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Ten Ways Data Integration Provides Business Value

By Philip Russom, Director, TDWI Research, Data Management

The results of data integration surround us, enabling highly valuable business activities in our organizations. Yet, we don’t always look past those activities to see data integration as the indispensable, behind-the-scenes enabler that it is.

If you need to corroborate the business value of data integration—which is a common prerequisite for the funding, sponsorship, or implementation of data integration—then you need to explain to your colleagues the enabling role that data integration plays for many data-driven business practices. Furthermore, if you want to keep data integration solutions fully aligned with business goals, then you need to be forever mindful of the specific types of business value that result from data integration’s teams, tools, and techniques.

This article provides a checklist of 10 ways that data integration provides business value. The discussion mentions numerous real-world use cases, illustrating the different kinds of business value that data integration provides. The checklist should help you express data integration’s business value to your peers and management, as well as plan and design data integration solutions that deliver the greatest business value possible.1

1. Data Integration Increases the Value of Business Practices

Let’s start with an overview. Many valuable data-driven business practices depend on one or more forms of data integration (DI). In fact, some business practices aren’t possible without DI:

Business intelligence (BI) and data warehousing (DW ). Effective decisions depend on aggregated, calculated, and time-series data values in a data warehouse—data and data structures that wouldn’t exist without data integration.

360-degree views of business entities. For example, success in sales and service often depends on complete views of each customer, which are typically assembled with data integration tools and techniques.

Business integration. Integrating diverse businesses and their processes through shared data requires a data integration solution. This it true whether the businesses are departments within a single enterprise or separate enterprises that share data through business-to-business (B2B) data exchange.

Real-time information delivery. Business practices such as operational BI or just-in-time inventory require a data integration solution that can operate in real time or close to it. As the pace of business has accelerated, data integration has sped up to collect and integrate time-sensitive data at speeds unthinkable a few years ago.

DI as a value-adding process. DI and related practices (such as data quality and master data management) add value to data, which in turn increases the value of the business processes that use the data.

2. The Visibility of Data Integration’s Business Value Varies

Recognizing the business value of data integration when you see it is harder than you might think, because a DI solution is typically separated a degree or two from the applications and integrated data that business users see. In general, however, DI’s business value is readily visible as valuable data. Here are common examples:

  • A BI user querying a data warehouse sees the warehouse’s data, its data models, and metadata, which were built by a data integration solution.
  • A business user sees (in the graphical user interface of an operational application) a complete view of a customer that was built with DI in the form of data synchronization.
  • A product manager sees a list of supplies available from a supplier in the data set that the supplier assembled via DI and delivered across enterprise boundaries via B2B DI.
  • A line-of-business manager sees current information in a dashboard or other report that’s refreshed in real time or on demand by a DI solution.

Even when information is visible in a report or an application’s graphical user interface (GUI), users may forget that DI provided the information. It behooves all of us to remember and appreciate that DI collects, prepares, and delivers much of the data we take for granted.

Today, DI is a fast-growing discipline that provides data for many types of applications, whether they are analytic or operational.
3. As Data Integration Has Expanded, So Has Its Business Value

DI has evolved impressively in the last 10 years, expanding in several directions, as illustrated in Figure 1.2

Multiple techniques. DI had its origins in extract, transform, and load (ETL). Yet, today, modern DI is a collection of several techniques, including ETL, ELT, data federation, replication, synchronization, services, event processing, and so on.

Autonomous practice. DI as a discipline has earned its autonomy from related practices such as data warehousing and database administration. Today, DI is a fast-growing discipline that provides data for many types of applications, whether they are analytic or operational.

Large teams and competency centers. TDWI survey data shows that the average data integration team includes between 13 and 16 DI specialists. As the teams have grown, many organizations have reorganized them into competency centers that regularly staff DI projects through shared human resources and common technical infrastructure.

Vendor maturity. Almost all vendor DI tools are now feature rich and massively scalable. DI vendors will continue to add new features to their DI platforms and absorb related tools, especially tools for data quality, data governance, and master data management.

