Explore the list of buzzwords in analytics and business intelligence in 2021
Advances in technology are on the unprecedented rise. These developments are increasingly changing the way businesses are done, paving new paths to digital innovation. Business intelligence is one such effective technology for today’s data-driven organizations. It converts raw data into actionable information. Business intelligence interprets data and understands trends, assisting enterprises to make data-driven decisions. While the adoption of this technology is surging rapidly, the buzzwords defining different BI software techniques are multiplying every year.
Here’s is the list of top 10 analytics and business intelligence buzzwords that will rule in 2021.
Machine learning algorithms being integrated into decision-making processes have created a field of decision models, decision intelligence. It encompasses a range of decision-making techniques to design, model, align, execute and track decision models and processes. It observes, investigates, models, contextualizes and executes decision models. As the human would not be able to process large volumes of data, decision intelligence will take care of such data volumes using ML algorithms.
Data science enables better decision-making, predictive analysis and pattern discovery by using modern tools and techniques. Most businesses today hire data scientists to examine and interpret their data. However, when BI software will advance in the coming years, data science will be automated, making it far more accessible and easier to interpret.
X Analytics, a term coined by Gartner, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc. It is the ability to run any type of analytics on all of an organization’s structured and unstructured data, regardless of where data resides and in what format. Combined with AI and other techniques such as graph analytics, X analytics will play a key role in recognizing, predicting and planning for natural disasters and other business crises and opportunities in the future.
Augmented Data Management
It utilizes machine learning tools and AI techniques to optimize and improve operations. Augmented data management helps businesses secure high-quality data for real-time analytics that converts into business decisions. Traditionally, businesses’ data analytics processes are dependent on the data team for traversing the organization hierarchy to scout the right data, cleans it, model it, analyze it and derive insights. With augmented data management, companies can harness data through cross-department collaboration and accomplish various tasks.
Predictive analytics defines as the practice of excerpting information from existing data sets to predict future probabilities. It has already been used by a wide range of organizations, regardless of types and sizes. And its market is projected to reach US$10.95 billion by 2022, with a CAGR of 21% between 2016 and 2022. Predictive models can assist organizations in enticing, retaining and nurturing their most valued customers.
Self-Service Business Intelligence
Earlier, business analytics was perceived mainly as an IT project. But today, the focus has shifted more towards diverse business users such as marketing managers or sales directors. Many leading BI software vendors provide some sort of self-service reporting capabilities. But these tools can be compromised as they do not entail the complete process of data analysis. This is where self-service BI comes to the rescue, analyzing data effectively. It can be used by non-technical staff and departments within an organization.
Mobile business intelligence is becoming more incorporated into BI solutions. It refers to the ability to access BI-related data such as KPIs, business metrics and dashboards on mobile devices. Mobile BI systems can be deployed to keep pace with rivals while gaining an edge over the competition. When done properly, Mobile BI can bring business intelligence and analytics closer to the user.
Embedded analytics combines analytic content and capabilities within applications such as CRM, ERP, EHR/EMR and intranets or extranets. It helps users work smarter by integrating germane data and analytics to solve high-value business problems. It allows them to work more efficiently as these capabilities are available inside the applications used every day. Embedded analytics tends to be deployed around specific processes, including marketing campaign optimization, sales lead conversions, inventory demand planning and financial budgeting.
Collaborating business intelligence is the combination of tools, including social media and other modern technologies, with online BI tools. It enables faster and more informed decision-making, knowledge sharing, employee satisfaction, security, auditing, and stakeholder involvement. Furthermore, collaborative BI empowers business information assets thanks to cooperation and data sharing with other companies.
Cognitive computing is a trending field in cognitive science simulating human thought processes in a computerized model. As it presents the third era of computing, it interprets and processes voluminous structured and unstructured data and transforms them into valuable information. Cognitive computing’s features include: adaptive, interactive, iterative and stateful, and contextual.
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