AI has emerged as a transformative technology that is revolutionizing various industries and processes across the globe. With its uncanny ability to mimic human intelligence and perform complex tasks, AI is driving innovation and efficiency in almost every field you can imagine, and the realm of data quality management is no exception.
Data and AI have a close relationship. After all, AI is essentially a child whose intelligence was born through the analysis of huge amounts of data. So how will AI-powered solutions change the scope of data quality management? Predicting the future is always difficult, and much of what we predict today might be completely wrong in 10 years. But just thinking about when cellphones first entered the market, who predicted that in 10-15 years we would be watching movies on a phone, using it to navigate on the road, or tweeting to the world?
So, while we can’t predict the future, we can already see some impressive feats from the use of AI. The possibilities of AI tools like ChatGPT have already shown off some impressive possibilities. We aren’t just saying this because we assume it, we’re saying this because we’re seeing transformations ourselves at Plauti. We’ve recently implemented some POC’s using ChatGPT into our data management tool, Plauti Data Action Platform (DAP), and the results are pretty mind blowing. We’ll get to that later in the article.
It’s clear that the impact of artificial intelligence extends beyond mere automation and efficiency improvements. In fact, the way in which AI will change the world is so vast it’s hard to discuss broadly. Rather, every industry itself can already form a separate discussion about the changes they face. In this article, the focus will be on how AI can help achieve better data quality in Salesforce, although many of the topics discussed can be applied to data management principles outside of Salesforce.
To kick the article off, we’ll discuss the evolution of data management methods in Salesforce. Salesforce, as well as third party solutions, have continually introduced new features and tools to enhance data quality management and streamline data cleansing processes, but where did it all start? Let’s investigate the past methods of data cleansing and how they evolved into the present-day approach.
Manual Data Cleansing
In the early stages, data cleansing in Salesforce was primarily a manual and labor-intensive process. Administrators and data stewards had to manually review and correct data errors and inconsistencies. Of course, this approach is time-consuming and prone to human errors, limiting scalability and efficiency. As time went by, the use of data played a more important role in organizations, and thus the need for automating tasks grew stronger.
Salesforce introduced rule-based validation to automate certain aspects of data cleansing. Administrators could define validation rules to enforce data quality standards, such as mandatory fields, data formats, and dependencies. This helped catch simple errors and ensure consistent data entry. However, it relied on predefined rules and lacked the ability to handle more complex data quality issues.
Third-Party Integration and Data Quality Tools
To address the limitations of native Salesforce functionalities, organizations started integrating third-party data quality tools and services. These tools offered more advanced features such as data profiling, address validation, deduplication, and data enrichment. By integrating these tools with Salesforce, organizations could leverage external algorithms and data sources to enhance their data quality processes.
AI-Powered & Machine Learning Data Cleansing
The latest wave of data evolution, in many ways driven by the adoption of cloud and SaaS, is the integration of AI and ML technologies into Salesforce. This has ushered in a new era of data cleansing. Machine learning algorithms can analyze large volumes of data, identify patterns, and automatically detect and resolve data quality issues. Salesforce's Einstein AI platform provides capabilities like automated data matching, record merging, and data enrichment. These AI-powered features significantly reduce manual effort, enhance accuracy, and enable proactive data quality management.
Artificial Intelligence (AI) helps machines “think” like humans. While this is a gross oversimplification of AI, in the case of data deduplication, it enables a system to learn from your data and identify duplicate records through a process known as active learning. For instance, as you label each record in your data as a duplicate or not, the system learns which data fields are the most crucial to you. It can then give those fields more importance or weight, compared to others. For example, it might recognize that the "Email" field is more critical than the "First Name" field, and it can calculate exactly how much more important it is. When it comes to deduplication, it will learn that you prefer certain spelling methods, abbreviations, and so on. As time goes by and as you add new records to your Salesforce, AI powered data management systems will apply the same learned weights to those fields and automatically adapt to your needs.
The significant advantage of AI-based deduplication is that the system takes care of all the work for you. You don't need to set up complex rules or standardize your data. Instead, an AI powered deduplication tool may scan your data and learn by itself what rules to apply. This approach is highly scalable, meaning it can handle large amounts of data efficiently, unlike traditional rule-based deduplication methods.
