Introduction to Data Analytics
Data analytics is referred to as a science of evaluating raw data with the intention of making conclusions regarding that information. Data analytics is employed in various industries to permit organization and companies to make improved business decisions and in science to disprove and verify existing theories or models. It is the quantitative and qualitative processes and techniques used to improve business gain and productivity. Data is categorize and extracted to analyze and identify behavioral patterns and data, and techniques vary based on the requirements of an organization (Grossman & Siegel, 2014).
Data analytics permits businesses to evaluate large sets of data to respond to current needs in individual industry of operation. With data being continuously produced by machines and humans, the sheer available data amount is much more massive as compared to the past. Data analytic technique is normally employed in organizations dealing with big data. Big data is the data whose variety, velocity, and volume make it hard for organization to extract, analyze, and manage value using conventional or current systems and methods. In this case, the term analytics is employed as the process which extracts value from data by distributing and creating reports, deploying and building data-mining and statistical models, visualizing and exploring data, sense-making, as well as other associated techniques. The organizational data might be external or internal, processing might be batch, near real-time, or real-time, and any of these combination is probable (Grossman & Siegel, 2014).
Data analytics in business started in 1950s during business intelligence era where analytics 1.0 was introduced. This was a time of progress since data regarding customer interactions, sales, and production processes among others were analyzed, aggregated and recorded for the first time. New competencies were needed to manage data. Sets of data were small in volume and static in velocity to be segregated for analysis in warehouses. More time was spent in data preparation than in analysis. This was followed by analytic two which was introduced through big data era. This was established with establishment of internet and increase in the volume of customers created data. More and more data were created and there was a great need of developing a new way to handle large volumes of data. This increased the need for effective data analysis to enable the organizations to use the received data to make right decisions in business. This was followed by analytic 3.0 which is data-enriched offerings era. This era focuses on employing analytics to support customer-facing features, services, and products. This is commonly used by companies such as Amazon that trade online, since they receive a huge volume of data from their customer. The main aim of the analytic 3.0 is to enhance customer’s decision making experience while using these sites (Davenport, 2013).
Advantages and Disadvantages of Using Data Analytics in Amazon
In 2013, Amazon employed Redshift form of data analytics. The main advantage to this form of data analytics include (Linthicum, 2013):
- Improvement in performance when blending data with huge data sets which a number of analysts use to make decisions regarding customer analysis, marketing campaign optimization, financial transactions, inventory levels and customer analysis.
- The aptitude to offer huge data as required, without experiencing a slow and costly process of procurement to acquire software and hardware.
- Provide the aptitude to scale to manage huge databases, probable well past the petabyte range.
- The ability to employ an elastic resources set to return result sets containing sufficient speed to be relevant when managing a business
- It provide the ability to save large amount of money with time as compared to the cost of utilizing own software and hardware.
Among the presented disadvantages include (Linthicum, 2013):
- The probability of outages since any cloud computing failure will public and thus creating a bad reputation for the company.
- The data integration and migration cost is high and one may require a large bandwidth for internal data transmission.
- Another major disadvantage is lack of the best practice since this technology is still new in the market.
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