DATA FOR ANALYTICS

Data for Analytics

            Business analytics uses data from three sources for construction of the business model. It uses business data such as annual reports, financial ratios, marketing research, etc. It uses the database which contains various computer files and information coming from data analysis.

            Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain. Data is extracted and categorized to identify and analyze behavioral data and patterns, and techniques vary according to organizational requirements.

Data analytics is also known as data analysis.

            Big data analytics refers to the process of collecting, organizing and analyzing large sets of data (“big data”) to discover patterns and other useful information. Not only will big data analytics help one to understand the information contained within the data, but it will also help identify the data that is most important to the business and future business decisions. Big data analysts basically want the knowledge that comes from analyzing the data.

Types of Data Measurement Scales

1. Categorical (Nominal) Data: Nominal scale represents the most elementary level of measurement. A nominal scale assigns a value to an object for identification or classification purposes. The value can be, but does not have to be, a number because no quantities are being represented. In this sense, a nominal scale is truly a qualitative scale. Nominal scales are extremely useful even though they can. be considered elementary. Marketing researchers use nominal scales quite often. Nominal scaling is arbitrary in the sense that each label can be assigned to any of the categories without introducing error.


2. Ordinal Data: Ordinal scales have nominal properties, but they also allow things to be arranged based on how much of some concept they possess. In other words, an ordinal scale is a ranking scale. The ordinal scale indicates the relative position of two or more objects or some characteristics. The consumers are asked to rank preferences for several brands, flavours or package designs. The measures of such preference are ordinal in nature.
3. Interval Data: The interval scale has all characteristics of the ordinal scale and in addition, the units of measure or intervals between successive positions are equal.
4. Ratio Data: Ratio scales represent the highest form of measurement. They have all the properties of interval scales with the additional attribute of representing absolute quantities. Interval scales represent only relative meaning, whereas ratio scales represent absolute meaning. In other words, ratio scales provide iconic measurement. Zero, therefore has meaning in that it represents an absence of some concept.