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Data Privacy Pitfalls? Not With These 5 Steps to Compliance Success

Salesforce Staff — Fri, 07 Mar 2025 07:30:00 +0000

Organisations are diving into deeper customer relationships and more personalised experiences, often with the help of generative AI. As they do, personal data and data privacy compliance becomes a critical part of that strategy. In fact, 94% of business leaders believe their organisations should be getting more value from its data — and it’s no secret that good AI needs good data.

But in this fast-moving technical landscape, using personal data requires extra special handling. This is especially true when it comes to building and maintaining a trusted relationship with your customers, while remaining compliant with the growing number of regulations around the world.

Why data privacy compliance is non-negotiable

Law No. 27 of 2022 on Personal Data Protection (PDP Law) in Indonesia, Personal Data Protection Act (PDPA) in Singapore, Personal Data Protection Act (PDPA) in Malaysia, set standards for data protection.

Organisations face growing pressure to protect individual privacy rights while navigating an increasingly complex regulatory landscape. At the same time, consumers are more aware and concerned about how their personal information is collected, used, and safeguarded. By strengthening privacy and compliance frameworks, businesses can build trust with consumers and reduce legal risks and potential fines.

So, how do you keep customer data secure and protected while staying compliant with global regulations? Here are five steps to help you strengthen data privacy and compliance in your organisation.

1. Understand the data you have — and classify it

Categorising data by sensitivity — such as personal information, financial data, or health records — is crucial for effective data management and protection. It’s vital to understand what a piece of data is, how it can be used, and the protections around it. In turn, that allows you to implement targeted security measures and access controls that align with regulations.

Proper data classification also helps pinpoint where sensitive information resides within an organisation. It sets you up to apply appropriate safeguards, such as encryption, pseudonymisation, or anonymisation.

This clear classification not only maintains compliance with data protection laws but also supports quick responses to data subject access requests, sticking to the principles of data minimisation and purpose limitation, all while creating a trusted relationship with your customers.

Salesforce provides a free data classification tool that simplifies the process of classifying every standard and custom field, helping you identify your most sensitive data and incorporate it into your security and privacy policies.curity and privacy policies.

2. Audit and update your access controls

With your data classified, you can now assess who in your organisation should have access to what data. Audit your access controls and determine whether access rights are appropriate.

Check if any accidental or intentional over-permissioning occurred, and review and update access permissions based on employee roles, data sensitivity, and regulatory requirements. Doing so can mitigate risks associated with data breaches and non-compliance.

By proving the process of access control management, you’re prepared for a number of global regulatory inspections or audits while ensuring only those who absolutely need access to sensitive data can see it. You can manage access controls effectively by using tools that allow you to set permissions at various levels, ensuring that only authorised individuals can access sensitive data.

Salesforce allows you to manage access at a user, objective, and field level using the permissions and access settings. This approach helps maintain security and compliance across your organisation.

3. De-identify data in your testing environment

One way companies experience data breaches is by using real data in their testing environments. Everyone wants realistic data to test their application. But by anonymising or pseudonymising sensitive information, organisations can simulate real-world scenarios without compromising individuals’ privacy rights or experiencing a data breach.

This practice ensures that any data classified as personally identifiable information (PII) in the first step (such as names, addresses, and social security numbers), is not exposed during software testing, reducing the risk of data breaches or unauthorised access.

De-identification is key to data minimisation because it ensures you use only the necessary data for testing. This limits the risk of data exposure and keeps you in line with privacy laws. By using solid de-identification techniques and following ethical data practices, you can protect sensitive information and build stronger trust with your customers.

Solutions that protect sensitive data in secure testing environments, like Data Mask, are available to assist in de-identifying data. These tools can help you create policies to mask or replace sensitive information with non-identifiable data — using methods like random characters, similarly mapped words, pattern-based masking, or even deleted data. Pairing these tools with data classification (mentioned in the first step) ensures all your sensitive data is included.

Additionally, consider solutions that provide complete visibility into your testing environments and manage security. With tools like Security Center, you can centrally monitor, view, and manage your security health across multiple environments from a single platform, making it easier to maintain a strong compliance and security posture with actionable insights.

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4. Set up monitoring and alerts on sensitive data

With your data classified, access controls in place, and apps tested for privacy compliance, it’s time to set up monitoring, logging, and alerting systems to keep everything secure.

Tracking and logging user activities lets you keep an eye on access patterns, spot anomalies, and respond quickly to potential security issues. Proactive and real-time alerts can help you catch and block unwanted activity and can stop data leaks before they happen.

