INTechHouse BLOG 25.07.2023 13 min.
6 Challenges in Implementing Big Data Analytics Based on Our Client’s Experience
INTechHouse BLOG 25.07.2023 13 min.

6 Challenges in Implementing Big Data Analytics Based on Our Client’s Experience

Jacek Suty

Global businesses are now constantly seeking ways to extract valuable insights from the vast amount of information available. Implementing big data analytics can unlock tremendous value for businesses by providing valuable insights, driving informed decision-making, and enabling competitive advantage. However, organizations often encounter various challenges during the implementation process. Based on our professional experiences, we have identified six data analytics challenges that businesses face when implementing big data analytics solutions.

What is Big Data and Why Does It Matter?

Big data refers to vast amounts of structured, semi-structured, and unstructured data that organizations generate and collect from various sources such as social media, sensors, online transactions, and more. This data is characterized by its volume, velocity, and variety, and it often exceeds the processing capabilities of traditional data management systems. Big data holds significant value as it contains insights, patterns, and trends that can drive strategic decision-making, optimize operations, and unlock new opportunities for businesses.

The importance of big data lies in its potential to revolutionize how organizations operate and compete in today’s digital landscape. By harnessing the power of big data and data analysis, companies can gain valuable insights into customer behaviour, market trends, operational inefficiencies, and more. This enables them to make data-driven decisions, personalize customer experiences, enhance product offerings, improve operational efficiency, and ultimately drive profitability.

Examples of Big Brands Using Big Data and Their Profits:

  1. Amazon: Amazon is a prime example of a company that leverages big data to fuel its success. With its vast e-commerce platform, Amazon collects and analyzes massive amounts of customer data on customer purchasing behaviour, preferences, and browsing history. This data powers personalized product recommendations, targeted advertising, and dynamic pricing strategies.

But there are some data analytics challenges that make the data collection process hard and useless.

Challenge 1. Defining Clear Objectives and Use Cases

One of the initial challenges is defining clear objectives and identifying relevant use cases for big data analytics. Without a clear understanding of what the organization aims to achieve and how big data analytics can support those goals, the implementation process can lack direction. It is crucial to align the analytics initiatives with strategic objectives, prioritize use cases based on their potential impact, and ensure stakeholders are involved in the process.

Solution 1. Be Aware of How to Get and Use Insights

Develop a compelling and feasible business justification for your project, involving business professionals to gain a deeper comprehension of their data collection requirements and the actions they can take based on it.

Incorporate advanced analytics to uncover novel approaches for interpreting and comprehending insights, ensuring that these valuable findings are easily accessible to all members of the organization.

Supply the organization with contemporary visualization tools, interactive dashboards, and user-friendly interfaces that enable data exploration, report generation, and seamless data communication within the company.

Challenge 2. Managing Vast Amounts of Data

Source: Dell

Big data lives up to its name, with organizations accumulating terabytes and even exabytes of data that continuously grows. Without proper management, businesses can struggle to keep up with this growth, miss out on extracting value from their data assets, and have an opportunity to receive more inaccurate data.

Solution 2. Using Management Tools

Implement management and storage technologies to address the increasing volume and challenges of handling big data. Whether you choose cloud, on-premises hosting, or a hybrid approach, ensure it aligns with your business goals and organizational needs. Establish a scalable architecture and utilize tools that can adjust to the growing data volume while maintaining data integrity.

Challenge 3. Data Integration and Quality Assurance

Data integration remains a significant challenge in big data analytics implementations.

Source: Digiteum

Organizations often have data scattered across various systems, departments, and formats. Integrating this disparate data into a unified and accessible format can be complex and time-consuming. Additionally, ensuring data quality, including accuracy, completeness, and consistency, requires robust data cleansing and validation processes. Organizations must invest in data integration technologies and establish data governance practices to address these challenges effectively.

Solution 3. Using Data Source Utilization

Storing, managing, and analysing large amounts of data is a problem even for large business enterprises. To tackle collecting meaningful data challenges, organisations are looking at options like big data analytics tools and data lakes that can help reduce the time and effort involved in deriving business value from “big data”.

