The digital-first world pushes businesses to capitalize on data to secure a competitive edge over their laggard counterparts. The C-level executives are privy to it, as data silos are recognized as a competitive disadvantage by 74% of organizations.
Capitalizing on data, however, requires mastering enterprise data management. If you don’t invest wisely in data projects across the value chain, you may end up paying 10% to 20% more in data ownership costs.
Below, our experts break down the essentials of enterprise data management, along with best practices and solutions for common challenges.
What Is Enterprise Data Management?
The term enterprise data management, or EDM, can be applied to two different but intertwined aspects of managing the organization’s data assets:
- Process: Enterprise data management is a set of policies, governance practices, and rules that regulate data collection, maintenance, security, and use.
- Technology: Enterprise data management is the totality of infrastructure components, tools, and software solutions that facilitate data collection, integration, and management.
Thus, Enterprise Data Management (EDM) can be defined as a comprehensive framework that combines both strategic processes and technological solutions necessary to manage an organization’s data and is aimed at ensuring that data is reliable, secure, and readily available to support business decisions.
Why Enterprise Data Management Matters
As a combination of processes and technology, enterprise data management aims to maintain data quality, reliability, accuracy, accessibility, and usability. Proper EDM ensures that:
- Analytics tools can produce reliable insights
- Employees can access relevant data whenever needed – and trust it, too
- Sensitive data is protected from data breaches and theft
- Data processing remains in line with applicable regulations (e.g., HIPAA, CCPA, GDPR).
Enterprise data management is also crucial for making sense of large volumes of data, unlocking new business opportunities, and enabling data-driven decision-making as a result.
3 Main Benefits of Enterprise Data Management
In the past, robust enterprise data management was a competitive differentiator rather than a standard practice. Today, however, any large business using AI/ML and Big Data has to invest in EDM to ensure that large volumes of data are managed efficiently – and reap the following three benefits.
Increased Operational Efficiency
In 85% of financial firms, the IT department spends between a quarter and half of their working time helping other employees access the data and insights they need. Eliminating this inefficiency is possible with an EDM framework that establishes a single source of truth and facilitates access to relevant data.
Readily available and easily accessible data becomes a productivity booster as employees can quickly locate relevant data (e.g., interaction history for a given customer) and use it in daily tasks. Quality data can also power intelligent automation tools that create additional operational efficiencies.
Smarter Decision-Making
The companies where CEOs have a high level of confidence in their data show a 21% higher projected annual growth than average and 24% higher operating margins.
Operational and financial data and analytics provide timely insights into organizational performance and health at all levels, from a single branch or team to the line of business and the whole enterprise.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“It’s not just the C-level management that stands to benefit from data-driven decision-making. A single source of truth with real-time data and BI tools enables decision-makers at all levels to select the best option at every step.”
Reduced IT Infrastructure Costs
Smart investment decisions across the data value chain can reduce the total data ownership costs by 10% to 20% and cut down time-to-market by three to six months, according to McKinsey. Data-mature organizations also see an average ROI of 162% for their investments in data technologies and tools, twice as high as their least data-mature counterparts.
Addressing issues like duplicate data sources and fragmented data environments can lead to 5-10% in short-term savings and 10-20% in long-term ones. Automating data management and streamlining data controls can help you save an extra 15%-20% in the short run and 30-50% in the long run.
4 Key Components of Successful EDM
To reap the benefits listed above, you need a comprehensive EDM strategy. EDM as a process encompasses several management areas: namely, data quality, security, access, and lifecycle management. Each of these management areas focuses on a specific dimension of managing data as an asset. Let’s break them down.
Data Quality Management
Data quality management encompasses practices, processes, and tools for ensuring your data remains accurate, complete, reliable, consistent, and timely. It can include:
- Data profiling to map existing data and its structure and evaluate its quality
- Automated data cleansing and standardization to prevent errors and ensure data usability and reliability
- Data deduplication, merge, and survivorship rules
- Validation checks to verify data quality
- Data quality metrics monitoring (e.g., consensus, relevance, validity)
- Regular data quality audits and optimization.
