Big data and analytics have become integral to the success of many organizations in the modern world. However, despite the advantages that big data and analytics can bring, projects related to these two areas often fail.

 In fact, a study by Gartner found that through 2022, only 20% of analytic insights will deliver business outcomes. This means that the success rate of analytics projects is actually quite low. 

Another study by VentureBeat, 2019 states that 87% of data science projects never make it into production. So, what are the reasons behind this high failure rate?

There are many reasons why big data and analytics projects fail. Here are 10 of the most common ones:

1. Lack of governance framework and standards.

One of the main reasons why big data and analytics projects fail is because there is a lack of governance framework and standards in place. The governance framework and standards mean that there are rules and guidelines in place that everyone involved in the project must follow. 

This includes things like how data should be collected, processed, and analyzed.  In addition, it can be difficult to ensure that data is accurate and of high quality. This can lead to Without a governance framework and standards, it is very difficult to ensure that the project is carried out in a consistent and reliable manner.

insights that are inaccurate or misleading, which can in turn lead to bad decision-making.

Solution: Creating a project charter.

The project charter serves as a document that outlines the goals, objectives, and scope of the project. It also establishes the roles and responsibilities of everyone involved in the project. This can help to ensure that everyone is on the same page and that there is a clear understanding of what is expected of them.

 In addition, the project charter can help to ensure that there is a clear plan in place for how the project will be carried out. This can help to reduce the chances of things going off track.

2. Lack of clear business objectives.

Another common reason why big data and analytics projects fail is that there is a lack of clear business objectives. The business objectives are the goals that the organization wants to achieve through the project. 

Without clear business objectives, it can be difficult to determine whether or not the project is successful. In addition, without clear business objectives, it can be difficult to prioritize the tasks that need to be completed and to allocate resources effectively. This can lead to a lot of wasted time and effort, which can ultimately make the project unsuccessful.

Solution: Defining key performance indicators (KPIs).

One way to overcome this challenge is to define key performance indicators (KPIs) before the project starts. KPIs are quantifiable measures that can be used to track and assess progress toward the business objectives. 

Defining KPIs upfront can help to ensure that the project stays on track and that everyone involved is aware of what needs to be achieved. In addition, KPIs can help to provide clarity on what tasks need to be given priority.

3. Lack of data quality control.

A third common reason why big data and analytics projects fail is that there is a lack of data quality control. Data quality refers to the accuracy, completeness, and timeliness of the data. It is important to have high-quality data in order to make sure that the insights that are generated are accurate and reliable. 

Without proper data quality control, it is very easy for errors to creep into the data, which can ultimately lead to inaccurate insights. This can be extremely damaging to the organization, as it can lead to bad decision-making.

Solution: Implementing a data quality management plan.

One way to avoid this problem is to put a data quality control process in place before the project begins. This process should involve things like data cleansing, data verification, and data validation.

 In addition, it is important to have clear guidelines in place for how data should be collected, processed, and analyzed. These guidelines should be followed by everyone involved in the project. In addition, regular audits should be conducted to ensure that the data quality control process is working as intended.

4. Complex Models

Complex models can be difficult to understand, interpret, and use. This can lead to confusion and frustration among those who are trying to use the model. In addition, complex models can be difficult to maintain, which can make it hard to keep the project up-to-date. This can ultimately lead to the project being abandoned altogether.

Solution: Keeping the model simple. But not too simple.

When creating the model, it is important to think about how it will be used and by whom. Ask yourself the following questions:

  • What decisions will be made with this model? (eg. pricing, product development, etc.)
  • Who will be using this model? (eg. executives, analysts, marketers, etc.)
  • What level of detail do they need? (eg. high-level overview, detailed analysis, etc.)
  • What data do we need to collect? (eg. customer data, financial data, etc.)
  • How often will the model be used? (eg. daily, weekly, monthly, etc.)

Remember: Creating a model that is too simple may lead to inaccurate results while creating a model that is too complex can make it difficult to use and understand.

5. Data Structure

Data structure is the most important factor for the success of big data and analytics projects. Without accurate, updated, and complete data, businesses cannot take full advantage of their big data analytics capabilities. 

Companies often manage data at a local level, which can lead to information silos and redundancies. Information Silos are often created within an organization when departments or groups fail to share information with other departments or groups. 

