Trusted Analytics in the Cloud Starts with Data Discovery and Privacy
Not long ago, a top executive at a leading global telecom who was responsible for the company’s cloud and data security engineering was preparing to present an event keynote. His topic: the impact of cloud data security and privacy on accelerating digital transformation.
A large sympathetic audience shared his pain on a modern IT dilemma. How do I prove that my customer data is safe to migrate and use with new cloud-hosted data analytics applications?
His challenge sounds familiar to many business and technical leaders today who are making the same transition. The answer to safe and trusted cloud data analytics would depend on data governance maturity.
Each of his organization’s line of business leaders focused on revenue expansion with new service offerings needed to migrate sensitive customer data to the cloud. New, modern cloud applications would fuel data analytics insights. However, he was blocked due to a board-level mandate not to move data off internal systems, unless data migrated to the cloud could be deemed as safe and trusted as data on premises.
But how does one measure and offer satisfactory trust assurance to satisfy the divergent interests of business value creation and data governance leaders? Equally important, how best to maintain customer trust that data would be handled responsibly in new cloud-hosted environments?
According to the Flexera State of the Cloud Report 2021, organizations are moving more of their data to the cloud. As of early 2021, half of enterprise data (46%) is already there, and organizations predict 8% more will be within the next twelve months. The message is clear—adapt, or get outpaced by your competitors who are accelerating workload migration to the cloud potentially faster than you can.
We are increasingly seeing this scenario of challenges—you may recognize a few of these from your cloud journey…
- What is appropriate data use? Is the cloud only appropriate for hosting non-critical business data for storage—or can we safely tap into data intelligence for greater innovation by expanding data analytics applications?
- How is data exposure relevant? What is the exposure of personal data and IP in cloud applications once we democratize cloud data access and use, and how do we determine if it is appropriate to enterprise policies?
- Who is responsible for trust assurance? Who is responsible for data, AI and application governance that can help to mirror the trust assurance already established with on premises systems—can it be made consistent?
Conquering new uncharted territory to achieve trust assurance during digital transformation
While cloud hosted service providers can manage and protect underlying platforms—hardware, OS, hypervisors, and the like—it gets more complicated further up the stack. Data access and use are the cloud data governance responsibility of tenants. Service level agreements often end at the point where it is the tenants who are solely responsible to control appropriate access to their applications and data— which is outside the purview of host providers.
It is up to each cloud tenant to govern their data responsibly! Getting back to my real-world example above, we can see a typical scenario:
Your organization’s business leaders want to better understand customer buying behavior and assess data value for greater data intelligence into customer experience and build better loyalty but need to establish trust in the cloud to ensure data is handled as responsibly as on premises.
Moreover, digital transformation is only accelerating in this post-pandemic world. Acquiring new infrastructure to spin up data analytics governance programs is too expensive and time consuming to consider on premises—at minimum, a less sophisticated organization may find it challenging to respond quickly to board-level demands for privacy and security assurances as a gatekeeper.
And while the cloud offers much improved time to value with lower operational and capital expenses, and greater elasticity and scale benefits in the long term, your nervous executives are leery about exposing customer data in untrusted, unproven environments. A cloud data governance and catalog solution can answer these trust assurance questions to accelerate cloud adoption and use, including data analytics.
And Now There’s Added Pressure: Post-Pandemic, All Bets are Off (or at Least, Accelerated)
Organizations are now exiting the pandemic with a new appreciation for accelerating time to value. They are doing more with less resources, outsourcing functions such as IT that has been hit hard, fixing supply chain disruptions, and similar digital transformation hurdles that come from a realigned workforce and operations after uncertain times.
What was previously a multi-year goal for cloud migration is now a differentiator today to those who can emerge in 2021 having increased business efficiencies through reliable cloud data governance. But risks to cloud migration for sensitive data use remain—the disruption of 2020 did not help hosted environments become more trusted, it only created an urgency to move faster or be left behind.
