Published on: Oct 20, 2022
How to Create Value with Data Quality and GRC
If you want to improve customer interactions, offer high-quality products or services, build loyalty, and generate more value, you need to understand the importance of data quality in your organization.
Data will allow you to make the right decisions and offer your clients the products and services they deserve. However, data could become a liability if the right systems to manage it are not in place.
In this article, you will learn how GRC helps you implement the best practices and governance to get the most out of your data and increase the value of your organization.
Let's get started!
Why Can Collecting Data Become a Challenge?
Data is fundamental to your business success. Moreover, it'll allow you to make the right decision at the right time and produce the high-quality, personalized products or services your customers want.
There is a challenge, though.
Businesses are gathering more data than ever, and new technologies keep accelerating this process dramatically. As a result, organizations have substantial volumes of data, making it difficult to manage, protect, and extract value from it.
Here is where Governance, Risk, and Compliance come in. GRC enables you to define and implement the best practices, procedures, and governance to ensure your data is clean, safe, and reliable.
Also, you can use GRC platforms like StandardFusion to create an organizational culture about security. The goal is to encourage everyone on your team to understand how their actions impact the business's success.
Now, the question is:
Are you getting value from your data?
To answer that, first, you need to understand the following two concepts.
What is Data Quality?
Data quality represents how trustworthy your information is and how it serves your organization's specific needs " mainly supporting decision-making.
Some of these needs might be:
Operations: Where and how can you be more efficient?
Resource distribution: Do you have any excess? Where and why?
Planning: How likely is this scenario to occur? Can you do something about it?
Management: What strategies are working? What methods do you need to improve?
To ensure data quality, you can implement GRC strategies by establishing guidelines and regulations that standardize data usage across the company. These regulations may encompass labelling, movement, handling, and retention of data.
What is Data Integrity?
Data integrity focuses on the reliability of the data both in terms of its physical and logical aspects. To ensure that data remains useable, it must be:
Consistency
Accuracy
Validity
Truthfulness
The goal of GRC is to maintain the integrity of your information by preventing unwanted changes during updates or modifications. Ultimately, the primary focus is on ensuring the accuracy, availability, and trustworthiness of the data.
What Does it Mean to Create Value with Data?
To leverage your data to generate value, you must ensure the information you gather is valuable and truthful. The following are the key characteristics of high-quality data:
Completeness: The expected data to make decisions is present.
Uniqueness: No duplicated data.
Timeliness: The data is up-to-date and available when you need it.
Validity: The information has the proper format and matches the requirements.
Accuracy: The data describes the object correctly in a real-world context.
Consistency: The data must be the same across multiple databases.
Creating value with data goes beyond maintaining quality—it's about transforming that data into actionable strategies. Here are several ways to achieve this:
Datasets: Provide structured and refined data from multiple sources, enabling others to make informed decisions.
Insights: Analyze datasets to uncover trends and patterns, then share these insights to drive strategic initiatives.
Algorithms: Develop and offer machine learning models that can be utilized by others to solve complex problems.
Optimization: Use data-driven insights to enhance and streamline your organization's operations, improving efficiency and outcomes.
Products: Combine various data strategies to create innovative, data-centric products or services that meet market demands.
Investments: Leverage data and analytical models to guide investments in financial plans, property, or equity, maximizing returns.
Personalization: Tailor offerings to individual preferences by utilizing personal or firm-specific data, enhancing customer experience.
Relationships: Foster and deepen connections, whether between your organization and others or among third parties, through data insights.
A powerful way to make sure your company's data holds these six characteristics is by leveraging the power of GRC.
Why?
Because GRC gives your organization the ability to establish guidelines, rules, and security measures to prevent mistakes, streamline tasks, and direct staff in handling important data. By integrating these practices with the diverse methods of creating value, you position your organization to not only maintain high data standards but also to unlock its full potential.
How GRC Empowers Your Organization to Achieve High-quality Data
To leverage your data to generate value, you must ensure the information you gather is valuable and truthful. The following are the key characteristics of high-quality data:
Completeness: The expected data to make decisions is present.
Uniqueness: No duplicated data.
Timeliness: The data is up-to-date and available when you need it.
Validity: The information has the proper format and matches the requirements.
