Why 85% of Big Data Projects Fail According to Gartner: Learn The Reasons

Why 85% of Big Data Projects Fail According to Gartner: Learn The Reasons

Big data has become an essential element in today’s business landscape. Companies are relying on it to make informed decisions, gain insights, and stay ahead of the competition. According to research firm Gartner, though, a staggering 85% of big data projects fail to deliver on their promises. In this article, we explore the reasons behind this high failure rate.

Reason #1: Lack of Clear Objectives

One of the main reasons why big data projects fail is the lack of clear objectives. Before implementing a big data project, companies need to identify their goals and specify the outcomes they want to achieve. Without clear objectives, projects are likely to spin out of control and fail.

Reason #2: Poor Data Quality

Big data relies on massive amounts of information. However, if the data used in the project is of poor quality, the outcomes will be unreliable, leading to project failure. To avoid this, companies need to invest in data quality processes, including data profiling, data cleansing, and data standardization.

Reason #3: Lack of Skilled Professional Resources

Big data projects require skilled professionals who know how to handle large amounts of data. However, companies often lack resources with the right expertise. To address this challenge, companies can train their employees or outsource to vendors who can provide the necessary skills.

Reason #4: Technical Challenges

Implementing big data projects can be very technical and complex. Companies often struggle to integrate different systems, handle large amounts of data, and ensure data security. To overcome these challenges, companies can work with vendors that specialize in implementing big data projects.

Reason #5: Failure to Involve Business Stakeholders

Big data projects are not just technical; they also involve business stakeholders. Companies often fail to involve these stakeholders, leading to project failure. To avoid this, companies need to understand the role of business stakeholders and involve them in the project from the outset.

Conclusion

Big data projects can be highly beneficial, but they also come with significant challenges. To ensure success, companies need to set clear objectives, invest in data quality, hire skilled professionals, address technical challenges, and involve business stakeholders. By doing so, companies can maximize the value they receive from big data projects and avoid being part of the 85% that fail.