Importance of Business Analytics

The story of business analytics begins with data. Lots of data. The volume of data is growing exponentially. It is estimated that the world creates about 2.5 quintillion bytes of data every day – that’s 2.5 followed by 18 zeros!

Much of that data is produced by business activities. This mountain of data is extremely important for business success when used correctly.

Businesses use business data analytics to analyze their data to gain insights and make predictions. Data has become one of the most powerful tools for businesses, yet, the majority of businesses fail to extract the full benefits from their data. That is partly because data analytics is poorly understood and partly because of a lack of data analytics skills.

Companies have no choice but to implement business analytics since it is the future of business. The insights gained from it will guide business decisions and therefore the destiny of businesses and the greater economy.

The trick is for companies to have the skills to translate the insights gained from their data into astute business decisions.

What is the relationship between business analytics and data science?

Data science uses modern tools and techniques to find unseen patterns, derive meaningful insights, and make sense of large volumes of raw data. Data science involves the use of complex machine learning algorithms, statistical models, and computer programming to understand raw data and build predictive models.

Business analytics is concerned with extracting meaningful insights from a company’s data to facilitate business decisions. In business analytics, statistical concepts are used to gain insights.

While coding is integral to data science, business analytics does not involve much coding. A data scientist must have good computer science skills; business analysts don’t need computer science knowledge.

Business analytics involves:

Collecting and processing historical business data
Analyzing that data to find trends and patterns
Creating predictive models
Making astute decisions based on the insights gained

Business analytics is the process of using quantitative methods to derive meaning from data to make informed business decisions.

The four business analysis methods

Business analytics applies four quantitative methods to understand what data is revealing.

Descriptive. Historical data is interpreted to find trends and patterns.
Diagnostic. Historical data is interpreted to find out why something has happened.
Predictive. Statistics are applied to forecast what may happen in the future.
Prescriptive. Testing and other methods are applied to determine an optimal course of action.

The method used will depend on a number of factors.

What process does business analytics follow?

Generally, the process of business analytics happens as follows.

Data collection

The first step is the collection of data from all possible sources, including social media, news reports, internal reports, spreadsheets, IoT devices, email, and apps. Data collection methods include interviews, surveys, forms, online tracking, and social media monitoring.

Data mining

Data mining entails the sorting and processing of data. Data mining uses machine learning algorithms to find patterns in large batches of data, which can lead data scientists to interesting insights that the business can leverage to grow and prosper.

Descriptive analytics

Once the data has been sorted and processed, data scientists apply descriptive analytics to find trends and patterns in order to understand why something is happening.

Predictive analytics

When data scientists have enough data that has been processed and analyzed, they can apply business analytics tools to build predictive models, which business leaders can use to inform their business decisions.

Visualization and reporting

Visualization and reporting tools are vital at this stage by making the results of analytics understandable to people other than data specialists. Visualization tools present the findings in charts, tables, pie charts, etc. so people can visualize what the data is telling them.

Benefits of business analytics

Business analytics enables organizations to make data-driven decisions. By quantifying root causes and identifying trends, business leaders get a clear overview of the business and what steps they could implement to make it more efficient. Business analytics will also reveal where automation can best be put to use.
Business analytics boosts efficiency. Business analytics helps organizations speed up their efficiency. For instance, large volumes of analyzed data can reveal what processes a company should improve. One example of greater efficiency can be found in a recent KPMG report on emerging trends in infrastructure. According to the report, many organizations have started using predictive analytics to anticipate and prevent maintenance and operational issues before they become worse.
Business analytics boosts decision-making. By presenting data in a format that is easy to understand and gain insights from, business analytics makes it easier for business leaders to make critical business decisions.
Business analytics helps companies to reach the top. It is no secret that organizations that make use of business analytics to guide their decision-making outperform competitors. Data-driven decision-making leads to consumer insights, and companies that leverage these insights excel. McKinsey has found that companies that strategically use consumer behavior insights outperform their peers in sales growth by 85 percent. According to IBM, 62 percent of retailers report gaining a competitive advantage from information and data analytics.
Business analytics leads to increased revenue. Companies that embrace data analytics practices can expect significant financial returns. Research by McKinsey shows organizations that invest in big data gain a six percent average increase in profits. For companies that have invested in big data for five years, the gain is nine percent.
Data-driven strategies can improve customer acquisition and retention. Using data and business analytics helps companies to know what customers need and like. Data can also explain why customers abandon a brand, and companies can take action to prevent the situation that causes it. According to Forbes, data-driven companies are 23 times more likely to acquire customers than those who have not embraced data.
Business analytics can mitigate risks. Companies face many risks, including theft, employee safety issues, legal liability, customer attrition, and more. Business analytics, specifically predictive analytics, can alert an organization to potential upcoming risks. In this case, preventive measures can be taken to avoid a negative outcome.

