Unlocking the power of data governance by understanding key challenges

We introduced Data Governance: what it is and why it is so important. In this blog, we will explore the challenges that organizations face as they start their governance journey.

Organizations have long struggled with data management and understanding data in a complex and ever-growing data landscape. While operational data runs day-to-day business operations, gaining insights and leveraging data across business processes and workflows presents a well-known set of data governance challenges that technology alone cannot solve.

Every organization deals with the following challenges of data governance, and it is important to address these as part of your strategy:

Multiple data silos with limited collaboration

Data silos make it difficult for organizations to get a complete and accurate picture of their business. Silos exist naturally when data is managed by multiple operational systems. Silos may also represent the realities of a distributed organization. Breaking down these silos to encourage data access, data sharing and collaboration will be an important challenge for organizations in the coming years. The right data architecture to link and gain insight across silos requires the communication and coordination of a strategic data governance program.

Inconsistent or lacking business terminology, master data, hierarchies

Raw data without clear business definitions and rules is ripe for misinterpretation and confusion. Any use of data – such as combining or consolidating datasets from multiple sources – requires a level of understanding of that data beyond the physical formats. Combining or linking data assets across multiple repositories to gain greater data analytics and insights requires alignment. It needs linking with consistent master data, reference data, data lineage and hierarchies. Building and maintaining these structures requires the policies and coordination of effective data governance.

A need to ensure data privacy and data security

Data privacy and data security are major challenges when it comes to managing the increasing volume, usage, and complexity of new data. As more and more personal or sensitive data is collected and stored digitally, the risks of data breaches and cyber-attacks increase. To address these challenges and practice responsible data stewardship, organizations must invest in solutions that can protect their data from unauthorized access and breaches.

Ever-changing regulations and compliance requirements

As the regulatory landscape surrounding data governance continues to evolve, organizations need to stay up-to-date on the latest requirements and mandates. Organizations need to ensure that their enterprise data governance practices are compliant. They need to have the ability to:

◉ Monitor data issues

◉ Ensure data conformity with data quality

◉ Establish and manage business rules, data standards and industry regulations

◉ Manage risks associated with changing data privacy regulations

Lack of a 360-degree view of organization data

A 360-degree view of data refers to having a comprehensive understanding of all the data within an organization, including its structure, sources, and usage. Think about use cases like Customer 360, Patient 360 or Citizen 360 which provide organizational-specific views. Without these views, organizations will struggle to make data-driven business decisions, as they may not have access to all the information they need to fully understand their business and drive the right outcomes.

The growing volume and complexity of data

As the amount of data generated by organizations continues to grow, it will become increasingly challenging to manage and govern this data effectively. This may require implementing new technologies and data management processes to help handle the volume and complexity of data. These technologies and processes must be adopted to work within the data governance sphere of influence.

The challenges of remote work

The COVID-19 pandemic led to a significant shift towards remote work, which can present challenges for data governance initiatives. Organizations must find ways to effectively manage data and track compliance across data sources and stakeholders in a remote work environment. With remote work becoming the new normal, organizations need to ensure that their data is being accessed and used appropriately, even when employees are not physically present in the office. This requires a set of data governance best practices – including policies, procedures, and technologies – to control and monitor access to data and systems.

If any or all of these seven challenges feel familiar, and you need support with your data governance strategy, know that you aren’t alone. Our next blog will discuss the building blocks of a data governance strategy and share our point of view on how to establish a data governance framework from the ground up.

In the meantime, learn more about building a data-driven organization with The Data Differentiator guide for data leaders.

Source: ibm.com

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Understanding Data Governance

If you’re in charge of managing data at your organization, you know how important it is to have a system in place for ensuring that your data is accurate, up-to-date, and secure. That’s where data governance comes in.

What exactly is data governance and why is it so important?

Simply put, data governance is the process of establishing policies, procedures, and standards for managing data within an organization. It involves defining roles and responsibilities, setting standards for data quality, and ensuring that data is being used in a way that is consistent with the organization’s goals and values.

But don’t let the dry language fool you – data governance is crucial for the success of any organization. Without it, you might as well be throwing your data to the wolves (or the intern who just started yesterday and has no idea what they’re doing). Poor data governance can lead to all sorts of problems, including:

Inconsistent or conflicting data

Imagine trying to make important business decisions based on data that’s all over the place. Not only is it frustrating, but it can also lead to costly mistakes.

Data security breaches

If your data isn’t properly secured, you’re leaving yourself open to all sorts of nasty surprises. Hackers and cyber-criminals are always looking for ways to get their hands on sensitive data, and without proper data governance, you’re making it way too easy for them.

