Data is an important aspect for the success of any organization, the insights you generate help in making the right decisions, avoiding future mistakes and therefore generating the output your organization rightfully deserves. Data gives the power to the organization heads to explore infinite possibilities and make predictions. Listening to your gut feeling is not the right way to make important business decisions, Fact-based decisions, obtained by data analytics, enable organizations to accurately create their strategy and be ahead of the competition. Because of data analytics, now the organizations can generate more profit, perform better, increase their market share and improve their operations. By starting a journey to become data-driven and letting business intelligence, analytics and data management be part of your next business strategy has become an important aspect of any business.
Performing data analysis and generating the results are important points to reach in your data analytics process. Finally, u feel relieved from the burden of the whole process, it seems like a cakewalk now. With only a few steps to go, you are finally feeling you have come to a conclusion until you get output from your data analysis. What do these numbers signify and what should be done with them, this question can be answered once we know about different types of data analysis.
The data revolution has sprouted different types, stages and kinds of data analytics. Most of the companies are experimenting with data analytics – which in turn offers them solutions for business success. The question is, what does it really mean to businesses? The key to successfully using Big Data is by gaining the right and clean information that gives businesses the power to gain a competitive edge. The main goal of big data analytics is to make sure the organizations make smarter decisions for better business outcomes.
The two most important aspects of data science are :
- The management and processing of data
- The analytical methods and theories for descriptive, predictive, prescriptive analysis and optimization.
The management and processing involve data systems and their preparation, including databases and warehousing, data cleaning and engineering, and data monitoring, reporting, and visualization.
The analytical methods and theories for descriptive, predictive, prescriptive analysis and optimization aspect involves data analytics and includes data mining, text analytics, machine and statistical learning, probability theory, mathematical optimization, and visualization.
We cannot fit a single type of data analysis to all businesses, it is not one size fits all type of things it requires careful consideration, a good data scientist will identify the best type of analytics to be used in order to make the best out of the outcome for benefitting the business.
The three main types of analytics:
- Prescriptive analytics
They are reticulated solutions that help companies make the most out of the big data which they generate. Each of these kinds of analytic presents a unique insight. The common thing in all of these is that they help to improve an organization’s operational capabilities.
The descriptive data can be summarized in many different ways.
They are used to see the count of the occurrences of values in a particular interval or group. Calculating the mean, median, the central tendency and mode of the data sums up the data into a single value that is typical or representative of all the values in the data set. Evaluating the range, the spread, quartiles, variance, or standard deviation shows how scattered the values are and how much they differ from the mean value.
The presentation or visualization of this data and analyses is an important way of conveying information. There are many ways to do this in a pictorial or graphic format, visualizations using bar charts, box plots, and scatter plots are common approaches.
The predictive analysis goes a step ahead of exploratory and descriptive analysis by generating information from data sets to determine patterns and precisely predict future outcomes and future trends. Predictive analytics can be used to test a particular hypothesis. There are many tools used for this analysis.
This is the type of analytics that is dedicated to finding the best action to take for a given situation.
Prescriptive analytics is associated with both the given type of analytics i.e. descriptive and predictive analytics.
Looking back at descriptive analytics, it aims to provide insight into what has actually happened and predictive analytics helps create and forecast what might happen, prescriptive analytics aims to come out with the best outcome or among various choices, given the known parameters. Prescriptive analytics can even recommend decision options for how to take advantage of an opportunity that might arise in the future or mitigate a risk you might face in the future, and outline the ramifications of every choice. In practice, prescriptive analytics can automatically and continually process new data to improve the accuracy of predictions and provide better decision making.
This task is process-intensive, the prescriptive approach starts with analyzing potential decisions, the interactions between decisions, the influences that bear upon these decisions and the consequence all of the above has on an outcome to finally come up with an optimal course of action in real-time. Prescriptive analytics is not fail-proof, however, but is subject to the same distortions that can upend descriptive and predictive analytics, including data limitations and unaccounted-for external forces. The fruitfulness of predictive analytics also depends on how well the decision model covers the impact of the decisions that are being analyzed.
Growth in the speed of computing and the development of complex mathematical algorithms applied to the data collected have made prescriptive analysis possible. Specific techniques used in prescriptive analytics include simulation, optimization, game theory and decision-analysis methods.
The technical advancements in data science methods and the proliferation of data and have presented tremendous opportunities for improving the analysis capabilities and therefore making the task of data interpretation simple and easy to understand.