Enterprise data management

Harnessing the power of accurate, consistent, and unified business data

Left to develop organically, enterprise tech stacks and the data architecture within them tend to grow more complicated over time. Businesses produce and consume increasingly large volumes of data that can very easily become siloed, hard to see, and hard to use.

Rather than an asset to build on, enterprise data can become a burden to overcome. This is why so many businesses turn to enterprise data management to harness its full potential.

What is enterprise data management?

Enterprise Data Management (EDM) is the process of adopting a coordinated, comprehensive approach to managing and governing organizations’ ever-expanding data assets. It involves deploying a range of practices, technologies, and tools to manage, integrate, govern, and utilize data across an enterprise’s tech stack.

The goal of EDM is to corral enterprises’ big data into an accurate, accessible, consistent, and secure source of business intelligence and competitive advantage – typically within a central enterprise data management platform. Managing enterprise data in this way is designed to give all business stakeholders the means to make better-informed decisions and help them achieve their strategic objectives.

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How does enterprise data management work?

There’s no cookie-cutter approach to enterprise data management. Each organization has its own commercial priorities, data infrastructure, skill base, and data governance frameworks, as well as its preferred enterprise data management tools or platforms. However, there are common components and phases that EDM leaders will typically guide their businesses through.

Typical components of enterprise data management

Enterprise data management spans multiple processes and business initiatives including:

Change management
Metadata management
Data audit/catalog
Data cleansing
Data integration
Data governance
Data quality management
Master data management
Data lifecycle management
Data stewardship
Data security
Data storage
Data analytics

Let’s unpack some of these core aspects of EDM in a little more detail.

Data audit, metadata management, and data catalog

Before an organization can unify its data in (or via) an enterprise data platform, it has to define, catalog, and assess the data it uses across its tech stack. This establishes the scope of the EDM initiative and starts with a comprehensive audit of all data sources across the enterprise. Cataloging this audit centrally typically includes careful metadata management, that is, ensuring the business records clear descriptions of each dataset, its purpose, format, quality, and usage.

Collating metadata in this way helps business leaders to understand, manage, and use the actual data more effectively. It also makes it far easier to determine where potential integration or data quality issues may exist and how to structure any enterprise data management solution. With an accurate, comprehensive catalog of data sources, quality, and uses – signposted by strong metadata descriptors – organizations lay solid foundations for EDM.

Data quality, master data management and data integration

Before creating a unified data environment, organizations have the opportunity (and requirement) to cleanse their data, identifying and correcting any errors as well as plugging any gaps.

This data quality cleanse typically includes the establishment of master data management (MDM), which ensures that the organization’s critical data (such as customer and product data) is consistent and accurate across different systems. The final piece of the data quality puzzle is the establishment of metrics to measure and monitor data quality (such as accuracy, completeness, or timeliness) on an ongoing basis.

Once data quality has been established, there is a range of data integration tools capable of unifying the disparate data sources. These typically perform ETL (Extract, Transform, Load) or ELT (Extract, Load, Transform) processes in which data is collated from across the cleansed data environment and its formats standardized. Enterprise data management platforms that perform ETL standardize the information before loading it to a central data warehouse, data lake, data lake house, or cloud storage facility. With ELT, the transformation takes place within the central platform. Either way, the data is centralized, accessible, interrogable, and transparent – the ideal source of actionable insights.

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Data governance

Any enterprise data management strategy should include the creation of a robust data governance framework. Without it, data accuracy and data integrity within the EDM platform are at risk of erosion over time. Organizations need to put data policies in place that define how data should be handled from creation to archiving and deletion (this is sometimes referred to as data lifecycle management).

These policies will include clear procedural governance over data usage, security, and regulatory compliance – including defined standards and metrics for tracking data handling performance. Crucially, however, data governance policies should include cross-business data stewardship. Policies can gather dust without champions to apply them, so it’s vital to assign specific roles and responsibilities for managing and championing data management and oversight to business stakeholders.

As well as guardians of valuable data assets, data stewards can help non-technical personnel understand the benefits of the EDM transition to individual teams. This helps ease the frictions and uncertainties common to any business change program.

Compliance and risk management

Risk and compliance management works hand-in-glove with data governance. Enterprise data managers creating a governance framework need to ensure that the policies and procedures they create are compliant with relevant regulations (such as GDPR or CCPA). This is not a one-and-done operation, it’s an ongoing cycle involving:

Data risk assessments to identify any compliance vulnerabilities (say in data security).
Application of data processing and security procedures (such as encryption and access controls) to ensure regulatory compliance.
Regular audits of data practices to ensure adherence to data policies.
Monitoring any changes in data regulations.
Conducting further risk assessments in the wake of regulatory changes.

The onus is on organizations to process people’s data within a secure, ethical, and privacy-first environment. It builds trust, avoids damaging data breaches, and eliminates legal risk.

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Data analytics and reporting

Data analytics and reporting are the pot of gold at the end of the EDM rainbow. The insights provided by cleansed, aligned, and unified enterprise data from every data source across the tech stack can create a considerable competitive advantage.

Effective enterprise data management democratizes these insights, providing intuitive, easy-to-access reports for stakeholders across the business, and enhancing decision-making. From the creation of business intelligence dashboards, reports, and visualizations, organizations can observe and analyze performance trends.

Moreover, having unified enterprise data provides the perfect basis for two key levels of data analytics. Firstly, business experts can often conduct their data exploration via self-serve analytics. This combines the assurance of a solid, single source of business truth with the freedom for functional specialists to extract maximum value from digital information. Secondly, EDM provides the platform and raw materials to conduct advanced analytics and predictive modeling to help forecast future performance with AI and machine learning tools.

