Future-Proofing SMBs Through Data Engineering

Want AI-Driven Success? Look to Your Data

Your data probably isn’t pulling its weight.

It’s a bold statement, but it serves as a wake-up call to challenge the way you manage your most precious resource:

  • A survey by Precisely reveals that 70% of respondents cite data quality as the biggest issue for their businesses. 
  • A report by ZipDo states that large businesses lose more than £1.59 million per year due to poor data quality. 

In the race to become AI-ready before the competition, your data is at the core of innovation, sharper decision-making, and unparalleled business growth. 

But here’s the catch: 

A pervasive obstacle stands in the way for countless organisations, preventing them from unlocking these advantages – the current state of their data.

Your Data Is Preventing You From Becoming AI Ready

The journey towards AI integration will throw challenges at you. From ensuring data quality and managing complex data ecosystems to establishing robust governance frameworks and addressing security imperatives. 

According to several sources, these are the biggest data challenges preventing businesses from making full AI integration a reality:

Data Quality Quagmire

Deloitte’s State of AI in the Enterprise survey points out that at least 40% of businesses dipping their toes into AI report only a “low” to “medium” sophistication in their data practices. 

This gap in data quality can skew AI models and misguide outcomes, making it a significant hurdle in the AI adoption process.

The Impact of Data Management

The journey to AI excellence is hindered by a maze of data management hurdles.

Topping the chart of technological barriers to AI/ML deployment is data management itself, 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 as more businesses migrate their AI workloads to the cloud, amplifying their data integration struggles.

Governance Gaps

In an era where data governance should be high on the priority list, an alarming number of businesses still don’t have a coherent strategy or dedicated resources to tackle the issue. 

This oversight can result in murky policies around personal data use, a lack of governance experts, and stunted development of an all-encompassing data strategy.

The Price of AI

One of the biggest challenges for small and medium-sized businesses (SMBs) in fully immersing themselves in AI are the costs associated with it.

The investment isn’t just in the technology itself, but encompasses the cost of curating and preparing data, training personnel, and fostering a culture to navigate the new AI landscape.

Unstructured, Siloed Data: A Roadblock to Progress

Let’s look at one of the core issues that most businesses face with getting the most from their data: Data Silos. When data is scattered across different systems, in various formats, and without consistent standards, it becomes nearly impossible to harness for strategic advantage. 

It not only muddies decision-making but also places a yield sign on innovation. 

CIO Dive reveals that data silos can cost large companies an average of £3.97 million per year in lost productivity and poor decision-making due to inaccessible or unusable data.

Companies that poorly manage their data assets might see a 20-40% reduction in potential revenue due to poor data quality and accessibility issues caused by data silos.

The Solution? Data Engineering

Dealing with a bit of sidekick syndrome, data engineering tends to take a backseat in the AI-driven success story. 

By turning raw, unstructured data into a well-organised, easily accessible format, data engineering helps businesses leverage their data properly—especially for AI endeavours.

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. 

In fact, 50% of respondents in a study done by Monte Carlo 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 and merging different data sets.

Cost Reduction

By improving data quality and reducing the need for manual data processing, data engineering can help businesses 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.


With access to accurate and integrated data, businesses 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.

Data Mesh

Data engineering plays a crucial role in implementing a data mesh architecture. This allows for the decentralisation of data management and enables businesses to scale and adapt quickly to changing data needs.

It’s also a critical aspect of any business that has data at the centre of its decision-making, helping them unlock the full potential of their data, make data-driven decisions, and gain a competitive edge.

Making AI Work for Your Business Begins with Data Engineering

Without it, even the most advanced AI algorithms and analytics tools can run into hiccups, hallucinations, and hindrances. 

Data engineering ensures that your data is not only clean and accurate but also integrated and aligned with your business objectives. It’s the critical first step in unlocking the value of data, enabling predictive insights, personalised customer experiences, and optimised operations.

Recognising the Role of Data Engineering

Recognizing the pivotal role of data engineering in leveraging AI and analytics is just the beginning. 

The real challenge lies in implementing effective data engineering practices that align with your unique business needs. This is where our expertise comes into play.

As specialists in data engineering, machine learning engineering, software engineering and UX Design, we’re readily equipped to tackle the complexities of preparing your digital environment for a world of AI.

By establishing a robust data engineering foundation, we pave the way for seamless AI integration and analytics that drive real results. Our team of experts is dedicated to transforming your data into a powerful asset that works tirelessly for your business, ensuring it’s not just hard at work but working smarter.

Is Your Business Ready to Turn Data Chaos into Clarity?

To unlock the full potential of AI and analytics, the journey begins with solid data engineering. 

Let us be your partner in this transformative journey. 

With our expertise and commitment to excellence, we’ll ensure your data is not just working hard but propelling your business towards unprecedented success.

Connect with us today, and let’s embark on a journey to harness the true power of your data. Together, we can build a data-driven foundation that not only supports but accelerates your AI-driven success. 

The path to innovation, improved decision-making, and business growth starts here.

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