Question

D. With the focus on ‘Big Data’ things should be becoming easier to forecast and predict. Is this true?

Answer 1

"Big Data" is a field of software-utility and information processing through which complex and large data sets can be engaged and dealt with (through the use of data processing software) for systematic analysis, and extraction of information from the data sets.

A focus on big data would indeed make it easy to forecast and predict. Even currently, big data is being actively used for predictive analysis as well as for other objectives including obtaining value from the data and for analyzing user behavior. The current data sets are extremely large, as data can be easily obtained through the digital platforms and other means, and reporting and data exchange have increased and are faster now. But, the advanced software makes it easy to spot the hidden trends and patterns, reveal the unknown, and help in the information that can be used for forecasting and for making valuable decisions.

A variety of cost-economic software including Apache Hadoop and others are used for data processing nowadays. Predictive analytics is one of the valuable branches of data mining and can be used for analyzing large and huge data sets automatically. These data sets may also contain multiple variables. The predictive analysis tools may include:

a.Regression modeling
b. Clustering
c. Decision trees
d. Neural networks
e. Hypothesis testing
f. Regression modeling
g. Text mining
h. Genetic algorithms

Predictive modeling may also run along with other approaches/technologies including artificial intelligence, pattern recognition, and integrated reasoning. There is a huge interest in the development of automated reasoning software and tools that can identify future scenarios and their measurements. Predictive models in use today analyze the data and identify the factors that may exist in the transactional and historical data sets, which also leads to the identification of potentials as well as risks. The predictive and forecasting software may correlate and identify the relations existing between different factors. The tool will evaluate the opportunity/risk associated with conditions and will direct decision making. The strategies adopted for predictive analytics may include:

1. Time series tracking- data points that are plotted over some time.
2. Sequential pattern analysis- identification of relationships between the rows of a database.
3. Data profiling and transformations- combining of the fields and modification of columns and rows, identifying attributes dependency, evaluation, record aggregation and building of columns and rows.

Real World Use Of Big Data For Prediction And Forecasting

Big data and the different tools and strategies associated with them are being used by both private and public organizations for different reasons, but prediction and forecasting are important aspects and functionality associated with them. For instance, the police department may use data sets to identify the crime hotspots and identify the trends to predict and resolve crimes. Financial companies are making it easy for lenders and investors to know the creditworthiness of the borrowers and startups through big data analysis and tools. Healthcare uses big data analysis to accurately determine the side effects and to create resources and methods for preventing diseases. E-Commerce companies including Amazon are using big data as well as artificial intelligence to suggest offerings to the online customer and to increase sales. Hiring managers are also using predictive algorithms to hire the best candidate for a given job and to improve their hiring practices so that organizational goals can be better achieved. Big data and technologies associated with it are likely to be exploited to a much greater extent in the future as well, and the result with benefit common people and businesses alike.