The world is changing dramatically due to the rising influence of technology. Things are becoming increasingly fast-paced and on-demand. News is on-the-minute. Ten years ago, nobody would care about yesterday’s newspaper. Today, nobody cares about news that is four hours old. Live news is now the standard. We’re engaged with the online world more than ever, and all this interaction creates something we often forget about, like dust collecting on a forgotten shelf; immense amounts of data.
Your data is leaving trails all over the internet, every hour, every day. Your preferences, location, browser, device, search history, and more! This rich, abundant data, stockpiled over time, is now seen as potential intelligence to fuel business, IF they can access and unlock the insights within it. To that end, whether we like or it or not, competitors are using this data to gain an advantage wherever they can. To put that statement in perspective, let's examine this statistic from Salesforce; “49% of the general population now uses generative AI, and 34% uses it every day”. To stay afloat in a world which is becoming increasingly competitive through the use of technology, businesses have to adapt quickly to remain relevant. This is especially true in any field related to IT and technology in general.
Now, before you start printing copies of Ted Kaczynski’s manifesto and handing it out to your neighbors, let's take a moment to realize that change has always been a constant in life. Take for instance, the introduction of the auto mobile. Many people thought it could never replace the time-proven reliability of the horse. Or take the development of the printing press and the profound impact it had on society at the time. How about the emergence of the internet itself? With every emergence of innovative technology, there are its detractors and critics. It is true that changes are taking place at an increasing rate, but the reality is still the same; adapt or fail.
Now, we stand on the precipice of a new era; the era of artificial intelligence (AI). The allure of AI has grown faster than any previous technology and presents a new world of theoretical possibilities. Just like the printing press, AI looks like it’s on its way to shake-up industries, reimagine processes, and redefine the way we approach tasks we thought would never change. And, at the very bedrock of all this change lies the one thing we’ve been collecting for years; data. Lots and lots of data.
It is no understatement nor is it hyperbole; the world of artificial intelligence is largely possible due to our vast collections of data. All this data has given us unfathomable amounts of information to analyze and use for models. And the more data we collect, the better we become in using data to predict outcomes. In fact, Google will often know things about your personal life before you do. Sounds crazy? Remember the story about Target predicting a woman’s pregnancy before she knew it? Although the story has some controversy attached, it still paints a picture of what is possible with vast amounts of data. “Data is the new oil” is a phrase many will be familiar with. However, as we move ahead, we are realizing that data quality is just as important as data itself.
For a moment, let us imagine AI as an engine. This engine, made from code rather than physical parts, has the power to propel your businesses into new territories and avenues of innovation. However, just like any engine, this engine requires fuel, and that fuel comes in the form of data. But here's the catch; if this "data fuel" isn't of the highest quality, you end up with an unstable and contaminated mix of combustible elements. It's like you've thrown sand into your gas tank. Rather than firing each cylinder in the engine in a precise and reliable manner, this sub-par fuel could grind your engine to a halt. With this analogy in mind, data quality makes its way into the spotlight and appears as the central cog that keeps an operation running as planned.
Okay, so data quality is important, but how can we define data quality? In essence, we can say that data quality is a measurement of a dataset's fidelity, accuracy, and relevance. It encompasses all aspects of information, from its timeliness, to its alignment with an organization's objectives. Within the discussion of data quality, we often refer to the notion of clean data—a term denoting the “purity of data,” or data free from errors, inconsistencies, and duplicates. When you look at data this way, clean data isn’t just a preference, but necessity required in almost every instance, regardless of industry, location or scale.
No great decision ever came from terrible data quality
As we delve deeper into the relationship between AI's potential and the vast sea of data that surrounds us, a profound reality emerges: AI algorithms, no matter how advanced, are only as reliable as the data they learn from. Imagine a sculptor attempting to carve a statue from a block of uneven, flawed marble full of cracks. No matter how great the sculptor's skill level, the imperfections of the medium used will inevitably reveal themselves in the final product.
In a similar way, AI algorithms are like students learning from the data patterns they are presented with. However, unlike humans, these “students” lack any form of innate understanding of the world. Instead, they develop their insights in a mechanical fashion, they are programmed to form patterns, and detect outliers in those patterns. Because good behavior is determined by “good patterns”, the nature of their understanding is bound to the quality of the data they train on. This intertwined relationship underscores the close link between AI's full potential and the quality of data. When data is accurate, complete and free of duplications, AI's capacity to predict, recommend, and automate actions in the desired way reaches new heights.
At the heart of achieving clean data lies a series of processes that cleanse, refine, and enhance data from its raw, unpurified state, into sought-after material rich in potential energy. In this process, raw data is forged into reliable sources of knowledge and truth. One of these techniques, often regarded as the most important, is data deduplication. The process of data deduplication—often casually referred to as "de-duping"—is quite literally the elimination of duplicate data entries within a given dataset. This step in the process of data cleansing not only enhances data accuracy, but also streamlines operations by minimizing redundancy and confusion.
However, even though data deduplication is a critical part of data cleaning, the realm of data quality extends far beyond that of deduplication. Data validation and verification are also important cornerstones that ensure data entries are accurate, authentic, and current. Another aspect is data enrichment, which breathes life into datasets by supplementing them with additional information. Lastly, we have data standardization which guarantees uniformity by establishing consistent formats, units, and structures across diverse data fields. Together, these techniques form the scaffolding upon which clean data stands.
As businesses explore this new realm of AI, the significance of quality data is becoming glaringly obvious. Imagine any scenario where decisions—be they strategic or tactical—are being made using AI-generated insights. These decisions have the ability to impact growth, profitability, and ultimately make-or-break your business. When the reliability of these decisions hinges on the quality of the data feeding them, making sure your data quality is top-notch becomes a priority. After all, who would feel comfortable making any strategic decision based on data filled with false and unreliable information?
As the world is more real-time and the digital world never forgets, the ramifications of relying on unclean data become even more disastrous. A chain reaction that might have taken weeks to occur now happens in hours. People often say they make poor decisions when they are tired, or hungry. Like walking into the supermarket on an empty stomach and leaving with a bag of crisps and loads of chocolate. Poor data also leads to poor decisions. However, the results can be a lot worse than eating a bag of crisps. When it comes to your business, a poor decision could end up in your business losing market share, not to mention customer trust, and the competitive advantage you worked so hard for. Additionally, the operational inefficiencies arising from poor data quality inflate your running costs and can throw a spanner in your growth strategy. It’s called the ripple effect, and this effect extends throughout the organization. Whether those ripples exude a positive change or turn things pear-shaped is often down to data quality.
To kick off this guide to artificial intelligence, we've begun the journey by unravelling the bond that intertwines AI's potential with the need for reliable data. We've defined the essence of data quality as an intrinsic component that serves as the lifeblood of AI's effectiveness, as well as mentioned the techniques that nurture data quality. Although there is still a lot to discuss on this vast topic, a truth we can take away from this part of the guide is that even the most sophisticated algorithms are reliant on quality data for quality results.
As we make our way further through this topic, remember that the real triumph of AI rests upon the integrity of the fuel it consumes. Clean data is the cornerstone of propelling AI's abilities to unmatched altitudes. In the chapters ahead we will continue to explore the dance that unites AI and data quality and examine the transformative impact they conjure when working together.