What Is Data Analytics?What Is Data Analytics?What Is Data Analytics?

More than ever, businesses and organizations have an astonishing amount of data at their fingertips. Sales and customer information, employee demographics, financial information, website and social media traffic — all of this information and more translates into data that a business can use to inform its operations and growth. For that data to become actionable and useful information, it must first be collected, sorted, and analyzed. That’s where data analytics comes in.

Data analytics is being heralded as the “next big thing” for businesses. Simply put, it’s the science of drawing insights from sources of raw information such as those mentioned above. When applied in a business environment, it can help drive growth and predict potential opportunities. Examples of how data analytics can be used include promoting business growth and operational efficiency, enabling informed decision-making, and predicting trends.

The ultimate metric of the impact of data analytics is its effect on a business’s growth — whether in sales, company size, or market share. Data analytics enables a business to analyze all relevant data rather than small sample sizes, which in turn can lead to the discovery of previously unknown correlations. For example, 91.7% of Fortune 1000 companies indicated in a 2019 survey that investing in data analytics was necessary to remain competitive. The same survey indicated that 91.6% of those companies planned on making such investments.

Data analytics helps leaders make decisions that will affect everything from a business’s goals to its strategy and operations. Data-driven decision-making enables a business to choose a way forward based on facts and evidence, rather than opinion. An MIT study found that data-driven companies had 4% higher productivity and 6% higher profits than average. Another important element of data analytics is real-time analysis, which enables a company to be nimble in its decision-making and pivot strategies when necessary. By decreasing the time it takes to make decisions, a business can better serve its customers.

Predictive analytics uses current and historical data to forecast future trends, activities, opportunities, and potential problems. This analysis uses raw data, statistical algorithms, and machine learning techniques to best predict what may happen in the future. This type of data analytics can help businesses in a variety of ways, from marketing and inventory management to risk analysis and cybersecurity.

For example, by looking at what happened to sales for an outdoor clothing retailer the last time there was a cold snap, that retailer can then reasonably predict what may happen the next time freezing weather hits. A motor vehicle insurance company might analyze a driver’s historical driving record in conjunction with broader historical demographic data, such as age and gender, when determining the cost of an insurance policy. Those working in cybersecurity may use predictive analytics to spot abnormalities in a network that may indicate something suspicious.

Predictive analytics is a rapidly growing segment of data analytics for businesses, as evidenced by the findings of a recent study by Reportlinker.com. According to the study, the global predictive analytics market is projected to grow by 22.1% between 2018 and 2024. It’s clear that many decision-makers are no longer asking, “What is data analytics?” Rather, they’re asking, “How can I make data analytics work for my business?”

Data analytics is key to helping businesses understand their customers. For example, it can be used to check products for performance trends and can help companies understand which of their products best align with various consumer demographics. All of this can lead to increased efficiency and wider profit margins.

Data analytics can help businesses gain real-time insights into their sales and marketing efforts. In addition to assisting in predicting future sales trends, it can help organizations be more productive and efficient. Data analysis can also help organizations build stronger connections with their customers, which can help improve revenue and customer retention rates.

Companies can use data to improve the customer experience across digital and brick-and-mortar interactions. The impact of data analytics can be seen in three main areas: improving customer engagement, maintaining customer loyalty, and optimizing marketing efforts.

By analyzing consumer data, a business can learn who its customers are, where they’re located, what they’re interested in, and the best ways to reach them. Retaining customers is made easier by analyzing the factors that impact loyalty. Marketers and advertisers can take advantage of big data to determine the best time to serve ads or the most effective channels for specific products. What’s more, these insights can remain consistently up-to-date as the data is analyzed in real time.

Data analytics can also help a company with something called customer churn, which is when a customer ends their relationship with a business. A churn prediction model can help determine which customers are likely to cancel their services or stop doing business with the company, enabling the company to then work to correct any problems and increase customer satisfaction.

Data analytics can help a business personalize its interactions with customers. A chatbot that appears on a web page is a good example of this personalization. Chatbots are built based on historical website data to answer the most common and likely questions and concerns a visitor might have. Amazon’s purchase recommendation engine is another great example of personalization based on data analytics. The online retailer analyzes similar purchases to determine the most likely products a customer may be interested in buying, enabling it to deliver personalized and relevant options to each individual customer. Whether across advertising or customer service, data analytics can help businesses adopt this trend.

The U.S. Bureau of Labor Statistics (BLS) classifies data analysts under the category of operations research analysts. According to the BLS, employment in this field is projected to grow by 26% between 2018 and 2028, which is much faster than the average occupation. Job growth in this field will be driven by companies’ demand for technology that can help them increase efficiency and decrease costs. The BLS further states that as of May 2019, the median annual salary for professionals in this field was $84,810, although earners in the top 10% reported salaries in excess of $140,790.

As you can see, data analytics spans a wide variety of applications across multiple industries. Jobs in the field are growing rapidly, and the demand for great data analytics professionals will only grow with the amount of data we have at our fingertips. As such, professionals who aspire to compete for jobs in this fast-growing field will be well served by completing an advanced education.

Maryville University’s online Master of Data Analytics program offers a comprehensive curriculum that drills down into such topics as data analytics, warehousing, mining, visualization, and beyond. It also helps prepare students to sit for their Microsoft SQL and AZURE certification exams.

Are you ready to pursue a career in data analytics? Discover how the online Master of Data Analytics program at Maryville University can help you achieve your professional goals.

Recommended Reading

Is a Data Analytics Degree Worth It?

Exploring the Current Use of Data Analytics in Digital Marketing

The Future of Big Data in Business: Using Data Analytics to Provide Insight

Sources

Business Partner Magazine, “5 Reasons Why Data Analysis is Important for Every Business”

CIO, “What is Predictive Analytics? Transforming Data into Future Insights”

Datapine, “Data Driven  Decision Making in Business”

Entrepreneur, “Why Data Analytics is Important”

Harvard Business Review, “How Companies Say They’re Using Big Data”

Investopedia, “Data Analytics”

Predictive Analytics Today, “What is Predictive Analytics?”

Reportlinker, “Predictive analytics market: Global Industry Analysis, Trends, Market Size, and Forecasts up to 2024”

Softweb Solutions, “What Hides in Your Data- Potential to Change the Business Paradigm”

Tech Target Network, Data Analytics

U.S. Bureau of Labor Statistics, “Operations Research Analysts”

ZDNet, “Fortune 1000 to ‘urgently’ invest in Big Data, AI in 2019 in fear of digital rivals