Accuracy: The data describes the object correctly in a real-world context.
Consistency: The data must be the same across multiple databases
A powerful way to make sure your company's data holds these six characteristics is by leveraging the power of GRC.
Why?
Because GRC gives your organization the ability to establish guidelines, rules, and security measures to prevent mistakes, streamline tasks, and direct staff in handling important data.
GRC helps you answer the following questions:
How are you ensuring that data is available for internal decisions and the clients?
Is everyone taking the right steps to collect and process data?
Have you removed redundancies?
Do you have a backup system?
Are the critical processes standardized?
Overall, GRC aims to build shared attitudes and actions toward security within your company.
Why You Need High-Quality Data, and How GRC Helps You
To leverage your data to generate value, you must ensure the information you gather is valuable and truthful. The following are the key characteristics of high-quality data:
Completeness: The expected data to make decisions is present.
Uniqueness: No duplicated data.
Timeliness: The data is up-to-date and available when you need it.
Validity: The information has the proper format and matches the requirements.
Accuracy: The data describes the object correctly in a real-world context.
Consistency: The data must be the same across multiple databases.
Creating value with data goes beyond maintaining quality—it's about transforming that data into actionable strategies. Here are several ways to achieve this:
Datasets: Provide structured and refined data from multiple sources, enabling others to make informed decisions.
Insights: Analyze datasets to uncover trends and patterns, then share these insights to drive strategic initiatives.
Algorithms: Develop and offer machine learning models that can be utilized by others to solve complex problems.
Optimization: Use data-driven insights to enhance and streamline your organization's operations, improving efficiency and outcomes.
Products: Combine various data strategies to create innovative, data-centric products or services that meet market demands.
Investments: Leverage data and analytical models to guide investments in financial plans, property, or equity, maximizing returns.
Personalization: Tailor offerings to individual preferences by utilizing personal or firm-specific data, enhancing customer experience.
Relationships: Foster and deepen connections, whether between your organization and others or among third parties, through data insights.
A powerful way to make sure your company's data holds these six characteristics is by leveraging the power of GRC.
Why?
Because GRC gives your organization the ability to establish guidelines, rules, and security measures to prevent mistakes, streamline tasks, and direct staff in handling important data. By integrating these practices with the diverse methods of creating value, you position your organization to not only maintain high data standards but also to unlock its full potential.
Why is Data Quality Crucial in AI and ML Strategies?
Data quality is the cornerstone of successful artificial intelligence (AI) and machine learning (ML) integration into any data strategy. Without high-quality data, even the most advanced AI systems can falter and produce unreliable outputs.
1. Accuracy Matters:
Poor data can lead to inaccurate predictions and insights. Whether it's predicting customer behavior or optimizing operations, the precision of AI-generated results hinges on the data's accuracy. Errors in data inevitably lead to flawed conclusions, which can steer business decisions in the wrong direction.
2. Consistency is Key:
Inconsistent data disrupts learning processes in AI models. Machine learning algorithms thrive on patterns, and irregularities can result in models that fail to generalize effectively. This inconsistency can lead to erratic performance and low trust in AI-driven outcomes.
3. Misleading Insights Can Emerge:
Faulty data often results in misleading findings. Decision-makers relying on such insights could make choices that harm the company’s operations or strategy. In high-stakes environments, this can even lead to financially detrimental or reputationally damaging outcomes.
4. Cost Implications:
Enhancing data quality is not merely a preventive measure; it's economically wise. Poor data leads to increased costs, due to inefficiencies and the need for corrective actions. Investment in data cleansing and validation from the outset can save resources and improve return on investment (ROI).
5. Model Efficiency and Performance:
High-quality data enhances model performance, allowing AI systems to learn more efficiently. Clean, well-organized datasets facilitate faster training times and more reliable testing phases, leading to AI solutions that deliver tangible results.
Ensuring top-tier data quality isn't just a best practice, by prioritizing data accuracy and consistency, businesses can unlock the full potential of their AI investments and achieve strategic goals with confidence.
What Are the Potential Consequences of Using AI with Poor Data Quality?
When AI systems are fed poor-quality data, the repercussions can be significant and far-reaching. At the very least, inaccurate or incomplete data can render AI outputs ineffective, making them of little to no practical use.