Lack of proper data quality management poses the risk of “garbage in, garbage out”. Your analytics and BI tools won’t produce accurate results if the underlying data is incomplete or full of errors. It can also entail compliance risks, operational inefficiencies, and subpar customer and employee experiences.
Data Security Management
Any business deals with some amount of sensitive data. Failing to protect it may lead to data breaches and regulatory fines, which in turn cause reputational and financial losses. An average data breach costs businesses $4.88 million worldwide.
Data security management is the ensemble of measures taken to prevent unauthorized access to, alteration, or destruction of data. These measures concern data loss prevention, key management, cloud data security, access control, data governance, and authentication and authorization.
Common data security measures include data encryption at rest and in transit, data masking, timely data erasure, data resiliency (disaster recovery, backups), and multi-factor authentication.
Dmytro Tymofiiev
Delivery Manager at SPD Technology
“Robust data security management involves both implementing the technical means of protecting data and training employees in matters of cybersecurity. The latter will help you prevent social engineering and accidental exposure.”
Data Access Management
Data access management regulates who can access which information, as well as the modalities of data access. It’s crucial for preventing unauthorized access to sensitive information and maintaining compliance with privacy regulations.
Managing data access involves defining:
- Appropriate access control model (e.g., role-based, rule-based, attribute-based, mandatory, discretionary)
- User identification, authentication, and authorization rules and procedures
- Metrics for access monitoring and regular audits.
Best practices in this area include granting users the minimum level of access by default (the principle of least privilege), implementing multi-factor authentication, and enforcing password hygiene. Continuous data access logging and monitoring is also a must.
Data Lifecycle Management
The data lifecycle begins at data entry and ends with its destruction. Data lifecycle management, in turn, means defining how you’ll handle data depending on its lifecycle phase and what criteria data has to meet to move from one phase to another.
When done right, data lifecycle management will help you maintain consistent data quality levels and reduce archiving, backup, and storage costs by removing data when it becomes obsolete.
The data lifecycle consists of five phases:
- Creation: Collecting relevant data from its sources (IoT devices, forms, etc.)
- Storage: Transforming and adding data to storage
- Sharing and usage: Making the data available to business and external users with appropriate data access controls
- Archival: Keeping copies of data that’s no longer needed for day-to-day operations
- Destruction: Securely purging data from archives.
Enterprise Data Management Framework
The four components of the EDM as a process listed above come together in an enterprise data management framework.
An enterprise data management framework is a high-level document that outlines the foundational principles of the data management process within the organization. These principles focus on managing data quality, security, and access. They also describe how data is handled depending on its lifecycle stage.
An enterprise data management framework establishes organization-wide data governance, defines the data management architecture, and helps foster a data-driven culture.
Enterprise Data Governance
Data governance focuses on processes, structures, policies, and procedures implemented across the organization to ensure data security, quality, and integrity. It’s a subdomain of data management that:
- States data goals (e.g., maintaining compliance, improving quality) and metrics to track
- Outlines roles related to data management (data owners, data stewards, stakeholders) and their duties
- Defines data standards regarding data formats, models, metadata, naming conventions, etc.
- Maps data flows and outlines data collection, storage, movement, and archival processes
- Establishes auditing and testing procedures.
Enterprise Data Management Architecture
A data management architecture is your organization’s blueprint for collecting, storing, securing, arranging, integrating, and using data throughout its lifecycle. There’s no one-size-fits-all architecture; it’s shaped by business needs, goals, and constraints.
Common approaches to enterprise data management architecture are:
- Monolithic architecture. All data is handled by a single centralized data store. It’s simple in implementation but comes with high latency and limited scalability.
- Distributed data mesh. Domain-specific data owners handle their data products and pipelines, with the architecture consisting of smaller decentralized components. It offers better scalability and flexibility but is more complex in implementation.
Data Culture and Literacy
How well your data management strategy is implemented depends on how people interact with data. Fostering data culture and data literacy are two key ways to ensure business users help you achieve the strategy’s goals.
Data culture is a type of organizational culture that encourages data-driven decision-making and communication. Data literacy, in turn, refers to the individual’s ability to understand, create, analyze, and communicate data.
As part of the EDM framework, data culture promotes an organization-wide commitment to leveraging data in both strategic and operational decision-making. A commitment to fostering data literacy, in turn, equips your employees with the skills required to use data in their decision-making.