By logging all of the actions in your system, you can research issues, learn from past behaviour, and improve monitoring management. Logging also sets you up to provide evidence of compliance during regulatory inspections or in response to data subject access requests.

Organisations can use toolsets to enhance compliance with data regulations and ensure data privacy. With tools like Event Monitoring, organisations can monitor security, track application performance, and glean product intelligence insights using event logs.

It’s important to have solutions that proactively find security threats and respond effectively, respond to audits with ease by storing and querying event data using SOQL, and stay on top of compliance requirements.

Lastly, one of the most critical aspects of a privacy and compliance program is respecting your customers’ wishes for their data use. Complying with data subject requests, practicing data minimisation, and managing consent effectively are key to complying with global privacy laws.

Regulations emphasise individuals’ rights to access, delete, and revoke consent for their data. By quickly addressing these requests, organisations uphold privacy rights and avoid legal risks and fines associated with non-compliance.

Implementing data minimisation ensures you collect and retain only the essential data, reducing the impact of potential breaches. And effective consent management means getting clear and informed consent before processing personal data, fostering transparency and trust. These practices will strengthen data protection and organisational credibility, showcasing commitment to ethical data handling practices in accordance with evolving privacy laws.

At the final step, consider solutions to help manage consent and data requests, allowing you to handle data privacy efficiently and maintain compliance. For instance, Privacy Centre is a suite of data management tools built to help you manage components of data privacy laws. It allows you to create, monitor, and track requests, automatically fulfilling data subject access and right-to-be-forgotten requests.

Customers can easily update their consent and preferences by hosting forms on your website or in Experience Cloud and updating their consent and preference data to your organisation, next-Gen Marketing Cloud, or Data Cloud. And you can de-identify, delete, or move personal and sensitive data.

With the right tools and practices in place, including de-identification, deletion, or relocation of personal data, you’ll maintain a classified, permission-minimised, and secure data environment, ready to tackle data privacy compliance with confidence.

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What are Large Language Models (LLMs)?

Salesforce Staff — Thu, 06 Mar 2025 07:24:00 +0000

As businesses look for ways to serve customers more efficiently, many are realising the benefits of generative AI. This technology can help you simplify your processes, organise data, provide more personalised service, and more. What powers generative AI? Large language models (LLMs) — which allow generative AI to create new content from the data you already have.

Most importantly, generative AI technology can save time on tedious processes, so you can provide better care for your customers and focus on big-picture strategies. Let’s dig into how generative AI can help your business do more, and learn more about large language models.

What are large language models?

Generative AI is powered by large machine learning models that are pre-trained with large amounts of data that get smarter over time. As a result, they can produce new and custom content such as audio, code, images, text, simulations, and video, depending on the data they can access and the prompts used.

To put things into everyday context, large language models provide answers depending on how a question is phrased. For example, “what are LLMs and how can they help my business?” versus “what are LLMs and what value can they bring to my business?” will yield different results. Although the questions are similar, responses can vary by context.

Because these models use natural language processing and machine learning capabilities, LLMs respond in a human-like, coherent, and relatable way. As a result, they excel in tasks such as text translation, summarisation, and conversations.

With generative AI helping businesses perform these tasks, trust has to be at the core of your efforts. To make sure you’re using this technology responsibly, you can invest in a customer relationship management platform that has an AI-focused trust layer — which anonymises data to protect customers’ privacy.

A trust layer built into a generative AI landscape can address data security, privacy, and compliance requirements. But to meet high standards, you must also follow guidelines for responsible innovation to ensure that you’re using customer data in a safe, accurate, and ethical way.

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How do large language models work?

Advancements in computing infrastructure and AI continue to simplify how businesses integrate large language models into their AI landscape. While these models are trained on enormous amounts of public data, you can use prompt templates that require minimal coding to help LLMs deliver the right responses for your customers.

Furthermore, you can now create private LLMs trained on domain-specific datasets that reside in secure cloud environments. When a LLM is trained using industry data, such as for medical or pharmaceutical use, it provides responses that are relevant for that field. This way, the information the customer sees is accurate.

Private LLMs reduce the risk of data exposure during training and before the models are deployed in production. You can improve prediction accuracy by training a model on noisy data, where random values are added in the dataset to mimic real world data before it’s cleaned.

