Trusted Data Access and Sharing — Why Automation Is the Key to Achieving Value from Data Democratization

Is Your Data MIA?

Once confined to specialized teams and restricted access points, sensitive data has emerged as the lifeblood of modern enterprises. Its rich nature for offering competitive and business insights drives innovation, informs decision-making, and unlocks new avenues for growth.

While this data often sits locked up, organizations realize it’s the key to game-changing business outcomes, requiring responsible use that unlocks its potential.

Today, business success hinges upon the democratization of data — a concept pivotal for organizations striving to achieve the much sought-after “data-driven” state. The basic tenet is “democracy,” meaning the open availability of capabilities, not limited to a specific team.

The ultimate data mission here is, as Indiana Chief Privacy Officer and MPH General Counsel Ted Cotterill puts it in a CDO Magazine interview, “We want the right individuals to be seeing the right data for the right purposes, for the right duration.”

Driven by digital transformation efforts over the last few years, the volume of data is growing at a never-seen-before pace, and so is the number and variety of consumers needing data empowerment. Enterprise data teams face growing responsibilities around enhancing data quality and trust, promoting data access and self-service, balancing security and usability, and scaling access efficiently.

Despite the apparent imperative for widespread data access, traditional data management frameworks grapple with inherent limitations. Centralized control mechanisms and complex access protocols placed on siloed systems that lack nuance have often stifled data accessibility, impeding the seamless flow of insights across organizational boundaries.

This restricted access undermines the agility of decision-making processes and erects barriers to innovation and collaboration. Such challenges pose a critical threat to organizational resilience and competitiveness in a landscape where agility and innovation are tantamount to survival. Consequently, the necessity to transcend traditional data management paradigms has never been more pressing.

This paradigm shift underscores the need for rethinking a data team’s approach to data access governance — a departure from traditional siloed approaches, recognizing data as a ubiquitous asset capable of empowering individuals across diverse functions.

There is a gap in striking a delicate balance between openness, scalability, quality, and security, fostering a data-driven decision-making culture without compromising compliance or integrity.

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Connected Data in the Post-Transformation Era

As organizations transition into the post-transformation era, characterized by widespread adoption of cloud technologies and the efforts to scale out operations, the focus has shifted towards connecting data owners and producers with consumers. This paradigm shift emphasizes making data accessible to more users within an organization by breaking down the silos that were common on-premises, irrespective of their technical expertise, to include more business stakeholders and foster a culture of accessibility, empowerment, and collaboration.

By democratizing access to data, organizations empower employees to make informed decisions, drive innovation, and contribute meaningfully to the company's objectives.

This topic surfaced as a key point of discussion during a panel around “Democratization of Data and AI” at CDO Magazine’s Toronto Summit last year. Panel speaker Ferial Sheybani, Colliers International, VP, Head of Technology and Data, pointed out that siloed and disconnected data led to business users having insufficient information and affected collaboration across business lines.

Collaboration is a natural consequence of accessibility and empowerment, enabling cross-functional teams to work together seamlessly toward shared organizational goals. Collaboration can help organizations harness the collective intelligence of diverse teams to solve complex problems, build new offerings, and achieve strategic objectives more efficiently.

During the discussion, Sheybani mentioned that democratization was a clear need for the business, eventually leading to the creation of the Connected Data Program in her organization. She further revealed that there have already been demands for integrating generative AI so business users can easily access insights from the data based on conversational prompts.

Sheybani added that the organization is doing something other than waiting for the data to be perfect and fully connected before enabling access. Users may lose interest if they have to wait too long to realize value.

However, as organizations strive to realize the benefits of democratization, they also need to navigate a myriad of risks and challenges. These include data security, privacy, compliance, and integrity concerns.

In the quest to democratize data access, organizations must strike a delicate balance between utility and security, ensuring that sensitive information remains protected while enabling widespread access.

With Great Democratization Comes Great Responsibility

As organizations endeavor to democratize data access and empower individuals across the enterprise, the last mile of data delivery emerges as the critical phase in the journey. This final stretch represents an opportunity to share data and data products responsibly, helping to ensure that insights reach the right users when needed and with appropriate context.

However, achieving individualized data access poses significant challenges and concerns that need addressing to realize the potential of data democratization.

The last mile of data delivery is where organizations must contend with a wide range of variables, including specific user requirements, use cases, rules, and contextual nuances that inform policies for granting conditional entitlements to access the data responsibly. Unlike traditional data delivery models, which often adopt a one-size-fits-all approach, the last mile requires personalized access management tailored to the unique requirements of individual users and the operating environment.

Regarding the balance of data accessibility for various stakeholders, Shahidul Mannan, Chief Data Officer of Bon Secours Mercy Health, says that governing data access is vital to democratization. “Having the right information at the right time is not only key for decisions but also innovation and experimentation with new ideas. This necessity can be balanced with proper governance, robust self-service infrastructure, and security setup. If you are in the data-driven innovation game, this is a critical equation you have to solve or stay focused on trying.”

