Data Management can be broken down into different subsections. This is because the topic of data management is quite broad and contains different facets and nuances that a general discussion would not do it enough justice. Below we discuss data management and the different aspects of it. It is impossible to cover all the different subject areas and topics of data management in one article but we will cover the main ones.
Data visualization in Data Management
Data visualization can be defined as the term used to describe any instrument or tools used to understand data and the significance of data using visual context and simulations. Visualization communicates data or information by encoding it as visual objects in graphs, to clearly and efficiently get information to users. With data visualization software, patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier.
Data visualization is important because it aids in fast decision making amongst other things. It is so useful that it has become the standard tool for modern business intelligence and any form of data presentation. From CEO’s presenting their quarterly earnings to their board of directors to a freshman with a presentation slide of his assignment, everyone uses and now even relies on data visualization tools.
Complex tools like Tableau and Qlik and the more simpler tools like Powerpoint presentation, Keynote and Excel all fall under data visualization tools. Data visualization is embedded in the finance industry that currently all Business Intelligence software has data visualization tools embedded in them.
Data visualization tools are usually easier to operate than more complex traditional statistical tools. This makes them easily accessible to a wide range of people. Data visualization tools have been important in democratizing data and analytics and making data-driven insights available to workers throughout an organization. Workers throughout the organization can implement the tools on their own without help from professional IT personals.
This also means smaller companies or start-up companies do not have to invest in an IT department- at least not right away. Data visualization software also plays an important role in big data and advanced analytics projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data.
Big Data Analytics and Data Management
Big data analysis can be defined as the process of analyzing large volumes of data (big data) and drawing patterns, trends, insights, processes and other useful information from them. These information types and conclusions drawn from big data enables better decision making and even prediction in the business industry, the healthcare industry and other industries as well.
Big data is analyzed using advanced computational capabilities of supercomputers. The new benefits that big data analytics brings to the table, however, are speed and efficiency. Whereas a few years ago a business would have gathered information, run analytics and unearthed information that could be used for future decisions, today that business can identify insights for immediate decisions. Big data gives companies the ability to work faster and adapt to new strategies and trends more rapidly. This flexibility allows for companies to have competitive advantages.
Though big data is a new buzzword that everyone is talking about, the concept has been around for years. Now, many organizations are getting on board with the idea in a more deliberate fashion but decades aga companies made use of big data they just didn’t call it that. Way back in the 1950s, decades before the term “big data” became mainstream, businesses were using basic analytics (essential numbers in a spreadsheet that were manually examined) to uncover insights and trends.
Machine Learning and Data Management
Machine learning can be defined as the process (a continuous process ) in which machines are able to study patterns and study data inputted in them and learn new things for themselves from this data. Machine learning requires human supervision especially in the early stages and as time goes on the machine starts to learn from itself and become more intelligent.
Natural Language Processing and Data Management
Natural language processing works hand in hand with machine learning. The objective of Natural Language Processing is to train machines with human soft skills to bring about changes in their language and responses; making them more human-like. So with natural language processing, the machine can learn little human ticks that they make when they speak for example “hmmm” or errrmm” and it can add this to its machine learning database. In time, the cognitive technology can start to speak in this form as well making it sound more human.
Google recently released a program that can call to make appointments for individuals and the technology was able to have conversations with a human and sound completely human and also was able to self-direct to flow with the human in a random conversation. So the machine was able to repeat itself when the person on the other end couldn’t hear it, it was able to change its original intention to book an appointment by 10 am and adjust its preferences to the availability in the restaurant.
This is all made possible by natural language processing and in time it is going to be impossible to tell if you are talking to a machine over the phone or an actual person. Siri right now sounds like a robot and you can tell but in time it’s going to be able to sound very human. Many people are not sure of the ethics of this kind of technology but if history is any indication, innovation and the convenience it brings would always thrive and eventually, the public will adopt new technology.