It’s also easier to maintain an individual’s data privacy using decentralised data sources that don’t have access to direct customer data. As data security and governance become a top priority, enterprise data platforms that feature a trust layer are becoming more important.

Businesses can also leverage how LLMs work with other kinds of AI. Imagine using traditional AI to predict what customers may plan to do next (based on data from past behaviour and trends), and then using a LLM to translate the prediction results into actions.

For example, you can use generative AI to build personalised customer emails with offers, create marketing campaigns for a new product, summarise a service case, or write code to trigger actions such as customer recommendations.

These large language models save time and money by streamlining manual processes, freeing up your employees for more enterprising work.

Now that you’ve learned what generative AI can do, let’s see how you can use it to help your business.

4 ways generative AI can help your business

The sky’s the limit when it comes to ways you can use generative AI for your business.

LLMs are great at recognising patterns and connecting data on their own. Predictive and traditional AI, on the other hand, can still require lots of human interaction to query data, identify patterns, and test assumptions.

Feeding from customer data in real time, generative AI can instantly translate complex data sets into easy-to-understand insights. This helps you and your employees have a clearer view of your customers, so you can take action based on up-to-date information.

Now let’s dive into some use cases where large language models can help your business.

Using sentiment analysis to gain context into post-purchase actions

Sentiment analysis can help marketing, sales, and service specialists understand the context of customer data for post-purchase actions. For example, you can use LLMs to segment customers based on their data, such as using poor reviews posted on your brand’s website. These insights can help you act immediately on negative feedback. A great marketing strategy would be sending a personalised message offering the customer a special deal for a future purchase. This can help improve brand loyalty, customer trust, retention, and personalisation.

Generating email text for marketing campaigns

Text generation can help marketers reduce the time that they spend preparing campaigns. Generative AI can produce recommendations, launch events, special offers, and customer engagement opportunities for your social media platforms. Then, you can polish up the text to make sure it’s in your company’s voice and tone. For example, you can use the copy produced by generative AI to deliver personalised emails informing customers about a new product launch. This helps to improve personalisation, giving your customers a more consistent experience.

Surfacing related cases for service agents

Case summarisation can help service agents to quickly learn about customers and their previous interactions with your business. Cases provide customer information such as feedback, purchase history, issues, and resolutions. Generative AI can help with recommending similar customer cases, so an agent can quickly provide a variety of solutions. This results in faster resolutions, time and cost savings, and happier customers.

Automating basic code generation

Automation helps developers and integration specialists generate code for basic but fundamental tasks. For example, you can use code written by large language models to trigger specific marketing automation tasks, such as sending offers and generating customer message templates. This way, the overall language is consistent, personalised for the customer, and in your company’s voice. Automation can save time and improve productivity, allowing developers to focus on tasks that require more attention and customisation.

When used as part of a hybrid AI strategy, large language models can complement various predictive capabilities and drastically improve productivity. While generative AI can do so much, this technology still needs human guidance to be most effective for businesses. Generative AI can surface the insights you need to make decisions that can move your business forward.

Think of it like a smart, automated assistant for your company, handling time-consuming tasks so your employees can work on complex problem-solving. When you blend the power of generative AI with the knowledge and expertise your company can provide, you’ll be able to do more for your customers.

Urvi Shah, Staff Technical Writer, contributed to this blog post.

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What is Big Data and Why Does it Matter?

Salesforce Staff — Tue, 25 Jun 2024 08:54:18 +0000

Today we are constantly generating and consuming vast amounts of data. From social media posts and online transactions to sensor data and scientific research, the volume, variety, and velocity of data are growing exponentially. This phenomenon is known as big data. In this article, we will explore the concept of big data, its origins, and its significance in the modern world. We’ll also look closely into how big data works, provide real-world examples of its applications, and discuss its impact on various industries and sectors. Finally, we will look ahead to the future of big data and its potential to transform our lives and businesses even further.

What is big data?

There’s an overwhelming influx of data that characterises our daily interactions. This phenomenon, commonly referred to as big data, encompasses a vast and ever-growing collection of information. It extends beyond the traditional structured data found in relational databases to include unstructured data such as social media posts, sensor data, and weblogs. The sheer volume, variety, and velocity of this data present both challenges and opportunities for businesses and organisations.

The term “big data” was coined in the early 21st century to describe the exponential growth and complexity of data. Its defining characteristics are often summarised by the three Vs: Volume, Velocity, and Variety. Volume refers to the immense quantity of data generated daily. Velocity pertains to the rapid speed at which this data is produced and processed. Variety encompasses a diverse range of data formats, including structured, unstructured, and semi-structured data.

