Entries by John Adler

Tackling the AI Black Box: Ensuring Transparency and Accountability in Financial Services

As Artificial Intelligence (AI) takes on a crucial role in Financial Services, concerns around the lack of transparency and accountability in AI systems have mounted. Deep learning models are often referred to as “black boxes” due to their complex inner workings, which can be challenging to understand and explain. In an industry where trust and clarity are paramount, the lack of transparency in these AI models can lead to significant problems. Moreover, when AI-driven processes don’t go as planned, pinpointing responsibility is a daunting task.

At Data Management Group (DMG), we understand these challenges and help financial institutions navigate this complex landscape. In this post of our series on how Financial Services companies can overcome the myriad challenges presented by deploying AI, we propose the following strategies and solutions to address these barriers and foster trust in AI systems:

• Strive to Implement Explainable AI – Ensure clarity around how your AI systems make high-stakes decisions

• Ensure Visibility into AI Processes – Provide clear documentation into how AI is being used

• Assign Accountability for AI Operations – Define who is in charge of managing the care and feeding of each AI system or technology deployed 

Emphasizing the Importance of Explainable AI for High Stakes/Regulated Decisions

One key aspect of achieving transparency and accountability in AI is ensuring explainability for high stakes/regulated decisions. The people in charge of deploying AI models must be able to explain the AI models’ processes and decisions in a human-understandable format. This is important to fulfill regulatory requirements and also to build stakeholder trust and enable effective oversight. I wrote about the explainability imperative in one of the previous posts in this series, How Financial Services Companies Can Use AI While Maintaining Regulatory Compliance.

DMG’s Recommendations: Meet the explainability imperative in all your AI initiatives involving high stakes/regulated processes.
We recommend that Financial Services companies do the following:

• Prioritize Explainable AI (XAI) in their AI strategy. XAI models allow humans to understand, trust, and manage AI effectively. This includes models that provide clear reasoning for their decisions and models that avoid black box methods

• Invest in training initiatives for AI developers, decision-makers, and other relevant stakeholders. These initiatives should focus on how AI models function, how they make decisions, and how to interpret their outputs

• Use AI auditing tools that can help dissect the decision-making process of AI models. These can be particularly useful in verifying compliance and explaining the AI’s operations to regulators and stakeholders

• Leverage the expertise of third-party AI consultants to conduct regular AI audits, ensuring the models remain transparent and accountable over time


Enhance Visibility into AI Decision-Making Processes

Financial institutions must provide transparency into their AI-driven processes. This includes clear documentation of the AI model’s decision-making process, the data it uses, and the governance rules that apply. In essence, enhancing visibility into AI decision-making processes involves clarifying the ‘how,’ ‘what,’ and ‘who’ of AI systems: how they make decisions, what data they use, and who oversees and takes responsibility for them.

DMG’s Recommendations: Publish documentation around how your organization uses AI to drive decision-making. 

We recommend that all Financial Services companies:

• Develop clear, comprehensive documentation about AI data usage, algorithms, and decision-making processes

• Implement advanced monitoring tools that provide real-time visibility into AI operations

• Regularly update stakeholders about AI deployments, their purposes, and their operations to foster trust and transparency

• Integrate AI systems with robust reporting tools to ensure all key activities and decisions are logged and reviewable

Assigning Accountability for AI Operations 

To ensure accountability, it’s essential to define who is responsible for AI operations at various stages. This means defining who is responsible for the oversight, maintenance, and outputs of AI systems at various stages of their lifecycle – from development, validation, and deployment to ongoing management and auditing. Detailed protocols for handling AI anomalies, errors, or issues related to ethics and compliance are also crucial.

DMG’s Recommendations: Establish clear lines of accountability and responsibility at each stage of AI deployment.

We recommend that all Financial Services companies:

• Define clear, organization-wide roles and responsibilities for AI oversight, from development to deployment and management

• Establish protocols for handling anomalies and errors, including incident reporting and resolution procedures

• Create a cross-functional team that includes members from data science, legal, risk management, and business leaders to ensure diverse perspectives on AI accountability

• Craft detailed contingency plans for potential AI errors or failures, including communication strategies, technical backups, and resolution procedures

Overcoming the AI Transparency and Accountability Challenge

The “black box” nature of AI doesn’t have to be a barrier to its adoption in Financial Services. By prioritizing explainable AI for high stakes/regulated processes, enhancing visibility into AI processes, and assigning clear lines of accountability, financial institutions can build trust in their AI systems and use them to drive significant benefits. DMG is here to support your journey towards transparent and accountable AI. Contact us today for a complimentary consultation on how we can assist your organization in creating AI systems that are not just powerful but also transparent and accountable.

AI-Driven Data Quality: The Path to Accurate and Reliable Business Decisions in Financial Services

The rapid adoption of artificial intelligence (AI) in Financial Services has underscored the immense value AI can bring to data-driven decision-making. However, the reliability of AI is only as good as the quality of the data it is trained on. Poor data quality or inaccuracies can lead to misguided decisions with far-reaching consequences. To address this, it is crucial for financial enterprises to embrace AI Data Quality and AI Data Governance tools, enhancing the integrity of Critical Data Elements and ensuring cost-effective scalability in their AI initiatives.

At Data Management Group (DMG), we recognize that data is the bedrock of AI reliability. Our suite of solutions is designed to empower your organization to maintain high-quality data, allowing AI to deliver more accurate and dependable business outcomes. 

