empowered to collaborate and make informed decisions. The future of BI is bright.