Rob Golden, Divisional Senior Vice President and Chief Data and Analytics Officer at Great American Insurance Group, says that balancing data accessibility with data integrity and security needs to account for the level of sensitivity of the data. “The more sensitive data (PII, PHI, or company confidential data) demands stricter access controls. A successful organization needs to invest in educating stakeholders about sensitive data and why those restrictions are so important. Partnership between CISO and CDA/O is absolutely essential to achieving the right balance,” he adds.

Golden further elaborates that Financial Services being a heavily regulated industry, his organization has long-standing policies and processes to ensure that the data complies with regulatory requirements. “This includes a variety of internal controls, independent audits as well as periodic external/regulatory reviews.”

The controls on sharing data need to be as granular as possible about who is requesting access and under what conditions to justify the data types for provisioning. However, the traditional manual approach to last-mile data delivery that requires negotiating with data consumers to understand their needs is a significant roadblock to democratization. Manual governance has inefficiencies, delays, and security risks and needs more scalability when handling large volumes of requests for data access.

Speaking at an Executive Boardroom Session on Data Democratization at CDO Magazine’s Toronto Summit, Todd Henley, VP and Chief Data Governance and Privacy Officer at Northwest Bank, mentioned that a lot of the modern approaches to data enablement are designed to provide speed to execution and remove friction in business processes.

He mentioned the traditional approaches of providing business intelligence to advanced analytics and data science as the “infinite loop of regret,” where data teams are “doing the same things over and over again.” He elaborated that when they get a data product out the other end, the market circumstances change, and the business users lose their opportunity.

The situation will likely worsen with organizations pursuing data literacy and upskilling talent to improve data use and seize the opportunities presented by the AI boom.

(The opinions presented by Rob Golden are his own and do not constitute an official statement from Great American Insurance Group.)

More Users and Use Cases Equals Even More Responsibilities

As organizations prioritize enhancing data literacy and reskilling to meet the pressing demand for analytics and AI, users across departments make way for a new and diverse array of data use cases and accompanying risks — to make smarter decisions, they need more insights.

This surge in demand for data-driven intelligence introduces newer challenges for data management teams, including data privacy concerns, user experience, security breaches, and regulatory compliance issues. It further necessitates improved controls through access governance to balance accessibility and security — especially across industries that handle more sensitive data, such as healthcare and financial services.

Speaking about the approach at Bon Secours Mercy Health, Mannan says that the organization’s custodianship role in healthcare information mandates ensuring compliance with privacy laws such as  HIPAA and HITECH, among others. “Our goal is to provide easy and meaningful access to data to become a data-driven organization, but we also strictly follow it on a need-to-know basis. Moreover, we utilize innovative de-identification and anonymization of data to further reduce any risk exposure, and still be able to fuel innovation.”

“Our approach tries to balance innovation and governance and it starts with stakeholders being educated on the opportunities and risks. The most impactful innovation happens at the edges, closer to the work,” says Golden.

The need for active vigilance while enabling both technical and business users to leverage the full potential of data raises the question – can automation augment stewardship?

Can Automated Data Access Management Help?

Automated data access management solutions promise more efficient and highly granular control over data access, crucial for navigating the complexities of data democratization. By leveraging metadata insights, these solutions can tailor access policies to specific user needs, conditions, and use cases, ensuring responsible data access across the organization.

Automated data access management systems minimize delays and administrative burdens by accelerating order request and approval processes and implementing policy-driven controls, facilitating timely access to data while reducing the risk of unauthorized access and breaches.

Similarly, user-friendly interfaces and automated self-service functionalities further enhance efficiency, empowering users to manage their access permissions independently and freeing up data stewards and resources for strategic initiatives.

That way, every time a user wants to execute a data project or use data, automation can save the team valuable time from going through routine and repetitive hoops of reviewing legal compliance, metadata, data quality, privacy and security mandates, and more.

“Ensuring a vibrant community of data and analytics stakeholders is essential to advancing the types of transformation that many of us believe is possible with analytics and emerging AI tools,” shares Golden. “Focused education and AI risk management and governance with the most invested stakeholders and analytics practitioners will yield the best results.”

Critical to effective data access management is data cataloging, including automated discovery and classification, enabling quick and efficient data exploration and analysis.

Automated access control mechanisms, role- and attribute-based permissions, and data lineage tracking ensure data integrity and compliance, safeguarding sensitive information while facilitating data sharing and collaboration by incorporating data intelligence into access policy development and enforcement.

Golden says that while the primary approach to ensuring data traceability hasn't changed, “the emerging tools are making the job so much easier – we are building automation and traceability into the core platform, whereas historically, this was a separate and manual process.” 

Regis Deshayes, Head of Data Quality at Zeiss Group, stated in a CDO Magazine interview that even if a data set “is of excellent quality, it will be of no value if it cannot be found or shared, and is documented poorly.”

