Predicting business growth through real-time data and to analyze the fate of the org. without leaving behind any residual or meta data. These systems that are capsuled can also be termed as DATA SCIENCE.
Veracity, speed and value for the data sets of today or any time in future would enormously impact daily business needs; which in turn, results in making informed decisions collectively or individually.
In the era of Digital transformation, calculated algorithms with the yield of predictive statistical analysis will help the org to make quicker, faster decisions. Thus, the limitation in terms of data storage, visualization, privacy, data transfer and usage will reduce exponentially.
Lifecycle of Data science explains the process followed for effect data exploration
1. Business Relevance - With right questions, understand the purpose and tackle the problems by defining objective to provide better business solutions.
2. Data Mining - Deep understanding of the data, to know what is needed, what to be scrapped and have maximum flexibility in choice of data mining tools.
3. Data Cleansing - Procedure to reduce data redundancy & not sacrificing trivial datapoints.
4. Data Exploration - Categorizing business essentials and business critical of an organization is the key to handle mission critical activities.
5. Feature Engineering - Use scalable, multiplatform execution for mission critical results. Integration of AI with existing processes and systems.
6. Predictive analysis - Superimpose organization growth to a speed of 30x using future forecast based on performance using machine learning techniques
7. Data Visualization - Exhibit the discovery to the decision makers using plots, graphs and interactive visualizations.