Please enable JavaScript.

Coggle requires JavaScript to display documents.

Unit 4 – Big data adoption Implementation of big data requires investment…

- - - - Descriptive Analytics
        In descriptive analytics, large volumes of data are split into reasonable chunks. Techniques like data aggregation and data mining use this data to give a summary of the findings and to show the underlying meaning in the data.
        For example, descriptive analysis can be used in targeted marketing, where companies mine historical data of the consumers to analyze the consumer behaviors and engagements with their businesses. MS Excel, MATLAB, and IBM SPSS are some of the tools used here.
      - Diagnostic Analytics
        This analytics method is used to determine why something took place in the past, analyzing the root causes of the event. Though limited, it helps in understanding what factors impacted outcomes and can provide actionable insights.
        For example, if a company faced a sudden decrease in their sales of a particular product in 2020 as compared to 2019, this method can help in understanding why the sales have fallen during this time period. Techniques like attribute importance, sensitivity analysis, and principal components analysis use diagnostic analytics.
      - Predictive Analytics
        This is a technique that makes future predictions based on current data. It builds models that work on the descriptive analytics data to predict future outcomes.
        For example, it can be used to analyze all the social media opinions on a particular product and then to predict whether a person’s response to the product will be positive or negative. It is most commonly carried out using tools and languages like Python, RapidMiner, Anaconda, and IBM SPSS.
      - Prescriptive Analytics
        Prescriptive analysis takes future prediction one step further. Given a specific outcome, it can suggest multiple courses of action to reach that point or it can present all possible outcomes to a particular course of action.
        For example, an online taxi service could analyze a customer’s distance to nearby drivers and make suggestions on the quickest option.
- - - - 2 WAYS TO USE THIS DATA
        
        Big Data As A Service
        Companies can use big data internally for revenue growth.
        Facebook is a prominent example. It collects vast amounts of data about its users including age, gender, work to their current location, and email address. Facebook uses this data to generate money, not by directly selling off such detailed information but by placing appropriate ads on the “News Feed” of a particular user, based on their activities and preferences. This user-targeted marketing is one of the major revenues for Facebook. In short, what Facebook is doing is “selling” its big data analysis to its customers by providing targeted advertising opportunities.
        
        Insights From Big Data
        Companies other than the tech giants may also be able to monetize their data. Insights generated through big data analysis may prove valuable to other organizations. In addition to generating more revenue, supplying big data analytics may help foster new partnerships with compatible companies.
        For example, financial institution BBVA sells value-added analytics based on anonymized transaction data of its customers to the tourism industry. A point to be noted here is that there is no exchange of ownership of data, and it remains the property of BBVA.