Enterprise data management (EDM) is a critical framework for organizations that want to leverage data as a strategic asset. In today’s digital economy, where data is the new oil, EDM ensures that data is collected, stored, and utilized effectively across an enterprise. This comprehensive guide delves into the core components of EDM:
It illustrates how these elements work together to drive business success.
Enterprise data management: Table of Contents
Understanding Enterprise Data Management
Enterprise data management encompasses the processes, policies, and technologies used to manage data throughout its lifecycle. It involves everything from data creation and storage to integration, quality assurance, and eventual data disposal.
Effective EDM enables organizations to harness the full potential of their data, facilitating informed decision-making, enhancing operational efficiency, and fostering innovation. Furthermore, enterprise data management is crucial for:
- Gaining insights into customer behavior and market trends,
- Promoting targeted marketing efforts,
- Improving customer experience, and
- Boosting revenues.
Why Is Enterprise Data Management Important
According to the Fortune Business Insights Enterprise Data Management Market report, in 2023, businesses spent over $92 billion on data management. Data has become a key asset for any organization.
Yet, Matillion’s survey found that over 90% of the companies interviewed struggle to convert data into an analysis-ready format. Moreover, a lack of proper enterprise data management can have devastating consequences that go beyond data analysis such as:
- Security issues (e.g., data breaches),
- Damaged reputation,
- Missed opportunities, and ultimately,
- Revenue losses.
This is where enterprise data management comes in. A robust EDM program will:
- Increase productivity. Effective data management will eliminate errors and duplicated data while enhancing employee engagement and productivity.
- Lower costs. Enterprise data management streamlined workflows will reduce data redundancy and bottlenecks, cut unnecessary expenses, and contribute to business growth.
- Enhance security. 32% of cyberattacks investigated by IBM in 2023 involved data theft. A correct enterprise data management will protect sensitive information from breaches and unauthorized access.
- Ensure regulations compliance. Enterprise data management will help reach compliance with the increasing number of rules and laws including the EU General Data Protection regulation (GDPR) and the California Consumer Privacy Act (CCPA).
- Boost Customer Satisfaction. Accurate data will provide businesses with valuable insights about their customers’ needs, fostering satisfaction and loyalty.
Data strategy: the starting point of enterprise data management
Data strategy is a cohesive, long-term road map that defines how an organization will gather, store, protect, and use its data assets to achieve its business goals and foster innovation.
A well-thought-out data strategy defines clear policies, processes, and technologies for:
- Data management,
- Data governance,
- Data quality,
- Data integration,
- Analytics, and
- Security.
Along with digital transformation, data strategy has emerged as one of the pivotal drivers for success in today’s rapidly evolving business landscape. A robust enterprise data strategy (EDS) is the cornerstone of effective enterprise data management that:
- Aligns data initiatives with business objectives, ensuring that data investments drive measurable outcomes.
- Empower businesses to anticipate market trends.
- Facilitate faster innovation.
- Assist organizations to adapt to changes quickly, setting them one step ahead of the competition.
Leading companies are prioritizing data strategies to remain competitive and accelerate digital transformation.. For instance, Coca-Cola invested $925M in its digital transformation in 2022.
4 key best practices to follow for creating a successful data strategy
83% of enterprises polled by Hakkoda in 2023, had an effective data strategy in place. However, only 55% of those organizations achieved their data strategy goals. Developing a data strategy that drives business success can be challenging. Here is a list of four winning best practices.
- Align IT and business leaders to set measurable objectives. It will facilitate the identification of critical business goals, prioritize data-driven decision-making, and engage senior management in digital transformation initiatives.
- Obtain organizational buy-in to ensure long-term success and data democratization. Promote a data-driven culture at all levels of the organization. Provide training sessions and simple resources to give employees a basic understanding of the value of data.
- Integrate, master, and enrich existing data to maximize its value and avoid data silos. Harness the power of data inventory, data quality assessment, and other data audits. Centralize data using data integration tools to integrate different data systems into a single platform.
- Acquire the right mix of talent suited for data strategy execution. A recent LSA Global research revealed that having the proper talents accounts for 29% of the difference between high and low-performing firms in terms of revenue growth and customer loyalty.
Data architecture: its role in modern organization’s enterprise data management initiatives
How businesses approach data has fundamentally changed in recent years. Many organizations are turning to modern data architectures as a means of strengthening their enterprise data management strategies and building up their data maturity levels.
Data architecture is the blueprint for managing data flow within an organization. It translates business requirements into data and system requirements, ensuring that data infrastructure supports business operations effectively.
Benefits of a Strong Data Architecture:
- Enhanced Data Quality: A well-structured data architecture ensures high data quality, which is crucial for reliable decision-making.
- Unified Data Strategy: It provides a framework for defining, demonstrating, and distributing a comprehensive data strategy across the organization.
- Continuous Improvement: A robust data architecture facilitates continuous improvement by enabling scalable and flexible data management practices.
Harvard Business Review cited that less than half of organizations’ structured data is used for decision-making, highlighting the importance of a cohesive data architecture to address inefficiencies and mitigate security risks.
Furthermore, companies need proper data architecture to leverage enterprise data management tools well. A strategy for storing, transmitting, and receiving data must be in place for every application.
Building a resilient enterprise data management foundation
Data architecture focuses on how data is stored, moved, and accessed. Therefore, having a robust strategy is key to managing real-time data and becoming more data-driven. System architecture diagrams and entity relationship diagrams should be part of any data architecture strategy.
