Want AI-Driven Success? Look To Your Data

Your data probably isn’t pulling its weight.

It’s a bold statement. But also a wake-up call to challenge the way that 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. On top of that, 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 is beset with challenges: from ensuring data quality and managing complex data ecosystems to establishing robust governance frameworks and addressing security imperatives. 

According to a variety of sources (thank you, Perplexity), 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 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: 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 with more organisations migrating their AI workloads to the cloud and amplifying their data integration struggles.

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

An oversight which 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, not to mention training personnel and fostering a culture to navigate the new AI landscape.

The stakes are high, but the rewards of navigating this terrain successfully are unparalleled—ushering in an era of unprecedented innovation and competitive edge.

For businesses that want to unlock AI’s potential, the path forward involves a commitment to elevating their data management practices, investing in comprehensive governance strategies, and insisting on stringent data security standards from their AI partners.

Unstructured, Siloed Data is 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.

So many businesses face this predicament, yet few address it effectively. 

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 up 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. These silos can lead to a 30% increase in the time spent searching for and validating data, impacting operational efficiency and decision-making processes.

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. Businesses with data silos are also 23x more likely to struggle with acquiring new customers and are 19x more likely to struggle with retaining existing customers due to fragmented data impacting customer insights and service delivery.

Imagine trying to complete a jigsaw puzzle without knowing what the final picture looks like, and some pieces are missing or don’t fit anywhere—this is the reality for businesses dealing with unstructured, siloed data.

The path to AI adoption is fraught with pitfalls, primarily rooted in the murky waters of data management. Here’s a sobering reality check: poor data management is not just an inconvenience—it’s a formidable barrier to leveraging AI’s transformative power. Let’s dissect the multifaceted challenges that underscore this dilemma.

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. 

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.

Data Mesh: Data engineering plays a crucial role in implementing a data mesh architecture, which allows for the decentralisation of data management and enables organisations to scale and adapt quickly to changing data needs.

Data engineering is 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 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.

In essence, data engineering transforms data from a static asset into a dynamic, strategic tool.

Recognising 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, cloud architecture, and UX services, we are equipped to tackle the complexities of making AI work for your business.

Target the cause of your data challenges

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.

Dive into the future. 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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