The benefits of enterprise data management

Implementing effective enterprise data management offers numerous benefits to organizations, enhancing their ability to flex data as a strategic asset. These benefits include:

Cost savings – eliminating redundant data and reducing duplicate or manual processes reduces storage and operational costs.
Enhanced data quality, consistency, and transparency – maintaining high data quality ensures accuracy and consistency across all organizational systems and departments. Decisions are rooted in reliable and, importantly, transparent business information. This transparency helps ensure a clear rationale for the basis of decision-making – particularly important if any AI-driven predictive analytics are deployed.
Customer experience and loyalty – accurate, consolidated customer information creates enhanced customer understanding and insights. Organizations can draw on these more nuanced views to create personalized engagement and experiences. At the same time, increased operational efficiency thanks to properly structured data means faster response to customer interactions. Fast, quality, individualized service drives customer loyalty.
Data democratization and enhanced collaboration – in creating an accurate, accessible single source of business truth, enterprise data management enables employees at all levels to create and share insights, collaborate more effectively between teams, and have a greater sense of participation in the achievement of business goals.
Agility, understanding, and innovation – well-managed data enables rapid response to changing market conditions or competitive opportunities.
Improved decision-making and strategic planning – the insights from accurate, unified, high-quality data enable data-driven decision-making at all levels of an organization. Objective business understanding forms the foundation for both functional day-to-day tactics and strategic planning in the C-suite.
Operational efficiency – efficient data management streamlines operations and optimizes resource allocation, reducing waste and manual effort.
Data security – a core component of EDM is the establishment of robust security measures through the data lifecycle. This not only protects sensitive information, it also builds trust and enhances organizational reputation.
Compliance and risk management – EDM includes data risk assessments, compliance audits, and the creation of defined data governance policies and procedures rooted in data processing laws. It helps minimize legal risks and mitigates the risk of data breaches, protecting sensitive information and customer trust.

Enterprise data management challenges

While the benefits of successful enterprise data management are undeniable, business leaders tasked with EDM implementation face several potential obstacles, including:

Data silos: It’s common for enterprises to store and process data in multiple disparate, function-specific, and often incompatible systems or formats. This lack of compatibility and interoperability impedes data consolidation and analysis.
Legacy systems / inflexible architecture: While legacy systems may continue to perform the standalone function for which they were deployed, their function and value within an evolving tech stack diminish over time. As these systems become more antiquated, performance falls and costs rise. Additionally, they tend to be less able to flex up to meet increased business demands.
Data integration: For EDM to be successful, and for it to create a unified view of enterprise data, there needs to be unimpeded data flow from every system. But data formats vary between sources, from highly structured and easily accessible configurations to unstructured, non-interrogable data (such as PDFs or free text fields for example). This makes data integration particularly challenging. Similar integration challenges can arise from inconsistent data formats, taxonomies, and data management processes.
Data quality: Over time it’s easy for enterprise data to become outdated, incomplete, misaligned with other systems, and inconsistent across teams or geographies. Consequently not only are like-for-like comparisons or cross-business analyses almost impossible, but compromised data quality undermines the basis and quality of any insights.
Future-proof scalability: The volume, complexity, and number of potential applications for enterprise data increase all the time. For example, the explosion of business applications for generative artificial intelligence has put a fresh premium on maintaining quality data to train and fuel AI platforms. Storage, processing, and management of enterprise data needs to be scalable to accommodate existing, emerging, and future use cases.
Change management: Moving from familiar function-centric data management to a unified business-wide approach using a new enterprise data management solution may meet resistance from some stakeholders. The transition may be disruptive in the short term and requires careful team engagement. Managing the change in processes and winning the hearts and minds of colleagues is essential to get full value from EDM.
The governance and compliance moving target: Navigating the ongoing shifts in the data management regulatory landscape is a significant challenge to EDM. Organizations consolidating and processing their data need to be mindful of the different data privacy and data security laws they must adhere to across each of the territories in which they operate – particularly with regard to personally identifiable information (PII). They also need to stay on top of changes to these laws or the addition of new regulations – such as the proposed American Privacy Rights Act (APRA). Endeavoring to satisfy data regulators in different territories can create data governance headaches, as can cross-border data transfers.
Lack of skilled resources: A successful EDM initiative can be hampered by a shortage of in-house data management specialists to oversee the process. In these circumstances, business leaders may need to lean heavily on outsourced expertise and/or cloud services.

Acxiom’s Enterprise Data Solutions

Acxiom has been the leader in building, maintaining, and optimizing enterprise data platforms for over 55 years. We’ve worked with some of the world’s biggest brands to deliver tailored data management solutions that grow audiences, build customer understanding, and provide tangible competitive advantage.

At each step of the enterprise data management journey, Acxiom can add value – ethically, compliantly, and in a privacy-conscious manner. If you are looking to level-set your EDM initiative by assessing your data, we can establish your data quality, hygiene, and architecture benchmark with a data health assessment.

To supercharge the process of data cleansing, data matching, and master data management, we can provide our proprietary identity resolution services to deliver consistent, comprehensive, and accurate unified data.

Our cloud-first unified data platform enables the easy integration of martech and adtech systems to acquire, engage, and retain customers. Meanwhile, our comprehensive marketing database solutions consolidate all customer data, identities, and interactions across your enterprise. This unified approach combines real-time and historical information, creating a robust data foundation that powers your entire integrated marketing ecosystem.

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