Even more business value. The recently gained diversity of DI teams, tools, and techniques means there are now even more ways that DI can provide business value.

4. Collaborative Practices Focus Data Integration on Business Goals

To assure that DI provides the best and the right kind of business value, DI work should be aligned with business goals relative to data. Luckily, a number of collaborative practices have arisen in recent years, so DI specialists can coordinate their work with a wide range of colleagues.

  • Data stewardship originated as a best practice for data quality (DQ) programs. A data steward identifies and prioritizes DQ work based on business need, pain, and bang for the buck. To prioritize credibly, a steward must collaborate with both technical and business people. In recent years, DI specialists have brought stewardship into their arsenal of techniques for greater credibility in the prioritization and alignment of DI work.
  • Data governance (DG) often has a focus on data issues relative to compliance, risk, security, and privacy. Yet, many organizations have stretched DG to also encompass standards, architecture, quality, infrastructure, and many other data issues. The collaborative DG committee can help DI specialists understand business goals that are pertinent to data and align their work accordingly. And the change management processes of DG enable DI specialists to make proposals that help raise the value of data.
  • Collaborative DI is a loose method for coordinating the work of DI teams that include many DI specialists. In a nutshell, collaborative DI takes team practices well known to application developers and applies them to DI—practices such as team hierarchy, code review, versioning, check in/out, and project management.
  • Unified data management (UDM) is a new practice that seeks to coordinate work across multiple data management disciplines, including DI, DQ, BI, DW, MDM, DBA, and more. But UDM also provides collaboration between data management and business management to assure that most data management work adds recognizable value by supporting the goals of business management.
5. By Definition, Data Integration Adds Value to Data

A common misconception about DI is that it merely moves data. However, all DI specialists know that you can’t simply move data. You must also improve it. In fact, every good DI solution is a value-adding process, as shown in Figure 2.

DI improves data as it integrates it. Data quality techniques are being folded into DI solutions more and more. This is natural because DI ferrets out quality problems that need fixing, as well as opportunities for improvement. DI also improves data models, metadata, master data, and other data characteristics, so the data becomes more clean, complete, and consistent.

DI builds new and valuable databases. Think of the aggregated, calculated, and time-series data found in data warehouses, data marts, customer data repositories, and master data hubs. The resulting data doesn’t exist anywhere else in the enterprise. Similar to a value-adding process in manufacturing, DI collects raw material (data from source systems) and assembles it into a product (new data sets).

DI transforms data to make it valuable to more business processes. DI doesn’t just move data from point A to point B; DI also transforms data so it’s fit to purpose in a target system. In other words, DI repurposes data so more business units and their processes can use it.

Now that we’ve defined the general ways that DI provides value, let’s drill into five specific use cases where DI improves data as a way of adding value to business processes.

6. Business Intelligence and Data Warehousing

DI in support of data warehousing is definitely a value-adding process. DI collects raw material (data from diverse sources) and combines it to create a new product (a data warehouse). A DW contains data and data structures that do not exist elsewhere in an enterprise. Furthermore, due to the requirements of business intelligence (BI), data going into a data warehouse must be repurposed considerably to create aggregated, calculated, and time-series data, assembled into multidimensional data models. DI doesn’t just collect data; it transforms the data into these required structures.

DI for business intelligence enables high-value activities. A DW built via DI enables decision making at strategic, tactical, and operational levels. Data prepared via DI is critical to BI practices, such as business performance management, reporting, dashboards, scorecards, online analytic processing (OLAP), and advanced analytics. These BI and DW activities—partially enabled by DI—can increase revenue, retain customers, enhance operational efficiency, enable accurate planning, guide sales and marketing, and many other valuable business outcomes.

7. Complete Views of Business Entities

DI collects data from multiple sources to complete a single view of a business entity. Common entities are customers, products, finances, employees, locations, and assets. This is similar to data warehousing, but for operations, not solely BI.

