Benefits of Data Analytics for Your Business

With data at the center of today’s global markets, a business’s ability to make sense of its data is vital to its success—and failing to leverage that data can put a business at risk of falling behind.

Raw data doesn’t offer much value on its own. Businesses need to employ data analytics—a systematic discipline focused on examining data to find insights, patterns, and trends—to make the data work for them. What are the primary benefits of data analytics? Data analytics enables business leaders to make informed, timely decisions and know more about their business and customers.

How Is Data Analytics Used in Business?

Data analytics, sometimes referred to as data and analytics (D&A) or big data analytics, is the broad term used to describe the processing of raw data to gain business insights. It involves various scientific methods, statistics, modeling, tools, and techniques.

There are several benefits of data analytics for businesses of all industries. Here are two examples:

• A marketing director can use data insights about buyer behavior to understand what customers want, creating business opportunities.

• An operations manager can leverage data to identify gaps, issues, and disconnects in production—as well as opportunities to optimize processes and improve performance.

4 Basic Data Analytics Types

A breakdown of data analytics approaches reveals four primary types: descriptive, diagnostic, predictive, and prescriptive. These approaches have different goals and can be used together to provide businesses with a holistic view of data and improve decision-making. So what value does each type bring?

1. Descriptive Analytics

For a business to create the best plan for its future, it needs to understand its past. Descriptive analytics reviews current or historical data, such as price changes or the number of customers, to draw conclusions relative to what has happened or is happening in a business. Insights drawn from descriptive analytics can help businesses understand their strengths and weaknesses as they create a business strategy.

2. Diagnostic Analytics

After quantifying performance in a particular area, a business may want to understand the causes of that performance. For example, if data shows that revenues fell in the last quarter, a diagnostic analytics approach can help identify the causes of the shortfall.

Analysts using a diagnostic analytics approach leverage techniques such as drill-down, data discovery, and data mining to get their “why” questions answered and provide decision-makers with essential data to help solve problems and improve business success.

3. Predictive Analytics

Like other data analytics methods, predictive analytics evaluates current and historical data to find patterns. A unique point of differentiation of predictive analytics is that it uses this data to forecast events and patterns that are likely to emerge.

In predictive analytics, analysts use various statistical techniques and models, conduct data mining, and use machine learning to analyze data patterns. While no one can predict the future with 100% accuracy, predictive analytics can help businesses plan for possible future events, anticipate risks, and uncover growth opportunities.

4. Prescriptive Analytics

A primary aim of data analytics approaches is to provide businesses with insights to make informed decisions. Prescriptive analytics takes this a bit further by offering recommended actions.

In prescriptive analytics, analysts use tools and techniques—including graph analysis, neural networks, and machine learning—to help businesses determine the best next steps based on the data.

5 Business Areas Leveraging Data Analytics

Data analytics is used in business in numerous ways, for different purposes, and across a wide range of industries. Here are just five critical business functions that can be optimized by data analytics professionals.

1. Supply Chain

Data analytics benefits businesses by measuring which products are in high demand and determining optimal inventory levels to meet demand while also minimizing costs and inventory management.

2. Customer Acquisition

Data insights about what’s working and what’s not in attracting new customers can help businesses determine the effectiveness of their customer acquisition strategy.

3. Product Development

Individuals working on product development can leverage data analytics to learn about a product’s most and least popular features and what improvements are desired by consumers.

4. Content and Marketing

Data analytics can even help inform the creative process with insights on content performance. This takes some of the guesswork out of addressing consumer needs with articles, illustrations, video content, and more.

5. Operations

Speed is vital in today’s fast-paced business world. Hiccups in operations can slow a business down. Data analytics can play a critical role in identifying gaps or flaws in operations and determining opportunities to improve processes and maximize efficiencies.

Data Analytics in Business Resources

Data is transforming decision-making in enterprises large and small, helping businesses expand their markets, grow sales, and improve operations. The following resources provide more information on how data analytics is used in business.

