At the dawn of 2020, the amount of data created globally was estimated to be 44 zettabytes - a figure only expected to grow. According to World Economic Forum estimates, by 2025, there will be 463 exabytes of data created daily - that's equivalent to 212,765,957 DVDs. That's a lot of data! Data can be a powerful tool and is used in business, fraud detection, healthcare, marketing, and advertising, gaming, augmented reality, and many other industries and applications. 

Data can help improve business processes, find potential customers and keep track of existing customers, monitor competitors, target advertising, and innovate. But collecting data isn't enough. For it to be powerful, data must be analyzed and then strategically applied. Unfortunately, that's where most businesses are struggling. According to IDC's Digital Universe Study from 2012, only 0.5% of data was analyzed, and many recent estimates are not much higher.  

To remedy this very large gap came the proliferation of business analytics or business intelligence - the process of analyzing data and pulling meaningful, actionable insights from it. Companies that use business analytics experience improved efficiency and productivity, faster, more effective decision making, better financial performance, and identification and creation of new product and service revenue. With more businesses experiencing the benefits of business intelligence, more companies are adopting analytics strategies. According to a 2018 MicroStrategy study, 71% of global enterprises planned to increase their investments in analytics over three years.  

Why Do Businesses Need a Business Analytics Strategy 

According to the International Institute for Analytics, businesses using data see an estimated $430 billion in productivity benefits over competitors who are not using data. Yet, 95% of businesses cite the need to manage unstructured data as a problem for their business. Statistics like these and all the other buzz around big data have had businesses of all sizes rushing to hire data analysts and implement data software. While that's important, businesses must have a business analytics strategy first and foremost. Having a clear strategy is vital when you consider the tremendous amounts of data being collected. 

Here are three questions businesses can ask to begin creating a big data strategy: 

1. What Do We Want to Know? Whether you already collect loads of data or none at all, it's important first to examine what information would be truly valuable (i.e., solve a problem, make a process more efficient, improve customer experience). Most businesses collect four kinds of data: personal data, engagement data, behavioral or historical data, and/or attitudinal data. Asking what you want to know is a good starting point if you're just beginning to collect data. If there's a lot of data just waiting to be analyzed, this can help you prioritize and sort through it. 

2. How Will We Collect the Data? Businesses collect data in many ways from many sources. There are three common methods: directly asking customers, indirectly tracking customers, and/or attaching other sources of customer data to their own. Many companies are now using artificial intelligence (AI) and other technologies to collect data from even more sources. Deciding how and where you'll collect data may mean purchasing software or technology infrastructure. 

3. How Will We Analyze the Data? There's far too much data available for business analytics professionals alone to sift through. Machine learning algorithms and other forms of AI can sift through large amounts of data 24/7/365 and don't even need a lunch break. These technologies are making it easier for businesses to make sense of the data and put it to good use. Analysis methods are commonly broken into several categories: descriptive analytics, which looks at historical data to identify trends and patterns; diagnostic analytics, which looks at historical data to determine why something has happened; predictive analytics, which uses statistics to forecast future outcomes; and prescriptive analytics, which uses tests to determine outcomes of a specific scenario. 

The answers to these three questions will guide the creation and implementation of your data strategy. They can also be referred to at any time and used when necessary to update or redesign your strategy. Other factors businesses need to focus on when creating a strategy are technology requirements, hiring needs, and data governance.  

Business Analytics Career 

Because the business world is so data-driven, big data, data mining, predictive analytics, data analytics, and so on are becoming increasingly more important. Business leaders want to hire professionals who are data literate to help their companies take advantage of data and business analytics, make informed decisions and drive data-driven change. Business analytics professionals can work in many different departments and industries. 

Careers in business analytics include: 

  • Data Business Analyst 
  • Data Scientist 
  • Quantitative Analyst
  • Business Analyst or Consultant  

Business analytics professionals report having a higher than average median base salary ranging from $100,000-$120,000. While a bachelor's degree is required, professionals with higher levels of education and training often have an easier time finding high-level jobs and earn a higher income.  

In partnership with Arizona State University's W. P. Carey School of Business, Thunderbird is proud to offer a three-course, online Business Analytics certificate program. The program is designed for data handlers, managers, and emerging leaders who seek to advance their careers through professional growth in the increasingly high-demand landscape of data analytics. 

Participants in the Business Analytics program learn: 

  • How firms compete with data analytics
  • How to derive value from data, lead data-driven analyses and create a business advantage
  • How to deepen quantitative and analytical skills
  • How to interpret and leverage the data within their organization to uncover trends, build predictive analysis models and get closer to the customer to increase profitability. 

Learn more and register: 

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