7 reasons for the failure of your big data project and ways to success

7 reasons for the failure of your big data project and ways to success

Big data is a huge buzzword in the tech world. However, you'd be surprised to know there are many reasons why data science projects fail and how many already have!

Data analysis keeps spreading its influence through more and more industries. Suddenly, everyone realized that properly interpreted information is an extremely valuable business asset. That’s why advanced analytics projects began to boom recently.

7 reasons for the failure of your big data project and ways to success

Hunting for data specialists becomes fiercer. Data market investments from companies have increased 87.8% since 2022. So, the data science market is rapidly growing, presenting opportunities for start-ups to enter the niche.

Having doubts? What if we tell you that 90% of all existing data in the world was created in the last 2 years? Despite such an impressive number, only 51% of companies hold a market share in data analytics in the U.S.

For someone new to the industry, it may seem like companies are spending days and nights doing some heavy data analytics. And, of course, are uncovering unbelievable superhuman solutions. 

However, despite all the hype around supersonic power of data, project failure statistics Gartner predicted in 2019 that 85% of data analytics projects fail, and so they did for the longest time.

7 reasons for the failure of your big data project and ways to success

Wow, right?

In this article we are about to uncover the 4 major reasons why IT projects fail for big data and how you can make yours successful by overcoming them.

So let's get straight into it!

Issues with big data – an overview

Here’s the untold truth about big data obsession: barely anyone knows how to build and use such projects in the right way. 

Which is why the prediction on why IT projects fail Gartner gained so much attention. So here's our expert opinion and guide to cover all the reasons in detail since the big data market is about to go wild!

Often companies become fooled with giant analytics projects like Amazon and Netflix and overlook big data project failure statistics. In most cases, succeeding in advanced analytics projects is rather exclusive than average, and requires high-end efforts. 

A recent study outlined in Harvard Business Review stated that most retail companies report having serious difficulties with adopting data analytics and AI technology. This complexity and lack of user-friendliness can also be found in every other industry and contributes to the many big data failure examples.

Contrary to popular opinion, big data projects rarely fail due to lack of budget. 

In fact, companies often get lost in details and technical aspects. They lose sight of the big picture, which turns out to be an indispensable part of data analytics projects. With that said, there are some very clear yet often unnoticed reasons for failed big data projects.

The 7 most common reasons why your data analytics project could fail

1. Companies forget about ethics and privacy policies

One critical reason why data projects fail is when companies neglect to prioritize ethics and privacy policies. Several high-profile cases serve as cautionary tales. 

For instance, the Racist Health Risk Scoring study published in 2019 revealed how an algorithm used to predict health risks systematically discriminated against black patients. 

The infamous Cambridge Analytica scandal in 2010 exposed the unauthorized access and misuse of Facebook user data for political purposes. Additionally, the Target Predicts Teen Pregnancy case in 2012 demonstrated how data analytics can reveal personal information without the individual's consent. 

Neglecting ethical considerations and privacy policies can lead to severe consequences for both businesses and individuals, damaging trust and reputation.

2. Long system response times due to complex calculations

Data analytics projects often involve intricate mathematical calculations to derive insights and predictions. However, if the system is not optimized to handle these calculations efficiently, it can result in long response times and eventually a high IT project failure rate.

Slow response times can frustrate users and hinder the effectiveness of the project. To address this issue, organizations need to invest in powerful hardware infrastructure, optimize algorithms, and employ techniques such as distributed computing or parallel processing to accelerate computational processes.

3. Costly data solutions for custom ML models or deploying ML services

Implementing data analytics projects can come with significant costs, especially when developing custom machine learning (ML) models or deploying ML services. 

Building and training ML models requires specialized expertise, extensive computational resources, and data labeling efforts. In addition, deploying ML services can involve substantial infrastructure costs, including the use of GPU resources. 

To mitigate these expenses, organizations should carefully assess the trade-offs between building custom models and leveraging existing ML solutions, considering factors such as time, cost, and the availability of skilled resources.

4. Incorrect evaluating and selecting of big data technologies

One of the common big data failures is the incorrect evaluation and selection of big data technologies. Individuals with expertise in big data may have limited exposure to only one service or tool within each category, leading to a lack of awareness about alternatives that may better suit the project's requirements. 

Thorough research, benchmarking, and consulting with experts can help organizations make informed decisions when selecting the appropriate technologies for their data analytics projects.

4.1 Confusion with Big Data tool selection

A related issue is the confusion surrounding big data tool selection. With numerous tools and technologies available, organizations can find it challenging to navigate the landscape and identify the most suitable options. 

It is crucial to evaluate factors such as scalability, ease of integration, community support, and compatibility with existing systems. Seeking guidance from experienced professionals or engaging in proof-of-concept projects can help organizations avoid the pitfalls associated with tool selection.