Create an inventory to gain a clear understanding of the data sources utilized and assess the feasibility of integrating them for comprehensive analysis. This process is primarily a business intelligence responsibility as it involves collaboration with business professionals who possess contextual knowledge and can determine the data necessary to achieve their BI objectives.

Embrace data integration tools that facilitate the connection of data from diverse resources, including files, applications, databases, and data warehouses, and enable their preparation for big data analytics. Depending on the existing technologies within your organization, you can leverage established platforms like Microsoft, SAP, and Oracle, or opt for specialized tools such as Precisely or Qlik, which specifically focus on data integration.

Challenge 4. Infrastructure and Scalability

Big data analytics requires a robust and scalable infrastructure capable of handling large volumes of data and processing complex analytics algorithms. Many organizations struggle with selecting the right infrastructure and technology stack that aligns with their requirements and can accommodate future growth. Building a scalable infrastructure involves considerations such as storage, computing power, network bandwidth, and cloud vs. on-premises solutions. Organizations need to carefully assess their current and future needs to avoid infrastructure limitations that hinder analytics capabilities.

Solution 4. Having an Inventory Approach

Create a comprehensive inventory to identify the origins of your data and assess its suitability for integration into a unified analysis. This task is primarily under the domain of business intelligence, as it requires input from business experts who possess contextual knowledge and can determine the data necessary to achieve BI objectives successfully.

Embrace data integration tools designed to facilitate the connection of data from diverse sources, including files, applications, databases, and data warehouses, and prepare it for big data analytics. Depending on your organization’s existing technologies, you have the option to leverage industry-leading providers such as Microsoft, SAP, and Oracle, or opt for specialized tools specifically tailored for data integration, like Precisely or Qlik.

Challenge 5. Talent Shortages Present Many Big Data Issues

Source: Towardsdatascience

Big data analytics demands specialized skills in areas such as data science, machine learning, statistics, and programming. Finding and acquiring talent with the necessary expertise is a significant challenge. The scarcity of skilled professionals in the job market often leads to intense competition for talent. Organizations must develop strategies to attract and retain top talent, including partnering with educational institutions, upskilling existing employees, and fostering a data-driven culture that encourages professional growth.

Solution 5. Right Global Collaboration

One effective and expeditious approach to address talent shortages is by collaborating with a proficient and dependable technology provider who can readily supplement your big data and BI requirements. Outsourcing your project could also prove cost-effective if in-house hiring exceeds your budget constraints.

As you and your team possess unparalleled knowledge of your data, consider upskilling your existing engineers to acquire the required expertise and retain the talent in-house.

Develop analytics and visualization tools that are accessible to non-technical specialists within your organization. Simplify the process for employees to obtain insights and seamlessly incorporate them into the decision-making process.

Challenge 6. Privacy, Security, and Compliance

Source: NewVantage

Big data analytics involves working with vast amounts of sensitive and confidential data. Organizations must prioritize data privacy, security, and compliance with regulations to mitigate risks and maintain customer trust. Implementing robust data protection measures, including encryption, access controls, and data anonymization techniques, is crucial. Organizations should also stay updated with relevant data protection laws and industry regulations to ensure compliance and avoid legal implications.

Solution 6. Put Security First

Ensure that the security of big data is an integral part of the initial planning, strategy, and design stages. Neglecting this aspect and considering it as an afterthought could result in significant big data issues and substantial financial penalties.

Thoroughly assess both your data and its sources to ensure compliance with the relevant regulations that pertain to your industry and location. This includes examining adherence to regulations such as GDPR in the EU, HIPAA, and HITECH Act for healthcare data in the US, among others.

Have some of these 6 data analysis challenges or problems with collecting meaningful data? INTechHouse is on the way to help you. Just book a call and your business improvements will not keep you waiting.