More critically, unreliable data can lead to misguided insights, causing decision-makers to take actions based on flawed information. This not only wastes time and resources but can steer a business off course, potentially leading to financial losses.
In the worst-case scenario, misleading AI outputs can damage a company's reputation. Compromised data integrity can lead to decisions that harm customer trust or even result in compliance violations. Ultimately, the integrity and viability of a business utilizing AI rest heavily on the quality of its data inputs.
Key Consequences:
Ineffectiveness: The AI fails to deliver useful insights.
Misguided Decisions: Poor data leads to strategic errors.
Resource Wastage: Time and money spent on wrong solutions.
Reputational Damage: Customer and stakeholder trust is at risk.
Compliance Issues: Potential for legal and regulatory pitfalls.
In essence, high-quality data is crucial for AI to function as a reliable tool for innovation and growth. Without it, companies risk making decisions that are not only unproductive but potentially destructive.
How Data Influences Investment Decisions
In today's rapidly changing marketplace, understanding where to allocate resources is crucial. Data plays a pivotal role in guiding these decisions, helping investors and business leaders make informed choices.
Whether you're operating within the complex environment of a large corporation or driving strategy in a small business, data provides key insights to navigate these complexities. Let's explore how:
Risk Assessment: By analyzing historical data, investors can identify potential risks and adapt their strategies accordingly. For instance, using predictive analytics, companies can foresee market downturns or shifts, enabling proactive measures.
Market Trends: Data uncovers emerging trends that can shape an investment strategy. By observing customer behavior patterns and preferences, businesses can tap into new opportunities and stay ahead of the competition.
Performance Metrics: Evaluating the performance of products or services becomes seamless with data. Business intelligence tools can highlight which offerings yield the most profit or require further attention, helping decision-makers refine their portfolios.
Resource Allocation: Data aids in prioritizing where to direct time and capital for maximum impact. By comparing various investment opportunities, businesses can more efficiently allocate resources to areas with the highest potential returns.
Real-World Application
From financial institutions engaging in public markets to enterprises exploring new territories, data serves as a guiding light. Consider a company deciding whether to enter a particular geographical market: historical sales data, regional market trends, and consumer analytics provide a robust foundation to make the call.
In summary, whether scaling up a successful product line or optimizing current operations, data transforms investment decisions from guesswork into a science. By leveraging data effectively, businesses can thrive in today’s competitive landscape.
How GRC Supports Data-driven Businesses and The Key Benefits of Clean Data
Data-driven businesses embrace data to get insights that can enhance the organization. Furthermore, the efficient management of big data through GRC software helps you recognize new business opportunities, strengthen customer experiences, increase sales, improve operations, and more.
For example, GRC can help your business by enabling you to create and manage the right policies to process and protect the company's data.
More importantly, you can also manage individual policies to ensure they have been distributed and acknowledged accordingly.
Clean data offers numerous benefits, some easily noticeable, while others may not be so obvious. Placing trust in data not only enhances productivity and yields better outcomes, but it also contributes to fundamental and crucial elements that impact the success and performance of your business.
What are these factors?
Fundamental benefits:
Profits/Revenue
Trust
Internal communication
Employees' confidence in sharing information
Company's reputation
Operational benefits:
Customer satisfaction
Better audience-targeting
Efficiency
Business outcome
Privacy issues
How GRC Protects The Value of Your Businesses And Your Data
In today's world, companies should be measured not only via existing financial measures but also by the quantity of monetizable data they can capture, consume, store and use. In particular, how data helps the organization's internal operations to be faster and more agile.
When people think of high-quality data or big data, they usually associate these with big companies, especially technology and social media platforms. However, quality data gives organizations of any size ton of benefits.
Data quality helps your organizations to:
Understand their clients
Deliver better results
Strengthen business culture
Understand industry best practices
Enhance business operations
Identify the best partnership options
Make more money
Using the proper GRC software helps you create and manage the policies and procedures to ensure your organization's data is valid, consistent, accurate, and complete " allowing you to get all benefits previously mentioned.
The key to using GRC tools is that your business can produce what customers desire on a greater scale and with higher accuracy and velocity.
