Transforming Customer Data Management with modern technologies

We want to harness the power of Artificial Intelligence (AI) and Machine Learning (ML); after all, it is being pushed into every corner of our lives. It is viewed by futurists as unavoidable. But, just how it will evolve further is still somewhat unclear.

While such cutting-edge technologies are revolutionizing the way businesses analyze large volumes of data, extract meaningful insights, predict customer behaviour, and automate various tasks at its core, humans are still humans. They’re fractious, temperamental and unpredictable. Humans are data contributors and data users and the output of their contributions and behaviours are in use in customer master data management by applications.

While we accept new media into our lives the way we think, what we find appealing, and what we care about may not be entirely different today from generations ago, and generations into the future.

A man reading a traditional broadsheet newspaper

Table talk topics

Integrating Artificial Intelligence (AI) and Machine Learning (ML) in customer data management is great table talk for industry pundits in particular, and undoubtedly triggers lively debate when thought leaders talk about customer data, and data technologies online, in the media and at conferences and trade shows.

While such technologies offer significant benefits, concerns have been raised and remain worrying, about their limitations in truly understanding human behaviours and preferences. I’ve put together and elaborated points, that are perhaps worthy of consideration.

There is substantial evidence supporting the increasing integration of AI and ML in various aspects of our lives, including customer data management in onboarding and engagement journeys, as well as in transacting with business. It is essential to approach the claims of utility with a balanced perspective that considers both the potential benefits and the limitations of these technologies.

At their core, humans are diverse and unpredictable.

AI algorithms in contrast to human thinking and behaviours, have distinct limitations in really predicting human activity due to the complexity and unpredictability of this human nature. Human behaviour is influenced by myriad variables. Those who elaborate and tweak the data models are themselves also tainted with bias and the historical nature of the data itself that is used to train ML and AI models is naturally also biased because it is tied to circumstances that would have been present in the past but may no longer be relevant today.

AI models are just models though, and as such, they lack the real-world experience and contextual understanding of humans in prescribing interaction scripts, suggestions and recommendations. They are trained on patterns in accumulated data, which may limit their ability to comprehend complex social situations that require nuanced understanding.

AI systems also don’t understand language. They look at the statistical correlations of certain word combinations but they have no innate understanding of what even the words themselves mean. This limited understanding of the nuances and subtleties of human language and communication means that models will sometimes serve up completely flawed or inappropriate questions, answers and responses. They are likely to struggle with sarcasm, irony, or figurative language, and cannot understand the context in which language is used, leading to errors or unexpected outcomes.

The immense computing capabilities and high-volume data storage requirements to perform even the most basic AI and ML analysis at scale are also quite far beyond the reach of many organizations so at best. Such businesses have to rely on prebuilt models and algorithms for many of their applications. They may not even have enough of the right kind of data to train home-grown models and most importantly the cost to serve them with custom models may well be very cost-prohibitive or offer a poor return on investment.

We already suggested that bias may be present in the modellers, data scientists and even the data itself and these elevate ethical concerns surrounding the use of AI in significant decision-making processes. Biases in the data often lead to biased outcomes. AI systems most often lack what we consider “common sense” and the necessary transparency of thinking and decision outcomes; i.e. explainability, which are likely to limit their effectiveness in truly understanding human behaviour.

AI cannot authentically simulate human emotions and creativity. Operating based on pre-fed data and past experiences, there is no capacity for creative thinking or genuine emotional understanding, responses are calculated, and scripted with only the smallest of variations over time. without data retraining.

Privacy and ethical concerns

Existing data privacy laws vary from country to country, but they exist to more directly regulate the collection, use, disclosure, cross-border transfer, and other processing of data about identified or identifiable individuals.

Each regulation is jurisdiction-based but gives one a sense of the importance of how you leverage your customer data and might consider the application of AI, and Ml in the markets in which you do business.

For more reading, consider the fifth edition of Global Legal Insights’ (GLI)  AI, Machine Learning & Big Data 2023 by contributing editor: Charles Kerrigan is a multi-jurisdictional guide, exploring key legal issues, rules and developments regarding AI, machine learning, and big data across a range of jurisdictions and worth an examination in this context.

