Why Big Data Projects Fail: Insights from Gartner’s Research on 85% Failure Rate
The concept of big data is no longer a new one. It has become a buzzword in recent years. Companies across various industries are increasingly relying on big data to make data-driven decisions. However, despite the promises of big data, the success rate of big data projects has remained low. According to Gartner, the research firm, 85% of big data projects fail to meet their objectives. This article takes an in-depth look at the reasons behind the high failure rate of big data projects and provides insights on how to avoid them.
The Cost of Failure
The cost of failure of big data projects is substantial. Failed projects not only result in a waste of resources and investments but also lead to a loss of confidence in big data and, in some cases, even reputational damage. Big data projects require significant investments in technology, people, and infrastructure, and the failure of such projects can result in significant financial losses.
Reasons Behind the High Failure Rate of Big Data Projects
There are several reasons why big data projects fail. The most common causes of failure include:
1. Lack of Clarity on Business Objectives
Big data projects fail because they lack clarity on business objectives. Companies spend too much time and money on technology, infrastructure, and data collection and processing without understanding how it will help them achieve business objectives. The focus should be on how big data can contribute to achieving business goals. Companies must first identify the business objectives and then align the big data project with those objectives.
2. Absence of Skilled Personnel
Data is only valuable if it is analyzed and turned into actionable insights. Big data projects require skilled personnel to manage and analyze data. In many cases, big data projects fail because of the lack of skilled personnel. Companies must invest in training and hiring personnel with skills in data science, data analytics, and machine learning.
3. Technical Challenges
Big data projects require handling a vast amount of data from various sources. Managing such data requires robust infrastructure, technology, and tools. In some cases, the cost of technology and infrastructure can be prohibitive, leading to poor quality solutions or systems that are overly complex.
4. Inadequate Data Governance and Security
Big data projects involve handling sensitive and valuable data. Companies must ensure that data governance and security are integrated into the project design to safeguard data from theft or fraudulent use.
How to Avoid Failure of Big Data Projects
1. Define Business Objectives
The first step in avoiding the failure of big data projects is to define clear business objectives. The focus should be on how big data can help the company achieve business goals. All stakeholders must agree on the business objectives and define how the big data project aligns with those objectives.
2. Invest in Skilled Personnel
Skilled personnel are essential for the success of big data projects. Companies must invest in training and recruitment of personnel with skills in data analytics, data science, and machine learning.
3. Adopt a Flexible Approach
Adopting a flexible approach to big data projects is critical to avoiding failure. Companies should break down large projects into smaller ones and evaluate the results regularly. Reviewing the results regularly allows for adaptation to changes in business objectives and market conditions.
4. Emphasize Data Governance and Security
Data governance and security should be integrated into the project right from the design stage. This will ensure that data is protected from unauthorized use or fraudulent activities.
Conclusion
The failure rate of big data projects is high. However, understanding the reasons behind the failures and taking appropriate steps can improve the success of these projects. By defining business objectives, investing in skilled personnel, adopting a flexible approach, and emphasizing data governance and security, companies can avoid failure and achieve the desired outcomes from big data projects.