Into the Future: Cloud Data Discovery and Privacy that Delivers Transparency
If your organization is like the example above, your goal is to achieve a tough, but achievable, balancing act, quickly with the data transparency and insights needed to make informed decisions:
Meet the demands of business leaders who want to spin up new cloud data analytics workloads faster, while demonstrating to your data stewards that the cloud offers no more or unusual risks to data privacy and trust than what you could already manage sufficiently on premises
Cloud data governance is your key to enabling the transparency and insights that fuel your cloud-native agenda. With Cloud Data Governance and Catalog from Informatica, you have the help you need to migrate workloads and data with confidence by enabling privacy governance and risk mitigation best practices that include:
- Data discovery to uncover new insights into sensitive data for safe migration
With data discovery applied in cloud analytics governance, the industry’s metadata management leader, Informatica, enables insights to assess data sensitivity for trust and privacy, and support informed decisions to minimize exposure risk for appropriate use policies. Do the data sets migrated to the cloud require unique dispositioning? Should they be made available to all without restrictions? You can begin to answer these questions for more responsible use of both the data itself as well as the analytics applications applied.
- Data lineage insights to determine appropriate data exposure
Not only do you need to know what sensitive data you have discovered and catalogued as potentially higher risk, you need to understand its movement and whether appropriate to your policies for democratization and exposure with use in the cloud. With regulations that impact cross-border data transfers, moving data to the cloud can impact how you share sensitive information beyond the safe boundaries of your on-premises systems, not just outside your corporate IT environment, but globally under appropriate conditions.
- Accountability to govern your data analytics programs appropriately
Visibility into data quality and a greater understanding of AI models that power analytics can help you stay ahead of quickly emerging data ethics legislation, and help guide privacy exposure decisioning on appropriate data uses with the trust and confidence to satisfy both enterprise users and when communicating with customers. As use of AI expands and comes with the urgency to monitor and manage performance, the resulting bias and compliance inconsistencies with your policies can be a board-level blocker to cloud migration, if not addressed through cloud data governance insights. Being able to identify responsible stakeholders, and correct and report on AI bias risk, helps avoid long-term scrutiny as regulatory pressures start to increase.
- Managed data exposure that scales for safer use by key stakeholders
By balancing privacy and transparency requirements with your data democratization needs, you can unlock the value of what cloud hosted platforms provide in terms of increased scalability and elasticity. But it is simple: as more data and workloads are migrated to the cloud, the greater risk exposure due to scale. By enabling data discovery and lineage insights in a cloud-hosted solution, you can better make smart decisions with continuity as you scale out for whether exposure is worth the benefit or an unnecessary liability. More importantly, decide on how best to discover, classify, prioritize, and protect data to maximize value and minimize risk.
- Focus on cloud analytics value creation, not the time needed to achieve results
Cloud data governance while on the surface a cost of doing business, is more like a key that helps unlock tremendous value behind the door. Through automated data discovery that is based on metadata-driven intelligence, you can spend less time finding, assessing, and preparing data needed for analytics, and instead spend more time focused on operationalizing trusted data sources that drive value with data types that generate business insights to help you act. A goal your business leaders will appreciate when democratizing data safely!
Too Big to Ignore: The Risk of Failed Digital Transformation Initiatives
The success of your digital transformation initiatives and application modernization in the cloud depends on the reliability and trust of the data you plan to migrate as a fundamental prerequisite to spinning up new cloud data analytics workloads.
Flaws and inconsistencies, accompanied by trust and privacy concerns in the data as well as AI models will impact long-term customer experience downstream. Left unaddressed, these concerns stall new investments and instead focus on remediation of trust and privacy risks rather than value creation programs. Ultimately, due to a lack of confidence, these concerns will throttle adoption of new SaaS applications that drive new enterprise products and services.
Cloud data discovery and cloud data lineage, along with the insights provided on how they can best guide your evolving data governance policies, are key to enabling the full potential of sensitive data hosted in the cloud. Data discovery and privacy assurance is a starting point to assessing and understanding data privacy risks, and enabling trust in AI with transparency, by applying cloud data analytics governance.
The bottom line is simple: If you can rely on cloud data governance to deliver the data intelligence insights needed to migrate your data responsibly, you can accelerate value creation by leaving privacy risks behind. That starts with data discovery to accelerate your journey.
For more insights to fuel your digital transformation, please check out the Cloud Data Intelligence Summit on-demand and accelerate your cloud journey!