Successful organizations appreciate the value of data as an industry differentiator. They know that managing data to drive digital transformation and business innovation is key to their success. So, when it comes to data modernization initiatives, it should be no surprise that chief data officers (CDOs) and other data leaders have the direct attention of the CEO and the board. Even in the wake of potential economic uncertainty, more than two in three data leaders plan to increase investments in data modernization programs1.
Despite this focus and sponsorship, becoming data-driven remains an aspirational goal for many organizations. One major hurdle is complexity: more than half of data leaders report more than 1,000 sources of data at their organization2.
Why Is Data Modernization Difficult to Achieve?
A fundamental reason for this is that many enterprises end up looking at data modernization as primarily a technology initiative and underestimate its cultural and behavioral aspects, such as data literacy. For modernization initiatives to deliver value, it is critical to supplement the technology interventions with a framework that aligns processes, policies, people and skills on data. This is where data governance becomes critical for data modernization initiatives. Here, the enterprise can identify decision rights and accountability, and enable appropriate behavior as they seek to create, consume, democratize and control their data assets.
Improving the efficiency of data management and analytics activities is one of the top-cited metrics among leaders predicting an increase in data management investments this year3. To succeed in their data transformation, the enterprise must make the most of their opportunities and innovate across multiple data governance disciplines, including:
Here are the top six ways data governance and cataloging can accelerate data-driven digital transformation.
1)Accelerate the Shift from Multicloud to Intercloud (and Super Cloud)
With the exponential rise in the volume and variety of data in the past decade, on-premises data infrastructure (like data centers) has become obsolete. To provide faster, more secure and cost-effective access to data, most organizations have already transitioned to a mature footprint on cloud infrastructure, using cloud service providers (CSPs) such as Amazon Web Services, Microsoft Azure, Google Cloud Platform and Oracle. According to Informatica’s report, CDO Insights 2023: How to Empower Data-Led Business Resiliency, data leaders predict that the problem of increasing variety and volume of data will only become more complex in 2023, when 77% of data sources will be cloud based4.
It is safe to assume that any contemporary data ecosystem now includes combinations of CSPs, data cloud providers, SaaS applications and more. And as ecosystems become more complex, the ability to find and track your data across multiple cloud domains is only going to become more difficult.
Fortunately, modern data governance and cataloging solutions have evolved to keep pace with this complexity. These solutions will not only provide out-of-the-box seamless integrations with CSPs, but also with the cloud applications along the entire data value chain – Snowflake, Databricks, SAP, Salesforce and many others. The ability to scan, gain context and govern data from and to all parts of the ecosystem — irrespective of source and destination systems — is powerful. And this can provide IT teams with accurate views of data lineage and better control of where and how they are using their data assets.
Recommendation: The evolution of solutions that provide end-to-end data governance as data traverses across an intercloud value chain will be a key driving force for enterprises that want to retain their cloud advantage. Flexible, scalable vendor-neutral data governance solutions will help you realize the greatest agility with multiple CSPs.
2)Catalyze New, Hybrid Models of Data Governance
We have seen a significant push for organizations towards data democratization and data self-service. But this creates a paradox: Distributed data ownership won’t work with a traditional, top-down, centralized data governance model. However, a decentralized approach — with its potential for inconsistencies, lower visibility, transparency and control — may not be the best approach, either.
Organizations have struggled to find the right balance. Part of the reason is the limited ability of data governance solutions to bridge the gap between technical IT and business requirements. This lack of flexibility with traditional solutions results in a one-size-fits-all approach to data governance and lacks agility digital businesses need.
Solutions that can adapt to the evolving needs of the organization will act as catalysts for adoption of new, hybrid models of data governance. A hybrid model allows you to equip each department and business unit with the tools to cleanse, curate and enrich data in the way best suited for their business objectives.
For example, different countries have different rules and laws for collecting and distributing customer data. The ability to customize a data governance solution allows data producers and consumers to configure and calibrate data processes and policies. And this lets you manage and administer data assets at a local and global level in a way that is secure and in compliance with applicable laws.
Recommendation: Data democratization holds enormous potential, but success requires support for data stakeholders with diverse needs and expectations. Evaluate solutions based on their ability to provide customizable, flexible options for your business and technical stakeholders. The right solution will allow you to address unique use cases, a variety of operating environments and diverse ways of working.
3)Enable Data Mesh Adoption
To be truly data-driven, enterprises need to empower those closest to data to own the data and manage it throughout its lifecycle. And that is precisely what a data mesh offers. It eliminates data access and dependency bottlenecks without compromising business context specificity, trust or data quality.
While many organizations have dabbled with the potential advantages of a data mesh — its comprehensive data discoverability, data lineage and data interoperability — few have been able to overcome the challenges of architecting one.
Now that intelligent data catalog solutions offer embedded advanced data discovery capabilities, both technical and business users can benefit. Google-like search and automated AI-driven tools deliver greater understanding about data assets. This visibility is key both for regulatory compliance and to be able to accelerate data value.
Success with a model of distributed data domains means that every user in the enterprise needs to be able to easily engage with data. You need to provide a common definition of standards. And you need to build a collaborative environment that encourages behavioral change. That makes it critical to find a balance between the autonomy and local accountability of data domains that allows for innovation while ensuring it remains compliant with regulation, risk management, security and compatibility.