• CIO, “What Is Business Analytics? Using Data to Improve Business Outcomes”: A descriptive article about business analytics, including its benefits, how it’s used, examples, and tools

• Investopedia, “Data Analytics: What It Is, How It’s Used, and 4 Basic Techniques”: A description of data analytics and four main techniques

• Talend, “Business Analytics vs. Data Analytics: Which Is Better for Your Business?”: A deep dive into two different analytics approaches and how they can be applied

10 Business Benefits of Data Analytics

The insights available to businesses from data analytics can provide a host of advantages, from improved decision-making and enhanced brand image to stronger operational performance and security. Yet 68% of data remains unleveraged by businesses, according to a Seagate report.

The top business benefits of data analytics include the following:

1. Improving Decision-Making

As much as most people would like to say they make decisions objectively, the process always includes an element of subjectivity. Leaders who have to make on-the-fly judgments often rely on their own experiences, meaning that a hunch or gut feeling may be driving a major business decision. While effective leaders are generally good at this, the process can be subject to bias and mistakes.

One of the benefits of data analytics is that it allows leaders to leverage data to make better business decisions based on factual information. An environment where data plays a significant role in decision-making includes data early in conversations. Stakeholders ask what the data says about a topic to help ensure a decision delivers a more impactful result.

2. Acquiring New Customers

Losing prospective customers’ interest can damage a business’s growth and success. Data analytics can drive customer engagement, conversions, and acquisitions. For example, a business can use performance data about the success of its content or track how customers respond to email and social media campaigns to determine which messages resonate and drive conversions. The business can then use these insights to develop high-impact marketing initiatives and create strategies that improve customer experiences, which can increase customer acquisitions.

3. Enhancing Brand Image

A brand is what distinguishes one business from another. Brands are commonly associated with some of the most recognizable companies out there—think Coca-Cola or Nike. But establishing a brand is vital to every business, and protecting it is especially important.

Data analytics can provide insights into how people view a business’s brand. For example, a business can learn which elements of its brand image are compelling by combing through social media channels. With this data, they can learn what their customers or the general public think about their brand and then decide what to keep, remove, or revamp.

4. Delivering Personalized Experiences

After consumers add products to their digital shopping carts, they may receive recommendations for additional products based on their choices and views. This is a type of personalized experience that communicates to a customer that their preferences are understood. A personalized experience also shows the customer that a business has taken steps to get to know them.

Curating personalized experiences with data analytics by drawing insights about a customer’s preferences based on customer data, sales, and marketing data is increasingly important. According to McKinsey’s “Next in Personalization 2021 Report,” 71% of consumers expect to receive personalized services and interactions, and offering personalization can lift revenues by 10% to 15%. A business that does not leverage its data to deliver these experiences may miss opportunities to drive performance and improve customer outcomes.

5. Strengthening Retention and Loyalty

After acquiring customers, the primary aim of a business is to keep them as continuing customers. Customer retention and loyalty campaigns maximize upsell opportunities and improve profitability, since the cost to acquire a customer is higher than the cost to retain one.

Businesses can leverage data analytics to learn about customer behavior and identify consumption patterns. These insights can help a business understand its customers better and reduce customer churn (the number of customers who fail to become repeat customers).

6. Improving Financial Forecasting

Whether in a rising market or amid an economic downturn, businesses need to understand their financial standing. While it’s impossible to predict every scenario that may impact a business’s financial health, data analytics is key to building financial resilience.

By analyzing sales trends, costs and expenses, consumer behavior, supply chains, consumer demand, and market sentiment data, businesses can prepare for worst-case market scenarios, make forecasts on where their business will be over the short and long term, and take steps to maximize financial returns.

7. Optimizing Marketing Programs

Data analytics can help businesses maximize the return on their marketing investments. In evaluating or preparing a marketing campaign, data analytics can enlighten decision-makers on the areas of a marketing strategy that offer the highest value.

For example, data collected about the factors that influence consumer behavior and buying patterns may reveal which marketing channels are working best. This data may also provide essential business metrics to help marketing organizations make effective decisions on future marketing programs. This information can help marketing leaders adjust their marketing plans to focus on those with the highest ROI.

8. Streamlining Operations

Improving how a business operates can result in cost savings, better business performance, and a stronger bottom line. One way to achieve these aims is by leveraging operational analytics.