Loss of credibility

If your data is unreliable or incorrect, it can seriously damage your organization’s reputation. No one is going to trust you if they can’t trust your data.

As you can see, data governance is no joke. But that doesn’t mean it can’t be fun! Okay, maybe “fun” is a stretch, but there are definitely ways to make data governance less of a chore. Here are a few best practices to keep in mind:

Establish clear roles and responsibilities

Make sure everyone knows who is responsible for what. Provide the necessary training and resources to help people do their jobs effectively.

Define policies and procedures

Set clear guidelines for how data is collected, stored, and used within your organization. This will help ensure that everyone is on the same page and that your data is being managed consistently.

Ensure data quality

Regularly check your data for accuracy and completeness. Put processes in place to fix any issues that you find. Remember: garbage in, garbage out.

Break down data silos

Data silos are the bane of any data governance program. By breaking down these silos and encouraging data sharing and collaboration, you’ll be able to get a more complete picture of what’s going on within your organization.

Of course, implementing a successful data governance program isn’t always easy. You may face challenges like getting buy-in from stakeholders, dealing with resistance to change, and managing data quality. But with the right approach and a little bit of persistence, you can overcome these challenges and create a data governance program that works for you.

So don’t be afraid to roll up your sleeves and get your hands dirty with data governance. Your data – and your organization – will thank you for it.

In future posts, my Data Elite team and I will help guide you in this journey with our point of view and insights on how IBM can help accelerate your organization’s data readiness with our solutions.

Source: ibm.com

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Make data protection a 2023 competitive differentiator

Data privacy regulations, such as the General Data Protection Regulation (GDPR) in the European Union or the California Consumer Privacy Act (CCPA) in the state of California, are inescapable. By 2024, for instance, 75% of the entire world’s population will have its personal data protected by encryption, multifactor authentication, masking and erasure, as well as data resilience. It’s clear data protection, and its three pillars—data security, data ethics and data privacy—is the baseline expectation for organizations and stakeholders, both now and into the future.

While this trend is about protecting customer data and maintaining trust amid shifting regulations, it can also be a game changing, competitive advantage for organizations. In implementing cohesive data protection initiatives, organizations that can secure their users’ data see huge wins in brand image and customer loyalty and stand out in the marketplace.

The key to differentiation comes in getting data protection right, as part of an overall data strategy. Keep reading to learn how investing in the right data protection supports and optimizes your brand image.

Use a data protection strategy to maintain your brand image and customer trust

How a company collects, stores and protects consumer data goes beyond cutting data storage costs—it is a central driving force of its reputation and brand image. As a baseline, consumers expect organizations to adhere to data privacy regulations and compliance requirements; they also expect the data and AI lifecycle to be fair, explainable, robust, equitable and transparent.

Operating with a ‘data protection first’ point of view forces organizations to ask the hard hitting, moral questions that matter to clients and prospects: Is it ethical to collect this person’s data in the first place? As an organization, what are we doing with this information? Have we shared our intentions with respondents from whom we’ve collected this data? How long and where will this data be retained? Are we going to harm anybody by doing what we do with data?

Differentiate your brand image with privacy practices rooted in data ethics

When integrated appropriately, data protection and the surrounding data ethics creates a deep trust with clients and the market overall. Take Apple, for example. They have been exceedingly clear in communicating with consumers what data is collected, why they’re collecting that data, and whether they’re making any revenue from it. They go to great lengths to integrate trust, transparency and risk management into the DNA of the company culture and the customer experience. A lot of organizations aren’t as mature in this area of data ethics.

One of the key ingredients to optimizing your brand image through data protection and trust is active communication, both internally and externally. This requires organizations to rethink the way they do business in the broadest sense. To do this, organizations must lean into data privacy programs that build transparency and risk management into everyday workflows. It goes beyond preventing data breaches or having secure means for data collection and storage. These efforts must be supported by integrating data privacy and data ethics into an organization’s culture and customer experiences.

When cultivating a culture rooted in data ethics, keep these three things in mind:

◉ Regulatory compliance is a worthwhile investment, as it mitigates risk and helps generate revenue and growth.

◉ The need for compliance is not disruptive; it’s an opportunity to differentiate your brand and earn consumer trust.

◉ Laying the foundation for data privacy allows your organization to manage its data ethics better.

Embrace the potential of data protection at the core of your competitive advantage

Ultimately, data protection fosters ongoing trust. It isn’t a one-and-done deal. It’s a continuous, iterative journey that evolves with changing privacy laws and regulations, business needs and customer expectations. Your ongoing efforts to differentiate your organization from the competition should include strategically adopt and integrate data protection as a cultural foundation of how work gets done.

