Actionable Insights from invisible information

Integrating real-world data into the Web, with its large repositories of data, and providing Web-based interactions between humans and IoT resources is what data driven insights.

Data analytics involves the use of statistics, computational models, visualizations, and machine learning techniques to extract useful knowledge from large and complex datasets. This helps users to make decision accordingly.

Each analytic insight is derived directly from one or more enquiries. Analytic insight can be easily quantified and traced back to the enquiries Examples of analytic insight could be a set of association rules among other rules extracted, a significant pattern in a visual graph, or particular relationship within a multi-regression model. Notice that the term “relevant observation” is used to imply only those observations which are relevant and meaningful in the user’s current objective and context fit the notion of analytic insight.

·          Analytic insight – understanding and interpretation of individual analytical results.

·          Synergic insight – comprehension of the connections between the analytic insights and understanding of the problem situation by contextualizing the resultant comprehension in the user’s objective, assumptions, and domain knowledge.

·          Prognostic insight – prediction of the problem situation’s future states. Prognostic insight can also include the assessment of how the future states will change as the effects of different solutions, scenarios, and assumptions.

Actionable insight from analysis of a manufacturing plant with present data and historical data enables the user to choose the right combination of strategies to improve the efficiency of the plant. It help users in understanding their data and to solve problems through informed decision making.

IoT projects are not cheap and an expensive technology, like a Smart Board, used sub-optimally, will lead to shocking results. However, used optimally, it will give a good return on investment (ROI) and give your business a competitive advantage.

·          Integrating IoT data with your other data sources from existing applications creates a comprehensive, real-time overview of your business.

·          Translate the insights gathered with the solution to everyday business operational tasks

·          Adapt your way of thinking about making business decisions.

By investing on Data- driven projects most of the businesses struggle on real ROI which comes from generating actionable insights.

First step to precise data analysis is preparation of the data. This can be a challenging as IoT continues to evolve with no true standards. By creating the right format required in the next stage and quality assurance of the collected data may leads to success of the project

Large data sets add complication to data analysis and affect precision. Visualization tools help humans to understand the results of the data analysis by presenting them in a graphical and easier to understand way.

Visualization separates those insights that are truly actionable. Optimizing your insights during a processing part of the cycle will drive better business decisions. An actionable insight has some requisites which should be Relevant, Contextual and Fit the environment.

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