Companies implement business strategies with key goals in mind: to reduce costs, improve efficiency, and strengthen profitability. Business intelligence can add tremendous value in these areas, which is why business intelligence and data analytics professionals are in high demand. A recent IBM report cites a growing need for data science and machine learning skills (40% and 17% increases, respectively).
Our Online Master of Business Analytics at Ohio University teaches tomorrow’s business intelligence leaders how to use data analytics to identify business trends, create data models, and solve complex problems.
What is business intelligence, and what role does analytics play in business strategy? Business intelligence entails the analysis of past and present data to create actionable insights for informed decision-making. The data can vary — sales performance over a period of time, customer activity, or operational costs, for example. Prescriptive analysis and predictive modeling often contribute to data analytics. Skilled professionals combine these methods and others with artificial intelligence (AI) and machine learning capabilities. As a result, businesses can find patterns in structured and unstructured data to glean key insights that may provide a competitive edge in the future. For example, a business can mine its data to identify what customers are buying certain products and at what times and, in turn, develop tactics to boost potential sales.
Filling the Gaps
Companies investing in business intelligence as part of an overall business strategy expect to see a range of benefits, including reduced costs, decision-making insight, and increased efficiency. However, only 4% of these companies are using the right mix of resources (people, tools, and data), according to a Bain & Company report.
Fundamentally, business intelligence professionals use data analytics to seek out patterns and trends in various forms of data. They then translate their findings into information that facilitates business decision-making at the executive level. But what is business intelligence without the right tools, data, and talent? Through the use of modern business intelligence tools, organizations can access more data than ever before. Organizations need talented experts to make sense of that data to support data-driven business strategies. As a result, 52% of data analytics leaders in companies are seeking to hire business professionals with advanced data skills, according to a Forrester report. The same report points to AI as a harbinger of a new era in business intelligence to fill the gaps of today’s business intelligence tools. With AI, business intelligence and data analytics professionals can work with larger data sets and retrieve insights faster.
While AI adoption is high in digitized sectors — with high tech and telecommunications leading the way, according to a recent McKinsey report — the lack of understanding of business intelligence versus advanced analytics is another gap to fill. As for what business intelligence is in relation to data analytics, the dividing line is not always clearly defined, as they are interconnected in determining business strategy. Using data analytics in business strategy is no crystal ball, but with the right technology, business intelligence and data analytics professionals can predict where improvements can be made and enact strategies to take advantage of those opportunities.
Talent plays a central role in filling these gaps. The Online Master of Business Analytics at Ohio University equips students with valuable skills in data analysis for decision-making, prescriptive analytics, and predictive analytics to prepare them for leadership roles in this growing field.
Data Analysis for Decision-Making
The ability to summarize, visualize, and manage data using software tools and techniques is important in the career market. Executives want to make informed decisions when striving to reduce risk and identify opportunities to give their organizations an edge. While today’s CEOs are typically in tune with many of the latest trends that provide competitive business advantages, they rely on business intelligence and data analytics professionals to examine and interpret data to help them make the right decisions.
The strategic use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management is central to improved decision-making in a global digital marketplace. The charge to utilize prescriptive analytics typically comes from the chief information officer or another member of the executive suite. Business intelligence managers then leverage traditional disciplines, such as statistics and operations research, as well as newer methodologies, such as data mining, digital dashboards, and online analytical processing, to meet executive demands.
Predictive analytics can provide the insights needed for executives to develop forward-thinking plans. Through predictive analytics, business intelligence professionals discover and apply relationships found within data sets from various sources, such as spreadsheets and databases. For example, they can combine data about customer buying patterns from a spreadsheet with related data from a sales tool. Then, they can build quantitative business models and leverage software tools to analyze these models and reveal new insights. Analyzing this data may then facilitate making predictions about future events.
Opportunities to Make a Difference
From business intelligence manager to machine learning engineer, business intelligence professionals can serve in different capacities. The U.S. Bureau of Labor Statistics (BLS), which includes business intelligence and similar roles under the category of management analysts, expects employment in this area to increase by 14% from 2016 to 2026. According to PayScale, the average salary for business intelligence managers is $101,059, while the salaries of machine learning engineers average $110,840.
Business intelligence professionals should possess analytical skills and knowledge in the use of reporting tools, including running database queries. For management roles in business intelligence, a master’s degree is preferred. An MBA with a robust data analytics component can provide professionals not only with the technical skills needed to succeed in a business intelligence or data analytics role but also with the business knowledge to lead teams and projects while helping to set business strategies.
According to the BLS, employment in this field is expected to continue growing, as organizations are always looking for ways to improve efficiency, control costs, and uncover revenue-enhancing opportunities. If you are considering a career as a business intelligence professional, the future is bright.
Become an expert in telling the data’s story by earning your Online Master of Business Analytics degree from Ohio University.
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Bain & Company, “The Value of Big Data: How Analytics Differentiates Winners”
CIO, “Business Intelligence vs. Business Analytics: Where BI Fits into Your Data Strategy”
Deloitte.Insights, “Automated Machine Learning and the Democratization of Insights”
Forbes, “What You Need to Know About Business Intelligence”
Forrester, “AI Unlocks the Business Intelligence in BI”
IBM, “The Quant Crunch: How the Demand for Data Science Skills Is Disrupting the Job Market”
McKinsey, “Ten Red Flags Signaling Your Analytics Program Will Fail”
PayScale, Average Business Intelligence Manager Salary
PayScale, Average Machine Learning Engineer Salary
Robert Half Technology, “2017 Salary Guide For Technology Professionals”
U.S. Bureau of Labor Statistics, Management Analysts