By now, IT and business staff alike understand that data is an organization’s most valuable asset. They’ve come to realize that data has a value beyond the application through which it was created and beyond the silo in which it resides. Accurate, consistent, current data is a strategic asset that has the power to increase competitive advantage, drive revenue growth and market share, and enhance an organization’s reputation. The key to unleashing its value is to make it available anywhere, anytime to the individuals and groups that can put it to work in the service of the organization’s mission and strategies. Distributed data management is essential to driving the enterprise mobility strategies that increase productivity, reduce expenses and create efficiencies in a tight economic environment and ultra-competitive situations.  

When discussing distributed data management, it is important to understand that in all likelihood a great deal of data distribution has already occurred in your organization. Data always seems to find a way out of application- and department-specific silos, databases, operational systems, data warehouses and other sources from mainframes to smartphones.

It is also important to take stock of the individuals and groups of people in your organization who create, collect, manage, move, stage, update, define and ultimately use data to make business decisions. These people go by many names. In IT departments they include application developers, database administrators, storage system administrators, network administrators, infrastructure architects and data architects. On the business side of the organization, there are additional data creators, consumers and other data stakeholders. These individuals also go by many names, including line-of-business managers, salespeople, service personnel, business strategists, planners and analysts, compliance officers and more.

Creating and implementing a distributed data management system that provides users with accurate, consistent, auditable, secure and timely information with which to perform their jobs has at least one other hurdle to overcome.

Customer? Just what do you mean by that?

Not all of the people touching and manipulating data speak the same language. Individuals and departments define common data elements differently, creating confusion across the enterprise. An example with which you may be familiar is when a senior manager asks for a list of customers. Sounds simple enough, right?

The problem is, however, that he gets different reports depending on the particular department or system he consults. The organization’s accounting system generates a report containing 500,000 customers. But the customer relationship management system indicates that there are more than a million customer IDs. That’s quite a discrepancy, but it all comes down to how the attribute “customer” is defined in each system. And while it’s understandable, given that it’s not unusual for an organization to have a dozen or more points of entry for customer information, the result is that getting something as seemingly simple as a consolidated, reliable view of “customers” can be frustrating - even maddening. To reconcile such discrepancies, a senior manager may have to assign a small army of IT staff to sort through the data to understand the different categorizations of data attributes and de-duplicate data from multiple applications and systems in order to arrive at a reliable answer. This, of course, can take time, which limits the organization’s agility and competitiveness. 

So, moving to distributed data management is not as simple as taking a technology wrecking ball to traditional silos and moving the data to the people who need it, where and when they need it, and in the formats in which they need it. (Although that would be challenging enough.)

Where to Begin

Whether your organization is brand new and building a database management system afresh, or more typically, an existing organization struggling to wrap its arms around enterprise data residing in multiple locations, in multiple systems on multiple platforms in order to gain a holistic view of customers, products or overall company performance, it is critical to begin by integrating your data before you begin distributing it around your enterprise.

As enterprise data environments have become increasingly diverse and complex, data professionals are turning to modeling tools to help them do that in an effective and efficient way. Using a data modeling tool, data and enterprise architects can create and capture the metadata needed to describe their data environments. They can establish requirements, create data models, data flows, process models and more that enable them to create and maintain a single version of the truth, from which everyone in the organization can work with confidence.

In organizations in which data resides in a plethora of departmental silos, application-specific databases and other locations, architects can use modeling to reverse-engineer existing data flows and then model and implement changes to those flows as required by new business processes. These capabilities allow the business to anticipate change more proactively and make rapid adjustments centrally as needed. Again, the goal is to ensure that across the enterprise, everyone is working from the same data. This is the essence of what is meant by the phrase “aligning IT and the business.”

Data modeling is extremely powerful and effective. But additional prework and processes should be put in place before deploying the technologies that will enable you to distribute data around your enterprise.

Data Governance and Master Data Management

Since enterprise data is such a strategic asset and so critical to the efficient functioning and growth of organizations, it must be subject to business rules and consistent definitions in order to deliver optimal value.

Data governance is a discipline and management category intended to improve the quality and accuracy of enterprise data. It does this through the creation and enforcement of standard data definitions, the designation of roles and responsibilities for the stewardship of data to ensure that enterprise data - regardless of its origin - is available, accessible, reliable, consistent, auditable and secure.

Data governance is also the prerequisite to and enabler for master data management, which puts those business rules and definitions to work to ensure that there is consensus across the organization on how data is classified, how it is to be integrated, accessed, created, updated, monitored and by whom.

Now You’re Ready for the Technology Discussion and Deployment

Only once you’ve got a handle on the business-critical data residing across your enterprise-data that is essential to understanding and advancing your business-are you ready to talk about technology solutions.

Yes, distributing data across the enterprise to various constituents with different requirements implies a number of technology solutions. These include high-performance transactional and analytical database management systems, replication technology, robust, high-speed networks, layered security systems, disaster recovery and business continuity systems and more.

Once you’ve broken down silos and decoupled data from applications, you may also find that you need to create a centralized location from which users can access master reference data, according to their requirements and permissions.

One approach that works well, particularly in organizations that have many silos and legacy systems, is to create a data services layer that becomes the source of the data against which users can run queries, and from which role-specific data can be pushed out to users on a scheduled or subscription basis. Such a layer, which spans multiple systems to bring together reliable, integrated, master data, can function as the heart of an efficient, maintainable, distributed data management system, while shielding users from the underlying complexities and preventing a reoccurrence of the confusion regarding questions like “How many customers do we have?”

Many companies have employed the approaches described in this article and have achieved significant results, including increased efficiencies, improved views of business performance, enhanced competitive advantage, simplified regulatory compliance and the peace of mind of knowing that business decisions are being made based on accurate, holistic information.

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