Dissection Of Data Analysis In Business?

Dissection Of Data Analysis In Business?

A coveted career as a data analyst has been doing the rounds of every mathematical or economic student now and is also catching up with the science lovers. For whom does a data analyst work and why a company is keen to keep the pocket of an able data analyst heavy? However novel and promising this career seems to be from its title, the roles played by a data analyst has long been existing as an integral part of almost all business entities, but under different departments. Data analysis just encompasses all of them under one large umbrella of higher stature. It can be summed up as the finding of data through extensive data mining, its interpretation in textual and graphical form, and deriving conclusion capable of putting to the real application. For business, the data here is obviously information on production, market, product, customers and business statistics.

 

Data analysis combines three aspects, integrated into business

 

The final objective of analyzing a set of data using the tools of data analysis is to help in improving the efficiency or the business, like the QProfit System analyzes the previous market data to predict better assets for tomorrow. The interpreted data is represented in a form most understandable and usable for the concerned purpose and this is mostly in statistical form. This is why data analysis is better handled by executives with a backhand knowledge of statistics. We dismantle multiple types of data interpretation and presentation in this post:

Text analytics: When you mine into the universal sources of collecting data, it will be scattered here and there with many types of information twisted into each other, probably in a whole lot of forms and languages. The first challenge in data analysis is to find, segregate and decipher the relevant data by applying linguistic and statistical tools to yield it in a presentable form.

Predictive analytics: You have the statistical data model, based on historical and present data and the manual and digital technology allow superimposing the model for predicting future patterns and probable results for a new business plan.

Descriptive statistics: The final report may be having the faces of graphs, pie charts, dimensional models and futuristic predictions. To effectively transmit them to the target and right implementation, a to-the-point description such as the median or average becomes useful and then a simple bar graph becomes capable of displaying much stronger data.

Exploratory data analysis: During the production process of your final business product, any new tool or approach that aids in the process finds its way to be tried, and if not available, efforts are made to discover or invent one. When the data to be analyzed becomes more and more complicated in its availability and expected deliverables, new approaches are always under search.

Confirmatory data analysis: You have the data, it has been interpreted and is expected to deliver some result on the basis of assumptions, known as a hypothesis. How valid are these and how true or false they become forms the core of probability and confirmatory data analysis.