Biggest Challenges of Data Analysis? How to fix them?

Now data is almost everywhere, in absolutely every industry, sectors and businesses. Huge amount of data is created daily and is generating every single second through many resources, presenting businesses with never-ending data analysis challenges. Especially, after the rise of social media data is generating in abundance and in mostly places taken the shape of “Big Data”!

Collecting right data from multiple sources with good quality and converting into right insights is becoming a big challenge for Data Analysts, Data Scientists, Data Engineers and Data Workers. With the ever-increasing amount of Data or Big Data available, data analysis has become more challenging than ever before. In this blog, we would try to unleash the biggest and common challenges of data analysis today with some relatable Stats and ways, which can help Data Professionals in fixing them.

Biggest Challenges of Data Analysis, Data Analytics today

1. Managing huge amount of Data:

Huge amount of data collected and not managed properly most likely lead to a mess and hamper efficiency of both employees and organisations. Today data is created for every interaction across different channels – campaign, email, social media, website, ads, and virtual store. Over a course of time, analyzing it can become overwhelming, hindering the insights’ completeness. 

How to fix it?

Some strategies for managing big data companies can adopt are –
Invest in the Right Technology
Cloud-based storage solutions
Distributed processing frameworks
Skills Development
Data Governance

2. Data from multiple sources:

Generally, data comes from multiple and disjointed sources. This is one of the biggest challenge as data from different sources often comes in different formats, different types and column names are also not consistent. And the data from third-party applications is not reliable and complete. Companies suffer enough time consumption of their employees in compiling, cleaning and preparing this data.

How to fix it?

Comprehensive and centralized system should be in place
Avoid third-party applications
Only use reliable sources
Monitor them periodically

3. Real-Time Analytics – Collecting meaningful and real-time data:

Collecting meaningful and real time data is a big challenge, it is is critical for effective decision-making and improving business operations. Businesses need to analyze data in real-time to make timely decisions. Real-time data analysis requires sophisticated tools and technologies that can handle large amounts of data in real-time effectively.

How to fix it?

Use Real-Time Data Collection Methods
Define Key Performance Indicators (KPIs)
Implement Data Integration
Use Machine Learning
Monitor Social Media
Conduct Surveys

4. Choosing the Right Analytics, Data Analysis Tools:

With so many tools available in the market, choosing the right one can be challenging but crucial. Selecting the right analytics tool is crucial for the success of any data analysis project. Many analytics tools such as Tableau, Power BI, RapidMiner, QlikView are available in the market which offer varying capabilities. Business analysts need to consider factors such as the size and complexity of their data, the level of expertise of their team, and the cost of the tools when selecting the right data analysis tool. You should choose the software that solve your purpose and fits in your budget. Other factors to consider when selecting the right analytics and data analysis tool:

How to fix it?

Analytics Capabilities
Data Volume and Complexity
User Interface and Ease of Use
Integration with Other Tools
Support and Community

5. Data Quality:

One of the biggest challenges of data analysis is ensuring data quality. Data can be incomplete, invalid, inconsistent, or contain errors, making it difficult to draw accurate insights and conclusions from it. Data cleaning, data profiling, and data validation are some of the techniques used to ensure data quality.

How to fix it?

To ensure data quality, businesses and organizations should implement data cleaning, preparation and validation processes (Data Wrangling Process). This involves identifying and correcting errors, inconsistencies, and incomplete data. In addition to this, Data profiling tools should be used to analyze data and identify data quality issues. As a whole, complete Data Management Process, Tool should be implemented early in the organization data management cycle and should be monitored closely.

6. Data Integration:

Data integration is another challenge in data analysis. Businesses today have data stored in multiple systems and may be with different cloud based service providers, such as databases, data warehouses, data centers and data lakes. Integrating and compiling these different sources of data to create a single, unified view of the data can be a complex and time-consuming process.

How to fix it?

Businesses should invest in technologies that enable data integration, such as data warehouses, data lakes, and APIs. This will allow different data sources to be integrated and provide a single, unified view of the data. If you are using data centers for storing your data, make sure data integration process should be consistent throughout your organization.

7. Data Security:

With the increasing amount of data being collected, combined and analyzed, data security has become a major concern. Ensuring the security and privacy of data is critical to prevent data breaches, unauthorized access and for organization success.

How to fix it?