As data sets grow and become more complex, it becomes increasingly difficult to keep them updated and accurate. This can lead to large-scale projects becoming bogged down by data quality issues. More importantly, this can have a negative economic impact on the business.

Solution: Use a data lake.

In order to avoid this problem, it is important to have a central repository for all data. This repository should be accessible by all departments and groups within the organization. Ensuring that everyone has access to the same data will help to avoid information silos and redundancies. In addition, it is important to have a process in place for regularly updating and cleansing the data. This will help to ensure that the data is accurate and up-to-date.

6. Lack of skilled personnel.

A critical element of any big data and analytics project is having the right team in place. The team should have the necessary skills and knowledge to successfully carry out the project. In addition, the team should be able to work together effectively. Without the right team in place, it is very difficult for a project to be successful.

Solution: Hire skilled personnel or train existing personnel.

If you do not have the necessary skills and knowledge in-house, you will need to either hire skilled personnel or train existing personnel. Hiring skilled personnel can be difficult and time-consuming. In addition, it can be expensive.

 Training existing personnel can be a good option if you have the time and resources to do so. However, it is important to ensure that the training is relevant and that it covers all of the necessary topics such as big data, data analytics, and data visualization.

In addition, it is important to provide employees with access to resources that they can use to further their understanding of these technologies.

  • Technical skills can be learned through online resources or through formal training programs.
  • Soft skills can be learned through team-building exercises, workshops, or seminars.

7. Inflexible IT infrastructure that isn’t designed to support big data workloads efficiently or cost-effectively.

Many organizations have an IT infrastructure that is not well-suited to support big data workloads. This can lead to a number of problems, such as slow performance, high costs, and difficulty scaling the system. As a result, it can be very difficult to make the project a success.

Solution: Choose the right big data platform.

It is important to choose a big data platform that is designed to support the specific workloads that you will be running on it.

  • Should be able to scale easily and efficiently as your needs change.
  • Should be cost-effective so that you can keep your project within budget.

For instance, Cloud-based solutions are designed to be more flexible and scalable than traditional on-premise solutions. In addition, they are often more cost-effective. Another option is to use a hybrid solution, which combines on-premises and cloud-based components.

8. Lack of operationalization & expenditure on maintenance.

Operationalization is the process of taking a big data project from development and testing and making it part of the organization’s day-to-day operations. This can be a challenge for many organizations, as it requires a significant investment of time, money, and resources. Without proper operationalization, many big data projects fail to meet their objectives.

Solution: Operationalize the big data project.

Operationalizing a big data project can be a challenge, but it is important to do so in order to make the project a success. There are a number of ways to operationalize a big data project, such as:

  • Developing an end-to-end process for data ingestion, processing, and analysis.
  • Creating a self-service portal for data users.
  • Automating processes where possible.
  • Monitoring and optimizing performance on an ongoing basis.

Investing in technology can help to reduce the amount of time and resources that are required to maintain the system.

9. Lack of leadership commitment and ownership.

Without strong leadership commitment, it can be very difficult to make a big data project a success. This is because leadership is responsible for setting the direction for the project and ensuring that it has the necessary resources. 

In addition, leadership is responsible for making sure that the project aligns with the organization’s strategy. Without this level of commitment, it can be very easy for a project to get off track.

Solution: Engagement, support, and ownership from leadership.

Leaders are the backbone of any successful big data project. In order to increase the chances of success, leaders should:

  • Set the direction for the project and ensure that it aligns with the organization’s strategy.
  • Ensure that the project has the necessary resources.
  • Make sure that there is clear ownership and accountability for the project.
  • Communicate regularly with all stakeholders about the progress of the project
  • Monitor the project closely and make adjustments as needed.
  • Be prepared to make changes to the project if it is not meeting its objectives.

Showing commitment and engagement in these ways will go a long way towards making the project a success.

Final Thoughts on Why Big Data and Analytics Projects Fail?

While there are many reasons why big data projects can fail, there are also many things that organizations can do to increase the chances of success. By taking the time to understand the risks and challenges associated with big data, and by putting in place the right processes and tools, organizations can greatly improve their chances of success.

When done correctly, big data can be a powerful tool that can help organizations to achieve their goals. With the right approach, big data projects can transform businesses and have a profound impact on the world.   

Christoph Hecking – Head of Sales  at kiimkern.