Together, data culture and literacy form the human foundation of effective enterprise data management, complementing the technical and governance structures to create a holistic approach to managing data as a core business asset.
4 Best Practices for Managing Data Within the Organization
Only 20% of business executives fully trust the data they receive. To ensure you’re among the lucky fifth, we recommend you follow these four best practices.
Align Enterprise Data Management Strategy with Business Strategy
Your business goals should drive the enterprise data management strategy, from its stated objectives and metrics to data quality standards and architecture.
To align business priorities with the EDM strategy, discuss those priorities and data and analytics needs with senior leadership and business users. These discussions will also help you secure the buy-in among key stakeholders.
Here’s what to discuss:
- Business initiatives and goals that require data and analytics
- Main data challenges that stand in the way of reaching those goals
- Privacy and security challenges that accompany self-service data access
- Potential data uses that are impossible at the moment
- Metrics currently used to measure success.
Prioritize Data Quality Management
Data quality directly impacts how reliable analytics insights will be. Subpar data quality can also easily lead to poor decision-making and operational inefficiencies as employees will have to double-check the accessed data.
Prioritizing data quality management in the overall data management means:
- Establishing unified data quality standards across the enterprise
- Identifying and addressing data quality issues early on
- Regularly conducting data profiling
- Implementing dashboards for monitoring data quality
- Establishing data stewardship to oversee data quality management.
Enable Self-Service Data Analytics
Self-service analytics tools allow business users without technical expertise to access and visualize relevant data, use it to obtain insights and run what-if simulations, and produce summaries, reports, or analyses.
Implementing self-service analytics is key to democratizing data access across the organization, which is crucial for establishing a data-driven culture. It also reduces the burden on IT staff as business users don’t require continuous guidance or assistance to leverage analytics.
Promote Data Collaboration and Sharing
Fostering data sharing and collaboration within and across teams is one of the cornerstones of adopting a data culture. Furthermore, it’s key to breaking data and organizational silos, which are detrimental to business agility, data analytics reliability, decision-making efficiency, and innovation.
To promote data sharing and collaboration, ensure all employees have access to the right data at the right time and invest in data literacy training to equip them with the necessary skills. Establish and communicate the link between your data sharing initiatives and the organization’s goals. On top of that, lead by example: ensure leaders encourage data-driven decision-making and excel at using data themselves.
3 Data Management Challenges and How We Counter Them
Enterprise data management is a complex process which comes with its challenges. Let’s explore them, along with the solutions that we at SPD Technology employ when devising effective EDM strategies for our clients.
Fragmented Data Silos Across Business Units
Large organizations’ enterprise data is likely to go back years or decades, scattered across multiple jurisdictions, business units, and departments, with disparate standards and formats, creating significant data silos.
The presence of data silos is one of the main challenges for effective enterprise data management, standing in the way of creating a single source of truth. This is where the importance of data integration is particularly felt. When data is siloed, decision-makers can’t rely on it due to inconsistencies or incompleteness, while reporting and analytics tools may return inaccurate outputs.
Our solution
To counter data silos, we:
- Implement a data integration solution to unify data from multiple sources into a single source of truth (data warehouse/data lake)
- Ensure seamless access to data for business users
- Enable a 360-degree view of your enterprise data with intuitive data visualization and presentation tools, for example, like those we implemented for HaulHub.
Scalability and Performance Bottlenecks in Data Infrastructure
It’s a safe bet to say that data volumes will only continue to grow, and so will the business impact of Big Data. Between 2020 and 2025, the total amount of data generated worldwide is expected to almost triple.
However, if your data infrastructure isn’t built with scalability in mind, it may struggle to sustain the same performance, especially when it comes to real-time analytics, AI/ML models, and high-throughput data processing.
Our solution
To futureproof your data architecture against rising data volumes, we:
- Help you move to cloud-based platforms to leverage cloud computing infrastructure benefits
- Implement scalable enterprise data platforms (e.g., Hadoop, Spark, Snowflake)
- Optimize your infrastructure and architecture
- Implement parallel processing and real-time data streaming capabilities.