By enabling an ethical, sustainable and adaptive data protection strategy that ensures compliance and security in an ever-evolving data landscape, you are building your organization into a market leader.

Source: ibm.com

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How to stay ahead of ever-evolving data privacy regulations

Enterprises are dealing with a barrage of upcoming regulations concerning data privacy and data protection, not only at the state and federal level in the US, but also in a dizzying number of jurisdictions around the world.

Kicked off several years ago by the groundbreaking introduction of the European Union’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), the regulation and compliance trend is only going to intensify. In August the Federal Trade Commission (FTC) released an Advance Notice of Proposed Rulemaking (ANPRM) titled Commercial Surveillance and Data Security that encompasses a wide range of data protection and privacy issues, including data monetization models, discrimination and algorithmic biases and data security, to name a few.

As these types ANPRMs continue to be released and regulation swiftly catches up to innovation, a recent Gartner survey predicts that 75% of the world’s population will have its personal data covered under modern privacy regulations by the end of 2024.

At IBM’s recent Chief Data and Technology Officer Summit on data privacy, I spoke with some of the world’s top data leaders about the two-pronged challenge they’re now facing: ensuring that data policies and practices meet regulatory demands, while also continuing to innovate with new technologies.

We agreed there is a way to navigate this complicated landscape and maintain a competitive advantage that delivers business value. The journey starts with having a multimodal data governance framework that is underpinned by a robust data architecture like data fabric. This framework can create a standard approach for meeting regulatory compliance while allowing for customization to address local regulations and being proactive when handling new regulations.

Adopting a privacy-centric approach built around a data fabric

A data fabric is an architectural approach that simplifies data consumption across a diverse and distributed landscape, while adhering to data privacy requirements. Think of a data fabric as a single pane of glass that creates visibility across an enterprise. By doing so, it greatly reduces the complexity of managing disparate regulations worldwide. What’s more, a data fabric can automate data governance and security by creating a governance layer across the lifecycle.

To understand how a data fabric helps maintain compliance to privacy regulations, it’s helpful to look at some essential elements of that single pane of glass.

Build a foundation using a common catalog and metadata

Building a data fabric starts with creating visibility using a data catalog, which is an inventory of an organization’s information assets. It lets appropriate parties, such as the company’s chief data analyst, know what the data is and where it resides. Without a data catalog, data can remain hidden or unused and become impossible to manage.

A proper data catalog has a common taxonomy that helps everyone communicate more effectively and solves a common challenge of data integration—different data sets describing the same terms differently. This is important for data privacy: If the wrong term is used, data that should be limited in access might accidentally be made available to the whole business.

Similarly, active metadata — data about data — is at the heart of how a data fabric delivers on privacy for the same reason as a common data catalog. If you don’t know the details about your data, how can you truly say who is meant to see it or how you can use it? In the context of a data fabric, think of metadata as an augmented knowledge graph displaying the network of data across an entire enterprise, along with the conditions that apply to these sets of data.

Operationalize data privacy through automation

Once metadata has been created, it can be tagged, signifying which data is sensitive, limiting who has access to it and so forth. Then intelligent automation begins.

Automated metadata generation is particularly important for access and privacy. Consider, for example, an enterprise that wants to bring in a new data set containing transaction information such as item descriptions, quantity purchased, name, address and credit card number. When this data set is ingested, automated tagging labels the item descriptions and quantity as general transaction data, the name and address as personal data, and the credit card number as financial data. This tagging allows policy enforcement at the point of access. If business users access the data set, they can see the general transaction data, but the personal and financial data is automatically made anonymous.

Govern data and allow self-service consumption

While many of the regulations coming down the pike will be similar or even identical, how they are enacted will look very different across countries and regions. The challenge lies with demonstrating compliance to regulators while providing business users with a way to easily access the information. Otherwise, compliance creates a speed bump for innovation. That’s where the self-service element plays a critical role.

While self-service suggests a lot of freedom, the data fabric must include multimodal governance, allowing only certain people to access that data. Again, that single pane of glass will bring together the privacy and the security aspects at a single access point, while offering users an easier way to serve the data they want accessible to others. The ability to conduct real-time monitoring and audits helps secure the systems and comply with regulations, but it also helps the business mitigate data loss through breaches and keep models accurate.

Find your holistic data privacy and security solution by getting started with a data fabric strategy.

To hear more from data leaders around privacy, watch the replay of our CDO/CTO Summit series and attend our upcoming in-person CDO Summit.

Source: ibm.com

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Overcome these six data consumption challenges for a more data-driven enterprise

Implementing the right data strategy spurs innovation and outstanding business outcomes by recognizing data as a critical asset that provides insights for better and more informed decision-making. By taking advantage of data, enterprises can shape business decisions, minimize risk for stakeholders, and gain competitive advantage. However, a foundational step in evolving into a data-driven organization requires trusted, readily available, and easily accessible data for users within the organization; thus, an effective data governance program is key.