In a nutshell, using Artificial Intelligence & Machine Learning for deduplication can save huge amounts of time and effort, as the AI system becomes a super intelligent helper that learns from your data. The more it learns, the more it ensures your records stay clean and organized without much manual intervention.
You might be wondering, what is the difference between machine learning and artificial intelligence? That’s a great question. While machine learning and artificial intelligence are related fields, they have distinct differences when it comes to analyzing duplicates in Salesforce. Okay, so what’s the difference?
Machine learning is a subset of artificial intelligence that focuses on building algorithms and statistical models that enable computers to learn from data without being explicitly programmed. In the context of analyzing duplicates in Salesforce, machine learning can be used to create predictive models that can automatically detect potential duplicate records based on patterns and features found in historical data.
For instance, you can train a machine learning model using labeled data (existing records marked as duplicates or not) to learn the characteristics of duplicate entries. The model can then use this knowledge to classify new records as potential duplicates or not. Common machine learning algorithms used for this task include decision trees, random forests, support vector machines, and deep learning approaches like neural networks.
Artificial intelligence is a broader field that encompasses machine learning but also includes other techniques and methodologies that enable machines to simulate human intelligence and decision-making. This includes natural language processing (NLP), expert systems, knowledge representation, and more.
When it comes to analyzing duplicates in Salesforce, artificial intelligence can be applied beyond just machine learning. For instance:
Natural Language Processing (NLP)
Natural language processing (NLP) algorithms into the analysis. NLP helps you process and understand text fields, such as addresses or descriptions, in the Salesforce records. This can be beneficial when identifying duplicates with slightly different textual representations but referring to the same entity (e.g., "123 Main St" vs. "123 Main Street"). This technique is also referred to as fuzzy matching and can include elements of AI and ML.
AI can incorporate domain-specific knowledge from human experts to assist in duplicate detection and resolution, creating more accurate and customized solutions.
AI techniques can be employed to model complex relationships between different data attributes, leading to more advanced duplicate analysis and identification.
Artificial intelligence, in a broader sense, includes various techniques and methodologies that can enhance duplicate analysis in Salesforce beyond just machine learning. The two fields can work together to improve the accuracy and efficiency of duplicate detection and resolution in Salesforce, or any other data management system for that matter.
Sometimes we collect data that isn't necessarily incorrect, but it lacks a couple tweaks to make it usable. For example, a customer leaves their phone number but does not include the country dialing code. Other times, we have good data, but it still requires manual intervention to sort it. An example of this would be email enquiries that need to be sent to relevant departments. We’re going to reveal a little trick we’ve now adopted into our data management tool, Plauti Data Action Platform (DAP). In this scenario, let’s imagine you have a list of customer queries in your Salesforce org that require assignment to the relevant departments. In the subject line of each inquiry there is a clue as to which department it should go to. For instance, a subject line that reads “need help configuring API”- this will likely be an enquiry for the support team. Or the subject line reads “need help with payment issue” -this will likely be a case for the billing department. Normally, these cases would be sorted manually by a human, assigning each enquiry one-by-one to its relevant department.
Leveraging the power of AI, Plauti has eliminated this need for manual assignment. Our new addition to Plauti Data Action Platform makes use of ChatGPT to automatically analyze and assign these cases based on the descriptions -all within your Salesforce orgs. Additionally, no manual intervention required at all! This alone can save loads of time every day that would normally be spent manually sifting through enquiries and assigning them to their relevant departments. This savings in time gives you freedom to prioritize on important requests.
Another powerful example of AI in Plauti Data Management solutions is the use of AI to enrich customer profile information. Using the ChatGPT API, DAP has a new tool that analyses customer profile information and makes an external search to learn more about the account. The crazy part comes next; it can then automatically populate the account information field with useful information about the customer profile, including suggestions for sales techniques, and so on. This is like having someone perform research on every account in your organization and detailing their findings inside the account information field, except it’s all automatic!
Prevention is always better than seeking a cure. Have you seen the movie “Minority Report”? If you have, you might remember how the Oracles had special powers to glimpse the future and prevent crimes from taking place. In a way, predictive analytics tools are like Oracles that can predict and prevent bad data crimes from taking place. While they might not necessarily be glimpsing into the future like the Oracles, they work in preventing bad data from gaining entry to systems in several ways:
Early Detection of Data Quality Issues
Predictive analytics can analyze historical data and identify patterns indicative of data quality problems. For example, it can spot data anomalies, inconsistencies, or missing values. By catching these issues early, organizations can prevent data-related errors and maintain higher data accuracy.
