Section 3 Data Science an advanced Data Management
Advancement only comes with habitually doing more than you are asked.
Gary Ryan Blair.25
The wiring of the world has given us ubiquitous networks and broadened the scope of issues that data management must now embrace. In everyday life, you might use a variety of networks (e.g., the Internet, 5G, WiFi, and bluetooth) to gain access to information wherever you might be and whatever time it is. As a result, data managers need to be concerned with both the spatial and temporal dimensions of data. Graph databases have grown in popularity because they are optimized for analyzing networks, such as social connections and supply chains. In a highly connected world, massive amounts of data are exchanged every minute between computers to enable a high level of global integration in economic and social activity. XML has emerged as the foundation for data exchange across many industries. It is a core technology for global economic development. On the social side, every day people generate millions of messages, photos, and videos that fuel services such as Twitter, Flickr, and YouTube.
Most organizations are interested in applying data science to analyzing the vast volumes of data they can access to learn about social trends, customers’ opinions, and entrepreneurial opportunities. The discipline of data science combines data cleaning and transformation, statistical analysis, data visualization, and machine learning (ML) techniques. Organizations need extensive skills in collecting, processing, and interpreting the myriad data flows that intersect with their everyday business.
Organizational or business intelligence is the general term for describing an enterprise’s efforts to collect, store, process, and interpret data from internal and external sources. It is the first stage of data-driven decision making. Once data have been captured and stored in an organizational repository, data science techniques can be applied.
In a world awash with data, visualization has become increasingly important for enabling executives to make sense of the business environment, to identify problems, and highlight potential new directions. Text mining is a popular tool for trying to make sense of data streams emanating from tweets and blogs. The many new sources of data and their high growth rate have made it more difficult to support real time analysis of the torrents of data that might contain valuable insights for an organization’s managers. Fortunately, Hadoop distributed file system (HDFS) and cluster computing methods are a breakthrough in storing and processing data that enable faster processing at lower cost. Dashboards are widely used for presenting key information. Furthermore, the open source statistics and graphical package, R, provides a common foundation for handling text mining, data visualization, HDFS, and cluster computing. It has become another component of the data manager’s toolkit.
The section covers the following topics.
- Spatial and temporal data management
- Graph databases
- XML
- Organizational intelligence
- Introduction to R
- Data visualization
- Text mining
- Cluster Computing
- Dashboards