Solutions that enable federated computational governance — where data governance standards are defined centrally but provide autonomy to domain teams to operationalize these standards in alignment with their objectives — will be very important in helping your organization realize success with data mesh.
Recommendation: To architect a successful data mesh requires finding the right balance. You want just enough data governance to maintain controls for compliant use of data without slowing innovation. Consider solutions with features that will incubate a data-driven culture and empower every team — central as well as local — to own faster data delivery, enhanced agility and business value of data without compromise.
4)A Unified Platform Approach to Policy Orchestration
A major roadblock to becoming data driven is maintaining confidence in data governance programs across the organization when scaling out and enforcing policies and raising standards. Fragmented tools in use across data silos, inability to share and access reliable data, and inconsistency in data quality does not build confidence and trust, especially as data moves away from its source5.
Faced with the pressures of delivering business value and return on investment, data leaders are taking a long and hard look at disparate technology stacks to determine opportunities for simplifying access to trustworthy data.
As a result, there is significant demand for end-to-end integrated data governance solutions capable of managing data quality, data discovery, data lineage, data self-service and more from a single pane for end-to-end transparency. It can be a game changer for an organization to consolidate key capabilities — such as data security, data access, reporting, provisioning and more — with a single solution. The right solution helps organizations put their data to work faster. And that helps them realize efficiencies, reduces costs, saves time and helps them stay in compliance with policies and standards.
Recommendation: Consider vendors that can unify data catalog, data governance, data quality, data privacy and data democratization and enforce standards and policies from a single platform. This can help bridge the gap between your IT and business and allow you to work together towards building a data culture on a foundation of data governance.
Powered by AI, the Informatica Intelligent Data Management Cloud™ (IDMC) lets you catalog, ingest, integrate, prep, cleanse, master and share all your data, wherever it is. And that allows you to advance your business outcomes with your own trusted data.
5)Accelerate DataOps Adoption
The complexity of data across the enterprise has increased — and so has the demand for high quality, trusted data from a burgeoning data community. In the race to get to insights faster, everyone wants to beat the competition. This urgency to be the first to get it right puts extreme pressure on IT teams to make accurate data available and deliver it at speed and scale. And that has created a demand for collaboration with data operations (DataOps).
DataOps follow outlined policies and standards and appropriate levels of security and data quality to automate the delivery of data. With help from DataOps, organizations can embed agility in their analytics development process and provide data users with more time to generate deeper, value-generating insights.
Successful collaboration with DataOps can boost organizational speed, increase trust in data and enhance data user productivity. But this requires alignment on data and its usage, the kind that advanced data governance solutions can provide. With the evolution of roles in data management, tools that can offer personalization and role-based access will provide the much-needed flexibility to realize the promise of agile data governance.
Recommendation: To accelerate DataOps adoption, consider solutions that help eliminate redundancies, automate workflows and processes, augment human knowledge and collaboration and empower data consumers.
6)Bring AI to the Heart of Data Governance
The manual efforts required to operationalize metadata intelligence often act as a single point of failure. The World Economic Forum suggests that “463 exabytes of data will be created every day by 2025,” which explains the crucial role that artificial intelligence (AI) will play in data governance in the modern enterprise6.
New AI- and machine learning (ML)-assisted data governance solutions help automate data stewardship. They can reduce risk exposure while they also maximize the value of data and underlying algorithms for competitive advantage. Here, organizations can position themselves to succeed now and in the future. They can automate time-consuming and resource-intensive processes today and enable smart systems that can get smarter over time.
The power of AI helps discover, ingest and inventory large volumes of data at high speed. It also helps data teams to enrich it with deeper insights that enable business context — and that makes the data even more valuable to data users. Capabilities such as intelligent data lineage tracking to understand data propagation enable visibility into data sets as they are generated and traverse across systems. And this supports insights into data trust and quality regardless of the platform.
Recommendation: Implementing AI and ML in silos only breeds newer silos and bottlenecks. To capitalize on its potential, look for solutions that embed AI and ML across the full set of data governance capabilities. This helps remove manual effort, reduces errors and puts data in the hands that need it. faster with confidence. For example, this is what they did at Banco ABC Brasil when they automated data cataloging and data quality to better understand their data.
Enterprises are growing increasingly aware of the critical role data governance plays in enabling access to high-quality data that drives digital transformation. In fact, improving governance over data and data processes is cited as their top priority in 2023 by data leaders7.
Although it could be argued that modernization of data governance capabilities has been on the backburner for far too long, there is real movement now. And the sphere of influence for data governance only continues to grow. Emerging use cases, diverse teams and the need for adaptive data governance will drive continuous innovation in products that solve modern data challenges.
To accelerate your data-led digital transformation and build a ROI-backed business case for cloud data governance, download our whitepaper, 5 Essential Business Value Metrics to Build a Robust Case for Cloud Data Governance.
Stay tuned to learn more about the latest innovations for Informatica Cloud Data Governance and Catalog.
5 IDC infographic, sponsored by Informatica, Delivering Data Value by Activating Data Intelligence, Doc #US 49588722, September 2022