Advantages & disadvantages of data analysis.

The rich types of data that companies generate contain valuable insights, and data analytics is the way to unlock them. Data analytics can help organizations with everything from planning marketing strategies for individual customers to identifying and mitigating risks to their business.


Let’s look at the five advantages of using data analysis.

Customize the customer experience

Companies collect customer data through many different channels, including physical shopping, e-commerce, and social media. Take a clothing business that has a physical and online presence. The business can analyze its sales data and data from its social media pages and create targeted advertising campaigns to promote its online sales for the types of products that customers are interested in.

Organizations can perform behavioral analysis on customer data to further enhance the customer experience. For example, businesses can perform predictive modeling on e-commerce transaction data to determine which products to recommend at checkout to increase sales.

Communicate business decisions

Businesses can use data analysis to guide business decisions and reduce financial losses. A forecast analysis can suggest possible actions in response to business changes, and a written summary can show how the business should react to those changes.

For example, a company may create changes in price or product offerings to determine how those changes will affect customer demand. Changes in product offerings can be A/B tested to validate the ideas generated by this model. After collecting market data on modified products, companies can use data analysis tools to determine the success of the changes and visualize the results to help decision-makers decide whether to implement changes. body in the meeting or they will not include it.

Streamline operations

Organizations can improve their performance through data analysis. Collecting and analyzing supply chain data can show where production delays or bottlenecks are occurring and help predict where problems may arise. If the demand forecast indicates that one supplier will not be able to handle the volume required for the holiday season, the company may add or replace the supplier to avoid production delays.

In addition, many businesses, especially in retail, are struggling to improve their inventory levels. Data analysis can help identify the best sources for an entire company’s products based on factors such as seasonality, holidays, and global trends.

Reduce risk and manage setbacks

Risk is everywhere in business. For example, a retail chain may use predictive modeling – a statistical tool that can predict future events or activities – to determine which stores are most at risk of the week. theft. The company can use this data to determine the level of security in the store, or even if it will remove itself from certain areas.

Investors can also use data analysis to reduce losses after a pullback. If a business is overstocking a product, it can use data analysis to determine the best price for tracking to reduce inventory. A company can create a statistical model to provide immediate advice on how to solve common problems.

Improve security

All businesses face data security risks. Organizations can use data analysis to determine the cause of past data breaches by organizing and using relevant data views. For example, IT can use data analysis tools to analyze, organize, and visualize its audit logs to determine the timing and origin of an attack. This information can help IT find vulnerabilities and fix them.

IT departments can also use statistics to prevent future attacks. Attacks often involve malicious access behavior, especially payload-based attacks such as denial-of-service (DDoS) attacks. Organizations can configure these models to be continuous, with monitoring and evaluation systems to identify and report anomalies so that security professionals can take immediate action.


Let’s look at the disadvantages of using data analysis.

No organization within the group

There is a lack of coordination between different groups or departments within a group. Data analysis can be done by members of the working group and the analysis can be shared with the administrative staff. However, the information these groups produce is not valuable or has much impact on organizational metrics.

This may be due to the way work is done in “silos” and each group uses only their current system disconnected from other departments. The research team will focus on answering the right questions for the business and the results produced by the data collection team must inform the appropriate employees to make the right actions and behaviors that can have a positive impact. and organisms.

No guarantees and patience

Collection solutions are not difficult to implement, but they are expensive and the return on investment is not fast. In particular, if existing data is not available, creating a system and process to start collecting data can take time. By nature, research models improve accuracy over time and require dedication to implement the solution. Since consumers don’t see immediate results, they sometimes lose interest, leading to a lack of confidence and failure.

When a company decides to implement a data analysis system, it is important to be a feedback system in order to understand what is working and what is not, and what improvements are needed to fix it. Without this closed process, the management may decide that the research is ineffective or not useful and may abandon the entire exercise.

The data quality is low

One of the main limitations of data analysis is the lack of access to quality data. Companies may already have access to a lot of data, but the question is, do they have the right data they need? A top-down approach is required where the business questions that need to be answered must first be known and the data required to answer those questions can be determined. In some cases, the data may be collected for historical purposes and may not be sufficient to answer the questions we are asking today.

At other times, even though we have the right metrics to collect data from, the quality of data collection can be poor. There may be situations where sufficient data is not available or missing to perform a proper analysis. As they say, garbage goes out. If the quality of the data is bad, the decisions made to use the data will also be bad. Therefore, steps must be taken to improve the quality of data before it can be used effectively in an organization.

Privacy Concern

Sometimes the collection of data can compromise the privacy of customers because their information such as purchases, online transactions, and subscriptions are available to companies that use their services. Some companies may exchange these data sets with other companies for mutual benefit. Some of the data collected may be used against an individual, a country, or a country.

Organizations should be careful about the type of data they collect from customers and ensure data security and privacy. Only the data required for analysis should be captured and if there is sensitive data, it should be annotated so that sensitive data is protected. Data breaches can cause customers to lose trust in organizations, which can negatively impact the organization.

Confusion and uncertainty

Some research tools developed by companies are like black box models. It is not clear what is inside the black box or that the concept of the system that learns the data and creates a model is not far away. For example, a type of neural network that learns from different situations to decide who should receive money and who should refuse.

Using these tools can be easy, but the logic behind making decisions is not clear to everyone in the business. If the company is not careful that the model is being trained with the wrong dataset, there may be hidden biases and the decision of these processes may not be fast and the organizations may break the law by discrimination of race, sex, sexuality, and age. , etc.

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