Proactive Data Cleaning
AI-driven predictive models can automatically clean and rectify data quality issues. They can identify incorrect or inconsistent data points and suggest corrections, improving the overall quality of data in real-time.
Continuous Monitoring and Improvement
Predictive analytics requires continuous monitoring of data quality, ensuring that any new issues that arise are detected and addressed promptly. This helps maintain data accuracy over time, even as data volumes grow.
With the advent of Artificial Intelligence (AI), the way data cleansing workflows can be optimized is almost unlimited. At this point, the imagination is really the limit as to how AI can make suggestions and improve the way we prioritize workflows and data quality issues.
Intelligent Task Prioritization
In your company, your sales teams are familiar with the biggest accounts. Naturally, they don’t want any information in those accounts compromised. Errors and issues slipping into the data of those accounts could result in errors that could damage your reputation. It would be neat if your data management solutions had such an intrinsic understanding of the value of each account, able to address any issues with a sense of priority. Well, that’s exactly what AI solutions are becoming better at.
Through advanced algorithms and machine learning, AI can analyze data patterns and historical records to recognize high-priority issues. For example, it can prioritize correcting customer information for key accounts or fixing duplicates that affect critical reports and analytics. By focusing on the most crucial tasks first, departments can respond promptly and with a clear sense of urgency on each issue. They can allocate their resources and ensure data quality improvements where they are most needed at the moment.
Today, businesses have to be able to scale. One of the quickest ways to scale any operation is with the use of automation. There are certain tasks we humans are good at, but there are also tasks where a computer wins every time. If a human was asked to re-arrange a set of data in alphabetical order, it would be very easy, but it would still take up some time, simply due to the limitations of our physical form. For a machine, rearranging a set of data is done at lightning speed. Using AI, you can drive intelligent automation in your data cleansing workflows. As an AI powered solution learns more about a company’s data habits, it will better perform repetitive and time-consuming tasks, such as standardizing formats, validating email addresses, and correcting common data entry errors.
As these tasks are extremely monotonous and time-consuming, delegating them to automation and artificial intelligence is a win for all. Also, by automating these routine processes, users can free up their time for more strategic and complex challenges that require human intervention.
So, we know that AI is capable of at this point, but what are the benefits of AI assisted data management? It should be no surprise that AI-enabled data cleansing in Salesforce brings numerous benefits and has a significant positive impact on data quality. Let’s highlight these impacts.
Improved Data Accuracy
AI-powered data cleansing algorithms can automatically detect and correct errors, standardize formats, and resolve duplicates in advanced ways. As they have the ability to learn with time, they also become better able to judge the needs and priorities of your data, and to make data accurate and error free.
Automating data cleansing processes with AI eliminates the need for manual intervention in repetitive tasks. This leads to faster data cleansing cycles and reduced turnaround times. Data teams can redirect their efforts towards more strategic data management initiatives, optimizing their productivity and contributing to better overall data quality.
As data volumes continue to grow, traditional manual data cleansing processes become increasingly challenging to manage. AI enables scalable data cleansing solutions that can handle large datasets efficiently. AI algorithms can process vast amounts of data quickly, making them well-suited for organizations with extensive databases or high data ingestion rates.
AI-powered predictive models can enrich Salesforce data by automatically appending missing information or updating outdated records. This ensures that the data remains up-to-date and comprehensive, leading to better customer insights.
AI-driven data cleansing in Salesforce offers numerous benefits, as we discussed. However, there are also some challenges and considerations that organizations should be aware of to ensure successful implementation and maximize the effectiveness of AI-driven data cleansing processes.
Data is complex!
Salesforce databases can be complex and heterogeneous, containing a wide range of data types, structures, and formats. AI algorithms might not always be able to handle all types of data effectively or discriminate discrepancies in the same way a human familiar with a business can. Data teams should carefully evaluate the compatibility of AI-driven data cleansing solutions with the specific data types present in their Salesforce environment and make necessary adjustments or augmentations to the AI models as needed.
In AI we trust…?