5. Incorrect or missing technical solution architecture

Successful data analytics projects require robust technical solution architecture that enables efficient data integration. Without a well-designed architecture, organizations may struggle to gather and combine data from multiple sources to create valuable and usable information. 

The architecture should address data collection, data storage, data preprocessing, data transformation, and data integration aspects. Thoroughly planning and implementing a solid technical solution architecture ensures smooth data flow and enhances the project's effectiveness.

6. Failing to test the solution

Testing is a critical aspect of any data analytics project, but it is often overlooked or insufficiently prioritized. Failing to conduct comprehensive testing is a serious reason why most IT projects fail and result in the deployment of flawed or inaccurate models and algorithms. 

A comprehensive testing plan should be established, covering various scenarios and ensuring that all stakeholders are aware of it. Rigorous testing helps identify and address issues early in the project lifecycle, improving the overall quality and reliability of the solution.

7. You neglect the overall project strategy

Data analytics projects require heavy tech support and constant maintenance and that's one of the biggest reasons why data projects fail.

No wonder it’s crucial to have skillful, experienced developers, and focus on technical solutions. Especially considering how costly and devastating errors can be in advanced analytics projects. 

That's why outsourcing software development to offshore companies is becoming more and more common. There are many locations for outsourcing IT operations, but Ukraine has been literally booming with offered services in the tech industry for recent years.

The case with the UK National Health Service project is probably the largest and the most expensive data project failure in history. The attempt of putting all patients records into a centralized system miserably broke down, flushing $15 billion. So it's clear how data project errors cost a very high price.

Of course, data startups are well-aware of the potential risks and try to avoid them at any cost. That’s why companies over concentrate on the ‘behind-the-scenes’ part while neglecting the big picture. Great role-models like Google and Netflix primarily succeed because of the formed vision and strategy, and not just the financial part.

5 ways to succeed with your big data projects (and avoid failures!) 

After discussing the likelihood of how many IT projects fail and common reasons why IT projects fail, it's time to focus on ways to succeed with them. 

Here are five tips to help ensure your big data projects are successful:

1. Start with a clear goal

It's obvious, but we'll say it again. The first step towards a successful big data project is to define a clear goal. Without a specific goal, your project will lack direction and purpose. 

You should identify the business problem you are trying to solve, and then use data to help you solve it. This will help you stay focused and avoid getting sidetracked by technical details. When everyone involved in the project knows what they are working towards, it will be easier to make progress and achieve success.

2. Focus on data quality

The quality of your data is critical to save you from the many unfortunate failed business projects, whether it's big data or not.

If your data is inaccurate, incomplete, or outdated, your analysis will be flawed, and your insights will be useless. Therefore, you must put in place robust data management practices to ensure that your data is accurate, reliable, and up-to-date. 

This means investing in data cleansing, data integration, and data governance to ensure that your data is fit for purpose. So before you invest in a big data infrastructure, start with improving the quality of your data itself, or make it the first step of your new project.

3. Solve your customers' problems

The current era is all about customer-centric development. All businesses that are rising above the mediocre do so because they put customers first. And you should too.

Ask yourself what your customers' problems are and how your project can help solve them. This will help you design your project with your customers' needs in mind, and it will ensure that your project delivers real value to your customers. This customer-centric approach will help you build better products and services and create more loyal customers.

4. Create an overall project strategy

The key to a successful big data project is to create a robust project strategy. 

This means defining your goals, identifying your target audience, and setting out a clear roadmap for achieving success, basically checking off all previous steps. 

You should also ensure that your project aligns with your overall business strategy and that you have the resources and support you need to achieve your goals. By having a clear project strategy, you'll be able to focus on the big picture and make strategic decisions that drive success even in the future.

5. Invest in technical expertise

Finally, to ensure the success of your big data project and avoid the biggest reason why data science projects fail, you must invest in technical expertise. 

We haven't talked about this enough, but having the right people, processes, and technology in place to manage your data effectively is what drives everything else for your project to be a success. 

Consider partnering with experts in data management, data analysis, and data visualization to help you make the most of your data. You may also need to invest in new technology, such as machine learning or artificial intelligence, to help you gain insights from your data more quickly and efficiently.

Final thoughts

Undoubtedly, the progress of a data analytics start-up hardly depends on managing tech operations at the highest level. 

However, ignoring all the reasons why do IT projects fail like a full-scale strategy, business objectives and customer-driven practices, you are likely dooming you for failure. Strive to balance both business and operational aspects, stick to a solid strategy, and rock!

Still looking for more answers? Consult the COAX experts and find out how you can maximize your data analytics projects for success!

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