Your business or your government agency runs on data. When your data is poorly organized this directly affects your employees' ability to understand the data, the forecasting and planning that is decided and being implemented, as well as the continued analysis of data by employees throughout the year. Ultimately, this will result in missed goals and reduced profits. The reality is that if your company isn't sure how to organize data, the data quality is bad or disorganized, your business possesses insufficient business intelligence to meet the company's data organization needs.
This results in employees at all levels of the company not having a true view of organizational data in order to analyze what is currently happening and they cannot set the right goals or design a road map to achieve the best results. Ultimately, having a disorganzied data infrastructure is setting every level of the company up for failure. How can the company have real growth or remain competitive if the data is disorganized or incorrect? How will anyone really understand where the company stands or what the best course of action should be?
High-quality data is accurate and complete, and therefore reliable. It's consistent. End users can easily understand it. This article will examine four common factors that lead to poor data organization and the disarray of analyzing, planning and missed goals that happen because of it. It will also provide an overview of Uniserv, which is a valuable software solution to resolve the many factors that exist within data challenged companies.
1. Duality Issues at Any Stage of Data Movement
Data warehousing is the foundation for Business Intelligence. However, issues of data duality happen not only in the first phase of data warehousing, but at all stages of the process. Your system extracts data from such sources as your web logs, Customer Resource Management program, Enterprise Resource Program, order data and other sources. It worked well when first designed, but now you have added more sources, more software and more formats.
2. Absence of Data Quality Guidelines
Most organizations have not set guidelines to control information quality. Therefore, no protocols exist for checking data or searching for information that is lost or missing. This is especially important when dealing with information your systems cannot validate for themselves, such as numbers of manufactured items, the amount of units sitting on warehouse shelves. Employees might cover for each other by clocking in and out for coworkers without a supervisor verifying the amount of hours worked.
3. Typing Mistakes Are the Most Common Cause of Data Inaccuracy
Human error is inevitable. However, organizations with guidelines to guarantee information quality build in controls to cross check data, and to identify gaps and possible errors. This includes validating the information website visitors fill into web forms when they register, buy or request free information. Sometimes credit cards pay despite differences in names, telephone numbers or address information. Some error is due to fraud, not mistakes.
4. Multiple Data Sources
Modern business and government organizations collect information from a variety of sources. This makes it vulnerable. The sources come in different formats, such as flat files and relational databases. Your system must transform extracted data into a format for the target in the data warehouse. This process includes the cleansing of data. This requires numerous operations to make it fully compatible with the data warehouse and consistent with all other records. Lookup, merge and reduplication result from joining different records. And you must design and control the process so it meets the needs of the entire organization. For instance, the marketing department would like to track all website visitors and associate them and their on-site behavior with how much money they spend with the company for years to come. In aggregate, that data affects marketing campaigns and website design.
Uniserv Achieves Better Data Organization
Uniserv specializes in helping businesses and government agencies improve their data organization and management. It's a set of solutions that solve those four factors that lead to poor data organization. Uniserv analyzes your data management, helping you to transfer it smoothly, without any redundant entries. That integrates it with your data warehouse. The Data Quality Service Hub and Data Quality Functions help you verify and validate full information, especially on your customers. They validate email addresses and shipping addresses. They resolve identities and use Geomarketing to verify IP addresses.
The reasons for poor data vary with your organization. However, neglecting invalid and poor data quality results in skewed, inaccurate BI about your organization's profits or your agency's mission. If you cannot comply with legal requirements, it can leave you open to regulatory actions by the government or to lawsuits by injured customers or suppliers. The continued failure to use KPIs based on accurate information means you will fail to perceive problems in time to solve them.
Learn more about solving your data issues and better data integration with our complimentary resource, Achieving High CRM Data Quality.