Solutions to Data Challenges in Enterprise Applications
By Vikram Chalana on Jun 7, 2017
In previous posts, I identified common data management challenges in enterprise applications such as duplicate data entry, data maintenance, and data collection and validation, and outlined the organizational impacts of these challenges in terms of direct costs and indirect costs.
In this article, I’ll talk about some of the solutions to these challenges. There are several strategies you can use, and you can start addressing some of these challenges by improving and increasing user education. Once the end-users have a better understanding of business processes and their own role in the entire business process, they are likely to take better care of data in the system. This user education can be augmented with data governance programs that document important data and their related rules and the relevant business processes. Companies can also implement application data management and other technologies to address challenges.
Improving user education
Educating enterprise application end users about end-to-end business processes offers multiple benefits. End-users will start to think horizontally about the business. In the early stages of a business process, users will begin to appreciate the consequences and importance of the data they enter. Users involved in the later stages of a process can troubleshoot process issues and start asking the right questions. For example, when an accounts payable accountant understands the upstream process of paying vendors, he or she can start asking questions such as whether the company purchased the wrong materials or the correct materials at the wrong price, or whether the vendor actually delivered what they thought they were buying.
Data governance and stewardship programs
Data governance is the process of setting up data-related policies in the enterprise. These policies may include a glossary of terms used in the business process, the documentation of the entire process, and a documentation of the fields and rules associated with each field for each step of the process.
After an organization has documented these data policies for one or more business processes, it can start to improve data quality by effectively governing the data that are entered into the enterprise system through various stewardship methods. One of these methods is manual stewardship, where the organization ensures data governance policies become part of its day-to-day operations, and every application user is trained to follow these policies. Another stewardship method is reactive governance, which dictates that once erroneous data are identified, they are corrected by data stewards through data cleansing projects. A third stewardship method is proactive governance, where governance rules and business processes are codified in software as a data quality firewall to prevent bad data from entering the enterprise application. I’ll discuss these methods in greater detail in future posts. Data governance as a missing approach to data quality is described in great detail by Jim Barker in his Ph.D. dissertation.
ADM and other technologies
There is a range of technologies that organizations can implement to effectively manage data in their enterprise applications. These technologies vary in their degree of complexity, cost, and effectiveness. Every organization needs to determine which technology to use based on its needs and maturity of its data management function.
Foremost among these technologies is application data management (ADM). These technologies sit outside the enterprise systems and help address many of the data management challenges identified. They’re often targeted at business analysts and data stewards in the organization, and address the challenges associated with data entry, data maintenance, data quality, and data collection. These tools work on both master data and transnational data and can be implemented with relative ease.
The next set of technologies are components of the enterprise information management (EIM) suite of tools. These tools include extract, transform, and load (ETL) tools; data quality (DQ) tools; and master data management (MDM) tools. As with ADM tools, these technologies also sit outside the core enterprise applications. However, in contrast to ADM, these tools are targeted at technical people who are typically part of the IT organization. They can take longer to implement than ADM tools, and are more involved. They are best suited for more complex or strategic data management problems. A complete data management strategy for enterprise applications will need a combination of the components identified in this post — user education, data governance, ADM, and other technologies.
About the author
As Winshuttle's Chief Technology Officer, and Co-Founder, Vikram has been focused on empowering people to transform their ERP-based businesses since Winshuttle's humble beginnings. He is passionate about technology that allows people to improve their lives and the way they run their businesses. Outside of work Vikram likes to spend time outdoors running, hiking, kayaking, and skiing.
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