With our lives becoming increasingly data-oriented, Analytics has proved beyond useful in strategic business decisions. The Big Data market is flourishing, where organizations as well as public bodies seek to enhance their competitive advantage by better understanding the ever-growing complexities of data.
Statistics has always been the backbone of Analytics and data science. However, that trend is changing with the paradigm shift in new age Analytics. There is now more reliance on Artificial Intelligence (AI) and Machine Learning algorithms for performing Big Data Analytics.
Fusion of Statistics + Computer Science + Data Management + BI
Big Data not only changes the tools one can use for predictive Analytics, it also changes our way of thinking about knowledge extraction and interpretation. At the edge of statistics, computer science and emerging applications in the industry, there is a rapid development of fast and efficient algorithms for real-time processing of data.
In recent times, there has been an increasing trend of using fully-automated and generic Analytics methodologies that greatly simplify many tasks associated with data science. Research organization and the academia have also adopted these emerging trends. Many Analytic courses are said to tap into the pulse of contemporary computer science. Some prominent branches of computer sciences that are helping to make this convergence of Analytics with the technical software world are:
- Intelligent machine learning algorithms to process huge amount of data
- AI for automated Analytics
- Neural Networks
- High Performance Computing
- Dynamic Web-based Systems
- Signal and Image Processing
- Computing Security
Contrasts and Compliments for AI and Analytics
The focal challenges of AI include the development of software that can reason, gather knowledge, plan intelligently, learn, communicate and perceive. AI has been used in research methodologies in the field of medicine, robot control, defense and remote sensing. One key distinction is that the AI diagnostic method has emphasis on the algorithm, the means and not on the underlying data set. Alternately, Predictive Analytics focuses on the data, not only mining historical data but also imputing the missing data values using sophisticated techniques. New age Analytics focus on both the means as well as the data, by making the machines intelligent and using the vast amount of available data.
The ideal Analytics team would include the right mix of science and engineering background along with quantitative web science and Big Data Analytical skills.
Business cases such as product recommendation, segmentation of customers, fraud detection or churn prevention emphasize real-time and highly scalable predictive Analytics.
The return of computer science would allow users of Big Data to automate and enhance complex descriptive and predictive analytical tasks that would be extremely labor intensive and time consuming if performed manually. The convergence of Predictive Analytics with AI will widen the horizon of the strategic decision making process through:
- Better enablement of key strategic initiatives
- Higher flexibility by allowing self-modification
- More efficiency allowing multiple iterations
- Multi-target prediction problems become possible under the purview of machine learning and statistics
- Model more number of variables and greater data points
- Being able to tweak, add, or drop different aspects of an algorithm.
- This keeps the time demands in check while the accuracy of the algorithm and its ability to predict are significantly improved
- As the model becomes more refined, machine learning allows multiple iterations to produce highest levels of accuracy
- Such scenarios include multi-label classification, multivariate regression, sequence learning, structured output prediction, multi-task learning, recommender systems and collective learning
Be Wary Of
A non-strategic merger of computer science and data science can lead to counterproductive solutions. Computer science brings in standardization and automation of processes. On the other hand, data science treats each data set individually. A standard approach of market mix modeling will be applied in a very different fashion on two different business scenarios, e.g. for distributing marketing spends across channels and across time.
Thus, the convergence of these disciplines needs to be handled tactfully, keeping the best of both the worlds.
Statistics and Data Management have been the two strong pillars for Analytics. With the computing-power and improvised techniques, computer science will strengthen the Analytics industry. The advance of progressive scientific technologies is increasingly finding its application in business Analytics, especially with Big Data converging upon the Analytics industry.