In this post, we offer our recommendations on how companies can improve data quality and accuracy, ensuring the maximum benefit from their AI initiatives. Our recommendations include: 

• Ensuring Scope and Definition of Critical Data – What you consider Critical Data Elements will impact data integrity

• Leveraging Advanced AI Data Quality Tools – Take advantage of AI tools to optimize data quality

• Employing AI-Powered Data Governance – Bring AI into your data governance processes to ensure your processes evolve as needed  


Ensure the Scope and Definition of Critical Data 

In large enterprises, data quality can be notoriously inconsistent. Companies traditionally identify critical data types and manage those data pipelines more rigorously, or they employ Master Data Management (MDM) tools to ensure data reliability. However, this focus on “critical” data leaves a significant portion of data that may not be reliable, and AI systems often lack the capability to discern between reliable and unreliable data.

Additionally, there is significant cost associated with maintaining Data Quality, and so most organizations are forced to make a difficult decision to reduce the scope of Critical Data to only the Most Critical Data. Our recommendation is to leverage AI Data Quality methods and tools to be able to ensure the quality of the actual Business Critical Data (usually more data) at a lower overall cost.

DMG’s Recommendations: Use AI tools to cost-effectively increase your company’s Critical Data
We recommend that Financial Services companies:

• Adopt AI-enhanced data cleansing tools that can aid in detecting, correcting, and preventing errors in their datasets

• Review their definition of data quality and critical data and refine their data governance processes accordingly

Institute a culture of data quality by providing regular training sessions for employees, reinforcing the importance of data accuracy in AI implementation

Leverage Advanced AI to Improve Data Quality 

AI itself can be a solution to the problem of poor data quality. AI models can be utilized to identify and resolve data quality issues before feeding the data into business-level AI models. These tools are typically built in to the Data Governance tools and processes. The process of data cleaning includes removing duplicate data, detecting outliers in your data, forecasting missing data, and validating and verifying data. 

DMG’s Recommendations: Evaluate and experiment with AI tools that can remedy poor data quality. 

We recommend that Financial Services companies evaluate:

• Employing AI models designed to detect and correct inconsistencies and errors in data as part of the their Data Governance methods, processes, and tools 

• Regularly subjecting your data to AI scrutiny, ensuring your data is clean, updated, and accurate before it serves any business-level AI models 

• Incorporating automated data cleaning and preprocessing routines using AI, further raising the data quality standard

• Leveraging AI-driven data profiling to understand the quality of data at a granular level and improve it prior to using in business models

Employ AI-Powered Data Governance to Drive Strategic Decision-Making

Data governance traditionally involves setting policies and standards for data usage, ensuring compliance with regulations, and maintaining data quality. AI augments this process, introducing capabilities such as predictive analytics and real-time monitoring, turning static rules into dynamic, responsive frameworks that adapt as new data trends and patterns emerge. 

DMG’s Recommendations: Incorporate AI into your data governance practices to ensure dynamic data quality

We recommend that Financial Services companies:

• Implement AI systems that can analyze vast datasets to predict potential data quality issues, flagging them before they impact decision-making

• Use machine learning models to continuously learn from data interactions, optimizing governance policies and ensuring they remain relevant in the face of changing regulations and business needs 

• Automate compliance checks with AI algorithms that can sift through complex regulatory requirements, reducing human error and ensuring faster adaptation to new compliance challenges 

Ensuring Reliable Data in Large Financial Institutions

Financial institutions are entrusted with making data-informed decisions daily. By proactively perfecting data quality and governance with advanced AI tools, these enterprises can both bolster their strategic decision-making capabilities and expand the universe of data that is considered CDEs.

At DMG, we are dedicated to guiding financial services through the complexities of AI in data governance. Our solutions are designed to deliver accurate, reliable data that powers your AI strategies effectively. Reach out today for a complimentary consultation on fortifying your organization’s data quality for superior AI performance. 

How Financial Services Companies Can Use AI While Maintaining Regulatory Compliance

In the Financial Services industry, regulatory compliance is an imperative. DMG has been helping a number of leading organizations get their arms around how to utilize Artificial Intelligence (AI) while maintaining and navigating the regulatory landscape.

As these institutions weave AI into the fabric of data governance, the question isn’t just how they can achieve compliance, but also how they can continue to innovate within the strict parameters set by regulations such as GDPR and other industry-specific mandates. This delicate balance demands AI solutions that not only drive progress but also operate transparently and accountably within the regulatory frameworks.

In this post, we’ll explore how organizations can ensure their AI efforts in the data governance realm meet regulatory requirements, despite challenges such as the need for explainability in AI systems and the lack of transparency provided by some AI models.

We recommend the following considerations and approaches to ensure you’re using AI in a way that maintains regulatory compliance:

• Meet the Explainability Imperative – Ensure you can explain how AI is making decisions

• Provide Transparency in AI Processes – Make your use of AI clear, consistent, and understandable

• Integrate AI with Compliance Monitoring – Utilize AI to continually monitor your adherence to regulatory requirements


Ensure You Meet the Explainability Imperative

A significant challenge with AI in regulatory compliance is the necessity for AI decisions to be explainable. Financial Services companies must be able to understand and articulate how AI systems arrive at their conclusions, particularly when those systems inform decisions impacting customer data governance.

DMG’s Recommendations: Meeting the explainability imperative is a non-negotiable to abide by regulatory requirements. 
We recommend that Financial Services companies do the following:

• Implement Explainable AI (XAI) systems that provide clear, understandable reasoning for their decisions, aligning with the transparency requirements of regulations like GDPR.

• Document the decision-making processes of AI systems meticulously, ensuring that each step can be reviewed and justified in regulatory audits.

• Invest in training for staff to understand AI decision-making, equipping them to provide explanations and justifications to regulators, auditors, and stakeholders.

Provide Transparency in AI-Driven Processes

Transparency in AI processes is essential for maintaining regulatory compliance, particularly when handling sensitive customer data. Providing transparency is a multifaceted challenge, requiring clarity in how AI algorithms make decisions, how they’re audited, and how they align with compliance mandates. Financial Services companies need to ensure that their use of AI is as open as it is effective.

DMG’s Recommendations: Develop clear documentation and policies governing how AI is used in your organization.

