Predictive Analytics and its origin:
Predictive Analytics is the process of collecting, analyzing, identifying & extracting patterns from the historical & current data and then to use this intelligence to determine the future outcome of any given business or event. Since its birth predictive analytics have extended its footprints in many disciplines like finance, retail, health care etc. Though this topic is gaining prominence in recent times, it has been applied even many centuries ago in astrological sciences. Credit scoring in financial systems, customer retention, marketing, risk assessment, fraud detection, crime trends etc are some of the applications of predictive analytics where it has proved its necessity. Its application in business strategic decision making helped organizations to scale new heights.
For every organization, information is a valuable asset & only if identified & used in the right way it can lead to a remarkable results.
“Knowledge is having information. Intelligence is the ability to use it in your advantage. Wisdom is being yourself in the process.” – Unknown
The problem is with the availability of high volumes of structured data collected from a controlled environment, unstructured data collected from uncontrolled environment and a mix of this. Decision making becomes more difficult with large unstructured, multi-dimensional & distributed data to process. Organizations should have a complete understanding of predictions based software systems & follow the right steps with very less assumptions to implement them. The return of investment will vary upon how the system is created.
Steps to get the technique work in the right way & to achieve success.
- Identifying the right business use case
- Limiting the scope of analysis
- Collecting the right data
- Preparing the right models
- Identifying , analyzing & filtering complex patterns from the collected data
- Classifying & ranking the data
- Revisit the data and the model back again
Predictive Analytics & SAP HANA:
The association of Predictive Analytics with SAP HANA has opened the gateway for infinite innovations. Processing large structured, multi-dimensional data that requires enormous computation capabilities and time is made simpler by this association. When dealing with Internet of Things, massive unstructured data from M2M systems, various sensors, appliances etc. can be processed efficiently by HANA.
The capability to analyze text in SAP HANA elevates it over the other softwares. Large quantity of unstructured data transacted in emails, blogs, call centers, social media etc. will add more value if they are streamlined and optimized. For example, a suggestion or a feedback from a customer or an analyst about a product exchanged in any forum can be converted into useful insights and channelized to R&D or any other responsible body to build better products and robust business processes. This should be an on-going process and to achieve this, organizations should step towards building machine learning applications for a long term success.
Trends and way forward:
Mobile experience can be enriched with more support for user-centric, context aware predictive information and capability to offer highly personalized user experiences. For instance, when an individual sets an appointment with his/her customer, all role-specific, context aware relevant information should be pulled out for the user. The challenge here is to understand and pull the semantic information, represent the data in the right fashion & to minimize the volume of data. A platform should be available with artificial intelligence at the core so that more self learning applications with capabilities of linked data, semantic understanding can be developed on top which can remember the user preferences & decisions.
Here are some of the opportunities where other application systems could make our life simpler.
- Similar to the detection of spam e-mails using Bayesian techniques, application systems can be built to detect security threats or breaches in a software system landscape.
- An intelligence system could read information from a knowledge repository of issues reported from customer/stakeholders and could predict the solutions or narrow down the problem for newly reported issues.
Even with all these cutting edge technology & their tools, there is a lot of space for growth & innovation. Context based predictions with all influencing factors including the environmental variations for making a decision is still not a reality. With more influencing factors the end user experience will be compromised due to the complexity involved. For a application user, the predictions would be more appropriate & highly useful only if it is easy to customize. Lets try to simplify everything , so we can do anything.