Salesforce has a built-in deduplication tool for removing duplicate records from leads, contacts, accounts, and opportunities databases. This tool is the primary method Salesforce offers for cleaning data. However, many customers find this built-in option lacking, especially considering the high-quality data that sales and marketing teams rely on.

For example, imagine a newly hired salesperson pulls up a list of contacts for a specific account in Salesforce. In the potential client’s contact record, a person named ‘Bob Smith’ is listed as the purchasing agent, so the salesperson adds them to the recipients list for an upcoming new product release announcement, assuming this would be the best point of contact for that potential client. Let’s say that, on a follow-up call, the salesperson discovers that Mr. Smith no longer works for that company. Bob Smith changed jobs, and somehow, a duplicate record was created for him under the new employer.

This may not sound like a catastrophic mistake, but this error created additional work, and now, that new salesperson has reason to doubt the reliability of the data in Salesforce. That remaining duplicate record could be the only one in the database, but this seed of doubt can translate into extra hours spent double-checking and verifying information. And suppose it’s not the only duplicate out there the salesperson encounters. In that case, they may even start maintaining a separate shadow database, relying on their notes rather than the database in Salesforce. It’s easy to see how a few stray duplicates can snowball into a bigger problem.

Salesforce Duplicate Management Tools

Duplication identification tools rely on field comparisons. By comparing the contents of a selected number of fields within each record and across the entire database, these tools can determine whether records are unique or duplicative. The record is classified as a duplicate if the content within the chosen fields matches. The system notifies relevant users of the potential duplicate so they can determine which records should be deleted or maintained. Restrictions can also be put in place to prevent users from creating duplicate records in the first place.

Each organization creates its deduplication logic. Data sources, workflows, and business processes determine how programming logic is applied to determine duplicates and decide which records are retained and which are removed. However, there are basic data-cleaning steps that can help optimize built-in tools.

Step 1. Normalize Data

Data normalization sets a standardized data format for all data sources. For example, if a field contains a date, users can set a consistent date format, such as MM/DD/YYYY or DD/MM/YYYY. If the field includes phone numbers, they can be standardized with either periods or dashes between the digits. Once data normalization standards are set, the data must be converted to the desired format either manually or programmatically.

Without clean data, fields can get skipped, calculations can be faulty, or records can be counted multiple times. Ensuring that your Salesforce data is error-free is essential for effective decision-making.

Step 2. Identify Duplicates

Salesforce has multiple fields that can be used to identify duplicate records. These include:

• email addresses
• telephone numbers
• website domains
• company names

While deduplication can work using just one field, matching on multiple fields generally produces better results.

If the data is being imported from multiple systems, removing duplicate records before importing reduces the time needed to remove them once inside Salesforce. Consider using an external platform if cleaned data needs to be pushed out to various systems.

Step 3. Develop Deduping Logic

Once duplicate records are identified, decisions have to be made about what to do with the duplicates. This step includes answering the following:

• Which record should be retained?
• Which records will be removed?
• Will the records be merged? And if so, which record retains what data??

Deduping can be a process of trial and error. That’s why Step 4 is so crucial.

Step 4. Test, Test, and Retest

Do not test deduping logic on the master database. Instead, create a test environment where logic can be tested at a smaller scale. Tests will run faster, and results can be analyzed to refine deduping logic continuously until 100% of accurate duplicates are removed. Once the logic has been thoroughly tested, apply it to the entire Salesforce CRM and validate the output. 

Why Do You Need Data Automation?

Although Salesforce duplicate management tools can help identify duplicates, the deduplication process is manual, making it time-consuming and labor-intensive. With limited matching rules and simple duplication algorithms, some duplicate records can be missed. Data automation tools often include comprehensive matching rules and duplication algorithms to ensure all duplicate records are identified.

Salesforce deduplication tools do not provide automated data normalization or check for cross-object duplications. This means inaccurate and inconsistent data can exist across leads, contacts, or accounts. Also, Salesforce management tools do not enforce data governance policies. For companies with regulatory or compliance requirements, separate processes must be put in place to ensure adherence to standards. With more advanced, intelligent data automation tools, data governance policies can be established to ensure all requirements are met.

Salesforce’s built-in duplication management tools are a starting point for most organizations. However, as businesses grow, they often need to improve their functionality. Most companies eventually need data automation to realize the full benefit of Salesforce CRM. If your business is facing any of the following scenarios, consider exploring a trusted AI-powered deduplication tool suite for Salesforce data import and cleaning,

Increased Data Volume

Growing companies generate more leads, acquire customers, and receive more opportunities. Humans are prone to making errors, and if data volumes increase, manual data entry errors will grow exponentially. In these scenarios, we recommend a leading data quality solution such as DataGroomr as they can process large datasets and prevent duplicate records from being created when importing CSV files. Their tools can review, update, and enrich existing records using new information in imported files, and they automate recurring duplicate analyses and reduce manual processes. Mass merges and conversions can be set to occur automatically, freeing up more resources.  AI technology definitively delivers a more robust duplicate management solution, while allowing organizations to reallocate resources to more critical tasks while maintaining a duplicate-free Salesforce CRM.

As we delve into the heart of 2024, the landscape of Machine Learning Operations (MLOps) continues to witness rapid evolution, propelled by the incessant growth of artificial intelligence and machine learning. With these advancements come many tools designed to streamline, optimize, and enhance the MLOps workflow. Staying abreast of the latest tools is imperative for practitioners and enthusiasts alike. Exploring the top MLOps tools in 2024, we uncover the technological marvels reshaping how organizations operationalize and scale their machine-learning endeavors. Whether you’re a seasoned data scientist or a curious industry observer, this guide unveils the tools that promise to redefine the MLOps landscape in the year ahead.