Some practical applications

Recent breakthroughs in machine learning are a leap when compared to preceding models and engines but current artificial systems still don’t have the many features of biological intelligence. The complexity of achieving artificial general intelligence (AGI) that matches or exceeds human intelligence remains a major challenge, with significant gaps in flexible, robust, innovative learning, reasoning, and behaviour to be closed.

With all that said, AI and ML can be used to improve customer targeting, campaign optimization, and lead generation in marketing as long as we recognise these limitations and watch for anomalies in the data continuously.

Many companies are incorporating AI and analytics into their customer data management strategies to drive towards better data quality and reach more authentic interactions; and also to help support the development of hyper-personalized recommendations, in pursuit of elevated customer satisfaction.

Data quality and security are paramount concerns in customer data management, and AI plays a significant role in addressing these challenges.

In theory, improved data quality will reduce human error through improved data accuracy.

AI technologies can dramatically improve the practice of customer data management by enhancing accuracy, reducing errors, and ensuring data security. ML algorithms present themselves as powerful tools to potentially enhance data quality by automating various tasks and identifying and rectifying inconsistencies, errors, and missing values in datasets. This not only enhances decision-making processes but also builds trust among stakeholders regarding the integrity of the data being managed.

Keeping data fresh and current is expensive but AI can be of assistance in updating and auto-correcting system data to facilitate businesses staying on top of customer relationship management.

The tooling can track conversations in real-time, and make suggestions based on a bank of prompts or any observed past behaviours where they correlate with the data. By providing feedback to service agents, and enabling the use of AI-driven customer service bots there is some degree of anticipated customer behaviours that might be tied to personalized information exchanges.

When outcomes are prescribed and inputs are clear, unambiguous and standardized, the concept of Autonomous Data Management can be adopted. Here, AI- and ML-powered autonomous data management enables self-service data protection and recovery, freeing up staff to focus on more strategic initiatives. These are effectively automation bound up in the characteristics of AI and ML implementations.

This ADM approach is a double-edged sword since this also introduces the potential for AI to replace human jobs. This raises concerns about potential unemployment and the need for retraining workers for new roles.

Crystal ball gazing

For years, the promise presented by historical data has been the idea that it can be used to predict trends. These trends are often focused on politics, environment and social conditions and then try to anticipate consumer and customer behaviour in response to these variables.

Historical data is used for sales forecasting trends, including seasonal variations, growth patterns, and revenue trends. By analyzing past sales data, businesses make more accurate and reliable sales forecasts, enabling them to plan, allocate, and optimize resources and strategies

Predictive analytics uses historical data to build mathematical models that capture important correlations. These models are then used on current data to predict future events or suggest actions for optimal outcomes. It has applications in various fields such as weather forecasting, economic forecasting, healthcare, engineering, finance, retail, and environmental studies

Making scientific predictions based on historical time-stamped data is referred to as time series forecasts. In business, historical data is used to make forecasts through time series analysis.

This can help in predicting outcomes in areas such as finance, retail, and environmental studies. The state of the forecasting and data makes a difference as to when it should be used. The forecast can be dynamic or static, and the quality of the data is crucial for accurate predictions. They are used in various industries, and the entire point of time series analysis is to facilitate forecasting. It has applications in climate forecasting, economic forecasting, healthcare, engineering, finance, retail, and environmental studies

Various forecasting techniques have also been developed for managerial forecasting problems. The selection of a method depends on the context of the forecast, the relevance and availability of historical data, and the degree of accuracy required. Each technique has its special use, and it is essential to select the correct technique for a particular application.

Final thoughts

The integration of Artificial Intelligence (AI) and Machine Learning (ML) in customer data management presents a potentially transformative landscape, offering both opportunities and challenges to organizations.

While these technologies contribute significantly to data accuracy, security, and automation, their limitations in understanding nuanced human behaviour, potential biases, and the absence of genuine emotional intelligence should be acknowledged.