Data analytics benefits business operations by identifying problem areas and making processes more efficient. Key data insights derived through predictive analytics can help identify potential problems in machines and equipment in advance, reducing repair costs and minimizing the risk of experiencing unexpected downtime incidents.

9. Mitigating Risks

Establishing a strategy to address risk includes three primary steps of a continuous cycle: identifying the risk, assessing it, and monitoring it. The first step focuses on identifying every potential event or circumstance that can negatively impact any part of the business, from its finances to its reputation. The next step assesses the likelihood of those events occurring and whether they will directly impact the business’s operation. The monitoring stage determines whether responses to risk events are appropriate.

At every stage, data analytics is vital in accelerating and improving the process and helping leaders make the best-informed strategic risk decisions.

10. Enhancing Security

Technological advancement brings convenience and improvements in business, but it also creates new opportunities for cybercriminals to exploit security gaps. Data analytics can help detect security threats as they emerge in several ways.

Cybersecurity professionals use data analytics tools and technologies to monitor where critical data is housed, how it is used, and who is using it. For example, an automated data analytics technique known as data discovery and classification can identify sensitive information, analyze data usage patterns in business systems, and assess and remediate vulnerabilities such as outdated software or a misconfigured system.

Data Analytics Careers in Business

Because of the many benefits of data analytics, the job outlook for data analytics and data science professionals is promising, with career opportunities spanning multiple industries. According to the U.S. Bureau of Labor Statistics (BLS), data scientists had a median annual salary of $100,910 in 2021, and they’re included on the list of what are predicted to be the fastest growing occupations in the coming decade.

Data analysts’ duties can vary by industry and business type, but their primary responsibility is analyzing and interpreting data. Job titles can also vary, with examples including data analyst, business consultant, insights analyst, and data analytics consultant.

Data analytics professionals extract patterns and trends from data in many ways. Here are some examples:

• Regression analysis: Analysts look through data to find correlations among different sets of variables. The primary aim of regression analysis is to estimate how one or more independent variables might impact dependent variables. This process helps to identify patterns that may be useful for forecasting future trends.

• Monte Carlo simulation: This mathematical technique enables an analyst to determine likely outcomes for different scenarios. Creating a Monte Carlo simulation can begin with a model, such as a spreadsheet that includes sales data. Through computerized techniques and statistical tools, an analyst can calculate the likelihood that a particular outcome will come to fruition.

• Sentiment analysis: Most types of analytical methods are quantitative―that is, data is expressed as numerical values. Sentiment analysis is a type of qualitative data analysis that interprets and classifies text comments generated by customers to determine how they feel about a business or its products. Customer qualitative data can be acquired from various sources, including surveys, social media mentions, or product reviews on an e-commerce site.

Many forms of data analysis require software tools, because the data sets involved are simply too big for manual processes. Thanks to automation and artificial intelligence technologies such as machine learning, statistical analysis software, and cloud technologies, analysts can leverage the power of data analytics more cost-effectively and efficiently.

Data Analytics Career Resources

How can an individual know which type of data analytics career is right for them? The following resources provide information on career options.

• Dataquest, “8 Data Analyst Skills Employers Need to See in 2022”: An overview of some of the most important data analyst skills

• Investopedia, “Data Analyst: Career Path and Qualifications”: Insights on the data analytics sector with classifications of different career paths

• The Muse, “Data Professionals Are in High Demand—Here Are 8 Jobs You Should Consider”: Information about data analysts, machine learning engineers, and other data professionals

Take Advantage of the Benefits of Data Analytics

Data analytics is increasingly important in business, which is a primary reason the field is attracting new talent every day. Businesses understand that they can obtain the best results from data analytics by hiring experts with the right education and training. However broad the duties of a particular role, individuals with demonstrated knowledge and proven data analytics skill set will be more attractive to potential employers.

Traditional options for preparation include self-study and enrolling in a college program. For individuals seeking other options, a data analytics bootcamp can help an individual launch their career in as little as a few months. Individuals interested in data analytics should carefully weigh their options to get the expertise they need to grow in their careers in a time frame that suits their professional pursuits.