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Key Components of Data Analytics

Release date

5/22/2019 12:00:00 AM

Topic

Technology

Key Components of Data Analytics

The modern data-centric world demands the implementation of data analytics in everyday business. Analytics-based performance management is in demand for successful data analysis. To achieve this, it utilizes machine learning, artificial intelligence and various internet hacks. Data Analytics (DA) examines data sets that are extensively used in commercial industries. If you are interested in learning about the data analysis process, read this blog to find out its key components.

Roadmap and operating model

Every organization tends to utilize mapping tools to make sustainable designs for their processes and capabilities. This allows organizations to effectively manage their data and deliver services to their customers. The application of data analytics also facilitates the discovery of remote data structures and database schemes. Road maps and operating models are handled by the executives of a team to:

  • Keep track of the employees’ actions and processes;
  • Validate the quality of the key performance indicators (KPIs) selected;
  • Formulate strategies to fulfil their organization’s mission and vision.

Data acquisition

Data acquisition (DAQ) refers to the process of measuring physical or electrical changes, such as voltage, current, temperature, pressure or sound fluctuations, within a computer system. The components of the DAQ measurement system are as follows:

  • Sensors – A sensor (or transducer) converts a physical phenomenon into an electrical signal, which can be measured. Some of the common sensors used in a DAQ system are:
    • Photoelectric sensor;
    • Microphone;
    • Strain gauge;
    • Piezoelectric transducer;
    • Potentiometer
    • Optical encoder
    • Accelerometer
    • Thermocouple and thermistor;
    • pH electrode
  • DAQ boards and devices – the main function of DAQ hardware is to digitize incoming signals, for the computer to interpret them. This acts as an interface between the signals received from the environment and the computer. The three key components of a DAQ device are:
    • Signal conditioning;
    • Analogue-to-digital converter (ADC);
    • Computer bus.
  • Computer and software – the role of a computer in a DAQ system is to control the operations of the DAQ device. A programmable software installed in the computer helps to process, visualize and store the measured data. Software components in a DAQ system can be categorized into two types:
    • Application software – it is a custom-made application that facilitates the interaction between the user and the computer. The software’s pre-defined functionality includes:
      1. Performing signal-processing algorithms;
      2. Displaying custom user-interfaces;
      3. Acquiring, analyzing and presenting of the measurement data.
    • Driver software – this provides the application software with the ability to interact with a DAQ device by simplifying the communication process. The driver software has the following functions:
      1. Abstracting low-level hardware commands;
      2. Exposing the application programming interface.

Data security

Monitoring and detection of malicious activities within corporate networks are very essential because of the rising global security threats. Big data security analytics is a collection of new-generation security tools that employ various correlation algorithms to detect anomalies. These tools generate a number of security alerts, enabling quick detection and reduction of cyberattacks. Security analytics collect data through several ways, such as network traffic, cloud resources, business applications and user-behaviour data. They analyze real-time data to predict suspicious activities and provide information about the latest vulnerabilities.

Data governance and standards

Data governance and standards are required to meet an organization’s increasing regulatory requirements and achieve high-quality data. Implementation of data governance and standards are based on the following reasons:

  • Enhancing the quality of data management;
  • Building comprehensive data structures for international real-estate portfolios;
  • Formulation of data stewardship and ownership;
  • Conducting international audits and compliance reviews become more rigorous.

Insights and analysis

The values learnt from the implementation of analytics are referred to as insights. They help you identify opportunities to improve your business and tackle market fluctuations. Analytics helps gain values from your data by discovering unique patterns and trends. Therefore, to get a better understanding of your data, you need to learn how to gather and interpret insights, in addition to applying analytics.

Data storage

Retaining data using computers and other devices is referred to as data storage. This retention and analysis of data is done using specialized technology, which in turn makes it accessible for future use. Data storage can be classified into three types based on storage products and services:

  • File storage – this is an inexpensive and simple type of data storage, in which the data is stored in files and folders of a hard drive. The hard drive stores the data in the same configuration as it is viewed by the user.
  • Block storage – this is a more expensive and complex form of storage, which is suitable for data that needs to be frequently accessed and edited. This storage method is less scalable and stores data in evenly-sized blocks.
  • Object storage – objects can be stored with metadata and unique identifiers, making this storage type less expensive. It is ideally meant for data that does not require editing.

Data visualization

Data visualization refers to the graphical representation of information gained through data analysis. It helps to present data in an effective and interesting manner, thereby, making the output interactive and easy to understand. The various types of data visualization techniques are charts, tables, graphs, maps, infographics and dashboards.

Data optimization

Data optimization strategies function to decrease the database system response time, in order to increase the speed and efficiency with which the data is retrieved. Data administrators and analysts utilize optimization techniques to improve a server’s data access methods.

Data Analytics helps companies to identify the most economical methods required to run their business. A career in this field will help you work in any business sector of your choice, where you can implement your skills to analyze data and communicate it effectively. Toronto School of Management (TSoM) provides students with a Diploma in Data Analytics Co-op course, which is a program based on Big Data. If you are interested in entering the analytics field, enrol for the course now!


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