The sources of big data are as varied as the data itself. Social media platforms, e-commerce transactions, sensor networks, and scientific research contribute to this ever-expanding pool of information. The proliferation of smartphones, IoT (Internet of Things) devices, and cloud computing has further accelerated the growth of big data.

History of big data

The history of big data is relatively short, but it has already had a profound impact on the way we live and work. In the early days of computing, data was scarce and expensive to store. In the early days, raw data was often collected and stored without much processing, making it challenging to derive meaningful insights. As a result, businesses and organisations were forced to be very selective about the data they collected and stored. However, with the advent of cheaper storage and more powerful computers, it became possible to collect and store vast amounts of data. This led to the rise of big data.

The term “big data” was first coined in 2005 by Roger Mougalas. Mougalas used the term to describe the massive amounts of data that were being generated by the Internet and other digital sources. He argued that this data could be used to gain valuable insights into human behaviour and to improve decision-making.

In the years since Mougalas coined the term, big data has become a major force in business, government, and society. Big data is used to improve customer service, develop new products and services, and make better decisions. It is also used to study human behaviour, track disease outbreaks, and fight crime.

The potential of big data is enormous. However, there are also challenges associated with big data. One challenge is the sheer volume of data that is available. This data can be difficult to store, process, and analyse. Another challenge is the privacy of big data. Big data can be used to track people’s movements, habits, and preferences. This information can be used for good, but it can also be used for malicious purposes.

Despite the challenges, the potential of big data is too great to ignore. Big data is changing the world, and it is important to understand how it works and how it can be used.

Data-driven innovation

Data-driven innovation is the process of using big data analytics to analyse data and derive insights for informed decision-making. This can help organisations improve efficiency and productivity, develop new products and services, and improve customer service.

Data scientists and analysts play a crucial role in analysing data to uncover trends and patterns that can drive business decisions.

One example of data-driven innovation is the use of big data analytics to improve customer service. By analysing customer data, businesses can identify trends and patterns in customer behaviour. This information can then be used to develop targeted marketing campaigns, improve customer service strategies, and develop new products and services that meet the needs of customers.

Another example of data-driven innovation is the use of big data analytics to improve healthcare. By analysing patient data, healthcare providers can identify trends and patterns in patient health. This information can then be used to develop personalised treatment plans, improve patient outcomes, and reduce healthcare costs.

The potential of data-driven innovation is enormous. By harnessing the power of big data, businesses and organisations can improve their operations, develop new products and services, and make better decisions.

However, there are also challenges associated with data-driven innovation. One challenge is the sheer volume of data that is available. Another challenge is the privacy of big data. Businesses and organisations need to be careful about how they collect, store, and use big data. They need to make sure that they are protecting the privacy of their customers and employees.

Despite the challenges, data-driven innovation is a powerful tool that can help businesses and organisations improve their operations and make better decisions. By harnessing the power of big data, businesses and organisations can gain a competitive advantage and achieve success.

How Big Data Works with Structured and Unstructured Data

In order to understand big data, it’s important to know how it works. A data lake is often used to store unstructured big data, allowing for flexible data management and quick access. The big data process can be broken down into five key steps: data collection, data storage, data processing, data analysis, and data visualisation.

The first step in the big data process is data collection. This involves gathering data from a variety of sources, such as sensors, social media, and customer transactions. Once the data has been collected, it needs to be stored in a way that makes it easy to access and analyse. This is where data storage comes in.

The next step is data processing. This involves cleaning and preparing the data to ensure data quality, which may include removing duplicate data and correcting errors. This may involve removing duplicate data, correcting errors, and converting the data into a format that is compatible with the analysis tools that will be used.

Once the data has been processed, it can be analysed to identify patterns and trends. This involves using statistical and machine-learning techniques to identify patterns and trends in the data. This information can then be used to make informed decisions about everything from product development to marketing strategies.

The final step in the big data process is data visualisation. This involves presenting the results of the data analysis in a way that is easy to understand. This may involve creating charts, graphs, and other visual representations of the data.