Data security can be improved by implementing strict data encryption methods, access controls, and monitoring systems regularly. Businesses should also develop data security policies and train employees on data security best practices.

8. Bridge Skill Gap:

Data analysis requires a high level of skill and expertise. However, there is a shortage of skilled data analysts, data scientists, data engineers in the market. Businesses face the challenge of finding and hiring skilled data workers or investing in training their existing employees. Finding a right data professional is crucial for business success.

How to fix it?

To address the skill gap, businesses should invest in training and development programs for their existing employees. This includes providing training on data analysis techniques, data preparation techniques, data wrangling tools, and technologies. Finding a right data professional or a data leader is important for any business success.

9. Data Visualization:

Data visualization is an important aspect of data analysis. It helps in presenting complex data in an easy-to-understand format. Usually, this is in the form of graphs, charts, infographics, and other visuals. Doing this manually and no expertise, especially with extensive data, is tedious and impractical. Creating effective data visualizations requires a high level of skill and expertise.

How to fix it?

To improve data visualization, businesses should invest in tools and technologies that enable easy data visualization, such as business intelligence software and dashboards. Employees should also receive training on effective data visualization techniques. Knowledgeable workers with good hands on experience with different BI tools & softwares like Tableau, Qlikview, PowerBI are essential for organization’s success.

10. Cost Effectiveness:

Data analysis can be expensive, especially for small businesses. Investing in the right tools and technologies, hiring skilled data analysts, and ensuring data security can all add up to a significant cost.

How to fix it?

To manage costs, businesses should carefully evaluate their data analysis needs and invest in the right tools and technologies. You need to fix the cost on spending Data Analytics. This includes considering open-source tools and cloud-based solutions, which can be more cost-effective than on-premises solutions.

11. Data Governance:

Data Governance refers to the policies, procedures, protocols, processes and standards that organizations should put in place to ensure the proper management of data. Business analysts, data analysts face the challenge of ensuring that data is governed properly, which includes ensuring that data is accurate, consistent, and secure.

How to fix it?

To improve data governance, businesses should implement rules, policies and procedures for data management and follow them strictly. This includes establishing and assigning data ownership, ensuring data privacy, data security and defining data quality standards.

12. Data Silos:

Data silos refer to isolated data sets that are not integrated or shared across an organization. Data analysts face the challenge of breaking down data silos and integrating data from different departments to gain a holistic view of the business. It is also a time consuming process.

How to fix it?

To break down data silos, businesses should invest in technologies that enable data integration and collaboration across departments. Review it regularly. This includes implementing shared data repositories and collaborative tools.

13. Stakeholder Management:

Data analysts need to work closely with stakeholders across the organization, including executives, managers, and other employees. Understand their requirements, provide them reports and solutions accordingly. Managing stakeholder expectations and ensuring that everyone is aligned on the goals of the project can be challenging and is a key aspect.

How to fix it?

To improve stakeholder management, businesses should involve stakeholders in the data analysis process and establish clear communication channels. This includes defining project goals, timelines, outcomes, what & how to improve and regularly communicating progress updates to stakeholders.

14-15. Other challenges include:

Data Analytics Strategy:

Developing a Data Analytics Strategy is crucial for the success of data analysis process. Business analysts need to consider factors such as the goals and objective of the project, the available data, data resources and skills of the team, and the desired outcomes, output when developing a data analytics strategy.


Business analysts need to consider the scalability of their data analysis projects, especially as data volumes grow. How reports, dashboards or code would react when data will grow?Would those handle more load and not falters when data grow! Check the durability of process in place regularly and this would only be possible, if process in place is Scalable. Ensuring that the data analysis process can handle large volumes of data and can scale as the business grows is a key challenge.


As data is growing exponentially, businesses face challenges daily of effectively handling data sets to draw valuable insights and inform business decisions. Data analysis is an essential part of modern business, and its importance is only going to increase in the coming years.

With the increasing amount of data available and generating, businesses face several challenges in data analysis. Ensuring data quality, data integration, data security, skill gap, real-time analytics, data visualization, and implementing data culture are some of the biggest and common challenges of data analysis today. Addressing these challenges requires a combination of the right skills, tools, techniques, technologies, and data governance. By thoughtful investing, businesses can overcome these challenges and leverage data analysis to drive business growth and success.


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