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The enterprise application landscape will continue to undergo significant changes with ongoing innovation in cloud computing, mobility, data security, and AI-enabled applications. Analysts predict over 50% growth in an organization’s application portfolio, and most of those applications running on multiple cloud platforms outside of the organization’s own data center. Data management in this complex hybrid application environment is critical to an organization’s successful digital transformation.
Managing data across applications and maintaining integrity has become the foremost challenge for organizations operating in this hybrid model. Data issues compromise the benefits of moving operations to the cloud. Organizations must ensure data integrity and minimize the friction of sharing data across their applications.
It is critical to identify what data is considered the master to avoid conflict between systems. This decision has both business and technical implications. From a technical perspective, this decision affects how transactions flow across the enterprise. From a business perspective, It is essential to consider how various departments use the data. This includes Marketing, Sales, Fulfillment, Customer Service, Finance, etc. Understandably, it can be challenging to negotiate conflicting sets of requirements across all of these teams.
Each department often maintains its own “source of truth” on its own systems. For example, the Marketing department maintains contact information in leads. That information is not meaningful to the Finance department until the lead becomes a customer. But what is the best way to avoid duplicating data and compromising data integrity? We recommend looking at every functional area involved and understanding how and when the data is used. It is equally important to assess if the data in one department could be valuable to another department.
Once this assessment is complete, it is possible to pull all the information into a master data management system. This system acts as a data hub, exchanging the appropriate data among applications. Typically systems such as these are purpose-built for the task and include features such as merge and dedupe to facilitate clean and consistent data flow.
Once the technical team sets up the master data system, it has to be business-driven. A new role of Data Steward has emerged to address the need for business-driven data management. This person is responsible for reviewing data across different systems and making important business decisions that determine what should be kept, merged, transformed, and more.
Ideally, the Data Steward understands a broad cross-section of the business and its data. Leveraging their broader perspective about all the data repositories within their company, they can effectively work with individual groups to better understand and integrate their needs. This ensures that each group’s data can make meaningful contributions to the organization.
It is valuable to appoint someone within each department to be the data steward’s data liaison. In addition to maintaining ongoing quality and processes, they can also negotiate needs within their group and act as a point of contact for the data steward.
With data shared across an increasing number of platforms, data quality issues become magnified. Be sure to run merge processes periodically to ensure there are no duplicates. There are several aspects to consider: What is the master record? What is the input criteria? How do you identify duplicates? What level of matching is needed?
It is essential to manage invalid or expired data as well. For example, an invalid email address in a marketing campaign should be routed to the data steward. The data steward can also determine if this update is valuable to other departments.
Data quality is an ongoing process. Often, the initial data cleansing effort can be a formidable undertaking for any organization. After the initial clean-up, data quality can be maintained using the approach discussed above.
At one of our clients, the Customer Service department implemented a new system to manage support requests. After two years, the system had accumulated data on over 33,000 contacts. Unfortunately, that data only lived within the support system. That contact information is valuable to the Marketing and Sales teams for doing upsells, cross-sells, and improved customer insight.
We helped our client uncover this hidden value and efficiently share this data across departments. In addition to solving problems and fixing defects, a good data quality audit can uncover revenue-generating opportunities as well.
When a company is in its initial accelerated and nascent growth, and it’s investing in initiatives that provide immediate returns. At first, the priority is to get new systems up and running as soon as possible, usually with a limited budget. Data management is typically not something companies deal with at that stage.
As systems proliferate and grow, and individual groups within a company start creating their own data repositories, it becomes more important to manage this in a systematic way. You can address it by determining who should be responsible, what systems and processes are involved, and defining a workable ongoing plan going forward.
This will also improve agility and set the company on a good foundation for future innovations and opportunities.