We advise companies to provide transparency into their AI processes by doing the following:

• Utilize AI systems that log decisions and data usage in a clear, comprehensible manner, facilitating easy review by compliance officers and auditors.

• Engage in regular transparency audits, using third-party experts to verify the compliance and clarity of your AI systems.

• Foster a culture of openness, where AI’s role in data governance is communicated clearly to customers, building trust and reinforcing a commitment to compliance.

Integrate AI with Compliance Monitoring

AI’s capacity to process and analyze large datasets can be a powerful tool for monitoring compliance in real-time, providing financial services companies with immediate insights into potential areas of risk. In this way, AI can actually help ensure companies maintain regulatory compliance rather than opening them up to additional regulatory risk.

DMG’s Recommendations: Utilizing AI as a tool to ensure compliance monitoring is a great way to put the technology to use.

We recommend that Financial Services companies:

• Implement AI monitoring systems that continually assess compliance across all data governance activities, flagging issues as they arise.

• Develop dashboards that provide a real-time view of compliance status across various departments and AI systems, enabling quick responses to potential discrepancies.

• Ensure that AI compliance tools are adaptable, allowing them to evolve as regulations change and new standards emerge.

• Develop AI systems capable of parsing and interpreting regulatory updates, guidelines, and notices.

Compliance as an AI Advantage

Financial Services companies using AI for data governance should not see it as a challenge to regulatory compliance but as an enhancement to it. By aligning AI capabilities with compliance requirements, financial institutions can gain a competitive edge, ensuring they are not only meeting the necessary standards but are also positioned at the forefront of innovation.

As Financial Services companies navigate this complex relationship between AI systems and regulatory requirements, DMG is equipped to provide the expertise and support your organization needs to take advantage of the game-changing technology while staying on the right side of regulation. Contact us to explore how your institution can ensure your AI initiatives in the data governance realm keep you compliant with regulations both now and in the future.

Using AI to Uphold Fairness and Combat Bias in Financial Services

The burgeoning role of Artificial Intelligence (AI) in Financial Services is not just a technological revolution; it provides the opportunity to redefine fairness in a sector that has historically seen biases perpetuated in practices like redlining, gender bias, and age discrimination.

At DMG, we’ve been helping Financial Services companies face what’s both a formidable challenge and a golden opportunity: ensuring that AI-driven processes like data classification and access control are free from biases that can lead to negative customer experience and significant legal and reputational risks.

We recommend the following considerations and approaches to address Fairness and Bias concerns:

• Establishing AI Principles – Establish clear intent and objectives

• Set Up Strategic AI Governance – Establish accountability and oversight

• Identify and Address Bias – Identify and address Legacy and New potential bias

• Understand AI’s Global Mandate – Understand and address cultural differences

• Embed Equity Into AI Operations – Identify and Operationalize equity into systems

Establishing AI Principles

In the journey toward fairness, the inception point is establishing robust AI principles. These principles act as the north star for AI development, guiding models and teams working on AI projects to adhere to the highest standards of integrity and fairness. We wrote about the importance of AI principles in our first post in this series as well, which covers the data privacy and security imperative of AI in Financial Services companies.

DMG’s RecommendationsEstablishing AI principles is the cornerstone of ensuring you’re employing AI ethically.

We recommend that all Financial Services companies

• Formulate comprehensive AI principles that prioritize non-discrimination and equality.

• Ensure AI systems’ training on broad, inclusive datasets, reflecting the diversity of global customers.

• Continuously evolve AI principles to address emerging social norms and ethical considerations, fostering a culture of inclusivity and responsibility within AI teams.

Set Up Strategic AI Governance

AI governance in Financial Services companies is not just about oversight. It’s about instituting a proactive, strategic approach to operational fairness for all customers. This governance must extend to the creation, deployment, and ongoing management of AI systems.

DMG’s Recommendations: Companies striving to deliver AI solutions that uphold fairness and combat bias must always have strategic AI governance in place. 

We work with Financial Services companies to:

• Create a robust AI governance structure with clear roles and responsibilities, ensuring that fairness objectives are integrated into every aspect of the AI lifecycle.

• Utilize advanced fairness monitoring tools to detect and mitigate biases, employing regular fairness audits and reporting to maintain accountability.

• Set up a diverse oversight committee that brings together different stakeholders, including ethicists, customer advocates, and technologists, to review and challenge AI decision-making from multiple perspectives.

Identify and Address AI Bias

Many financial services are deeply rooted in historical data. This includes credit scoring and lending, insurance underwriting, mortgage and loan approvals, and risk management. Reliance on historical data can unwittingly encode and perpetuate past prejudices. The onset of AI affords organizations the opportunity to hit the reset button by identifying and mitigating biases present in historical datasets and decision-making algorithms.
DMG’s Recommendations: Rooting out historical bias in data sets is increasingly important as AI gets trained on this data.

We help Financial Services companies:

• Undertake detailed bias impact assessments for different AI applications, recognizing that biases can vary significantly across services and products.
Implement specialized AI fairness tools and methods tailored to the unique challenges of financial datasets, ensuring they are capable of evolving with changing industry patterns.
• Commit to transparency in AI decision-making processes by publishing fairness metrics and methodologies, thus building trust with stakeholders.

Understand AI’s Global Mandate

Many Financial Services companies increasingly operate on a global stage, where AI systems must navigate a tapestry of cultures. This diversity demands AI models that are not only technically proficient but also culturally competent.

DMG’s Recommendations: Financial Services companies must be prepared to meet the needs of an increasingly diverse customer base.

We work with Financial Services companies to

• Incorporate diverse cultural datasets and insights into AI training processes, avoiding the one-size-fits-all model and instead embracing a multifaceted approach.

• Engage local communities and cultural experts in the AI development process to ensure models reflect the nuances of different cultural norms and values where your organization operates.