For example, one of our clients, HaulHub, struggled with scaling its mission-critical platform that handled 70 million tickets and stored large numbers of projects and images. We helped the company ensure the platform’s stability while integrating AI models and implementing a client-facing OLAP system.
Underutilization of Data Assets for Advanced Analytics
Collecting swaths of data in a single source of truth is only the first step to maximizing its ROI. Realizing its full potential is impossible without advanced analytics and AI/ML capabilities. However, ramping them up can prove difficult due to a lack of strategic alignment, expertise, or infrastructure.
Our solution
To help our customers maximize the ROI of their data, we:
- Offer end-to-end AI/ML development services, from data preparation and model development to deployment and ongoing support
- Align the delivered solution with your strategic goals and business needs
- Ensure your AI/ML models and advanced analytics remain scalable and flexible.
Resources, Tools, and Technologies Required for Efficient EDM
To address the challenges above effectively, enterprises need specific tools, technologies and resources, with robust data management expertise as the foundation. Here is an overview of what is necessary to establish an effective data management process and support it with the right tech infrastructure.
Resources
Make sure you dedicate enough human, technical, and financial resources to your EDM strategy:
- Human resources. Common EDM roles include data managers and stewards, database administrators, data architects and engineers, and data and BI analysts. Implementing an enterprise data management solution also requires a business analyst and project manager.
- Data resources. The collected data requires enough storage and computing resources for access and analytics, which entail additional costs.
- Financial resources. Implementing a data warehouse, for example, can cost $50,000-$200,000 for small and midsized projects and $500,000+ for larger ones. EDM also entails running costs (e.g., data quality audits, data warehouse/lake maintenance).
Tools
No two organizations have identical data infrastructure, challenges, and objectives, and neither should their software stack be. In other words, your toolkit should align with your strategic objectives and business needs. That said, it should include:
- Data integration and ETL tools
- Data storage and management platforms
- Data analytics and business intelligence (BI) tools
- Data governance and quality tools
- Master data management (MDM) solutions
- Data security and compliance tools.
Technologies
A typical tech stack for our enterprise data management project includes:
- Data processing technologies: Hadoop (distributed data processing), Spark (real-time analytics)
- Programming languages: Python, Java, SQL
- Cloud technologies: AWS, Azure; Kubernetes (cloud orchestration)
- Security technologies: SSL/TLS (data encryption), OAuth (user authentication).
Consider Professional Enterprise Data Management Consulting
The advantages of strategic technology consulting include optimized costs, efficient resource allocation, proper alignment with business goals, and superior productivity, while enterprise data management is easy to get wrong. Here are four reasons why turning to an experienced partner for enterprise data management services is a wise choice:
- Access to specific expertise. You gain access to the experience and expertise required to tackle your particular technical and business requirements – without in-house recruitment costs.
- Improved cost efficiency. Deep technical expertise will minimize the likelihood of having to rework your solution too soon. It’ll also ensure prompt delivery and allow you to leverage every cost-saving opportunity along the way.
- Advanced technology adoption. An EDM partner with AI/ML and advanced analytics expertise will help you ramp up your analytics capabilities. Its experts will also ensure your data infrastructure remains compatible with cutting-edge technology.
- Faster implementation. An experienced EDM partner leverages automation tools and custom accelerators to speed up development, all while eliminating common bottlenecks and implementing best practices.
Why Choose SPD Technology for EDM Consulting?
At SPD Technology, we rely on our technical expertise and industry-specific experience to deliver solutions that align with your business goals, maximize your data ROI, and give you a competitive edge with advanced analytics.
Our data management consulting services stand for:
- Comprehensive services and solutions. We’re here to support you in designing, implementing, and maintaining your EDM strategy from A to Z. Our end-to-end services include AI/ML and advanced analytics development, data warehouse and data lake design and implementation, and cloud migration.
- Proven methodology and best practices. With almost two decades of experience, we can identify the right solution for your unique business needs, all while maximizing its performance, flexibility, and scalability with time-tested best practices.
- Both technical and strategic support. We help you ensure your organization has the right processes and skills to maximize the efficiency of your EDM strategy. To that end, we can provide training, strategy consulting services, and ongoing support post-deployment.
