What Is Business Analytics? An Inside Look at the Merger of Business and STEM

Thursday, February 20, 2020

A female business analyst sits at desk in front of laptop looking at printed pie charts showing data.

Business analytics can be transformative for companies, both big and small. With data analytics’s recent popularization, businesses have more opportunities to refine their practices than ever before — analytics has completely changed the business landscape. What exactly is business analytics? How does it work, and how can businesses make the most of this powerful technology?

What is Business Analytics?

Business analytics is the practice of collecting and analyzing data to gain business insights. Business analysts use data to draw conclusions about everything from customer behavior and how to attract new customers, the efficiency of business operations, the success of marketing campaigns, and the nature of product trends. They also use data to predict future outcomes, using this information to drive business decisions. Everything from untapped revenue streams to opportunities for streamlining processes can be identified and optimized through analytics.

In short, analytics is a powerful tool that businesses can use to make operations more efficient, by solving problems and increasing productivity. Analytics can also be a game-changer for enhancing the customer experience, providing powerful insight into customer behaviors, preferences and frustrations.

Benefits of Business Analytics

Using business analytics can make a substantial impact on companies’ efficiencies, effectiveness and bottom line. Business analytics can help with everything from generating real-time reports to discovering untapped markets and establishing a data-driven culture of improvement, analytics can optimize nearly every business facet. Data pulled from current and past business operations can reveal valuable insights that help businesses take strategic action, solve problems quickly and effectively, and optimize future business plans. In addition, analytics can provide real-time feedback during a project, strategic plan or marketing campaign, enabling businesses to measure success, maximize resources and course correct quickly, if needed.

Uses of Business Analytics

Data analytics is a versatile tool for every industry, from global business and technology to education and nonprofit businesses. There are many uses for business analytics, from analyzing operational efficiencies and assessing sales and financial trends, to targeting audiences and creating data-driven business plans.

Targeting Your Audience

Businesses typically have an idea of who their ideal customer is, but this doesn’t always align with their actual customer base. For example, a business in the outdoor industry may identify solo backpackers as the target demographic for its products, but discover that the majority of its revenue comes from families looking for easy access to outdoor recreation.

While gaining any customer’s business is a good thing, understanding customer demographics can help businesses better target their efforts in the future to maximize their budget and resources. Analytics is an ideal tool for analyzing this data, combining internal customer data with social media analysis to gain insight into a brand’s reputation, customer demographics, digital ad campaign effectiveness and more. This data can be used to improve everything from product development to marketing strategy.

Monitoring Customer Retention Initiatives

Customer retention is a key business challenge for companies fighting for market share in today’s hyper-competitive landscape. Analytics can also be a valuable tool for supporting this goal, measuring and analyzing customer behavior to develop and monitor customer retention initiatives. For example, a data analyst might explore variables like customer transactions (e.g. frequency of purchase), customer demographics (e.g., age and gender), customer interactions (i.e. the number and nature of interactions with a customer service team), and customer experience or reviews. Identifying trends and patterns among this data can not only help companies gauge the current customer climate, but also predict future behaviors and outcomes.

Measuring Operational Efficiencies

By examining resource input and comparing it to the output gained from business objectives, companies can use analytics to gain valuable insights into demand, inventory, and sales forecasting. One key application of operational analytics is supply chain analysis. Through operational analytics, businesses can examine their supply chain, identifying areas with the potential to increase efficiency and reduce cost, from sourcing and manufacturing to warehousing and transportation.

Performing Supply Chain Analytics

The supply chain is a diverse and interconnected web of business processes— inventory, customer service, budgeting, and a host of other functions—that, when working together well, improve a business’s efficiency, profitability, and customer relationships. Supply chain domains include inventory, vendor relationships, sales and delivery, returns, accounts payable and receivable, budgeting, and more. What occurs in one supply-chain domain affects the rest of the domains.

Businesses have long grappled with finding ways to analyze and predict supply-chain outcomes with an eye to improving their competitiveness, efficiency, and profitability. By delving into the complexities of the supply chain, business analytics uses observations about data to make predictions that result in a proactive approach to supply-chain management.

The goals of supply-chain analytics include improving a business’s operational dynamics, devising scalability strategies, reducing costs, and identifying policy dynamics. To set these goals in motion, business analysts measure current and historical data through strategic use of innovative statistical methods, statistical software, and augmented reality.

Planning for Expansion

If a business sees an increase in its customer base, brand awareness and revenue stream, it has an opportunity to expand. As it grows, a business can use analytics to assess its current operations, for example, looking into its customer base to establish whether it has identified the best target audience or evaluating logistics scheduling, looking for costly and time-consuming processes to simplify. Analyzing data like this can provide valuable insights and identify areas of opportunity, helping businesses build a comprehensive, strategic expansion plan.

Types of Business Analytics

Analytics falls into four categories: descriptive, diagnostic, predictive and prescriptive.