• Develop region-specific AI models where necessary, ensuring decisions are locally relevant and culturally sensitive, thus bolstering the global appeal and trust in AI-driven services.

Embed Equity Into AI Operations

Operationalizing fairness requires embedding equity into the DNA of AI systems, not just as an abstract principle but as a tangible, actionable framework within all AI operations.

DMG’s Recommendations: Ensuring your AI initiatives promote equity and fairness is a must to combat bias.

We recommend that all Financial Services companies:

• Integrate fairness protocols into the development pipeline, with checkpoints at each stage of the AI model’s lifecycle to assess and ensure fairness.

• Embrace explainable AI frameworks to demystify AI decisions, providing clarity on how outcomes are derived and enabling stakeholders to assess fairness directly.

• Train AI developers, data scientists, and operational staff in the nuances of fairness, equipping them with the skills to identify bias and the tools to address it effectively.

Pioneering Fairness Through AI

Capturing the benefits of AI carries significant responsibility, especially for Financial Services firms – we must act as pioneers in the realm of digital fairness. By committing to comprehensive AI principles, robust governance, vigilant bias monitoring, and cultural sensitivity, financial institutions can lead the charge against bias, setting a new standard for equity in the industry.

If you’d like support in figuring out how your AI initiatives can ensure fairness and remove any semblance of bias, please feel free to schedule a complimentary consultation with DMG today.

AI in Financial Services Companies: The Data Privacy and Security Imperative

In an era where Artificial Intelligence (AI) reshapes financial services, dramatically improving operations and revolutionizing customer experiences, the imperative of data privacy and security has emerged as a number one priority for business leaders. At Data Management Group (DMG), we’ve been at the forefront of this shift, collaborating with leading financial entities to navigate and neutralize AI-related data risks. This guide distills our insights, offering a blueprint to safeguard your company’s data and your customers’ data against emerging threats.

In this post, we’ll delve into the potential vulnerabilities introduced by AI and provide recommendations institutions can put in place to ensure robust data protection. Subsequent posts in this series will cover concerns around implementing AI in other areas, like bias and fairness, regulatory compliance, data quality and accuracy, and transparency and accountability.

The AI Paradox: Potential vs. Pitfalls

AI holds immense promise, from predicting market trends to offering personalized banking solutions. However, its very nature, which involves sifting through vast datasets, can inadvertently introduce vulnerabilities. Unauthorized access, potential breaches, and misuse of data are genuine concerns. Financial institutions must proactively counter these threats.

Here are 5 keys to help your company get all the benefits of AI while at the same time neutralizing threats around data security and privacy.

1. Develop and Adhere to AI Principles

The foundation of any AI-driven initiative in the financial sector should be built on sound AI principles. These principles serve as a compass, ensuring that AI models are transparent, fair, accountable, and designed with privacy in mind. By embedding these principles into the AI lifecycle, from data collection to model deployment, financial institutions can mitigate risk and uphold their commitment to data protection.

DMG’s Recommendations: We recommend that companies outline a set of AI principles that can be used to guide how LLMs are trained on data and provide guardrails around their outputs. This includes:

• Establishing a set of AI principles that are tailored to your organization’s values and goals.

• Ensuring that these principles are communicated across teams and are at the forefront of any AI project. Principles often include a commitment to data privacy, ensuring AI is trained in a way that prevents bias, and adding requisite safeguards to ensure data is only accessible by those people or systems who truly need it.

2. Establish Comprehensive AI Governance

Beyond principles lies governance — a robust framework that encompasses policies, procedures, and oversight. Effective governance ensures that AI models align with organizational goals and regulatory standards, offering clarity on data usage, model interpretability, and unforeseen outcomes.

DMG’s Recommendations: To ensure that your organization has appropriate checks and balances in place, we recommend that organizations:

• Implement a multi-disciplinary AI governance committee, comprised of data scientists, legal experts, and business leaders.

• Conduct a regular review of existing and proposed AI initiatives, ensuring they align with established principles and address emerging concerns.

• Provide avenues of recourse for the committee to raise red flags if and when they encounter potential activity that runs counter to the organization’s principles.

3. Bolster Security by Fortifying the Data Itself

AI models, by their nature, process vast amounts of data, making them potential targets for cyber-attacks. Ensuring the security of these models, the data pipelines feeding them, and the infrastructure supporting them is paramount. While AI can accelerate data processing, enhance insights, and automate tedious tasks, it can also introduce vulnerabilities if not secured properly.

These vulnerabilities include threats like data poisoning, where malicious actors can introduce “poisoned” data into the training set, causing the model to learn incorrect behaviors. Model inversion and extraction, where attackers can use the outputs of an AI system to reconstruct valuable information about its training data or even replicate the model itself, is another threat.

DMG’s Recommendations: We work with companies to regularly conduct AI-specific vulnerability assessments, implement end-to-end encryption, establish robust access controls, and set up real-time monitoring to detect and thwart potential threats. Among the specific steps we recommend companies take are:

• Robust model training to recognize and resist adversarial inputs, making them more resilient to such attacks.

• Data validation, which ensures that the training data is clean, relevant, and free from anomalies. Companies should regularly update the training set to account for new data patterns.

• Differential privacy techniques should be implemented to ensure an individual’s data cannot be reverse-engineered from the AI’s output, thereby adding a layer of privacy protection.

• Regular security audits should be performed to evaluate the AI system for vulnerabilities. Employ red teams to simulate real-world attacks and gauge the system’s resilience.

• Continuously monitor and log AI operations. Any anomalies or unexpected behaviors should trigger alerts for immediate investigation.

4. Implement PII Data Security and Controls

Personally Identifiable Information (PII) is a goldmine for malicious actors. Financial institutions must implement stringent controls to ensure that PII is not only stored securely but is also processed in a manner that respects individual privacy. Large companies handle vast amounts of PII, which is often stored in disparate systems, making it difficult to track and secure.