- Seamless integration with modern technologies. We’re here to help you capitalize on AI, cloud computing, and data warehousing by integrating these capabilities into your enterprise data management solution.
Conclusion
How you design and implement your enterprise data management strategy will directly impact your data’s ROI, the organization’s ability to complete, and its business growth and agility.
An effective EDM process promotes data quality, ensures employees can trust available data, protects sensitive information, and mitigates compliance risks. When designed and implemented well, enterprise data management can also enable smarter decision-making, improve operational efficiency, and optimize costs. As data variety, volumes, and sources continue to evolve, organizations across industries have to ensure they can maximize the strategic advantage that data offers. To secure your competitive edge in a digital-first world, ensure your EDM strategy is comprehensive, actionable, and tailored to your goals and needs.
Getting your EDM strategy right requires deep technical expertise and a full grasp of your needs and objectives. If you’re looking for an experienced data management partner, we at SPD Technology have the hands-on experience to deliver an EDM strategy that delivers long-term value to your business. Learn more about our data management services to discuss how we can help you address your data needs with a robust EDM strategy.
FAQ
- What is the EDM framework?
The enterprise data management (EDM) framework is a document outlining the fundamental principles upon which data management processes will rely. Those include data governance, data management architecture, and data culture and literacy.
Your EDM strategy will be based on this framework. So, you should create the framework before tackling the strategy.
- What is the difference between EDM and MDM?
EDM stands for enterprise data management and refers to the ensemble of processes, practices, and technology solutions for managing all of the organization’s data. It includes master data management as a component.
MDM stands for master data management and focuses on how an organization manages master data in particular. Master data encompasses information about customers, products, locations, etc. that remains constant and serves as a reference for transactions and interactions.
- What is an example of enterprise data?
Enterprise data typically includes customer information (e.g., name, phone number, history of interactions, past orders) and transaction details (e.g., transaction amount, origin, payment method). It can also involve supply chain information like supplier ID, inventory levels for each product SKU, financial performance indicators like EBITDA, and payroll lists.
- What is enterprise data management?
Enterprise data management can refer to the processes, policies, and practices that comprise data management. Alternatively, it can also describe the technology solutions that power the said processes and rules.
EDM spans four key components: data quality, security, access, and lifecycle management.
- How to develop an enterprise data management strategy?
To develop an EDM strategy, you first need to outline the EDM framework that establishes key principles for data governance, data culture, and data management architecture. Once the framework is ready, you should decide on the methodology (defensive vs offensive data management), establish a clear vision and objectives, and secure executive buy-in.
Your EDM strategy should cover:
- Current state assessment
- EDM vision and goals
- Data governance framework
- Data architecture
- Data quality management
- Data security and compliance
- Data lifecycle management
- Data analytics and reporting
- Implementation plan
- Monitoring and evaluation.
- Why is enterprise data management important?
Proper enterprise data management ensures that the data collected or generated, stored, and used within an organization remains accurate, up-to-date, accessible, and trustworthy. It also prevents data downtime, prepares data for analytics tools to ensure output reliability, and protects data against exposure.
Furthermore, mature enterprise data management is also a prerequisite for implementing advanced AI/ML and Big Data analytics solutions.
- What are the benefits of data management in every enterprise?
Strategic data management allows businesses to increase operational efficiency, enable data-driven decision-making, and reduce IT infrastructure costs. Furthermore, solid EDM also lays the groundwork for adopting other advanced technologies. Those include AI/ML and Big Data analytics.
- What is enterprise cloud data management?
Enterprise cloud data management refers to the processes and technology that regulate how an organization uses the data stored in the cloud.
Cloud storage and computing come with numerous benefits like instant scalability and optimized costs. However, they also require additional considerations for ensuring data privacy, security, and performance.
- What is the key element of enterprise data management (EDM)?
Four key elements of enterprise data management are:
- Data Quality Management: Measures, standards, and tools put in place to maintain consistent data quality
- Data Security Management: Measures, policies, and tools for preventing unauthorized alteration or destruction of enterprise data and maintaining compliance
- Data Access Management: Practices and rules for protecting data privacy and preventing unauthorized access to sensitive data
- Data Lifecycle Management: Rules and measures for handling data across its lifecycle, from collection to deletion.