Big data examples

Big data is being used by businesses across a wide range of industries to improve their operations and deliver better customer experiences. Here are a few examples:

Retail: Big data is used by retailers to track customer purchases, analyse customer behaviour, and develop targeted marketing campaigns. Retailers use big data analysis to uncover customer preferences and optimise inventory management. This information can be used to improve the shopping experience, increase sales, and reduce costs.
Healthcare: Big data is used by healthcare providers to improve patient care, reduce costs, and develop new treatments. Healthcare providers, as business users, leverage big data to enhance patient care and operational efficiency. This information can be used to identify patients at risk for certain diseases, develop personalised treatment plans, and track the effectiveness of treatments.
Finance: Big data is used by financial institutions to detect fraud, assess risk, and develop new financial products. This information can be used to protect customers from financial crime, improve the efficiency of financial transactions, and develop new investment opportunities.
Transportation: Big data is used by transportation companies to improve logistics, reduce costs, and improve safety. Big data helps transportation companies in resource management by optimising routes and reducing fuel consumption. This information can be used to optimise shipping routes, track the location of vehicles, and predict traffic patterns.
Manufacturing: Big data is used by manufacturers to improve quality control, reduce costs, and develop new products. This information can be used to identify defects in products, optimise production processes, and develop new products that meet the needs of customers.

These are just a few examples of how big data is being used by businesses to improve their operations and deliver better customer experiences. As the volume, velocity, and variety of data continue to grow, we can expect to see even more innovative and groundbreaking uses of big data in the years to come.

Big Data Technologies in Today’s World

Big data has become an integral part of our daily lives and has revolutionised the way we interact with technology, businesses, and information. In today’s world, the amount of data created every day is simply mind-boggling. According to recent estimates, the global data creation is a staggering 2.5 quintillion bytes of data every single day, and this number is only expected to grow exponentially in the years to come.

The impact of big data can be seen across various industries and sectors. For instance, in the healthcare sector, big data is used to improve patient care, reduce costs, and develop new treatments. By analysing vast amounts of patient data, healthcare providers can identify trends and patterns, leading to more personalised treatment plans and better patient outcomes. Similarly, in the financial industry, big data plays a crucial role in detecting fraud, assessing risk, and developing innovative financial products.

The retail industry also leverages big data to enhance customer experiences and drive sales. By tracking customer purchases, analysing customer behaviour, and developing targeted marketing campaigns, retailers can gain valuable insights into consumer preferences and provide more personalised services. Big data also plays a significant role in the manufacturing industry, where it is used to improve quality control, reduce costs, and develop new products.

Furthermore, the entertainment industry has embraced big data to create more engaging and personalised experiences for consumers. By analysing user data, content providers can tailor recommendations, improve streaming quality, and develop new content that resonates with their audience.

The growth of the Internet of Things (IoT) has further amplified the significance of big data. With billions of devices connected to the internet, from smartphones and smartwatches to industrial sensors and home appliances, the volume of data generated is immense. This data holds valuable insights into consumer behaviour, operational efficiency, and asset tracking, enabling businesses to make informed decisions and optimise their operations.

The world of big data continues to evolve rapidly, presenting both opportunities and challenges for businesses and organisations. Harnessing the power of big data effectively requires robust data management strategies, advanced analytics capabilities, and a commitment to data privacy and security. By embracing big data and leveraging its potential, businesses can gain a competitive edge, drive innovation, and transform their operations.

Future of Big Data and Machine Learning

The future of big data is bright. As the amount of data in the world continues to grow, so too will the need for tools and technologies to process and analyse it. This growth will create new opportunities for businesses and organisations of all sizes to use big data to improve their operations, develop new products and services, and make better decisions.

One of the most important developments in the future of big data will be the continued growth of artificial intelligence (AI) and machine learning (ML). These technologies are already being used to automate many of the tasks associated with big data processing and analysis, and they will become even more powerful in the years to come. As AI and ML become more sophisticated, they will be able to identify patterns and trends in data that are invisible to the human eye. This will allow businesses and organisations to make even better decisions and to develop new products and services that are tailored to the needs of their customers.

Another important development in the future of big data will be the increasing use of data visualisation tools. These tools make it possible to present big data in a way that is easy to understand and interpret. This will allow businesses and organisations to communicate the results of their big data analyses to their stakeholders in a way that is clear and concise.

Finally, the future of big data will also see an increasing focus on data privacy and security. As more and more data is collected and stored, it is important to ensure that it is protected from unauthorised access and use. Businesses and organisations will need to invest in data security measures to protect their data from cyberattacks and other threats.

The future of big data is full of potential. As the amount of data in the world continues to grow, so too will the opportunities for businesses and organisations to use it to improve their operations, develop new products and services, and make better decisions.

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