DMG’s Recommendations: There are a number of approaches we recommend to ensure security of PII. Companies should always:

• Anonymize or pseudonymize PII data before feeding it into AI models

• Implement data masking techniques and ensure that access to PII is strictly on a need-to-know basis. Data masking conceals the original data with modified content (characters or other data) that is structurally similar to the original data. This is especially useful for testing and training environments.

• Encrypt PII, both in transit and at rest. This is a vital step to ensure that even if data is accessed, it remains unreadable.

5. Set Up PII Network Screening

Ensuring the security of the network through which PII data flows is crucial. Any weak link in the chain can lead to potential breaches, undermining trust and leading to regulatory repercussions. Modern enterprises often have intricate networks that span multiple regions, cloud providers, and on-premises data centers. As networks become more complex and interconnected, the challenge of ensuring PII is securely handled within these networks grows exponentially. The sheer volume of network traffic can also make real-time screening a resource-intensive task.

DMG’s Recommendation: As with PII data security and controls, there are a number of approaches financial institutions can take to ensure the safety and security of networks that handle PII. We recommend:

• Deploying advanced network screening tools that monitor data flow in real-time

• Implementing intrusion detection systems and ensuring regular patching of network vulnerabilities

• Setting up Deep Packet Inspection (DPI), which involves examining the content of network packets to identify any PII data being transmitted. DPI can detect PII even if it’s not part of regular database traffic, such as in emails or file transfers.

• Network segmentation, or creating isolated segments within the network to ensure that PII is only accessible and transmissible within designated secure zones.

Data Privacy and Security Around AI is Difficult But Far From Impossible

While AI introduces a new dimension of concerns in the realm of data privacy and security, with a principled approach, robust governance, and stringent security measures, financial institutions can harness the power of AI without compromising on their commitment to data protection. As the financial landscape continues to evolve, staying proactive and informed will be the key to navigating the challenges and opportunities that lie ahead.

If your firm is working through some of these challenges and would like outside expertise ensuring you can take advantage of all that AI has to offer while mitigating the risks, please reach out to us today for a complimentary consultation. 

Emerging Data Risks: More Data = More Risks

The Benefits of Being Frugal With Data

In today’s digital age, enterprises are generating and processing more data than ever before. One estimate stated that 1.7 MB of data was created every second for every person on earth. With the amount of data in the world said to double every two years, we’ve far surpassed that estimate by now.

The massive amounts of data that enterprises have access to can be valuable assets for companies when managed well. We tend to want to save all data since it may be valuable in the future. After all, this data provides insights into customer behavior, shines a light on emerging market trends, and offers opportunities for personalization that most companies would have only dreamed of a few short years ago. On the flip side, data can also be a massive liability for companies if they try to retain too much of it or it is not managed properly.

Emerging Data Risks_1Datensparsamkeit: Keep What You Need, and Nothing More

One approach that we’ve found helps clients successfully frame their approach to data management and retention is to adopt the principle of Datensparsamkeit, which is a German term that roughly translates to “data frugality.” This principle states that organizations should only retain the data they absolutely need and nothing more.

There are a number of benefits to adopting a Datensparsamkeit approach. First, it can help to protect the privacy of individuals. Second, it can reduce the risk of data breaches. Third, it can reduce storage and security costs. By being more selective about the data they retain, organizations can improve their privacy, security, and bottom line.

Emerging Data Risks_2Considerations Around What Data to Keep and What to Discard

One of the key challenges of data management is determining what data to retain. There are a number of factors to consider, including the following:

• Legal requirements: Some data must be retained for legal reasons, such as to comply with regulation or to document business transactions.

• Business needs: Other data may be needed for business purposes, such as to provide customer support or to track product usage.

• Privacy concerns: Organizations must also consider the privacy implications of retaining data. In some cases, it may be necessary to delete data to protect the privacy of individuals.

The “keep-it-all” approach to data management is neither sustainable nor advisable. It’s not financially feasible to retain all of the data that is generated by an enterprise and, even if it were, in many cases the downsides to trying to retain everything far outweigh the upsides. Instead, DMG recommends that organizations take a selective, strategic approach to the data they retain.

Emerging Data Risks_3Putting Datensparsamkeit Into Practice

As you figure out what the most critical data is for your company to retain, here are some additional tips and questions you can ask to help determine what is kept and what gets discarded:

Consider the purpose of the data. What will the data be used for? Is it needed for a specific purpose? To cite one example, do you really need to keep the IP addresses of all website visitors or people who are logged into your site forever? If so, how will you use that data to improve customer experience?

Potentially summarize and delete data outside the value timeframe.  How long is data valuable? At what point is it more of a risk than a benefit? At what point does granular data lose its value, and summarized data is sufficient? Consider how you might update corporate data retention policies and schedules to ensure that data gets purged upon reaching the point where the risks outweigh the benefits.

Evaluate the risk of data breaches. How likely is it that the data could be breached? If the risk is high and there’s personally identifiable information in the data set, what is the benefit to keeping that data?

Consider the cost of storage and security. How much does it cost to store and secure the data? If the cost is high and there’s no obvious use case for the data, why are you paying to store it?

By following these tips and thinking through these questions, organizations can make informed decisions about what data to retain. This will help to protect the privacy of customers, reduce the risk of data breaches, and save money in the long run.

Data management is a complex and challenging task. However, by adopting a Datensparsamkeit approach, organizations can make it easier to manage their data and protect the privacy of individuals.

Need help determining which data is mission critical for your organization? Reach out for a complimentary consultation today. 

Data Quality Metrics and A Data Quality Culture

Data Governance Best Practices

Formalizing and improving your Data Governance framework is one of the soundest ways to ensure that your company does more than just pay lip service to being data-driven. In this post, I’ll cover two Data Governance best practices that can set you well on your way to what I called in a previous post A Minimally Invasive Approach to Formalizing or Improving Data Governance.