Now, what does this have to do with value?
By protecting the value of data, you are protecting your company's overall worth. GRC empowers your company to create a value culture, giving everyone education and agency so they can make better decisions.
Also, GRC enables you to tell better security stories. These stories seek to build trust with your customers and partners, join new markets, and shorten sale cycles.
To summarize:
A better understanding of your operations " through data " will lead to better products and services, enhanced customer experiences, and ever-lasting relationships with clients and partners. All these symbolize growth and more revenue for companies.
Can Unprotected Data Damage Your Company's Value?
Trust is a vital part of any interaction (business or personal) and, as such, is required for you to protect it " without trust, there is no business.
When data is not protected, the chances of breaches are higher, causing direct and indirect costs.
Direct costs are:
Stolen information
Lawsuits
Fines
Compensations
Potential business loss
Indirect costs are:
Reputation/Trust
Lost revenue from downtime
New and better protection
PR activities
Often, reputation damages can generate long-term harm to your organization, making it hard for you to cultivate and maintain businesses. In reality, reputation loss is most companies' greatest worry, followed by financial costs, system damage, and downtime.
So, what does all this mean?
It's not just about you gathering data; it is also about how you can lower risks, leverage, and protect the data you already have. GRC integrates data security, helping your organization be better equipped against unauthorized access, corruption, or theft.
In addition, GRC tools can help you elevate data security by controlling policies, regulations, and predictable issues within your organization.
The bottom line?
When your company can't get or keep customers because of a lack of trust, your organization's value will be significantly lower " or even zero. Unfortunately, this is even more true for small and medium size companies.
How GRC Can Help You Achieve High-Quality Data
Many organizations have problems managing their data, which unfortunately leads to flawed decisions and a lack of trust from employees and customers.
Moreover, although companies understand how costly wrong information is, many are not working on ensuring quality data through the proper processes and controls. In fact, Harvard Business Review said that 47% of newly created data records have at least one critical error.
Why is that?
Because there is a lack of focus on the right processes and techniques that need to be in place to ensure quality data.
What do poor processes cause?
Human errors
Unawareness of risks
Incorrect data input or extraction
Wrong data handling
Inaccurate formatting
Different sets of data for various departments
Fortunately, GRC's primary goal is to help you develop and manage the right policies and procedures to ensure everyone in your organization appropriately handles data.
GRC aims to build a data structure based on the proper governance that will dictate how people organize and use your company's information. As a result, Governance, Risk, and Compliance will empower you to get value from your data.
Understanding the Digital Value Proposition of GRC
A digital value proposition is essentially a unique blend of benefits that a product or service offers to meet the needs and desires of its customers. It leverages digital tools and data to enhance customer experience, solve problems, and provide tangible advantages over competitors. Constructing a compelling digital value proposition using data requires a structured approach.
Constructing a Data-Driven Value Proposition
Identify Your Initiative or Offering
Start by clearly defining what you are offering. Whether it's a new app, an online service, or a digital tool, understanding your initiative is the cornerstone for building value.Recognize Your Customer Segment
Who are you serving? Pinpoint the specific group of customers who will benefit from your offering. This involves considering demographics, preferences, and unique needs.Understand the Customer’s Needs
Identify what specific tasks or 'jobs' your customers need to accomplish. This step is crucial for aligning your offering with their expectations.Leverage Data Sources
Use data to enhance your product. Identify which sources of data—such as customer feedback, market trends, or usage analytics—will inform and improve your offering.Address Customer Pain Points
Determine how your product can alleviate specific customer frustrations or challenges. Use data insights to target these pain points effectively.Enhance Customer Gains
Beyond solving problems, focus on how your offering can provide new benefits or make existing benefits more accessible, using insights from data to highlight these enhancements.Differentiate from Competitors
Use data to highlight what sets your offering apart from others in the market. This could be superior features, unique benefits, or a better user experience.
Practical Implementation
Imagine you're launching a music streaming service. Your digital value proposition might use data to offer personalized playlists that alleviate the hassle of finding new music (pain point) and boost user satisfaction by introducing them to artists they might love (gain). By analyzing listening habits and leveraging sophisticated algorithms, your service stands out in a crowded market.
