Dealing With Big Data - Essay Example

Big data When dealing with big data, there are two important things that are usually taken into account; data generation and data analytics. Data analytics deals with the analysis of data and occurs on two levels. Level 1 deals with data collection and regeneration while level 2 is predictive and prescriptive. Disney has invested 1 million dollars in data in data generation and handling. They have developed a wrist pen that is given to customers and it collects data and relays it to a large central server where it is analyzed.

This data is used in marketing and improving customer service. IBM on the other hand has invested 24 billion dollars in data analytics and through a company called Watson has employed about 15000 analytical practitioners to handle data collection and analysis. This data is used to analyze the market and improve their business. Another example of the use of data analytics in business is the BMW motor company whereby a survey conducted showed that people who were getting into cars were always having their windows broken in winter as a result of ice accumulation.

In order to improve the customer confidence in their product, the company took it upon itself to wash the parked cars and give them back to the customers whenever they wanted to leave. In this way, customer confidence in BMW improved. Facebook uses data analytics to conduct surveys and improve its business and the quality of service they offer to its customers. Recently, Facebook conducted a survey asking the question of which gender between males and females spends more time sharing photographs on Facebook and the data collected showed that women spend more time sharing photographs than men.

About 350 million photographs were shared daily on Facebook. For one to become a data scientist he must have data handling skills such as programming, database creation and analysis, mathematical modeling, statistical analysis, and above all he must be creative. If we analyze the trend in the use of big data by big companies, it is evident that companies are hesitant in investing in big data. About 55-60% of investments in big data fail. This can be attributed to the fact that the companies start on technology first rather than an understanding of the fundamentals of the business.

Today, there is a very high demand for data in business performance and market analysis and hence the need for companies to invest in big data. However, a major setback in handling big data is the shortage of data scientists to work in this field. This comes as a challenge to educational institutions to train experts to work with this large data.