What is unified data management?

Unified data management (UDM) is a process where a range of disparate data sources are consolidated to create a single source of data, stored within a data warehouse. This data management strategy incorporates people, processes, and technology to treat both the data-silo model that has evolved over time, and the huge quantities of information processed by organizations, resulting in data fatigue.

Historically, organizations have grown their software systems on an ad-hoc basis, installing a range of different programs and data management techniques and evolving as they have expanded. The resulting structure is disparate, with duplicate tools and data that serve an identical capability. There is siloed data, disintegrated across teams and areas, with little to no ability to share. This results in poor access to data when needed, loss of business insight and trend analysis, and increased business costs.

Under these fragmented and problematic circumstances, unified data management creates a framework to consolidate information from a range of sources. It does this by identifying integration factors in those sources of data and then storing it in a common data repository in a data warehouse. This then initiates data integration throughout the entire system into a single framework that supports complete data optimization.

Unified data management also provides a common space for data cleansing, parsing, and transformation. This is completed universally across all data in the warehouse to set business standards and rules. The cleansing process is vital to have consistency across all areas of the business, enabling better data compliance and to derive better business insights.

The unified data management system has been compared to a heart: data comes in from a range of sources, and it is centralized. While in the data center, it’s oxygenized and unwanted carbon dioxide and waste products—or, flaws, inconsistencies and bad data—are removed. The data is then pumped through to those places where it is needed, the departments that use it.

How does this look in an organization?

Unified data management isn’t a one-size-fits-all software tool that an organization can use out of the box. A platform consists of multiple tools across data management disciplines, including business intelligence, data integration, data quality, data governance, and master data management. For the unified data management to be successful, it should weave all these functions together in one simple interface that allows for administration and development. Servers need interoperability, and all tools need the same development artifacts such as data models, metadata, and master data.

An example of unified data management in use is in online clothing retailing. An e-commerce store instituted a unified data management to combine all aspects of customer and marketing information. This meant that all customer information was centralized to one point:

  • The customer journey on the website, using website analytics
  • Advertising and marketing campaign information
  • Sales information
  • Customer information

Using all this knowledge together, the business discovered a range of key insights that drive business decisions. The consumer would often peruse the website on their phone in the morning, maybe while on their morning commute. Then, they’d log in to their desktop once they reached work and then purchase.

They mapped the relationship between products to allow for better marketing. If a customer purchased a floaty, sheer top, they would likely want to buy a simple fitted t-shirt or camisole underneath. Linking the two items so one becomes visible when the other is purchased increases sales.

A thorough analysis of actual data from marketing campaigns can unearth some interesting, unexpected data. Google Adwords campaigns that were thought to be effective, when compared with other campaigns from social media, found that previously discarded strategies actually yielded better results, dollar for dollar.

Once a customer’s journey is tracked, the company can send highly-targeted, individualized marketing campaigns to them. These messages can be delivered via email, social media, or phone, depending on the user’s preferences.

A unified data management system allows huge gains for businesses, increasing revenue, market share, alongside traditional goals like decreasing data errors and saving time by making data accessible.

5G and unified data management

Mobile networks and the Internet of Things (IoT) have combined to create an industry behemoth. As 5G is rolled out across the world, the sheer volume of user data is completely overwhelming. From any standard home, there are laptops, mobile phones, fitness watches, printers, TVs, cars, and even the fridge, all transmitting and receiving data. All this data needs to be accessed and managed in a way that benefits the network.

Data management and storage have become key issues for service providers, and a holistic unified data management system is helping to manage data goals. Using the cloud, centralized user data, and separating the data from function has helped network providers manage the unmanageable. The benefits of unified data management have included reduced network complexity and improved data consistency. As the world transitions from 4G to 5G, the need for unified data management has increased.

Why an organization needs unified data management

While it might seem like this is an exercise in technology—and certainly, the technological requirements are there—this is actually about strategy and good business management. The ultimate goal of unified data management is to be able to make data-driven, educated, and objective business decisions. It is about operational excellence, ensuring governance, compliance, integration, and business transformation happen consistently and for maximum benefit of the organization and staff. It is about business intelligence; where is the business right now, and how can it be better?

There is a balance that needs to be attained between two distinct goals. Having excellent data management practices and aligning this with business goals.

Data access in unified data management

Any internal or external stakeholder should be able to access the information they need. The data should be relevant, easy to locate, and accurate. This means that a unified data management system provides all users with the data they need to order a product, know if it is in stock, invoice a client, assess a claim, or do a million other tasks. Data silos are a thing of the past, with every sector of an organization connected.

Analytics accuracy in unified data management

There’s no point performing analytics or predictions if the data is flawed or incomplete. It results in incorrect information, wasted time for data scientists and end users of the reports, and represents a huge risk to the business. Unified data management allows real time insights, new opportunities, and fully-optimized decisions.

Components of successful unified data management

A successful unified data management isn’t simply getting the technical elements right and having all data stored in an accessible way. It needs to satisfy two components.

Co-ordination of diverse data management

There needs to be a balance between interoperability of servers and data development among teams. Unified data management can share and unify technical infrastructure using relevant data architecture components. Doing this should result in a practice that is holistic, collaborative, and unified. Only then can data be leveraged on an enterprise scale.

