The UNC Pembroke Master of Business Administration with a Concentration in Business Analytics Online prepares you to gather, organize and optimize data to understand trends and forecast opportunities. Businesses have never been so reliant on data and business analytics. They want maximum access to big data, and they want to know how to discover, understand and manage it for the most robust results.
This is evident in U.S. Department of Labor 2016-2026 statistics for expected job growth in business analyst positions: operations research, 27 percent; market research, 23 percent; and management, 14 percent — (all much faster than the average for all occupations).
With these opportunities in mind, aspiring business analytics professionals should keep these seven vital industry trends in mind:
Self-Service Business Analytics
Gone are the days when business users had to depend on IT to fulfill their analytics requests. Thanks to intuitive tools, users have the ability to access and analyze corporate information, all within a supported architecture and BA tools portfolio.
Business users can now modify reports and dashboards, filter and produce ad hoc reports, and study new data models. Having access to data tools any time they're needed gives executives and managers a more nimble response to shifting markets. Once the competition within an industry has this capability, self-service business analytics becomes a competitive mandate, and the "trend" is the new norm.
Data Quality Management
With the explosive growth in data sources, types and users, it is little wonder that the quality of reporting in many organizations has declined. Poor data quality, according to Gartner, costs organizations approximately $15 million a year in losses.
A Business Application Research Center survey identified data quality management (DQM) as the most prominent BA trend in 2019. Data quantity has taken a back seat to quality, as companies come to realize the latter's impact on decision-making. They are implementing advanced data curation, management and distribution practices with greater oversight, while also responding more quickly to stricter compliance regulations. To these ends, many organizations are implementing DQM policies, departments and procedures.
In conjunction with DQM, many businesses are also employing greater data governance, or the management through a governing body of the availability, usability, integrity and security of business data. Increasingly, decentralized forms of data governance balance the need for control against the need for business agility.
Data Discovery and Visual Analytics
This is the process by which software tools are used to generate findings that bring value. Data discovery involves understanding the relationship between data, visual analysis and guided advanced analytics. Understanding these concepts and having advanced visualization tools enables users to better see trends developing, to gain valuable facts and figures, and to make sound decisions.
Artificial Intelligence and Machine Learning
Imagine AI robots continuously monitoring business data through live dashboards and alerting business leaders to statistical anomalies in real time. Such technology exists, although it may not mimic the Skynet scenes from The Terminator. At least not yet. But algorithms based on advanced neural networks (as depicted in the Skynet corporate logo) already do instantly mine data without human assistance. People just set the programs to focus on certain variables and the systems do the rest — monitoring, analyzing, reporting and gleaning insight.
In environments such as manufacturing, IoT devices are another data source feeding into machine learning systems. Auto manufacturing facilities are among the most sophisticated examples of AI and machine learning in practice today.
Seamless Predictive and Prescriptive Analytics
Businesses use predictive analytics to understand current and historical data from multiple sources. From there, it can extrapolate information about customers, products and market trends (or other data) and generate statistically reliable predictive models. Far from an emerging trend, sports stadiums are using this information for ticketing; hotels are using it for booking; and retailers are using it for inventory. Now this data is being merged with prescriptive analytics to determine what decisions business leaders should make when statistical thresholds are crossed. AI can often perform this task, minimizing the need for human involvement. The emergence of predictive and prescriptive analytics introduces the beginning of automated business decisions. One can start to imagine, in our lifetime, companies being managed largely by machines.
As analytics continues to evolve at an accelerated pace, so does the software that analytics professionals use. Fortunately, cloud-based "software as a service" (SaaS) allows hassle-free updating and the ability to add features as needs change. The traditional model of buying software for on-premise use could never meet today's business analytics challenges.
Taken as a whole, these trends illuminate a business world of pervasive analytics. This bodes extremely well for anyone investing in the field's long-term future.
Learn more about UNCP's online MBA with a concentration in Data Analytics program.
Datapine: Top 10 Analytics and Business Intelligence Trends for 2019
Bureau of Labor Statistics: Management Analysts
Bureau of Labor Statistics: Market Research Analysts
Bureau of Labor Statistics: Operations Research AnalystsBI-Survey.com: Self-Service BI: An Overview
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