Clean Data's Business Impact
We’ve covered some of the damage poor data can do to an organization, but now it’s time to change the focus, and look at how improving data quality can dramatically change this scenario in a positive way. It’s time to flip the perspective, and explore just how positive the impact of clean data can be. As we’ve stressed earlier, clean data isn't just a mere buzzword; it in in fact the lifeblood to deriving success from AI-driven processes. And as we navigate this landscape of change in depth, we'll unpack how clean data is not only a foundation of truth, but a cornerstone of business excellence.
Imagine a sales team powered by the latest AI intelligence processes, deriving insights from clean and pure CRM data. Using AI, their predictions for the next quarter are remarkably more accurate. Don’t scoff or shake your head, because this isn’t science fiction or magic; it's the direct result of the clean data. This isn’t a theoretical, untested ideology, it’s a fact that’s proven itself in many organizations to data. In fact, according to Salesforce, “98% of sales leaders say forecasting accuracy will be improved by AI (41% say major improvement)". Clean, quality data enables AI algorithms to discern patterns and trends in historical data, market trends, and customer behaviours with accuracy. As a result, the sales team can not only make informed decisions on which leads to prioritize, but also focus on the exact opportunities to pursue, and which strategies to employ for a better chance of success. In this way, clean data data transforms AI from a guessing game into a strategic powerhouse.
As we’ve mentioned earlier, you can think of AI models as students in a classroom. But just imagine if every textbook contained spelling mistakes and grammatical errors. When it came to test time, could you blame the students for making mistakes? When AI models are trained with clean data, they learn from relevant examples, like students learning from good textbook. In healthcare, for example, AI models trained on clean medical records can provide accurate diagnoses and treatment recommendations. Yet, if they happened to be trained with inaccurate data that contained errors in treatment guidelines and outcomes, they would likely make critical errors that impact patient outcomes -a scary thought! Clean data ensures AI models are well-educated, leading to dependable outcomes.
Data deduplication is most often the unsung hero in the fight against poor data. In this example, just picture a marketing campaign powered by AI by leveraging customer data readily available in your CRM like Salesforce. If this data contains duplicates or inconsistencies, AI might mistakenly classify some customers as high-value when they're not, leading to misguided marketing efforts and wasted opportunity to increase trust with the customer. As this example highlights, for marketing professionals, clean data is important, in fact “63% of marketers say trusted customer data is important for using generative AI successfully.” Clean data, fortified by data deduplication, eliminates such errors, ensuring that AI operates with unwavering accuracy.
A tense scenario for any company; imagine a business executive navigating a complex merger. In this merge, they’re relying on AI-driven analytics to guide their every step in the process. The accuracy of these analytics hinges on the quality of the data in the organization. All aspects of data quality come into play; how well has the data been maintained? Is it full of errors, or duplicates? If you know your data is clean and void of these issues, you can also be confident in the power of AI to decipher your complex internal datasets, unveiling trends and identifying opportunities. The executive can make well-informed choices based on laser-accurate finding, leading to a successful merger with half the stress. However, if the data is marred by inaccuracies, the AI's guidance could lead to decisions riddled with assumptions that may come back to bite.
We’ve mentioned the importance of personalizing customer interactions earlier. Here’s a statistic to help us paint this picture in more detail; According to a report from Salesforce ‘’73% of customers expect better personalization as technology advances’’. And, in a world of personalized experiences, clean data is the absolute key to perfection. Consider an e-commerce platform using AI to recommend products to customers. Using AI, they craft personalized recommendations that cater to their customers individual preferences, such as price, product type, brand, and more. This has a great effect in increasing customer satisfaction which, as a natural by-product, leads to better sales. The reason for success? They made sure the data they had on each customer is and accurate and up to date with the latest interactions in their system. This effective approach would be near impossible if the data is cluttered with inaccuracies, customers may receive irrelevant recommendations, leading to dissatisfaction and decreased sales.
Clean data isn't just a trust builder that helps tailor better experience for customers; it's an efficiency enhancer – it’s like giving everyone in your organization a Red-Bull! Picture an organization automating its customer support through AI chatbots -no, not those dreaded annoying chatbots we learn to hate – smarter chatbots armed with robust and comprehensive knowledge about every facet of your organization. Clean data ensures that these “bots” operate seamlessly and help customers rather than chase them away. With a vast array of knowledge, they excel in reducing response times and customers find the information they’re looking for -which sometimes might just be talking to a human! Hey, some people just prefer old fashioned.
Happier customers, a company running like it was given a caffeine injection. Sounds like the dream come true right? If only it could go on that way forever. Alas, as any business knows, innovation is always such an important part of survival. Remaining stagnant is not an option, and when it comes to innovation in AI, fuelling it with clean data is like adding fertilizer to your data garden. Clean data gives businesses more confidence as the begin to explore new possibilities, experiment with AI applications and uncover new avenues of success. For example, in the automotive industry, clean data is critical for developing self-driving cars. The data collected from sensors and cameras must be accurate and consistent to make split-second decisions. Clean data becomes the canvas upon which innovation is painted.
As it should be clear by now, a business armed with clean, accurate data gains a competitive edge in a world where every advantage, no matter how small, plays in the overall long-term success. It’s also been demonstrated that AI-powered initiatives are more likely to produce accurate results and drive outcomes that have a meaningful impact on business. While clean data is often overlooked in this area, any company that’s had experience with AI driven experiences knows the truth; clean data is a key differentiator between an average improvement to a great success. When processes heavily rely on data, the ability to harness clean data really is the hallmark of excellence.
Data deduplication plays a pivotal role in forming a robust foundation for AI applications. By eliminating duplicates and ensuring data consistency, data deduplication enhances the efficacy and reliability of AI systems. Let's take an example from finance. Clean data, prepared through data deduplication, is essential for algorithmic trading. Any discrepancies or duplicates in stock data can lead to disastrous financial losses. In this symbiotic relationship, data deduplication becomes the bedrock upon which AI excellence is built.
Although it’s been said before, it’s essential to highlight it again; clean data isn't a one-time effort; it's a ongoing commitment that needs to be delivered with consistency. Data deduplication is a cornerstone of data management that contributes significantly to a data quality, and it is an essential part of providing a seamless, personalized shopping experience to customers. The synergy between data deduplication and AI sets the stage for AI to begin to work it’s magic, but also keep the data in check which helps achieve continued success. We’ve seen it for ourselves and through the testimonies of our clients; clean data is the catalyst that kickstarts outcomes to be proud of.
As we conclude this chapter, you've journeyed through the profound impact of clean data, data deduplication, and AI on business outcomes. If business want to succeed in their AI strategy, they need to ensure synergy between the AI's transformative potential and the data it uses to fuel its next action.
Now, brace yourself for the next leg of our expedition.
In Chapter 5, we'll take a look at the marriage between Salesforce and AI. We'll delve into the advanced AI capabilities that Salesforce offers—a match made in heaven for businesses seeking insights, automation, and personalization. Get ready to unravel the intricate tapestry of Salesforce AI, empowering businesses with a realm of possibilities.
So, fasten your seatbelts as we step into the world of "Salesforce and AI: A Match Made in Heaven." The synergy between these two powerhouses promises to reshape the way businesses harness data, insights, and automation for unparalleled success.