Also known as Business Intelligence, reporting focuses on the question – What Happened?Although often seen as lower level analytics it is essential for an organization to understand themselves. In addition, higher level analytics cannot be conducted if the underlying data cannot be trusted. Data issues that are detected are fixed as an integral part of building a reporting capability.

Version IT Analytics team has the deep experience and professional expertise in the techniques required to successfully implement comprehensive reporting.Some key techniques include:

KPI Definition

Agreeing on the Key Performance Indicators, including how they are calculated, is essential to have a common understanding of what the reports mean.

Data integration

Involves combining data from different sources to provide users with a unified view. Reporting across organizational silos often results in immediate benefits.

Data cleansing

Cleansing or scrubbing detects and corrects (or removes) corrupt or inaccurate data from a record set, table, or database. It increases trust in the data.

UI/UX Design

Ensuring that the resulting reports are useable (UI) and can be integrated with the day to day operations of your company (UX).

Reporting provides insight into what happened. Analyzing the data brings insight into how and why things happened. The analysis utilizes many statistical methods, such as clustering, regression and sensitivity, to understand the relationships between data and to learn something new. Other techniques include:

Natural Language Processing

Natural Language Processing (NLP) is the ability of a computer program to understand human speech as it is spoken or written. NLP taps into a vast amount of unstructured data.

Modeling

A model describes the relationships between data variables, usually in the form of mathematical equations. The interaction of these variables generates insight.

Data mining

The grandparent of analytics, data mining explores data in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new subsets of data.

Nothing is static and that is why it so important that your analytic capabilities can learn and adopt as things change. This never ending cycle of optimization is the essence of Predictive Analytics – What is likely to happen? Learning is a growing spectrum where there has been explosive growth in analytic capabilities thanks to the abundance of data, cheap storage and increasing processing power. This includes advances in artificial intelligence. Techniques here provide the best solutions to many problems in image recognition, speech recognition, and natural language processing.

After the data itself, the most important aspect of any analytics activity is the ability to take action. Analytic insight is not valuable if you cannot or do not take action with it. Action to Insight is where all organizations want to be – to enjoy a state of information superiority.