Support of strategic business objectives

There are two parts to this; the management team must understand clearly what the goals of the business are, and then these goals need to be translated into data requirements. There should be an alignment between management and technology. If management requires data in a certain timeframe, or to meet certain milestones, this needs to be clarified and communicated.

Unified data management by any other name

Unified data management is not a hugely common term, but its principles are practiced by many. Already most organizations are implementing practices and strategies of unified data management but often calling it something else. Many businesses don’t have a name for their data management discipline, but when questioned about their strategies, the processes and outcomes align with unified data management.


Enterprise Information Management (EIM) and Enterprise Data Management (EDM) are two terms that are commonly used when unified data management would be more appropriate. Often, these terms are linked with commercial software vendors that promote them erroneously. These terms are also narrower in scope, revolving around the practices of data management, integration, and retrieval, and not on the holistic package and the benefits to the business.

Master data management

Master data management (MDM) is when the business and information technology work together to ensure master data is accurate and consistent and that there is accountability for it within the organization. While it has similarities with unified data management, it is less focused on tying in with the business goals and ideals and more on strategies and programs to increase the accuracy of data across the business.

Data and information governance

Data and Information Governance is not about the technical systems used to manage software but is about the ethics, standards, and rules around the data. Governance is more concerned with definition of use, access to information, security, the information lifecycle, and categorization. It has more of a theoretical role in planning, and it aids in making decisions around how data is managed but in itself is not a management system.

Benefits of unified data management

Avoids information overload

Data is everywhere. The volumes of data created and consumed by organizations is vast, from social media, reviews, videos, and external sources of information, as well as internal purchase information and customer data collection. It is not unusual for businesses to deal with terabytes of data each day, and it can be completely overwhelming for people and for systems.

Unified data management works to minimize the volume. Data is received, processed, stored in the right place, and only in one place. This makes the whole process less overwhelming and management of data a far more realizable goal.

Business insight and trend analysis

One of the primary goals of unified data management is business insights and accurate trend analysis. Unified data management transforms data, cleans it, and creates a usable data set for insights. It also arranges the data in an accessible place and form, making it far more usable. This leads to agile innovation based on data and business insights that otherwise would not have been possible.

Reduced costs

There are a variety of ways that unified data management reduces overheads of business:

  • No double up of data or data silos; save on errors and missing information
  • Because unified data management is largely cloud-based, there is no need for investment into costly hardware and servers
  • Better business analytics and predictions allow for informed decisions that are more likely to lead to growth
  • Because the data is automatically cleansed, there is less wasted time for data scientists and other valuable resources

Regulatory compliance

Data is a big business, arguably the biggest currently. A whole raft of laws, legislation, and business rules have been created to try and manage this. Not only do organizations need to manage the data itself, but it needs to be managed in a way that keeps it safe and compliant with all regulations. Unified data management automatically applies rules to data, so there is no chance of non-compliance. Reducing the risk of fines and non-compliance is essential to best business practices and in remaining a trusted brand by customers and vendors.

Challenges of unified data management

Too broad scope

An organization that tries to get all their data unified in one process will fail. This process is not designed to be a one-hit wonder, with one tool that unifies everything.

Solution: It is unlikely that an organization will want to coordinate 100 percent of their data and that could be an impossible goal. Instead of trying to do everything at once, tackle the problem in logical steps. Tie in a few parts of the system at a time and work incrementally, building the system until it is unified to a level that meets management goals. Only choose items to unite whose collaboration and coordination creates benefits.

Lack of flexibility

One of the biggest problems with any data architecture is a lack of flexibility. If an organization is a medical provider, the infrastructure needs to be able to cope with millions of live streams of monitoring data, needs to be able to easily add products, and manage the data correctly. Software, data sources, and data types are constantly changing for all organizations, and any data management system needs to be incredibly agile in order to stay relevant.

Solution: Keeping data in the cloud, using applications wisely, and not forcing a rigid structure on the system goes a long way to keeping outcomes fluid. The data and business needs should be dictating the architecture, not the infrastructure itself. Using autonomous technology, check and maintain integrity and performance across the system and optimize the system when required.

Data standards and governance ambiguity

There are laws, standards, and guidelines around almost every aspect of data collection, storage, and distribution. Setting the standards for the business could be one of the most challenging aspects of creating a unified data management system. Implementation of legislation can be costly and cumbersome, especially in complex organizations. Compliance laws are constantly changing to suit the fast-growing needs of an increasingly connected world.

Solutions: The system must be designed and developed with governance experts, data scientists, information technology professionals, and business owners all involved. There is no simple way to do this, other than establishing good data stewardship from the start and not taking shortcuts. Personally identifiable information must be given particular attention, tracked, and monitored for compliance to privacy regulations. Tools that track and review data while identifying connection chains and monitoring compliance are vital.

Not understanding the data

It is common for an organization to misunderstand its data, which becomes expensive when information is stored but never used. Then, companies have uncertainty on how that data can be used or repurposed, making data processing a waste of resources.

Solutions: Creating a discovery layer helps to identify the data that exists and makes the data accessible and usable.