Data Governance Best Practices Header 1

Why You Need to Establish Data Quality Metrics

As we have mentioned previously, Data Governance is largely about modifying peoples’ behavior with relation to data. Metrics drive behavior, and are critical to the establishment and success of Data Governance programs.

Most Data Governance programs are concerned about Data Quality, so let’s delve a little deeper into Data Quality metrics. Data quality metrics are important to measure the effectiveness of your Data Governance program and ensure that data quality and integrity are maintained. By establishing data quality metrics, you can make adjustments as needed to ensure that data quality is maintained.

These are some common data quality metrics that DMG frequently helps companies measure:

Completeness measures the percentage of required data fields that are populated in a dataset. A company might measure the completeness of customer records to ensure that all required fields are present.

Accuracy measures the degree to which data reflects the actual state of the real-world object or event it represents. You likely want to measure the accuracy of sales data, for example, to ensure that it reflects the actual sales transactions that occurred.

Consistency measures the degree to which data is consistent across different sources or systems. A company could measure the consistency of product data across different sales channels to ensure that customers receive consistent and accurate product information.

Timeliness measures the degree to which data is up-to-date and reflects the current state of the business. The timeliness of financial data to ensure that reports and analyses are based on the most recent data available would be an example of such a measure.

Validity measures the degree to which data conforms to predefined business rules and standards. You could measure the validity of customer data to ensure that it conforms to established data quality standards.

Relevance measures the degree to which data is useful and applicable for the intended purpose, like the relevance of marketing data to ensure that it is applicable to the target audience and can be used to make effective marketing decisions.

Duplication measures the number of duplicate records in a dataset. Duplicate data can cause inaccuracies and inconsistencies in data analyses and reporting.

Integrity measures the degree to which data is complete, accurate, and consistent over time. A company might measure the integrity of employee data to ensure that it remains accurate and up-to-date throughout the employee lifecycle.

Automated data quality checks can be implemented to run in the background so they identify any issues with data quality in real-time. DMG recommends at minimum setting up rules that check for missing data, inconsistent data, and duplicate records. If and when these issues can be caught early, they can be flagged for troubleshooting before they’re allowed to permeate and erode data quality.


Data Governance Best Practices Header 2

Develop a Data Quality Culture

Developing a data quality culture is perhaps the most important aspect of ensuring data quality and integrity. A data quality culture is one in which all members of the workforce understand the importance of data quality and are committed to maintaining it.

Just a few of the ways you can do this include establishing data quality goals, objectives, and metrics; providing regular feedback to your team on data quality metrics and measurable impact your Data Governance efforts are having on the business; and recognizing and rewarding individuals or teams that contribute to maintaining data quality. By establishing a data quality culture, you can ensure that data quality and integrity become tightly intertwined with your overall approach to Data Governance.

Capturing the Benefits of Data Governance Doesn’t Have to Be Painful

Implementing a robust Data Governance program at the enterprise level can be challenging, but there are a number of strategies you can take to ensure you minimize the additional strain placed on your team. This is an area where we have successfully led numerous transformations for enterprise clients to help them go from questioning their data to having their data help answer some of their most vexing questions.

If you haven’t read the first 2 posts in this series yet, you can read ‘A Minimally Invasive Approach to Formalizing or Improving Data Governance’ here and ‘Automated Data Quality Checks and Standardized Data Formats’ here. You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing Data Governance approach.   

Automated Data Quality Checks and Standardized Data Formats

Data Governance Best Practices

Unlocking the power of accurate and reliable data is no longer a daunting task. Companies that are able to do so will be well-positioned for the future, as they use data to enhance customer experience, increase customer retention, and drive innovation.   

Data Governance plays a prominent role in fulfilling the promise of doing more with your data. Companies that take advantage of revolutionary advancements in areas like automated data quality checks, paired with best practices like standardized data formats, will be well on their way to having actionable data that informs mission-critical business decisions and opens up opportunities for growth.  


Why You Should Implement Automated Data Quality Checks

Data Quality is a typical priority for most Data Governance programs. Automated data quality checks are an effective way to ensure that data quality is maintained throughout your organization without adding significant additional demands on your workforce. They provide the following benefits to organizations that are looking to elevate their Data Governance efforts:

1. Improved data accuracy: Automated data quality checks can help companies identify and correct errors in data quickly, improving data accuracy and reducing the risk of making decisions based on incorrect data.

2. Better compliance: Companies that employ automated data quality checks ensure that they are meeting regulatory requirements and industry standards, reducing the risk of penalties or fines for non-compliance.

3. Greater transparency: Automated data quality checks can help companies build trust with stakeholders by providing a clear audit trail of data changes and updates, improving transparency and accountability.

4. Cost savings and risk mitigation: By identifying and correcting errors in data early, companies can avoid the costs associated with correcting data quality issues downstream, such as lost productivity, reputational damage, or customer churn.

5. Improved decision-making: Automated data quality checks can help companies make better-informed decisions by ensuring that the data used to inform those decisions is accurate and reliable.

Automated data quality checks can be implemented to run in the background so they identify any issues with data quality in real-time. DMG recommends at minimum setting up rules that check for missing data, inconsistent data, and duplicate records. If and when these issues can be caught early, they can be flagged for troubleshooting before they’re allowed to permeate and erode data quality.


Why You Should Standardize Data Terminology, and How You Can Get Started

Standardizing data terminology is another way to ensure data quality and integrity without adding significant additional demands on your workforce. Some examples of data terminology are: product identifier, states, colors, amortization value, cost of goods, etc. These are largely based on the industry and business that you are in, and which data is considered to be of high value to the organization.

