THIS is Why Your AI Projects Probably Keep Failing

So many AI projects fail to deliver on their promise.

Whether it’s the data, the algorithms, or the wrong teams – most AI projects unfortunately fail to meet expectations or offer even a glimpse of the vision you sought. 

But what’s at the core of this AI roadblock?

A survey by Datanami reveals that 33%-38% of respondents reported failures or delays in AI projects due to poor data quality.  And according to Forbes, 60%-80% of AI projects are failing and among the few companies using AI successfully place data at their core.

To put it bluntly: Data quality, data biases, algorithms and the wrong teams. 

The culprit lies in a frequently underestimated area

With data being the biggest culprit to obstructing your pathway to AI success, data engineering might be the answer.

Just to remind you of the profound impact that poor data infrastructure and quality can have on AI initiatives: 

  • Gartner reveals that the average financial impact of poor data quality on organisations is around £7.69 million per year.
  • IBM estimates that businesses lose around £2.46 trillion annually due to poor data quality.  
  • Precisely reveals that 70% of respondents cite data quality as the biggest issue for their businesses.

From inaccurate data collection to flawed processing, data integrity is not just helpful, but essential for the success of AI projects and should never be treated as an afterthought.

Without clean, complete, and accurately processed data, even the most advanced AI models are doomed to fail. Which can lead to misguided insights, poor algorithms, hallucinations and project failures.

These are the biggest data challenges preventing businesses from making full AI integration a reality:

Data Quality: At least 40% of organisations dipping their toes into AI report only a “low” to “medium” sophistication in their data practices. 

This gap in data quality is a great way to skew AI models and misguide outcomes.

The Impact of Data Management: Topping the chart of technological barriers to AI/ML deployment is data management, cited by 32% of those surveyed in an S&P Global Market Intelligence study. 

Cleansing, integrating disparate data sources, and training AI models start compounding with more organisations migrating their AI workloads to the cloud and amplifying their data integration struggles.

Your Solution: Data Engineering

This is where the indispensable role of data engineering shines. 

Data engineering ensures that your data is not only accurate, complete, and consistent, but also integrates data from diverse sources, providing a holistic view that is crucial for informed decision-making. 

It lays the foundation for AI by building robust, scalable, and reliable data infrastructures. This helps facilitate faster access to data, enabling real-time decision-making and allows you to swiftly adapt to market changes. By improving data quality and streamlining data processing, data engineering significantly reduces the need for manual intervention. This helps save costs and enhance operational efficiency. 

In the context of decision-making, access to accurate and integrated data means organisations can make more informed decisions, improving performance and competitiveness.

What are some of the biggest impacts that data engineering has on businesses?

Data quality: Data engineering helps ensure that data is accurate, complete, and consistent, reducing the risk of errors and improving the reliability of data for decision-making.

Did you know: 50% of respondents reported that data engineering is primarily responsible for data quality, compared to 22% for data analysts, 9% for software engineering, and 7% for data reliability engineering.

Data integration: By unifying data from different sources, data engineering gives businesses the ability to make informed decisions based on an overall view of their data, streamlining processes and driving operational efficiency.

Almost 40% of projects fail due to difficulty integrating different data sets, and 40% of projects fail because they struggle with merging different data sets and making them work well together.

Cost reduction: By improving data quality and reducing the need for manual data processing, data engineering can help organisations save costs and increase efficiency. 

With businesses losing more than £1.59 million per year thanks to poor data quality, it’s safe to say that data quality needs to be treated with the utmost care.

Decision-making: With access to accurate and integrated data, organisations can make better-informed decisions, leading to improved performance and competitiveness.

And while it might seem like an obvious choice: 78% of executives still struggle with utilising their data for decision-making, and over a third admit they don’t use it for decision-making.

Back Yourself By The Best In Data Engineering

Are you ready to navigate the complexities of data and tackle your AI projects with confidence? 

We understand the pivotal role of data engineering in unlocking the full potential of AI technologies. Our expertise in data engineering, combined with our prowess in machine learning engineering, cloud architecture, and UX services, positions us as your ideal partner in the journey towards AI success. 

If you’re ready to transform the challenges of data management into opportunities for innovation and growth, then contact us today and ensure your AI projects are built on a foundation of high-quality, well-integrated data. 

Don’t let the true cost of ignoring data engineering undermine your AI ambitions. 

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