Read More: C2090-552: IBM InfoSphere Optim for Distributed Systems Fundamentals

Ensuring data quality and access within an organization, while establishing and maintaining proper governance processes, is a major struggle for many organizations. Here are a few common data management challenges:

1. Regulatory compliance on data use

Whether data protection regulations like GDPR, CCPA, HIPAA, etc. are put in place by governments or a specific industry these data privacy and consent controls go beyond sensitive data to outline how organizations should allow their employees to access enterprise data in general.

2. Proper levels of data protection and data security

Certain data elements are critical for competitive advantage and business differentiation; therefore, those data assets need to be protected against data breaches, ensuring that only authorized users have data access.

3. Data quality

For data to be trusted, it needs to be complete, accurate and well understood. This requires data stewardship and data engineering practices to curate data standards and track data lineage, increasing the value of data. AI and Analytics is only good as the quality of data been used for it.

4. Data silos

A typical organization’s data landscape consists of a large number of data stores across workflows, business processes and business units, including but not limited to data warehouses, data marts, data lakes, ODS, cloud data stores, and CRM databases. Integrating data across this hybrid ecosystem can be time consuming and expensive.

5. The volume of data assets

The number of data assets and data elements that a typical organization stores continues to grow. This extremely large amount of enterprise data – comprising thousands of databases and millions of tables and columns – makes it difficult or impossible for users to find, access and use the data they need.

6. Lack of a common data catalog across data assets

Lack of a common business vocabulary across your organization’s data and the inability to map those categories to existing data leads to inconsistency of business metrics and data analytics in addition to making it difficult for users to easily find and understand the data.

Why you should automate data governance and how a data fabric architecture helps

The challenges outlined above demand a data strategy that includes a governance and privacy framework. Furthermore, to help the framework scale across the enterprise, it needs to be automated.

To help avoid vulnerability and inability to innovate caused by a lack of proper data governance, an architecture that enables the design, implementation and execution of automated governance across the enterprise is needed. This is especially important for organizations that operate in hybrid and multi-cloud environments.

A data fabric is an architectural approach to simplify data access in an organization. This architecture leverages automated governance and privacy to facilitate self-service data consumption. Self-service data consumption is crucial because it improves data users ability to easily find and use the right governed data at the right time regardless of where it resides using foundational data governance technologies such as data cataloging, automated metadata generation, automated governance of data access and lineage, data virtualization, and reporting and auditing.

Source: ibm.com

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Three mega-trends shaping the data economy

Data Economy - A European Perspective

I recently had the pleasure of chatting with Vilmos Lorincz, Managing Director of Data and Digital Products for Lloyds Banking Group in the United Kingdom. Data is a fundamental currency in financial services, and so developing new approaches for banking protocols is critical to formulating progressive solutions for both clients and industry colleagues.

In response to a demand by the U.K. government for more transparency in financial services, the Open Banking Implementation Entity (OBIE) was set up in 2017 to deliver architectures that give customers more control of their data within a secure framework.

Lloyds Bank undertook a decisive transformation by moving their big data to the cloud and advancing data literacy for its employees, upgrading their capacity to provide benefit to clients. “We had to design the new agile operating model for more than a thousand colleagues,” said Vilmos, “helping them land in their newly defined roles, making the right technology investment choices, while engaging with more than 20,000 people.”

Vilmos emphasized that ethical behavior is absolutely critical to gaining and maintaining client trust, establishing a company’s brand as honest and responsible partners.

When asked about mega trends that are shaping the data-driven economy, Vilmos suggested three fundamentals.

1. Customer awareness: As citizens become more digitally sophisticated, they are keenly attuned to privacy and security issues. They rightfully want control of their data, expanding their ability to explore and select personal options.

2. Maturing corporations: The corporate world is advancing its ability to adopt new processes that keep pace with emergent technologies to add value to their business models and to benefit their customers.

3. Regulatory bodies: Regulators and governments are playing active roles in adjusting to new market realities, both protecting individual rights and positioning their nation to take full advantage of the opportunities of the rising data economy.

“Organizations are realizing that data is a mission-critical competitive factor and a must-have to meet and exceed customer expectations,” Vilmos explained. “They are becoming much better at deploying machine-learning and artificial-intelligence capabilities as an increasing part of their data estate.”

Vilmos advises business executives to prepare for a fast-evolving future by establishing frameworks that can accommodate the growth of the data economy and by planning how to deliver their products within the data-driven landscape.

Source: ibm.com

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