No matter where a business is in its digital transformation journey, consulting with industry experts can help establish a baseline, identify goals, and leverage EDM to achieve those targets.
Data governance: creating trust
Digitalization is making data acquisition easier than ever before. However, the growing volume of data being gathered is increasingly complicating the trustworthiness of the data. Gartner predicts that 30% of GenAI projects will be dropped after proof of concept, due to, among others, poor data quality.
Data governance transforms the data quality challenge into a strategic asset. It enables firms to leverage the power of this resource by ensuring that the extracted data can be trusted.
The Benefits of a Successful Data Governance Program
Data governance includes a set of roles, policies, processes, and tools that promote the availability, security, and quality of data. An effective data governance program will:
- Enhance data quality through quality improvement initiatives such as data profiling and proactive data stewards.
- Break down data silos by enabling collaboration across departments.
- Ensure security and compliance with privacy laws and industry regulations through strong IT governance frameworks, and
- Foster trust by getting users and decision-makers working using the same, accurate information.
Good data governance is invaluable. A recent survey shows that organizations with a reliable data governance program reported improved data quality and better analytics and insights.
Discover how to deploy non-invasive data governance across your organization. Download our free whitepaper.
Data quality: boosting financial performance
In today’s digital world, data has become a “make or break it” factor for any company. Within the framework of enterprise data management, data quality is paramount. Poor data quality can have disastrous effects on organizations. According to a recent Wakefield Research survey, it can impact over 25% of a business’s revenue.
Data quality defines how well a set of data meets the requirements and purpose of its use in a real-world scenario. Accurate data quality, an integral aspect of EDM, helps decision-makers make informed decisions and:
- Achieve their goals
- Build successful business strategies and processes
- Integrate artificial intelligence (AI) and automation into their workflows
Steps to Ensure Data Quality within EDM:
- Data Auditing: Regular data audits help identify and rectify data quality issues, ensuring accuracy of data and reliability.
- Data Quality Dimensions: Define data quality dimensions such as accuracy, completeness, consistency, and timeliness.
- Data Quality Assessments: Conduct assessments to measure data quality against defined metrics, providing objective insights into data reliability.
Discover more about why data quality is important and how to improve it in our blog: How the Right Data Quality Strategy Can Boost Financial Performance. Investing in data quality is essential for integrating advanced technologies like artificial intelligence (AI) and automation into business workflows, ultimately boosting financial performance and operational efficiency.
Data integration: breaking down data silos thanks to enterprise data management
Enterprise data is often stored in three places: on-premises, the cloud, and third-party servers. There’s a lot of information to manage, which explains why companies are only able to utilize 57% of their captured data.
This is where modern data integration and cloud integration help minimizing latency, adapting seamlessly to cloud infrastructure, and incorporating machine learning and artificial intelligence inside enterprise data management initiatives.
Get High Quality and Consistent Data With Data Integration and ETL
Data integration is the process of consolidating data from multiple sources into a unified, coherent format that can be used for analysis and decision-making. It is one of the main components of enterprise data management.
Depending on their goals, organizations can get a comprehensive view of their data through different data integration techniques (e.g., batch data integration, ETL, and ELT). The most widely used is the data integration extract, transform, and load (ETL) approach, a common choice for businesses focusing on data quality and consistency.
What Is ETL?
The ETL process consists of three steps:
- Data extraction. The information is extracted from different sources through database queries or change data capture in case of real-time data integration.
- Data transformation. The raw data extracted from different sources is converted into a consistent format outside the data storage system (i.e., staging area).
- Data loading. The transformed information is loaded into the target repository (e.g., a database or a data warehouse).
This data integration technique offers the possibility to include rigorous data validation and cleaning steps for datasets requiring complex transformations. It also guarantees a fast and accurate data analysis.
Importance of Data Integration:
- Enhanced Decision-Making: By breaking down data silos, data integration provides a holistic view of business operations, enabling more informed decisions.
- Operational Efficiency: Integrated data reduces manual data handling, streamlining workflows and improving productivity.
- Competitive Advantage: Modern data integration techniques, including cloud integration and machine learning, enhance agility and competitiveness.
Having a robust data infrastructure is the starting point for becoming a data-led organization, a status modern companies strive for. Using this new bird’s eye perspective, companies can enhance decision-making, reduce manual work, and provide better customer service.
| Benefits | Challenges |
| Improved Data Velocity for Agile Decision-Making | Diverse Systems and Complex Data Environments |
| Scalability That Supports Business Growth | Data Proliferation |
| Cost Efficiency and Reduced Maintenance Overheads | Data Security and Compliance Concerns |
| Enhanced Customer Experience | Resource Constraints, Skills Gap, Data Integration Tools Selection |
Conclusion
Enterprise data management is a multifaceted discipline that plays a pivotal role in today’s data-driven world. By developing a comprehensive data strategy, building a robust data architecture, ensuring high data quality, and effectively integrating data, organizations can unlock the full potential of their data assets. This not only enhances decision-making and operational efficiency but also drives innovation and competitive advantage.
In conclusion, mastering enterprise data management is essential for organizations aiming to thrive in the digital age. With the right strategies and tools, businesses can transform data from a mere resource into a powerful catalyst for growth and success.
Whether you are at the beginning of your data management journey or looking to enhance your existing practices, embracing EDM will set you on the path to becoming a truly data-driven organization. Book a consultation call with Infoverity today to start optimizing your enterprise data management strategy.
