Data has many variations – business transaction data, master data, data warehousing, Big Data, legacy data, Cloud/SaaS data, machine data, social data – however, there is a commonality among all of these and that there is a path or a journey that all data goes through.
It starts with an entry point. Information is entered, be it product, customer or supplier information. It then morphs into records containing multiple points of reference such as contact, recent sales or duplicate versions of the same information. The information could then develop multiple variables – such as attributes that augment or narrow down the specifics. It could carry numerous changes and historical references and it could have various usages – what we refer to as “contexts” at BackOffice.
When data is looked at in this way, you begin to see a lifetime being played out. Data can begin its lifetime anywhere in your organization at any particular stage. The question is, how is a data lifetime managed? How do you make sure you’ve cared for and fostered that data? How can the entire company benefit from it? How is it archived appropriately when it’s no longer useful in daily activities?
This is the concept of a Data Journey. Being able to look at data through the various paths it takes, provides an incredibly helpful lens into understanding and managing that data. Separating the data from its originating system and making it a corporate asset becomes a different task than constantly fixing errors across repetitive data in multiple systems in the organization.
In a Data Journey approach, companies need to have organization around data versus systems. This is often called a “data-centric” approach but today that term seems more typically defined by the data’s domain, or silo, in which it is used. You can find data-centric approaches within these domains, such as in security or healthcare, where breaches and risks are evaluated and mitigated by a singular focus on that particular data rather than across systems, or in banking or chemicals, where compliance and best practices are the driving need. Interestingly enough, Information Technology (IT) has already begun to address this data transformation, although their newest challenge is applying the same “data first” consideration to new types of data, such as Cloud, Mobile and Big Data. While all of these are excellent starting points, businesses also need to think about how much of the data within these independent systems is duplicated and used elsewhere in their organizations, which requires a much wider lens through which to view and manage that data.
Challenges in Managing Data
One of the greatest challenges in managing data through a Data Journey is the consistency of approach and manner in which the data is maintained. Do all your business systems call data by the same field names? Do your data entry teams use the same definitions of data when they fill in text fields? Do you support multiple geographies with language, currency and other differentiations? Do you support the same customer in multiple ways? All of these situations can lead to data errors in business systems, ending in disrupted business processes.
Another challenge rests in your organization’s appetite for data quality. If you decide on a Data Journey approach, you must prepare and plan for a data quality program that supports the lifetime of your data. This means that you need to be able to
- See across all systems touched by that data
- Be able to easily manage changes to the data from an end-to-end visibility standpoint
- Be able to prepare and run metrics across that data’s lifetime (not just within each system) from inception to archival
- Establish common rules for handling and changing data as it passes through each system
If your organization has already established a data governance program or Center of Excellence, then you are already prepared to address your Data Journey. However, as you develop your governance programs, think about the lifetime of your data and how it touches your global and remote systems, partners and customers. A Data Journey approach allows your data quality or data governance experts to look across your business data and decide how to prioritize and categorize it, based on its lifetime. Considering your data’s journey will save you countless hours of data management work in the future.