With the advent of data socialization and data democratization, many organizations organize, share and make information available to all employees in an efficient manner. While most organizations benefit from liberal use of such a source of information available to their employees, others struggle with the quality of the data they use. As most companies also implement artificial intelligence systems or connect their businesses via the Internet of Things, this becomes especially important. Business analysts determine market trends, performance data and even current executives with insights that help guide a company's future. And as the world becomes more data-driven, it's important for businesses and data analysts to have the right data, at the right time, in the right way so that they can turn it into insight. The basic model a company follows while implementing data socialization is: Data socialization Business analysts often spend most of their time focusing on data quality. This is a problem because data preparation and management is not the primary responsibility of the business analyst. But they don't even need to depend on IT to do it. Some of the most common data quality issues faced by analysts and organizations in general are: 1. Duplicates Multiple copies of the same data sets burden computation and storage, but can also lead to skewed or inaccurate insights if not detected. One of the main problems could be human error - someone accidentally entered data too many times - or it could be an algorithm that got it wrong. The proposed solution to this problem is called "data duplication". It is a mix of human insight, data processing and algorithms to identify potential duplicates based on probability scores and common sense that records look like a close match. 2. Incomplete data Because the data was not entered into the system correctly or some files may be corrupted, the data that resides often contains many missing variables. For example, if an address does not contain a ZIP code at all, the rest of the information may be of little importance because its geographic aspect would be difficult to determine. 3. Incompatible formats If data is stored in inconsistent formats, the systems used to analyze or store the information may not interpret it correctly. For example, if an organization manages a database of its consumers, the format for storing basic information must be determined in advance. Name (first name, last name), date of birth (US/UK style) or phone number (with or without country code) must be stored in the exact same format. It can take a long time for data scientists to easily sort out multiple stored versions of data. 4. Accessibility The information most data scientists use to create, evaluate, theorize, and predict results or final products is often lost. From departments, sub-departments, branches and eventually teams working on data – the way data goes to business analysts in large organizations – leave behind information that the next user may or may not have full access to. The method of sharing and making information available to everyone in an organization in an efficient manner is a cornerstone of enterprise data sharing. 5. System Upgrade Every time a data management system is upgraded or hardware is upgraded, there is a chance that information will be lost or corrupted. It is always advisable to take multiple backups and update the system only from certified sources.
