Top Data Topics - What are they?

Published on Jan 27, 2019

Ever been in a room when people are throwing phrases and acronym's like they have a word generator from another planet? Tech is fast expanding and each new wave of technology brings with it a new words and phrases and techniques. We have come up with a top ten trending analytics topics, these are short cuts to understanding what its all about.

Predictive Analytics – What is it? Predictive analytics is a combination of techniques from data mining, predictive modelling, and machine learning, these techniques analyse current and historical data to make predictions about the future or unknown events. Where is predictive analytics used? An example environment of predictive analytics is financial transaction data. Banks and other financial services can use their data and predictive analytics to identify risks and consumer patterns.

Cognitive Search – What is it? Cognitive search can search for and extract relevant information from large and small sets of data. Where is cognitive search used? When you search for a query via multiple sources of structured and unstructured data cognitive search will pull all relevant information based on your search criteria.

Stream Analytics – What is it? Stream analytics takes multiple streams of information from multiple sources such as, IoT devices, websites, mobile phones etc and then feeds the live data back to a single analytics platform. Where is stream analytics used? It’s used in everyday businesses to analyse their multiple sources of data social media, emails, audio files, images the list goes on.

NoSQL – What is it? NoSQL is a provider of databases that allow for storage and retrieval of data in a structured way. Where is NoSQL used? NoSQL databases are used in big data and real-time web applications.

Distributed file stores – What is it? Distributed file stores store data on multiple nodes verses storing all data to a single source. This is similar to blockchain technology. Where is distributed file stores used? A use case example could be in insurance where identity and the history of a policy holder is crucial. The information is easily accessible as the data replicated which improves processing performance.

In-memory data fabrics – What is it? In-memory data fabrics gathers single sources of data into a grid. Grouping the data into a grid allows each data source to remain independent but also as a collective group. Where is data fabric used? It can be used across multiple industries who want to analyse multiple sources of information independently or as a group.

Data virtualization – What is it? Data virtualization is strategy of data management the benefit is data can be produced and visualised quickly and from multiple sources in a unified manor. Where is data virtualization used? Big data companies can benefit from data virtualization as this technique allows users to gain an overview of their data in real time.

Data Preparation – What is it? Data preparation is when the act of preparing and editing raw data (primary data). This is imporant task as all data anomalisie need to be found, data needs to prepared before the data is loaded into a system for analysis. Where is data preparation used? The primary reason for preparing data is for that data to be then loaded into a data analytics application. Many industries will preparing data on a daily bases for analysing their data.

Data Interation – What is it? Data interation is when one or more data sources are linked together, the interagtion (linking) is useually done through products like MongoDB. It’s done to improve communication through unconnected sources of data. Where is data intergration used? When companies have 2 or more data sources that are unconnected and the company would like the data to be intergrated. Data interation is a part of a whole process that involves many aspect such as data prep and analysis.

Data quality – What is it? Data quality is quite literally the quality of the data. There are many definitions to data quality but if the data is compatible with its intended use for example in analysis or decision making then the data quality is good. There’s also a difference between data quality and data quantity, never get these mixed up. Where is data quality used? Data quality or quality of data is a part of everything we do like reading a book or making a financial business decision, you need good data.

Posted in Data Analytics, Insurance AI Blog on Jan 27, 2019