Constructing a digital value proposition with data is about crafting a compelling narrative around your offering. By systematically addressing customer needs, reducing pains, and increasing gains, you can leverage data to deliver unparalleled value and stand out in today’s digital landscape.
Ways to Achieve and Maintain High-Quality Data
Data governance: Data governance is more than setting restrictions and telling people what to do. Instead, it is a collection of procedures, roles, policies, standards, and metrics that will lead to a cultural change to ensure effective information administration throughout the organization.
Education: Achieving good data quality is not easy. It requires a deep understanding of data quality principles, processes, and technologies. GRC promotes the education process by allowing the organization to seamlessly implement, share, and communicate its policies and standards to every department.
Everyone is involved: Everyone must understand the organization's goal for data quality and the different processes and approaches that will be implemented. GRC focuses on cultural change.
Be aware of threats: The mission of GRC is for the organization to recognize and deal with potential threats effectively. When companies are aware of risks, they can implement the necessary controls and rules to protect the data.
One single source of truth: A single source of truth ensures everyone in the organization makes decisions based on the same consistent and accurate data. GRC can help by defining the governance over data usage and manipulation. Furthermore, GRC makes it easy to communicate policies, see who the policy creator is, and ensure employees are acting according to the standards.
Key Roles in Data Value Creation
Creating value with data requires a team with diverse skill sets and perspectives. Here’s a detailed look at the roles essential in this process:
Data & Business Strategist: This role involves crafting business models and generating hypotheses. The strategist bridges the gap between data insight and business outcomes.
Data Analyst: Analysts are crucial for making sense of raw data. They are responsible for direct data inference, data validation, and normalization, ensuring the integrity and usability of information.
Data Scientist: At the heart of innovation, data scientists design and refine algorithms. They apply machine learning techniques and test hypotheses, pushing the boundaries of what data can reveal.
Data Scout: This role involves identifying and evaluating potential data sources. Scouts are explorers who discover new data terrains, opening fresh avenues for analysis.
Data Engineer: Engineers focus on building and maintaining the infrastructure necessary for data processing. They develop the tools and systems that enable seamless data collection and analysis.
Head of Data: The leader who contextualizes and connects datasets, the Head of Data is pivotal in strategizing how data can be monetized and effectively integrated across the organization.
Data Application Specialist: This could be anyone who applies data insights to improve business processes, products, or decision-making, regardless of their official title.
These roles are not rigid but serve as a framework to consider the various thinking capabilities needed at different stages of your data-driven journey.
How Personalization Creates Value in Data-Centric Products
In today's digital landscape, personalization is a driving force for adding value to data-centric products. Instead of forcing businesses to choose between personalization and scalability, modern technology enables both. Companies can leverage vast amounts of big data and specific insights from little data to enhance their services substantially.
Balancing Big Data with Little Data
A key strategy involves the smart use of big data—trends derived from a large user base—alongside little data, which focuses on individual user behavior. This combination helps businesses operate efficiently at scale while providing highly personalized experiences.
For instance, streaming services craft movie suggestions tailored to individual preferences, while tech giants use location and personal data to curate custom content. This approach satisfies user needs more precisely, increasing perceived value and engagement.
Engaging Users with Behavioral Nudges
Data-centric devices, like fitness wearables, exemplify how personalization works in real-time. They prompt users with specific, actionable nudges—encouragements that are neither generic nor repetitive. For example, a fitness tracker might suggest, "Take a brisk walk to hit your goal!" These personalized suggestions, grounded in behavioral psychology, foster a sense of connection and trust between the user and the device.
Encouraging Reciprocity through Personalization
In the financial sector, personalization is pivotal. Financial apps often ask for user data in exchange for valuable insights, such as credit score improvements or savings tips. This reciprocal relationship makes subsequent personalized product recommendations, such as credit cards, feel more relevant and less intrusive.
By aligning personal data usage with user benefits, companies cultivate loyalty and trust. This approach not only adds value but also justifies the data exchange, making users more inclined to share sensitive information willingly.
In essence, personalization transforms interactions between users and digital products. By strategically leveraging big and little data, companies can offer services that are both personalized and scalable, increasing user satisfaction and enhancing the perceived value of their offerings.