By standardizing the terminology associated with data formats (for example), you can ensure that data is consistent across different data sources and that it can be easily integrated between systems and analyzed. Standardizing data formats can also help reduce errors and inconsistencies in data by ensuring that all data is entered or captured in a consistent and uniform manner.

Here are some examples of how to standardize data formats in a Data Governance model, along with some of the benefits of each:

Use of data dictionaries: A data dictionary is a centralized repository that defines the names, definitions, and formats of data elements used in an organization. By establishing a common data dictionary, all data elements can be consistently defined and documented, reducing ambiguity and improving understanding and use of the data.

Data validation rules: Validation rules are used to check data values against predefined criteria to ensure that data is accurate and consistent. They ensure that data can be standardized to a common format and structure.

Data modeling: Data modeling is the process of creating a conceptual or logical model of data, which defines the relationships between data elements and the rules governing their use. Taking this step means data can be structured consistently across different systems and applications.

Data transformation and mapping: Data transformation and mapping tools can be used to convert data from one format to another, while preserving the meaning and integrity of the data. By standardizing data transformation and mapping processes, data can be easily moved and shared across different systems and applications.

Master data management: Master data management (MDM) is a set of processes and tools used to manage the most important data elements in an organization, such as customer, product, or location data. MDM empowers organizations to ensure that critical data is accurate, consistent, and accessible across the enterprise.

Use of data exchange standards: Data exchange standards, such as XML, JSON, or CSV, or industry data standards (such as MISMO in mortgage banking) can be used to define a common format for exchanging data between different systems and applications. This allows for data to be easily shared and integrated across different platforms, reducing data silos and improving data quality.

In addition to all of the above, standardizing data formats also makes it easier to implement the previous best practice of automated data quality checks.

And remember that data formats are just one type of data terminology. There are many others out there. That is a larger subject, however, and one that we may revisit in a future post.

Let’s Discuss Data Quality Metrics and A Data Quality Culture

Establishing or improving your Data Governance framework can feel like an overwhelming challenge, especially with the deluge of data and systems that even small to medium sized businesses have to manage. The good news is that it has never been easier to automate data quality checks, and standardizing your data terminology and data formats can go a long way toward helping you do so.

The first post in this series looked at how organizations can take a minimally invasive approach to formalizing or improving their data governance efforts. In the third and final post of the series, we’ll cover the importance of choosing the right data quality metrics and establishing a data quality culture where everyone is invested in making sure your Data Governance efforts bear fruit.

You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing data governance approach.

A Minimally Invasive Approach to Formalizing or Improving Data Governance

Since the days of Moneyball, many leaders have pushed their organizations to become increasingly data-driven. What few of these leaders fully grasp, however, is the additional workload this kind of fundamental shift places on existing staff. This is especially true when it comes to creating a high-functioning Data Governance model, which relies heavily on data quality and integrity to serve as the bedrock upon which business decisions can be made.

We frequently work with clients to establish strategies, approaches, and methods to ensure that they are  operating with data of sufficient quality and integrity to establish a data-driven culture. Fortunately, there are a number of ways you can do so without adding substantial workload to your organization.

Many large organizations have a standard architecture approach which will need to be considered as part of Data Governance adoption. For example, some use The Open Group Architecture Framework (TOGAF) which is a high-level approach to design. It is typically modeled at four levels: Business, Application, Data, and Technology. If you are familiar with TOGAF or other frameworks, you will likely recognize these levels in the approach described here.

In this series of three blog posts, we will explore minimally invasive ways to ensure data quality and integrity in your Data Governance model without adding significant additional demands on your workforce.

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A Data Governance Strategy is a Must

One of the first steps in ensuring data quality and integrity is to have a clear Data Governance strategy in place. This strategy should outline the goals and objectives of your Data Governance program and provide a roadmap for achieving them.

Typical goals and objectives of a Data Governance program include:

Data quality: Ensuring that data is accurate, complete, and consistent across different systems and departments.

Data security: Protecting data from unauthorized access, use, and disclosure.

Data privacy: Ensuring that data is collected, processed, and used in compliance with privacy laws and regulations.

Data management: Developing policies and procedures for the creation, use, storage, and disposal of data.

Data integration: Facilitating the integration of data from different sources and systems to improve decision-making and operational efficiency.

Data standardization: Establishing common standards and definitions for data elements to improve consistency and reduce errors.

Data accessibility: Ensuring that data is available to authorized users when and where it is needed.

Data Governance management and oversight: Monitoring compliance with Data Governance policies and procedures, and identifying areas for improvement.

Data Governance education: Providing education and training to employees on the importance of Data Governance and their roles and responsibilities in maintaining data quality, security, and privacy.

Data Governance culture: Creating a culture of Data Governance throughout the organization to ensure that data is valued and managed as a strategic asset.

A Data Governance strategy will help you identify the data sources that are most critical to your business, determine the level of data quality required for each source, and establish processes for ensuring that data quality is maintained.

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Measuring Business Value – Quantifying Success and Improvement

One of the significant challenges in standing up a data governance program is the daunting amount of data that organizations today have. It seems overwhelming…so where do you start?

In our experience there is some set of data that has more business value than other data. Sometimes we call this critical data. Critical data elements might include: key transactional data, financial data, or regulated data (e.g. PII, HIPAA, etc). Part of the Data Governance strategy and charter will define the critical data and areas that the program needs to impact. The success metrics need to be aligned with the strategy, and this may include metrics for the handling, quality, and security of these data elements.

Additionally, the Data Governance program itself will have metrics associated with it. These may include key milestones such as when the Data Governance Charter and Policy were approved, how many systems are in the adoption phase of Data Governance, and when the initial set of critical data were identified.

The bottom line is that Metrics Drive Behavior, and since Data Governance is largely about modifying the organization’s behavior regarding data, we must have a set of initial (and improving) metrics to understand the impact of the Data Governance program and the value to the organization.

