Data Analytics

The science of analyzing raw data is called data analytics. You can make conclusions based on the data using different methodologies.

Data analytics have become extremely important today. It is applied in multiple fields such as aerospace, weather predictions, business planning, and product development, healthcare, and even in the entertainment industry to assess how people consume television programs and how much time they spend on particular channels. It has always been in existence but on a smaller scale.

Large-scale computerization has made it easy to make data analytics on a massive scale; especially in weather forecasting and space exploration, even in oceanography. It is also used by governments all over the world for making investment decisions.

The days of using pen and paper to analyze have been replaced with computers. Researchers and investigators simply collect raw data and feed it to computers and get back usable information that can be put to practical use. Using computers and software has a distinct advantage – results are received faster, it is also easier to feed multiple data and see the result in different models.

The result data analysts deliver is only as good as the software and solutions that they use. If the algorithms are faulty, then the chances of getting wrong conclusions also go up. The tools are really software developed by master coders who have expertise in a specific domain for a specific purpose of analysis.

Our expertise in data analytics is vast and is of two types – developing data analytic tools and delivering analytic services. We also develop special tools or adopt specialty tools with customization for complex projects that require a high degree of domain knowledge.

The types of services we deliver to data consumers fall under four categories:

Descriptive analytics : This type of analytics is about giving description as the name itself suggests – it will reveal events (what happened) within a time and answer questions like increase/drop in viewership (in the TV industry) for example; whether sales have shot up or has dropped and at what rate. As you can see, there is no conclusion; the user decides what to do with the result.

Diagnostic Analytics : These types of analytics revolve around giving a scientific reason(s) for the occurrence of an event. This involves more data crunching than descriptive analysis and a little bit of applying known or new hypotheses that will allow the users to draw a meaningful conclusion. For example, a soft drink manufacturer might want to know whether inclement weather was behind a reduced sales figure. Data analytics can give the answer to these types of questions that may have a significant impact on the manufacturer. These types of data analytics are done so that businesses can test the effectiveness of their current strategy and make changes if necessary.

The other two categories are : Predictive analytics and Prescriptive analytics – these provide either a prediction of an event or give enough information to change a course of action that a business is currently following.