How Does Data-Analysis Enable Better Decision-Making?

The arena of business is unpredictable yet predictable to a certain extent. This paradoxical scenario enables us to venture into the nitty-gritty of the business world and extract data that may be of paramount importance. Data Analytics plays an essential role in the entire set-up. It allows a business to forecast the reach of their actions to an extent where they can determine its viability without having to plunge into it, head-first. Through the course of this blog, we’ll discuss the types of analytics and the impact it has on each department of a business. We have generalized the segments to ensure helping a larger group of people.

Descriptive Analytics

Descriptive Analytics refers to describing the past data to understand the current state of the business. Descriptive analytics means applying descriptive statistics to existing data like mean, median, max, etc.

There are two techniques in achieving these analytics

• Data mining means extracting historical data
• Data aggregation means collecting the mined data to analyze consumer behaviors and their impact on the business.

Diagnostic Analytics

Diagnostic Analytics focuses on determining the cause of past performance and the root cause of events to find out the factors responsible for them. It doesn’t give a full picture of the circumstances, but it helps in understanding the relationship between the variables of the situation. These analytics involve performing training algorithms like regression and classification.

Predictive Analytics

Predictive Analytics focuses on forecasting the possible outcomes based on past situations by utilizing statistical models. It helps in determining the probabilities of all possible outcomes. It is a derivative model based on descriptive analytics and relies on machine learning algorithms like random forests, SVM, etc. and statistics of testing and learning the data.

Prescriptive Analytics

Prescriptive Analytics relies on predictive analytics and suggests future solutions. One advantage of this analytics is that it allows you to formulate various courses of action and suggests the most desirable course. Due to its accuracy, it is considered as an essential tool of advanced analytics.

Prescriptive analytics uses a strong feedback system that constantly learns and updates the relationship between the action and the outcome.

Now that we’ve seen a brief on the types of analytics; let’s understand its impact on and utilization by the various industries.

Human Resources

The HR department makes use of employee work history, salary and promotion information, personality information, attendance records, etc. to gather all the descriptive analytics to help them make insightful decisions. They try to understand the reasons for employee performance and how initiatives are working out for the employees. Hr Personnel can find better hiring practices and improvise employee experience. Prescriptive analytics help them design incentives to bridge the gap between organizational budget and employee satisfaction.

Purchase & Production

The purchase and production departments ensure that the company’s resources and assets are in proportion to its budget limits. They gather information about raw materials and assets, the suppliers of the materials, the cost of assets requirements for production. They diagnose the reasons for buying raw materials at a particular cost, why we need those costly machinery for production. They predict the required quantity of raw materials for a certain amount of production. They can predict machinery failure and prepare for it in advance. They can prepare for the supplies and machinery parts based on the conditions that might prevail.

Sales & Marketing

The sales and marketing departments are responsible for converting prospective customers and cater to after-sales services. They consider product reviews and assess customer surveys to know how customers feel about the company. They have to know about market analytics and prepare for demand forecasting shortly. They have to understand their competitors’ policies and prepare for competitive pricing. They have to weigh the various sales & marketing channels to get the best optimum results for themselves.

These departments prepare themselves by gathering information about their customers, competitors, market trends, and marketing channels that are available to pursue. They diagnose the factors responsible for the price of their products, weigh in the needs of the customers, and the sales channels for continuous improvement. They can predict the demand for products based on marketing trends. They can optimize their sales & marketing channels and come up with pricing strategies to match up with their competitors.

Accounts & Finance

The accounts and finance departments are responsible for knowing the financial ability of a company. They study customer participation in profit creation, assess the sales revenue from various products, and cash flow from various investments. They diagnose the reasons for slow sales and predict profit margins achievable based on customer behavior. Most of the companies have 80–20 rules (Pareto Principle) in their customer profit contribution, where 20% of their customers contribute to 80% of their profits. They find out the strategies to maintain the financial contributions from their customers & investments and prepare for cash reserves for the tougher times based on these analytics.

Now that we know the kinds of analytics and its department-wise utility — let’s look at the industry-wise utility.

Manufacturing Industry

The manufacturing industry makes use of descriptive analytics to find out when the machine parts fail to perform. Using diagnostic analytics, they find out the reasons for the failure. These reasons further help to predict such failures in the future based on various reasons. With the help of prescriptive analytics, companies can predict the tools they have to arrange in the case of failures.

Healthcare Industry

The healthcare industry understands the seasonal impact of the diseases on the medical staff with the help of descriptive analytics and diagnostic analytics. They forecast the number of cases that may arise in various seasons with the help of predictive analytics. Accordingly, with the help of prescriptive analytics, they predict the type of medicines and quantity of medicines that they may require in various seasons.

Banking Industry

The banking industry faces the biggest challenges of debt payments. They try to cope with the effect of it by finding out the defaulters who couldn’t make the repayment. They track the reasons for the non-payment and type of defaulters for it. They predict the losses for the future. They try to improve the metrics for their credit score to lessen the defaulters.

Telecom Industry

With the rise in the use of mobile phones, this industry has tons of information to crunch. Over some time, companies have come up with ways to handle customer data and design data usage and talk time plans for customers. They made use of descriptive analytics and diagnostic analytics to find out how much people surf on the internet and talk over their phones. After making the interpretation of the data and predicting how much a customer can spend on their data needs. They were able to come with data plans based on it.

Conclusion

We can conclude that the analytics are not only useful to most of the industry but also to the various departments of an organization. We, at ZealousWeb, have a team of data analytics and data scientists to harvest the power of data analytics to help your business.

Originally published at https://www.zealousweb.com.