In an opinion piece exclusively penned for CDO Magazine, Chathuri Daluwatte, Head of AI Diagnostics, Alexion, AstraZeneca Rare Disease, mentions that self-served data platforms follow a data-as-a-product approach to get rid of the bottleneck of a centralized data team via domain-oriented decentralization for analytical data.

“AI can be used within self-served data platforms during the DataOps processes (e.g. clean data, transformations, and ingesting) at the policy automation process of the data platform,” Daluwatte adds.

Automation from AI-powered data management plays a pivotal role in enabling organizations to expand the scope of business opportunities while maintaining compliance with evolving privacy regulations, AI ethics, ESG responsibilities, and other data-related obligations.

Automating appropriate access to data, data assets, and data products streamlines operations and also enhances transparency and simplicity in the delivery process.

“I believe if done right we can and should use automation to bring efficiency and speed to market, for data innovation and products. Like any new change, we need to manage it carefully through experimentation, incrementally, and while building the right process and framework,” says Mannan.

Mannan further elaborates that to fully extract value from data and AI innovation, data teams need to implement end-to-end automation with access to DevOps/MLOps-driven releases integrated into the life-cycle. “I don’t think we are far from the day when we can see efficient, low-cost, and much shorter go-to-market cycles for AI products based on the foundation of automation.”

By automating data delivery, organizations can ensure that appropriate individuals can access the best data when needed, facilitating quicker decision-making and realizing high-value business outcomes. This seamless and streamlined process reduces the likelihood of errors and minimizes the time and effort spent on manual or complex processes, ultimately increasing productivity and reducing risk.

Moreover, automation enables organizations to ensure compliance with regulatory requirements and ethical considerations – and maintain a balance between data accessibility and security – while maximizing the potential for better analytics, AI applications, and enhanced customer experiences.

Golden agrees that automation is the “only way to achieve scale” on an operational and/or analytical platform. While he is aware of tools that help with navigating the highly evolving regulatory space, he maintains that they are not a replacement for a robust partnership with the legal and compliance teams - which are “essential to interpreting the emerging regulation and risks, and what they mean to your business. No tool can interpret a new piece of regulation and how it will impact how you do business.”

Executive Action – Overcoming the Data Democratization Dilemma

Data can be considered the lifeblood of business. Still, it needs to circulate freely across the business’ organs (read: departments and users) while keeping the body healthy to be truly impactful. Many organizations struggle with the challenge of data democratization – making data intelligence and insights readily available across the organization while safeguarding the security and privacy of sensitive information.

Traditional, siloed data management approaches create bottlenecks, policy inconsistencies, and repetitive hoops to jump through, hindering timely access and stifling innovation at the speed of business.

The Roadblock: Friction in Data Access

• Limited Scalability: Traditional methods need to catch up with the ever-growing volume and variety of data, making managing universal access for a widening pool of data consumer users challenging.

• Security Concerns: Achieving data utility while protecting critical data is a balancing act that creates risk exposure if unresolved. Furthermore, manual processes add risks, creating errors with improper access when provisioning data or data products.

• Delayed Decision-Making: Cumbersome access request fulfillment procedures slow down business users who need data to make informed decisions quickly. Without automated data management and handling, trust assurance diminishes between data producers and consumers.

The Remedy: Automated Data Access Governance

Automated data access governance offers a remedy. By leveraging AI, machine learning, and analytics, these tools can:

• Grant Fine-Grained Access: Tailor access entitlements based on specific user roles, conditions, needs, and use cases. Tailoring helps ensure appropriate data consumers can access relevant data when needed by enforcing proper controls.

• Automate Request and Approval Processes: Eliminate bottlenecks from manual processes by automating data access requests and approvals aligned to fine-grained data-use policies, streamlining workflows, and accelerating decision-making.

• Enhance Security and Compliance: Enforce data governance policies and security protocols universally and consistently, reducing the risk of data security breaches and helping to ensure compliance with evolving regulations.

The Benefits: A Data-Driven Advantage

By implementing automated data access governance, organizations can unlock a multitude of benefits:

• Empowered Employees: Faster, relevant, and appropriate access to data empowers employees at all levels to make smarter data-driven decisions, fostering innovation and problem-solving across departments to grow and expand the business.

• Improved Agility: With quicker access to data intelligence and insights, organizations can rapidly adapt to changing market conditions and customer needs, putting the competition in the rearview mirror.

• Enhanced Collaboration: Breaking down data silos fosters collaboration with knowledge-sharing to accelerate data literacy, supporting the development of new products and services as data delivery is achievable with trust and confidence.

• Reduced Operational Costs: Automated processes streamline data management, freeing up valuable IT resources for higher-level tasks and reducing administrative burdens.

In an increasingly competitive data-driven economy, automated data access governance is no longer a luxury but a necessity when connecting data sources and producers, and delivering data products to consumers to realize greater value. By streamlining access management and minimizing unnecessary barriers while ensuring appropriate use with security, data leaders empower the workforce, unleash the true potential of data, and position organizations for sustainable digital growth.

About Informatica:

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