6 Ways Big Data Analysis Impacts Enterprise Decision Making

Digital transformation is restructuring the business process of the organizations by making it highly integrated.  The increased reliance on technology and its continuous advancement have reshaped the modern workplace. It is changing the way you operate business activities and deliver value to end customers.

Over the past decade, Big data along with Artificial Intelligence has created a lot of buzz in the corporate industry.  With the ever-increasing use of electronic devices, tremendous growth in computerization, and massive technological evolution, there is a major improvement in information availability. It leads to the generation of enormous amounts of digital data at the organizational level on a daily basis. 

It is very clear that today information is the most important factor that influences the decision-making process. There is a huge amount of data available for companies but in the raw form. In order to extract structured and valuable information, these huge data sets need to be thoroughly analyzed. The correct analysis of data sets aware organizations about their product quality; whether there is a need to bring efficiency in the operational process or any improvement required in customer services.

So, if you’re still using the old-style data processing applications to manage and store data then let me make it perfectly clear it is not going to deliver valuable insights that can thrive your business.

Nowadays, a large number of organizations are integrating Big data analytics into the decision-making process to gain valuable insights from high volume data. This technology examines huge chunks of data and reveals hidden patterns that help identify new growth opportunities. To capture the true value of big data and harness its potential to the greatest extent, it is essential to align big data analytics with traditional processes of decision making. Earlier business decisions were based on experience but with digitalization, these decisions are made on the basis of data, analytics, and logistics.

Big data is probably the most significant factor in decision making nowadays, and here are the 6 reasons:

1. Demand Forecasting

Big data processing enhances forecasting accuracy and improves cross-department collaboration. Especially in enterprises related to mass markets, like retail & ecommerce or Automobiles, Big data plays a crucial role to understand preferences, buying patterns, vacuums, and other opportunities, for making production-related decisions. With the use of this technology, businesses can predict future requirements of inventory level, plan the production activities, and allocate resources with minimum distribution cost. For instance, automobile companies can cut down their production if data indicates a market slowdown. Data analysis allows companies to make realistic estimates and targets to prevent morale from crashing.

2. Price Optimization

Price is arguably the single most important factor influencing buying decisions, and hence price analysis is one of the key functions of big data. It plays a key role in establishing any brand in the market. Thousands of companies fail to survive just because of the wrong pricing decision. It is well said that “A price can make or break your product”.  By identifying the optimal price of a product that a consumer is willing to pay, Big data also help companies make better pricing decisions. The organizations that deal with multiple products cannot overpower the market by following the traditional pricing approach. Hence, the decision-makers need to practice analytics strategies to gain a competitive edge.

3. Promotes Strategic Decision Making

Business decisions related to expenses or investments are prone to volatility, which can be devastating at times. However, with Big data technology, decision-makers and analysts can identify the areas where investments are required and select the one with better returns. This helps to make a decision that allows the flow of capital in formidable and potent areas. For instance, a smartphone company understands that the requirement of generic cell phones hasn’t declined in certain regions after big data analysis, and can now safely make investments in launching new ‘dumb phones’.

4. Improved Customer Services

Big data analysis can track all the activities and actions of your previous buyers and also determines their personal preferences accordingly. Besides this, CRM integration has made it easier to collect information from prospective customers in order to frame the right marketing strategies. In-depth insights into customer needs and preferences have helped decision-makers to better understand and observe the customer requirements. Also, it has reduced response time and increased problem-solving efficiency that promotes business success.

5. Better Allocation of Budget

With the help of market mix modeling (a big data analytics technique), decision-makers can analyze past company data and enumerate the impact of several marketing activities on sales, profit, and ROI. With the correct prediction of successful marketing strategies, Big data allows companies to determine the right channel that elevates the brand value and scale up the profit margins.

6. Enhances Operational Efficiency

Big data enables the business organization to have better insights by analyzing and managing huge sets of data. It impacts all the operational areas as it predicts the most accurate future trends, suggests successful marketing strategies and increases customer satisfaction by providing better services. Thus, enhances the overall operational efficiency of the organization.

Also Read: How Big Data Analytics Can Benefit an Enterprise

Summing up

In a nutshell, Big data integration indeed supports the decision-making process in any organization. This technological advancement has helped companies to gain a better understanding of demand and supply units of products and services by predicting the future. Hence, it is very essential for any organization to recognize the concept of big data analytics to leverage its full potential.

 

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