These are just some of the many benefits of business analytics that are still being explored by organizations.

Business Analytics Use Cases

Business analytics has many applications. Let’s look at a few of them.

Fraud reduction

Companies are using data about customers’ transaction history to identify potentially fraudulent purchases. They use predictive analytics to analyze customer profiles and rate the level of risk. In this way, predictive analytics helps companies prevent losses. Business analytics stand to be very useful in the medical insurance industry, where fraud costs in the US amount to around $68 billion a year.

Improve marketing

Online marketers use analytical techniques to optimize their marketing efforts and ensure a return on investment. They can test landing pages, banners, pop-ups, and product descriptions to ensure they get value for their marketing efforts.

Marketers can also use analytics to know how successful their marketing campaigns are. Analytics can tell them what the return on investment was of a marketing campaign and how it can be improved in the future. In this way, information gained from data analytics can drive strategic marketing decisions.

Reduce manufacturing costs

Business analytics have massive applications in manufacturing. With data gained and the application of data analytics, supply chain horrors can be avoided. Analytics can be used to ensure supply chain transparency to meet the needs of customers, suppliers, and partner companies. Data analysis can improve efficiency on the floor as managers track individual machines and employees.

In addition, predictive analytics can spot potential production bottlenecks and alert managers so downtime can be avoided.

Improve product management

Data analytics can identify a company’s most popular products at a particular time, season, or in a particular region. The company can then use this information to launch a product at the right time to the right target audience.

Improve sales

Wouldn’t you love to know exactly at what point in the sales journey a lead converts to a customer? That is what business analytics can do. It can dissect the sales journey, noting all the factors that influence a purchase, such as price, availability, season, geography, and other variables. Business analytics can use all this information to determine the exact moment.

Finance

Finance probably has the most application potential for business analytics. In fact, finance is the sector with the highest demand for data scientists.

One application of business analytics is for portfolio managers that can use the analytics tools to track the performance of a particular stock and advise a client to retain it or sell it.

The crippling shortage of data experts

We need data experts desperately, but there are too few of them. The mind-boggling explosion of data necessitates the skills of business analysts to make sense of it all. According to a recent study, 59 percent of finance and accounting managers say data science and analytics skills would be required of all managers by 2020.

So, companies are going to require data and analytics skills in the future, but they will have a hard time finding candidates.

According to some estimates, the world is currently experiencing a shortage of 150,000 – 200,000 hardcore data science professionals. In the meantime, the number of data scientist roles is expected to increase by 26 percent through 2026. That translates into roughly 11.5 million new data science jobs worldwide!

The strategic importance of the role cannot be overstated. After all, it’s the responsibility of business analysts and data scientists to provide the crucial information that can make or break a company. No wonder Glassdoor rates the data scientist role as the second best job in America.

All institutions are doing their best with plans to fill the talent gap. Universities and colleges have started offering targeted master’s degrees in the field of business analytics, and the three major software development companies, Microsoft, Google, and Amazon are offering certification courses in a wide range of data-related skills.

Final thoughts

In our digital age, it is crucial for companies to obtain expertise in the field of data analytics and make an effort to understand the importance of business analytics. No matter what the size of the venture, whether it’s a startup, a small business, or a multi-national, data will be the deciding factor determining its success or failure.

In all aspects of an enterprise, from sales and marketing, supply chain management, human resources, finance, and customer acquisition, to product development and discovering new markets, business analytics will be at the heart of them all.

And for those who want to be part of this revolution, there are more than enough job openings.