Recognizing these constraints is crucial for leveraging AI and ML effectively in areas such as customer targeting, campaign optimization, and data quality enhancement.

As this evolving technological frontier develops, ethical considerations, transparency, and a balanced approach are essential to harness the benefits of AI while mitigating potential risks. The path forward involves continuous monitoring, adaptation, and responsible deployment of these advanced tools to ensure they align with our evolving understanding of both technology and humanity.

Generative AI and Large Language Models for Consumer Data Management

2023 has seen an explosion in the amount of interest in focusing AI and ML on data generated and collected, especially within the consumer realm.

With the rapid advancement of generative artificial intelligence (AI) and large language models, such as OpenAI’s GPT, the possibilities for consumer data management have expanded exponentially.

What are Generative AI and Large Language Models?

Generative AI refers to systems that can create new content, such as text, images, or videos, that resemble human-generated data. Large language models, like GPT, are designed to understand and generate human-like text based on the patterns and information they have been trained on. These models have been trained on vast amounts of data, making them capable of generating coherent and contextually relevant text.

Generative AI and large language models have the potential to revolutionize consumer data management by appending synthetic data, artificially created data mimicking statistical properties and patterns of real-world data. This approach helps to address privacy concerns by minimizing the need for sharing sensitive consumer information while still enabling effective analysis and model development.

Large language models can also analyze vast amounts of consumer data to extract insights and patterns to enhance personalized experiences for consumers. By understanding user preferences, habits, and sentiments from say online interactions, businesses can provide tailored recommendations, advertisements, and customer support.

The ability of generative AI and large language models to understand and generate human-like text opens up new avenues for consumer data management.

Advanced natural language processing capabilities enable more seamless and efficient communication between businesses and consumers. Chatbots and virtual assistants can use these technologies to interpret user queries accurately, resolve issues, and provide personalized assistance.

The sophisticated algorithms powering generative AI and large language models can also analyze historical data to identify patterns associated with potentially fraudulent activities. By examining past instances of fraud and being trained on them, the models can learn to recognize and predict potential risks, thereby enhancing consumer data security. This is particularly relevant for the financial sector, where fraud prevention is a continuous struggle.

The models can also be used to uncover hidden trends, preferences, and consumer behavior patterns that might otherwise be overlooked using traditional data analytics. With these new approaches, organizations can make data-driven decisions, develop targeted marketing strategies, and optimize products and services to meet consumer expectations.

Special Considerations

Generative AI and large language models are trained on existing data like that contained in the Pretectum CMDM. They can unintentionally include biases present in everyday society. If not carefully managed, these biases can be perpetuated and even amplified by the models. It is crucial to ensure the data used for training is diverse, representative, and thoroughly evaluated to mitigate potential biases and discrimination.

While generative AI and large language models offer innovative ways to manage consumer data without directly exposing sensitive information, privacy, and security concerns remain.

The Pretectum CMDM offers best-in-class controls and security to protect your data but you must still exercise appropriate management controls and configuration to prevent unauthorized access or misuse of your precious customer data. Transparency regarding data usage and consent is essential to maintain consumer trust and protect their privacy which is why we also offer verification and consent capabilities.

As these AI and ML technologies evolve, your organization needs to maintain robust frameworks and regulations to govern data usage. Clear guidelines regarding the responsible development and deployment of generative AI and large language models are crucial to avoid unintended consequences and protect consumer rights.

We at Pretectum, see Generative AI and large language models as having the potential to revolutionize consumer data management.

From data generation and augmentation to enhanced personalization and fraud detection, these technologies offer numerous benefits for businesses and consumers alike.

Ethical considerations, including bias mitigation, privacy protection, and regulatory frameworks, must be at the forefront of how your business uses them. By leveraging these technologies responsibly, in conjunction with the CMDM, your business can harness the power of consumer data while ensuring transparency, fairness, and respect for individual privacy.

Machine learning and Artificial Intelligence and the Customer Master

robot fingers on blue background

Coming to a decision around customer data is a complex mental process involving weighing up and choosing from a number of options.