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Preventing Big Data Challenges Starts with a Solid Strategy

In the ever-evolving world of big data, organizations must proactively tackle challenges to unlock their full potential and drive success. A solid strategy serves as a foundation for addressing these obstacles and making the most of valuable data assets. By considering the following sectors, businesses can build a comprehensive approach to overcome big data challenges:

  1. Defining Clear Objectives and Use Cases: Before diving into big data analytics, it is crucial to establish clear objectives and identify relevant use cases. By aligning analytics initiatives with strategic goals, organizations can prioritize use cases with the highest potential impact and ensure data-driven decision-making drives the organization forward.
  2. Data Integration and Quality Assurance: Data integration and quality assurance are critical for accurate analysis. Organizations should implement data governance practices and validation processes to maintain data integrity across different sources, ensuring reliable and consistent insights.
  3. Infrastructure and Scalability: Building a scalable infrastructure is vital to accommodate the growing volume of data. Cloud-based services offer cost-effective solutions with pay-as-you-go options, enabling organizations to match their budget and computing needs.
  4. Data Security and Privacy: To safeguard sensitive data and comply with regulations, robust security measures, encryption techniques, and privacy frameworks must be in place.
  5. Skill Gap and Talent Acquisition: To leverage big data effectively, skilled professionals are essential. Organizations can bridge the skill gap by investing in training programs, partnering with educational institutions, or collaborating with external experts.
  6. Real-time Data Processing: For industries requiring real-time insights, implementing real-time data processing systems can facilitate prompt decision-making and analysis.
  7. Cost Management: Optimizing costs related to data storage and processing is crucial. By adopting cost-effective storage solutions and managing data lifecycle efficiently, organizations can ensure cost-effectiveness.
  8. Cultural and Organizational Challenges: Cultivating a data-driven culture involves change management and organizational buy-in. Encouraging data literacy, collaboration, and data-driven decision-making across the organization can foster a data-driven culture.

Your step-by-step plan for preventing poor-quality data and risk management process doesn’t work? We recommend you start consultations with the specialists.

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Final Thoughts on the Business Data Challenges

In conclusion, the journey of implementing big data analytics may present challenges, but with a well-thought-out strategy, these obstacles can be addressed and transformed into opportunities for growth and success. The key lies in aligning business goals with data analytics initiatives, utilizing the right tools and technologies, fostering a data-driven culture, and investing in talent and infrastructure. By overcoming big data challenges, organizations can harness the power of data to drive innovation, make informed decisions, and gain a competitive edge in today’s data-centric business landscape.


It is crucial to have a well-coordinated approach when implementing system changes that may affect data across different departments. Before making any changes, conduct thorough data impact assessments to understand potential implications and mitigate risks.

Navigating data analytics challenges can be made easier with the right strategy. Start by clearly defining your business objectives and identifying the specific data analytics requirements to have accurate information. Invest in the appropriate analytics tools and technologies that align with your needs.

Choosing the right analytics tool depends on your specific business needs and objectives. Evaluate various tools based on their functionalities, ease of use, scalability, integration capabilities, and cost-effectiveness. Prioritize tools that align with your data infrastructure and analytical requirements.

Properly defining user requirements is essential for successful data analytics projects. Engage with end-users, stakeholders, and subject matter experts to understand their specific needs and pain points. Conduct thorough requirement-gathering sessions to capture all relevant details. Regularly communicate and validate requirements throughout the project.

To consolidate data from multiple sources, invest in data integration tools and techniques. Utilize Extract, Transform, Load (ETL) processes or Data Integration Platforms to extract data from various sources, transform it into a unified format, and load it into a central repository. Prioritize data quality and ensure data consistency and accuracy during the consolidation process.

Ensuring the quality of collected data is critical for reliable analysis. Implement data quality checks and validation mechanisms at the data entry points. Regularly perform data audits to identify and address data quality issues promptly. Establish data quality standards and monitor adherence.

Real-time data access is essential for many business scenarios, especially in dynamic and time-sensitive industries. To access data in real-time, deploy real-time data streaming and processing technologies.

Building a data culture starts with top-down leadership support and encouragement. Promote data literacy among employees by providing data training and awareness programs. Foster an environment of data-driven decision-making by recognizing and rewarding data-backed meaningful insights. Encourage data sharing and collaboration across departments to promote data-driven initiatives and empower employees to leverage data effectively.

Building a data culture among employees involves creating awareness and promoting the value of data-driven decision-making. Provide training and resources to enhance data literacy and analytical skills. Encourage employees to share insights and successes derived from data analysis. Incorporate data-driven practices in performance evaluations and recognition programs.

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