AI algorithms rely on patterns in the data they are trained on, but they may not always fully understand the context or domain-specific knowledge required for accurate data cleansing. Furthermore, the data they trained on could even contain errors. For instance, some data issues may require human judgment or expert input to resolve correctly. Integrating domain-specific knowledge into the data cleansing process, either through human involvement or expert rules, is essential to handle complex scenarios that AI may struggle to address alone.
Training and Adaptation
AI algorithms need continuous training and adaptation to changing data patterns and evolving data quality challenges. Data landscapes are dynamic, and new data issues can emerge over time. Regularly updating and retraining AI models based on fresh data is necessary to maintain their effectiveness and relevance.
Human Oversight and Data Governance
While AI can automate many data cleansing tasks, it should not replace human oversight entirely. Human involvement is crucial in reviewing AI-generated suggestions, especially in cases where the potential impact of data changes is significant. A robust data governance framework is essential to establish rules and guidelines for data cleansing, ensuring that AI-driven decisions align with organizational policies and objectives.
Right now, businesses all over the world are making significant investments in artificial intelligence and machine learning (AI/ML) technologies. However, it is crucial to understand that the success of these AI initiatives heavily relies on the quality of the datasets used to train the algorithms. Ensuring clean and accurate data is one of the most important ways to make sure that these technologies learn good habits. Consider the scenario where your customer database contains improperly formatted addresses or missing income levels. If not specifically instructed otherwise, a machine learning algorithm will include such flawed data in its predictive models for purchasing behavior among different demographic segments. Consequently, using this data to build demand forecasts will lead to inaccurate results.
While AI/ML holds immense potential for generating business value, it is crucial to be mindful of potential pitfalls. The quality of the data plays a pivotal role in achieving precise and reliable AI outcomes. Neglecting data quality can result in significant issues that may only become evident after it is too late to rectify them.
As this article emphasizes, AI-powered data quality management is set to revolutionize the way organizations manage and leverage data in Salesforce. But where is it all going? Can we identify some likely paths and trends in the near future that AI is looking at? While we can’t ever say for sure, we can begin to round this article off with some fun and speculation.
AI-powered data cleansing systems become self-healing, capable of identifying and correcting errors in real-time without human intervention. These systems continuously monitor data streams, instantly recognizing anomalies, and automatically applying corrective measures, ensuring datasets are consistently accurate.
AI-Powered Data Curators
In the future, data curators will be AI entities equipped with advanced natural language processing and contextual understanding. They communicate with human data experts and learn from their guidance to adapt data cleansing strategies, making the process more intuitive and collaborative.
Quantum Data Cleansing
AI algorithms harness the power of quantum computing to process immense datasets with incredible speed and efficiency. Quantum data cleansing unleashes unprecedented capabilities in detecting subtle patterns, resolving complex relationships, and achieving unparalleled data accuracy. Perhaps it’s a bit far-out there – but hey, who knows?!
AI Ethical Data Cleansing
AI algorithms are programmed with ethical principles to ensure data cleansing respects individual privacy, cultural sensitivities, and avoids bias. This leads to more inclusive and responsible data management practices, fostering trust and transparency with users.
AI Data Archeologists
AI systems evolve to become data archeologists, uncovering hidden insights from historical and unstructured datasets. By combining advanced data cleansing techniques with sophisticated data mining, AI breathes new life into previously underutilized data reserves.
These imaginative scenarios may seem futuristic. However, looking at the evolving landscape of AI and data management suggests that some of these possibilities may become a reality in the not-so-distant future. Either way, the transformative impact of AI on data cleansing is bound to be an exciting journey of innovation – exactly where we’ll end up is still open to debate.
When it comes to data quality management, artificial intelligence looks set to bring about a huge wave of disruption. While there might be some downsides, it appears the impact will overall be extremely positive. As it stands, there are numerous challenges faced when it comes to managing complex sets of data, often requiring huge amounts of monotonous manual input. The first way AI is going to liberate the field of data quality is by performing many of these manual tasks for us.
As AI technology continues to evolve and is more widely adopted, it will likely become an indispensable and entrenched tool in most organizations, supporting them in their pursuit of excellence and continuous improvement. Since AI poses a great potential for disruption, it is extremely important that ethical considerations, transparency, and responsible implementation should always remain a priority when adopting such technologies.
If you find this article insightful and have data quality concerns of your own, head over to Plauti.com today and check out the latest innovations in data quality management.