Each option you might choose has various differentiating characteristics, what you decide upon is not necessarily a reflex action.

When we make a master data decision there are always some expected and foreseen consequences. There are possibly also unexpected outcomes or unforeseen consequences. One might have predicted, for example, that some data might fall foul of privacy laws at some point data collection about consumers was the wild west where anything goes, and in some places, this is still the case.

You should take note of the point that in making any data mastery decision, you are planning the use of information that has been accumulated in your brain through your past experiences. You would take these big and micro-decisions right up to the final moment that you commit your decision.

Every decision we make, also involves some element of risk, as there is a degree of uncertainty and even incompleteness or imperfection around making our decisions. For master data, many decisions can have long-term consequences on our organization and data quality. If you decide to not capture certain pieces of data at the time of initial contact, will you be able to recover them at some point later?

As an example, is your customer, a customer from the moment you first capture their data or the moment you first capture a transaction related to them? You might say, simply enough, the transaction drives the decision point but that’s not true if that customer has been a loyal customer for years but was anonymous.

Machine Learning Artificial intelligence and automation, when combined with structured data curation is a triumvirate of intelligent technologies with the added data management assurance through manual or automated methods. But we really want to minimize the mental effort associated with what for many, is a mundane but necessary activity – namely the management of that customer data.

Data syndication

We talk about data syndication with Pretectum’s CMDM as a systematic process of data distribution and availability.

When dealing with customer data, data syndication is the automated distribution, access or export of the schemas, rules and actual customer master data. You do this from your Pretectum customer data schemas and repositories to other users in your Pretectum Business Area and receiving or feeding systems in your defined systems landscape.

Pretectum CMDM is able to be configured so that the control, use and access to data and metadata entities in Pretectum by you, require minimal intervention by end-users depending on your use case.

Pretectum also allows you to run remote demand and push requests with secure credentialing via the Pretectum CMDM API stack.

Using the CMDM APIs, Pretectum CMDM takes care to keep the datasets updated with the latest master data from source systems of record or in target recipients of syndicated data.

Within Pretectum you use mapping rules to align schemas and APIs with datasets.

Pre-programmed data syndication helps you to configure the frequency of syndicated master data in the targets you require.

Show me the ML and AI

So where are the ML and AI you might ask? Well, behind the scenes, from the moment you add a schema traditional data management and integration practices become amplified in their capability and effectiveness through three key aspects:

Best Practice – You will be familiar with how most common data should look to the naked eye. Sometimes, particularly when dealing with digital data, unwanted artefacts are introduced that we may want to avoid or limit. Pre-processing or filtering records is a natural activity in the data curation process. Many of the methods and approaches that you likely use, are standard data engineering data prep activities. Accordingly, Pretectum tries to handle most of those for you, avoiding you having to apply those actions manually.

Templates – the most common systems of record, have predefined schemas. Data schemas are often stacked by industry vertical and within those specialisms, you will have some minimum expectations around how you gather, collate and manage customer data. Pretectum CMDM brings templates to the front of how you might consider your customer data curation and data management practice.

Recommendations – through the power of community across different industries we are able to relate the kinds of curation criteria that look most appropriate to your customer master data schema definition and suggest which attributes should be mandatory, the types of data patterns and in-ranges you might expect and the validation lists that you should be using.

Your data management decisions must always be considered to be evolutionary. The decisions that you take today may need to be different for the scenarios you encounter tomorrow.

Some data management decisions require a high level of thinking and calculation with referral to past experiences and results, and they require taking the long-term outcome of the decision into consideration. For long-term decision-making, we voluntarily focus on various information sources when we do this mentally, and then we decide what is and is not relevant in achieving our long-term goals. The reality though, is that our decisions continually change according to changes in our environment and our systems should adapt accordingly.

This decision-making is another area where the platform also learns from your decisions and behaviours within it. Over time, recommendations, decisions and results will evolve to be tightly aligned with your organization’s unique needs. The CMDM platform is there to support that.

To learn more about the Pretectum CMDM platform – contact us.