Descriptive

Descriptive analytics is the simplest form of business analytics, answering questions like how many, when, where and what. Descriptive analytics aggregates and analyzes data to summarize what’s happened or what’s currently happening in a business. For example, descriptive analytics is often used to generate annual reports with details about the past year’s operations, sales and finances.

Diagnostic

Diagnostic analytics help answer why questions, analyzing past events and performance. Diagnostic analytics uses drill-down, data discovery and data mining techniques to determine how different factors can influence trends, to alert businesses to potential issues and pull useful information from past events. For example, diagnostic analytics can examine the success of a recent advertising campaign as a way to increase sales.

Predictive

Predictive analytics uses machine learning and statistical modeling to forecast trends. Predictive analytics builds upon the data collected through descriptive analytics, extrapolating it to estimate the likelihood of various future outcomes. For example, predictive analytics can suggest the effectiveness of a future ad campaign based on past campaigns

Prescriptive

Prescriptive analytics goes one step beyond predictive analytics by providing recommendations that can help optimize business outcomes. Prescriptive analytics answers what-if questions, testing variables to determine the best path forward. For example, it can be used to determine the most impactful areas to invest a surplus, testing whether investing in personnel or product development would have a better return on investment.

Develop a Successful Business Analytics Strategy

An analytics strategy can help businesses implement analytics successfully. A business needs to think through every stage of the analytics process, from initial goal setting to final analysis and data-driven change implementation.


Specify a Goal

To collect useful data, businesses need to establish a goal, for example, finding an answer to a key question about customer behavior. Analytics goals can be tailored to fit any business’s unique needs, but the best results come from focusing on one goal at a time. Once a goal has been identified, any relevant questions should be listed. For example, a business may set a goal to learn more about customer behavior, posing questions like, “When do customers buy from us,” “Why do they buy our products” and “What channel do they prefer to use to communicate with us?”

Identify and Track KPI Metrics

Once a business has generated a list of questions, it can home in on what metrics need to be measured. Modern business analytics platforms make this process simple. For example, finding the answer to the question, “When do customers buy from us?” is as easy as setting up analytics to track which hours of the day customers make purchases. The biggest mistake businesses make in tracking metrics is not focusing on a specific goal or not tracking metrics at all.

Analyze Data

After data has been collected, it needs to be analyzed. The most effective way to analyze data is through data visualizations, which consolidate complex datasets into easily digestible graphics. Charts, graphs and tables are a few of the many different data visualization models to choose from, and most business analytics tools have integrated visualization capabilities to make this process as straightforward as possible. After initial visualizations, analysts may run additional exploratory models to determine if any additional patterns can be derived from the data. Through many iterative rounds of visualizations, descriptive statistics, and exploratory modeling, analysts are able to identify the model with the highest levels of accuracy and insight.

Take Action

Collecting and analyzing data can provide valuable business insights, but the true impact of analytics comes from turning those insights into action. Businesses can use their findings to develop or refine business objectives, marketing campaigns and sales strategies, but a company-wide commitment to taking action is key. In addition, it’s important to continue tracking and analyzing data when implementing organizational changes to measure success and reevaluate strategy if necessary.

Business Analytics Trends

Business analytics is a rapidly evolving field. Experts continually push the limits of current analytics capabilities, developing powerful new tools with impressive possibilities.

Artificial Intelligence and Machine Learning

Recent improvements in artificial intelligence (AI) have presented huge benefits to business analytics. As AI becomes smarter and more capable, it can be used to analyze complex datasets, quickly pull insights and recommend next steps — processes that were manual and cumbersome in the past. One company making impressive strides in AI analytics is Salesforce.com, a cloud computing company that develops customer relationship management (CRM) software. The Salesforce Einstein platform uses AI to process data and predict outcomes that help sales and marketing teams to make the best use of their efforts.


Big Data

As digitization takes over more and more aspects of daily life, user data has become the business world’s biggest resource. These massive datasets—known as big data—have given businesses more information on customers and potential customers than ever before. While many companies are finding ways to capitalize on big data, tech giant IBM is considered the industry leader in big data analytics. In recent years, IBM pivoted to big data services. Today, IBM boasts the world’s largest portfolio of big data and analytics services, including software, hardware and analytics research.

Codes of Ethics

The European Union enacted the General Data Protection Regulation (GDPR), which created stricter rules for how businesses can collect, store and use data, in 2018. At a time when privacy concerns are widespread and consumers are more cautious than ever about sharing their personal data, the GDPR has brought ethics concerns to the forefront of analytics practice. While many industries, like law and medicine, are already bound to professional codes of ethics, the analytics industry is just beginning to explore the ethics of using analytics for business.


Neural Networks

Inspired by the brain’s neural networks, artificial neural networks leverage the computing power of vast, connected computer systems. These neural networks are capable of processing information and learning to perform new tasks automatically, rather than waiting for someone to start up a particular operation. While relatively new, neural networks have already proved to be an impressive tool,  processing data, identifying patterns and making predictions with unparalleled accuracy. AI company Twosense has found a way to leverage neural networks for improved cybersecurity. The company uses neural networks to analyze biometric user data⁠ — everything from device location to the length of a cell phone user’s stride when walking⁠ — to track when a device changes hands and help to detect and prevent fraudulent activity.

