How data types impact overall business functions.
There is a lot of data generating exponentially day by day which is not only impacting storage size and costs but also query performance. Specially built applications and non-traditional data storage and analysis methods have heavily relied on vast datasets including transaction logs, email, sensor readings, images and video sources, among others. These datasets are significantly essential for organizations to derive information from the sea of data that was previously mostly ignored. Different databases have different data types that define the kind and range of data that can be stored in a given field.
There is no wonder that the upcoming days/months/years will see a massive upsurge in data volume, nearly 2.5 quintillion bytes of data daily. This voluminous amount of data is giving the rise of big data technologies capable of storing and processing data. Already, the global big data market is valued at nearly US$169 billion in 2018 and anticipated to reach US$274 billion by 2022.
Big data has ascended as the amount of data that will not practically fit into a relational database for analysis and processing caused by the voluminous information being created by human and machine-generated processes. Relational databases have standardized many elements in customer records, which previously had been paper-based and stored in physical file folders.
Structured and Unstructured Data
All data has some sort of structure whether structured, semi-structured, or unstructured. Today, businesses and their partners and consumers started requiring access to computerized data records. In this regard, structured data types made it easier than it had been for these records to be shared and understood by systems operated by organizations. Typically, structured data refers to highly organized information that can be readily and seamlessly stored and accessed from a database through simple search engine algorithms.
Significantly, having a dataset in place which is vital to data scientists or other data science experts within an organization who are working on systems that are tasked with predicting best action style models, or performing journey analysis, it has become possible to replay a user’s steps through a system, learn from changes over time and address challenges.