Picture this: You’re a sales representative at a global organization gearing up for an urgent meeting with a prospective client. You dive into your customer relationship management (CRM) system, expecting a clear, streamlined profile, but instead find multiple accounts under the same name, each with fragmented bits of information. What should have been a quick task now consumes hours of your time.
This scenario clearly demonstrates why having a robust enterprise data management (EDM) strategy is so important. Without it, even the most valuable insights can slip through the cracks, leading to a poor customer experience, higher costs, and missed revenue opportunities. To avoid such outcomes, you need a thorough understanding of how EDM works, its key components, and how they can add value to your business. This guide is designed to address these topics.
Table of contents:
Enterprise data management is a strategic framework that helps businesses ensure their data stays accurate, accessible, and secure across the organization. It leverages technologies such as artificial intelligence (AI), machine learning (ML), and data analytics to precisely define, easily integrate, and effectively retrieve data for both internal applications and external communication.
Let’s revisit the earlier example. Instead of combing through fragmented records and transactions, an effective EDM framework would allow you to create a unified client profile. This streamlined approach saves time, reduces errors, and enhances data quality, leading to data-driven decision-making.
Enterprise data management systems allow analysts to easily integrate internal and external data into centralized repositories, helping them build models, business dashboards, and KPI reports that guide a business’s operations.
However, in practice, data can be messy and inconsistent. That’s why it’s crucial to set clear standards and implement systems to ensure data remains accurate, secure, and reliable. When creating your enterprise data management strategy, there are some key components to consider to maintain data integrity and reduce inconsistencies:
Data governance
Data governance includes implementing consistent standards and protocols for how data should be managed throughout the organization. By establishing and enforcing such policies, you minimize the risk of data breaches, improve data literacy, and even reduce the need for extensive data cleansing and data enrichment activities. For example, developing data dictionaries and using metadata tagging will help those transforming data while minimizing errors. Similarly, a solid data governance policy allows data owners to control who can access what, ensuring sensitive information is restricted to the right departments or individuals.
Pro tip💡: To deliver self-service analytics at scale without sacrificing data governance, you can rely on an analytics partner like ThoughtSpot. With enterprise-grade built-in controls, you can manage and enforce how anyone across your organization uses your data. ThoughtSpot’s advanced human-in-the-loop system also ensures the accuracy and precision of results.
Data quality
Data quality is all about ensuring your data is accurate, fresh, complete, consistent, and unique. It begins with data profiling, a crucial step where you assess the existing state of your data to uncover inconsistencies, errors, and gaps.
After data profiling, you should schedule validation checks to track various data quality metrics such as consensus, relevance, and validity. Apart from analyzing metrics, you can also perform tests like datatype tests and anomaly testing (looking at mean, median, z-score, etc.) to ensure your data remains fresh and up-to-date.
Data integration
The right data integration process allows you to quickly and easily bring data from a variety of sources into a centralized destination like a cloud data warehouse, where it can be effectively analyzed and accessed. Creating an efficient data integration process involves providing the necessary tools and technologies to support the data teams. This tech stack might involve using ETL tools and APIs, or even establishing data pipelines to gain a unified view of your organization’s operations.
Data warehouse architecture
Data warehouse architecture refers to a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management's decision-making process. It essentially consolidates disparate data sources into a single repository to support business intelligence (BI), AI/ML, and analysis efforts. While designing your data warehouse architecture, it is important to outline how you will manage and integrate diverse data formats, where the data will be stored, and who will access those data assets.
Master data management (MDM)
Master data management (MDM) includes all the tools, processes, and methodologies an organization leverages to establish a single source of truth for its critical master data assets. This master data refers to the unique information that defines and describes the core entities of a business, such as customers, products, suppliers, and locations. By establishing good data governance policies and standards, you can ensure this foundational data remains accurate, consistent, and reliable across all systems and applications.
Master data management and enterprise data management are two concepts that are often confused. While both serve crucial roles in conveying information and facilitating decision-making processes, they possess unique characteristics tailored to specific needs and preferences. Let's explore how you can gain the most value out of these distinct data strategies:
| Aspect | Master Data Management | Enterprise Data Management |
|---|---|---|
| Definition | Involves establishing and maintaining a single source of truth for all master data assets | Oversees and manages an organization’s entire data lifecycle, ensuring every data asset is accurate, accessible, and secure |
| Scope | Specific to master data, focusing on key business entities such as customers, products, suppliers, and locations | Broader scope, covering all aspects of data management including data governance, quality, integration, architecture, and more |
| Implementation | Involves consolidating data from various sources, transforming it, and creating a central repository for master data | Involves creating a comprehensive data management framework that includes data integration, governance, quality assurance, and architecture |
| Data types | Master data—which includes key business entities and their attributes | All data types—including operational, transactional, and analytical data |
| Use cases | Creating a 360-degree view of customers, centralizing product information, managing supplier records, and ensuring compliance with regulatory standards | Integrating data from various sources into a central repository, improving data quality and consistency across systems, enforcing data governance policies, and leveraging data for business intelligence and analytics |
As organizations rely increasingly on data to drive all aspects of their businesses, the need for enterprise data management becomes increasingly clear. Here are a few noteworthy benefits of implementing this strategy:
1. Improved visibility and accessibility
By providing users with a comprehensive directory of all the datasets within their organization, you give them the power to quickly find and access the data most relevant to their needs. Without this visibility, users often struggle to get started, overwhelmed by the sheer volume of data and the lack of clarity about what information is available. This inability can lead to wasted time, missed opportunities, and decisions made on incomplete or inaccurate information. With a streamlined data management strategy, teams are empowered to make decisions grounded in data, taking guesswork out of the equation.
2. Enhanced data discoverability
Effective data management practices such as metadata tagging and data cataloging allow data teams to quickly identify relationships between different pieces of data, giving them an overview of their entire data landscape.
When paired with an augmented analytics solution like ThoughtSpot, data discovery becomes even more powerful. With an intuitive search experience, you can create interactive Liveboards that provide real-time insights into your KPIs and automatically sift through data to reveal hidden trends and anomalies.
3. Streamlined data management
A well-designed enterprise data management strategy makes it easier for users to find the right datasets and build and launch new use cases. By eliminating time-consuming manual tasks like configuring data pipelines, managing data flow, and integrating disparate systems, this approach allows businesses to innovate faster and maintain operational efficiency. Moreover, today’s augmented data management tools can intelligently map, merge, and harmonize data from disparate systems, leading to a more efficient and streamlined data management process.
Firms that have established a strong enterprise data management strategy enjoy several advantages, including the ability to infuse data and AI into their business processes and foster a data-driven culture. But to reach this stage, you need the right set of tools.
As a pioneer in the augmented analytics category, ThoughtSpot is more than a disruptive technology—it is the perfect vehicle for delivering value to your organization. By integrating AI and natural language search, ThoughtSpot enables users to explore and analyze data in new and exciting ways. Whether you want to receive personalized insights via SpotIQ or drill into specific data points, ThoughtSpot ensures your data is secure and accessible at all times.
Experience the power of AI capabilities for yourself by scheduling a ThoughtSpot demo.