Microsegmentation

Big data’s emergence has given businesses huge datasets and, therefore, more opportunities to segment data into smaller pieces. This granular segmentation gives businesses specific insights into customer demographics and behaviors. Rather than providing broad summaries, microsegmentation can provide hyper-specific details of individual customer behaviors and attributes. Microsegmentation has proven so effective that many marketing and analytics agencies now offer it as a dedicated service.

Deep Learning

Much like neural networks, deep learning — a subset of machine learning — imitates the function of the human brain to extract patterns from data. However, deep learning is able to process much larger and more complex data sets, making it particularly useful for analyzing big data. Unlike the traditional linear approach to analyzing data, deep learning enables machines to process data in a nonlinear way, resulting in faster and more advanced analysis of complex data sets. Descartes Labs in Sante Fe, New Mexico, uses deep learning to fuel their data-refinery service, analyzing massive data sets to help companies identify and apply data insights. Their use of deep learning has driven advancements in everything from disease control to disaster mitigation to satellite imagery.

Business Analytics Tools

Coding languages such as R, SQL, and Python are the foundation of modern data analytics. However, many powerful analytics tools are also available for businesses today. Each platform offers different advantages, giving businesses the ability to choose the best tool for their needs.

Coding Languages

Coding languages are the core tool of any data analyst. Expertise in the three most common coding languages — R, SQL, and Python — is key to success in the analytics industry. These languages provide powerful capabilities for processing, modeling, and visualizing data. R offers the most sophisticated options for statistics operations. SQL is ideal for preparing data structures within an organization’s internal infrastructure, in addition to pulling data out of the infrastructure as needed. Python, on the other hand, is best for data visualizations and other machine learning applications. Despite its traditional use as an object-oriented software development tool, Python has become a powerful data analytics tool as data packages such as pandas, NumPy, scikit-learn, and Nl2K have risen to popularity in recent years.

Dundas

Dundas is a browser-based analytics tool offering a significant amount of control over visual design elements. A highly customizable platform, Dundas gives businesses more finite control over visual modeling and reports. In addition, its HTML5 interface makes it compatible with a wide range of devices, including mobile.

Google Analytics

One of the most well-known analytics platforms, Google Analytics is free and extremely popular. In fact, many other business analytics platforms — including Microsoft Power BI and Tableau Desktop — offer Google Analytics integrations. The free version of Google Analytics isn’t as robust as some of the other platforms, but companies looking for great data capacity can upgrade to the paid enterprise version: Google Analytics 360.


Microsoft Power BI

Microsoft Power BI uses features like quick measures, grouping, forecasting and clustering to help businesses to develop actionable insights. Power BI is compatible with hundreds of on-site and cloud-based services, including Microsoft Dynamics 365, Microsoft Azure SQL Database, Microsoft Excel and Microsoft SharePoint. The platform’s data modeling tools are designed to be highly customizable and easy to use. Unlike many of the other services, Power BI offers downloadable software in addition to web-based access, so businesses can run analytics via server or cloud service.

Sisense

A popular choice among Fortune 500 companies like General Electric Co., Philips and NBC, Sisense uses AI to simplify the data analysis process. Sisense is an enterprise program accessible on site or on cloud. It boasts impressive features, including the ability to combine live and cached data and assess data across an entire system.

Tableau Desktop

The biggest selling point for Tableau Desktop is the ability to view live visual analytics, making it possible for businesses to spot data trends almost immediately. It’s compatible with most databases and offers a wide selection of data visualization options. Tableau Desktop is also mobile-friendly.

Zoho Analytics

Zoho Analytics integrates data from a large number of sources, including Microsoft Office documents, URLs, databases, and data from external applications like Dropbox, Google Drive, Salesforce and QuickBooks. Zoho enables businesses to pull data from all of these sources into statistical models that provide more comprehensive insights.

 

Sources

Dundas, Dundas BI Pricing

European Commission, EU Data Protection Rules

Google, Welcome to Google Analytics

IBM, “5 Ways Business Analytics Can Grow Your Business”

Investopedia, “Deep Learning”

Microsoft, Getting Started with Power BI — Analyze for Free

MicroStrategy, Business Analytics: Everything You Need to Kno”

Oracle, “4 Types of Data Analytics”

Oracle, “6 Steps to Building a Powerful Customer Analytics System”

PCMag.com, “Google Analytics”

Sisense, Sisense Pricing

Tableau, 2019 Business Intelligence Trends

Tableau, Pricing for Data People

TechRadar, “Best Business Intelligence Tools in 2019: Find the Right BI for Data Visualizations”

TechRepublic, “Mini-Glossary: Business Intelligence and Analytics Terms You Should Know”

Zoho Analytics, Pricing Plans

Contact:

Renata Ramirez
renataramirez@sandiego.edu
(619) 260-4658