A career in MLOps offers an exciting intersection of machine learning and operational efficiency, playing an important role in deploying and managing machine learning models at scale. Pursuing an MLOps course equips individuals with the specialized skills to navigate this dynamic field. The course covers key aspects such as model deployment, monitoring, and automation, providing hands-on experience with cutting-edge tools. By gaining proficiency in MLOps practices, individuals become valuable assets for organizations seeking to bridge the gap between machine learning development and production. This strategic combination of skills positions them for success in a field where seamless integration of machine learning into operational workflows is paramount.

What is MLOps?

MLOps, or Machine Learning Operations, encompasses practices that streamline and automate machine learning (ML) workflows and deployments. Leveraging core machine learning and artificial intelligence capabilities, organizations can address intricate real-world challenges and provide value to customers. MLOps fosters a culture that integrates ML application development (Dev) with ML system deployment and operations (Ops). This unified approach allows organizations to automate and standardize processes throughout the ML lifecycle, covering model development, testing, integration, release, and infrastructure management.

Top MLOps Tools to Use

MLOps tools are crucial in standardizing, simplifying, and optimizing the machine learning (ML) ecosystem. They serve various purposes, including experiment tracking, managing model metadata, orchestration, optimizing models, versioning workflows, deploying and serving models, and monitoring models in production. These tools collectively contribute to a more efficient and organized ML workflow, enhancing machine learning operations’ overall productivity and effectiveness. Here are the popular MLOps tools to use in 2024

Kuberflow

Kubeflow is a dedicated open-source platform designed to simplify and streamline machine learning (ML) workflows on Kubernetes, a container orchestration platform. It provides a comprehensive set of tools and components that facilitate the entire ML lifecycle, from data preparation and model training to deployment and monitoring. Kubeflow enables the deployment of scalable and portable ML workloads, enhancing collaboration and reproducibility within data science teams. With features such as model serving, hyperparameter tuning, and versioning, Kubeflow is a powerful MLOps tool, making it easier to manage, scale, and operationalize machine learning applications in a Kubernetes environment.

Hugging Face: Hugging Face is a prominent player in the MLOps landscape, offering tools and libraries, such as Transformers, to streamline natural language processing (NLP) workflows. It provides pre-trained models and facilitates efficient sharing and deployment of NLP models. While not a conventional MLOps tool, Hugging Face’s offerings significantly enhance the operational aspects of deploying and managing machine learning models, particularly in natural language processing.

 Pachyderm: Pachyderm is a powerful MLOps tool focused on data versioning and lineage within machine learning workflows. It provides version control for datasets, ensuring traceability and reproducibility. With its containerized data architecture, Pachyderm simplifies data management, allowing teams to track changes seamlessly. Pachyderm enhances collaboration and transparency by maintaining a structured history of data transformations, making it a valuable tool for organizations seeking robust data versioning and lineage capabilities in their MLOps processes.

 Metaflow: Metaflow, developed by Netflix, is a human-centric MLOps tool that simplifies and streamlines data science project management. It facilitates creating and scaling real-world data science workflows, emphasizing collaboration and ease of use. Metaflow enables data scientists to focus on building models rather than dealing with infrastructure complexities. With features like built-in support for AWS, it enhances productivity by seamlessly integrating with cloud resources, making it an ideal tool for end-to-end MLOps.

 Seldon: Seldon is a platform available as open-source that facilitates the deployment, scaling, and management of machine learning models within Kubernetes environments. It facilitates the deployment of machine learning models as RESTful microservices, making integrating them into larger applications easier.

 KubeFlow Pipelines: KubeFlow Pipelines is a part of the KubeFlow project, which is a machine learning toolkit for Kubernetes. KubeFlow Pipelines provides a way to build and deploy scalable and portable end-to-end ML workflows on Kubernetes. It helps in orchestrating and managing the entire machine learning lifecycle.

 MLflow: It is an open-source platform for managing the end-to-end machine learning lifecycle. It includes tools for tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow supports a variety of machine learning libraries and can be used with any existing code or data.

 AlgoTrader: AlgoTrader is a platform designed for algorithmic trading. While it is not an MLOps tool, it can be integrated into MLOps workflows in financial industries. AlgoTrader enables the development, testing, and deployment of algorithmic trading strategies, which may involve machine learning models for market prediction.

 Google Cloud AutoML: Google Cloud AutoML is a suite of machine learning products provided by Google Cloud Platform. It offers various AutoML services, such as AutoML Vision, AutoML Natural Language, etc., allowing users to train high-quality custom machine-learning models with minimal effort and machine-learning expertise.

 Big Panda: Big Panda is an IT operations platform that uses machine learning to correlate and analyze data from various sources to detect and resolve incidents faster. While not specifically tailored for MLOps, it can be employed in operations where machine learning models are used, helping to manage and respond to incidents efficiently.

Conclusion

 Staying abreast of cutting-edge tools is paramount in the constantly evolving landscape of machine learning operations (MLOps). As of 2024, Seldon, KubeFlow Pipelines, MLflow, AlgoTrader, Google Cloud AutoML, and Big Panda stand out for their model deployment, orchestration, and lifecycle management roles. Embracing these tools optimizes efficiency and scalability in MLOps workflows. To navigate this dynamic field and harness the potential of these tools, an MLOps course is invaluable. Such courses offer hands-on experience, imparting the skills and knowledge required for a successful career in managing the end-to-end lifecycle of machine learning models.