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Data Governance Tools are Key To Enabling and Accelerating Efforts

Once a Data Governance strategy is in place, you’ll want to evaluate which tools and technologies are needed to delivery on your strategy and roadmap. Technology is undoubtedly a cornerstone of getting your Data Governance efforts firing on all cylinders without placing huge additional demands on your team members.

There are a wide variety of tools that DMG has used to help enterprises elevate their Data Governance efforts. These include:

• Data Quality Management tools like Informatica Data Quality and Talend Data Quality; and AI tools such as DataRobot, Trifacta, and DataWrangler by Google Cloud

• BI tools like Qlik, Tableau, and PowerBI

• ETL tools like Talend, Microsoft SSIS, and Oracle Data Integrator

• Data Governance tools like Collibra, Alation, and BigID

• Cloud-based data quality services like Microsoft Azure Data Factory and AWS Data Quality Services.

There are many other tools and methods available. These are representative examples. DMG is partners with or certified in a number of Data Governance tools and technologies. We regularly assist customers with optimization of their existing tools and selection and implementation of new tools based on their Data Governance strategy.

Let’s Consider Implementation Best Practices

Once a Data Governance strategy, success metrics, and the requisite Data Governance tools are in place, it’s time to move on to the most rewarding parts of your Data Governance efforts: implementation, execution, and iteration. In our next two posts, we’ll look at best practices we guide customers through to ensure their Data Governance efforts are successful with minimal impact to their teams’ workload. 

You can learn more about our lean approach to Data Governance and schedule a complimentary consultation today if your organization would like outside expertise formalizing or improving your existing data governance approach.

5 Keys to Establishing a Successful Data Governance Program

Data Management Group (DMG) works regularly with clients who are interested in establishing a Data Governance program, but may not know exactly what that entails.  Data Governance is a powerful approach to aligning interests in, and furthering improvements in Data Quality and Accountability.

It’s worth mentioning that Data Governance is the foundation of Data Management, and it plays a critical role in ensuring that data is managed in a way that supports the goals and objectives of an organization. As businesses continue to rely on data to make decisions, Data Governance has become a vital component of the modern business landscape.

What is Data Governance?

Data Governance refers to the overall management of the availability, usability, integrity, and security of the data used in an organization. It includes the processes, policies, standards, and technologies that are used to ensure that data is managed effectively across the enterprise. The importance of Data Governance in today’s business landscape cannot be overstated, as it provides a framework for organizations to manage data effectively, efficiently, and in a compliant manner.

The Benefits of Data Governance 

One of the primary benefits of Data Governance is that it helps organizations make better decisions. By ensuring that data is accurate, reliable, and timely, Data Governance enables organizations to make informed decisions based on reliable information. This is particularly important in today’s fast-paced business environment, where decisions need to be made quickly and based on the most up-to-date information available.

Another key benefit of Data Governance is that it helps organizations manage risk. As data becomes more valuable and more vulnerable to cyber-attacks, Data Governance provides a framework for managing data security and privacy risks. By establishing clear policies and procedures for managing data, organizations can reduce the risk of data breaches and ensure that data is used in a compliant manner.

In addition to managing risk, Data Governance can also help organizations comply with regulatory requirements. Many industries, like financial services and healthcare, are subject to strict regulations that govern how data should be managed, stored, and used. Looking beyond specific industries, consumer protection laws like GDPR and the California Consumer Privacy Act (CCPA) also have vast ramifications for businesses of all shapes and sizes. Data Governance provides a framework for ensuring that organizations comply with these regulations, reducing the risk of fines and other penalties.

Data Governance is also essential for managing data quality. Poor data quality can lead to costly errors, lost revenue, and damaged reputation. Industry estimates show that more than $3 trillion dollars was lost annually due to bad data all the way back in 2016. With the amount of data in the world estimated to be doubling every two years, the annual dollar amount lost due to bad data is undoubtedly even more staggering today.

Data Governance ensures that data is accurate, complete, and consistent, improving the overall quality of the data used in an organization. This, in turn, can lead to improved decision-making and better business outcomes.

5 Keys to Establishing a Successful Data Governance Program

Data Governance can be complex and difficult to implement. However, with the right approach, organizations can overcome these complexities and reap the benefits of an effective Data Governance program. Here are some key steps to establishing a successful Data Governance program:

1. Define Data Governance goals and objectives. Without clear goals and objectives, Data Governance programs will flounder. Goals and objectives will help ensure that the program is aligned with the needs of the organization — data quality, data security, data integrity, data access — and that the right resources are allocated to achieve these goals.
2. Define Data Governance roles and responsibilities. Data Governance requires a team effort, and it’s important to define clear roles and responsibilities for everyone involved. These include defining the roles of the Data Governance team, data stewards, and other stakeholders.
3. Develop Data policies and procedures. Once roles and responsibilities are defined, it’s essential to develop clear policies and procedures for managing data. This includes defining data standards, data quality requirements, and data security and privacy policies.
4. Establish Data Quality and Governance metrics and measurement. Data Governance requires ongoing monitoring and measurement to ensure that it’s effective. Establishing clear metrics and measurement criteria will help ensure that the program is achieving its goals and identify areas for improvement.
5. Engage Data Governance stakeholders. Effective Data Governance requires the engagement of all stakeholders. This includes executive sponsors, business users, and IT staff. Engaging stakeholders early in the process will help ensure that the program is aligned with the needs of the organization and that everyone is invested in its success.

Data Governance at Data Management Group

Data Management Group works every day with companies that need a framework for managing their data, as a strategic approach to Data Governance is essential in today’s business landscape. We provide companies with a framework for managing their data effectively, efficiently, and in a compliant manner. This leads to improved decision-making, reduced risk, and improved data quality. While Data Governance can be challenging to implement, organizations that are successful at creating Data Governance programs stand to improve data quality and business planning, increase security, and mitigate risk.