So-called 360-degree views improve many types of business operations:

  • Complete customer data adds value to any customer-oriented business process, from sales and marketing to customer service.
  • Complete product data adds value to business processes for procurement, supply chain, manufacturing, and product management.
  • Complete asset data adds value to the management of assets, facilities, inventories, fleets, and office equipment.
  • Complete employee data adds value to staffing, shift scheduling, payroll, OFAC compliance, and benefits management.
8. Data Synchronization

Synchronizing or replicating data across multiple applications and databases is done in different ways. For example, data sync may assemble 360-degree views in a central database for access by (or publication to) multiple applications and user communities. This is seen in hubs for customer data, product data, and master data. Or synchronization may update related data strewn across multiple applications and their databases. For example, each customer- facing application (for CRM, SFA, or call center) is restricted to a partial view of a customer, unless a complete view is created by synchronizing customer data across these applications.

The business value of data synchronization is that more business users have a more complete view of an individual entity, such as customer, product, financial, and so on. However, data synchronization solutions tend to move and integrate data frequently, sometimes multiple times a business day. This increases the freshness or currency of data in applications. Data isn’t merely more complete; it’s also more current. And the currency enables more nimble, time-sensitive business practices.

9. Real-Time Information Delivery

As the pace of business has accelerated, DI has sped up to integrate data at speeds unthinkable a few years ago. Real-time information delivery—which is often enabled by modern DI solutions—enables a number of high-value business practices:

  • Operational business intelligence typically gathers data frequently—say, three of more times in a business day—from operational applications, and makes that data available for dashboards and other types of operational or management reports. This gives a manager fresh information for tactical and operational decision making.
  • A number of continuous monitoring applications, such as business activity monitoring, facility monitoring, and utility grid monitoring, would not be possible without real-time information delivery.
  • Real-time information delivery has made possible modern practices that we now take for granted, such as just-in-time inventory management, build-to-order manufacturing, and overnight shipping.
10. Business-to-Business Data Exchange

This is an exciting growth area for data integration, because organizations are using data integration tools and techniques in areas where these have been rare. The majority of B2B data exchange solutions are hand-coded, low-tech legacies that need replacing. We’re currently seeing a broad modernization of B2B data exchange solutions in product-oriented industries (such as manufacturing, suppliers, and retail), as well as in healthcare, financials, and any firm with cross-enterprise supply chain or procurement processes.

The need for modernization is driving change in B2B data exchange. But there’s also a need to build business value. After all, B2B partnerships are valuable in terms of revenue, market reach, brand development, and so on. And achieving greater operational excellence through modern DI helps to grow and maintain partnerships.


Depend on data integration to contribute additional business value to data-driven practices. These practices include BI and DW, 360-degree views, B2B data exchange, real-time information delivery, data governance, and so on. Realize that there are many forms of DI, hence many forms of value.

Strive to improve data as much as possible as you integrate it. Complement data integration with data quality tools and techniques. Build new, valuable data sets via DI, providing unique business value. For maximum business value, transform and repurpose data—don’t just move it.

Let’s all remember and appreciate that DI does, indeed, deliver business value. It provides aggregated, synchronized, current, and quality data for a wide range of valuable business processes and practices. A DI solution isn’t always visible, but the valuable data it delivers and the resulting business value are.

1Some of the content of this article comes from the TDWI Webinar The Business Value of Data Integration, available for replay on

2For a complete description of DI’s current, evolved state, see the TDWI Best Practices report Next Generation Data Integration, available on

Philip Russom is a research director at The Data Warehousing Institute (TDWI), where he oversees many of TDWI’s research-oriented publications, services, and events. Prior to joining TDWI in 2005, Russom was an industry analyst covering BI at Forrester Research and Giga Information Group. He has also run his own business as a BI consultant and independent analyst, plus served as a contributing editor to leading data management magazines. You can reach him at

Further Reading in Data Integration

Want to get up to date on the current state of data integration? Read these recent major reports from TDWI, available on

Next Generation Data Integration
Unified Data Management
Operational Data Integration
Data Integration for Real-Time Data Warehousing
Top Ten Best Practices for Data Integration
Data Requirements for Analytic Applications
Can Data Integration Be Agile?
Three Altruistic Goals for Data Integration
Four Nutshell Guidelines for Data Integration

This article originally appeared in the issue of .

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