Predictive Analytics

With advancements in technology, the time employed for decision-making has been reduced drastically with higher accuracy rates. Hardware prices got diminished from ages, which resulted in huge increase of digital storage spaces. Organizations started making use of this data to escalate their growth in terms of generating revenue and customer satisfaction. Big Data has made a revolutionary change in each and every sector ranging from Academia to Military. There was an enormous growth in Analytical start-ups in recent years, in which most of them are acquired by Global Tech Leaders. 

Hiring Managers opted Predictive models to select best applicant, In the same way Human Resource departments at VMware started using new prediction software “Workday” to detect when employees are about to quit. Workday brought tremendous change in work culture, VMware tried to fill those gaps in between management and employees by promoting them to higher designations or by offering salary hike to avoid migrations. 

From the statement of Amy Gannaway, VMware’s Senior Director, we can emphasize that VMware took full advantage of Workday tool for making accurate predictions on employees work status. Various factors affect employee’s decision to leave organizations. Workday effectively discovered interesting patterns after analyzing Tons of data and made certain assumptions based on its training data and algorithms. 

Tech companies like Google, Amazon, Facebook and Twitter are generating several Peta Bytes of data every year. They analyze this data in order to get insights on valuable information. Several statistical tools and machine learning algorithms are employed to extract knowledge from raw data sources. Companies like Amazon started tracking customers browsing history to recommend interesting products. Netflix use recommender systems to recommend favorite movies and TV show based on user interests. 

Apart from giant tech companies gobs of start-up companies like Airbnb, Box, Revolution Analytics, and Skytree are relying on Machine Learning Tools to gain better insights about customers. In Business world Major companies are stepping forward for acquisitions rather than building their own Departments. Recently Microsoft Acquired “Revolution Analytics” to strengthen their “Azure” platform. In the same way Box acquired DLoop, Auto tagging software, which is more concerned about user’s private data and security. In the same manner “Identified” joined its hands with Workday to serve customers better. 

Big Data & Analytics started grabbing majority space in technology. Decision-making skills no longer have human intervention. Almost all organizations adopted novel approaches in Data Mining, Statistics and Machine Learning to maximize their growth curve in terms of profitability. It created an impact on computing world, where machines turned into smart decision makers crossing human barriers. 

Our Organization’s role in Data Mining and Analytics

I worked with Delta Solutions for 2 years as Jr. Data Analyst; we used to work with different clientele of various domains ranging from Pharmaceuticals to Stock Market. We had dedicated team to extract data from corporate databases and information repositories for Data manipulation and pre-processing to get actionable data from raw formats. We had well experienced senior Data Analysts to train us according to end users requirements. Generating Daily, Monthly reports and pivotal analysis is our daily routine. 

Apart from regular analysis tasks we are trained in Statistical programming language “R” to work with marketing department projects, where we have to perform sentiment analysis to report customer’s satisfaction levels towards particular products. I felt justified at my work by integrating advanced statistical approaches by moving beyond traditional practices. 

During my tenure at Delta Solutions I was deployed in one of the award winning projects for “Typhoon Yolanda” deployed by SBTF (Stand by Task Force) in response to Digital Humanitarian Network, United Nations OCHA to assist them with media mapping for Typhoon in Philippines. Being a core member in Geo-Statistics team we are provided with spatial data to analyze geographical conditions in Philippines. Our work mostly relies on getting raw data from media and few micro-blogging websites like Facebook, Twitter and YouTube API’s using Geo-location of tags. We mapped frequency and severity of typhoon around Philippines, categorizing and prioritizing of reports that need immediate action were sent to troops working in most typhoons affected areas with clear visualizations and well-documented reports. 

Our organization collaborated with few start-up companies working in the domain of Machine Learning and Natural Language Processing to start Research and Development/Incubation cell within our organization. Different departments working on different platforms are utilizing R&D services to better design and program tools. Most of the companies changed their way from traditional practicing to novel approaches in their project development life cycle. At some point Data Mining tools are being used by employees to strengthen their program usability. 

Manual software systems were totally replaced with automated programs to reduce minimal effort from programmers and end users perspective. Firms using Data Mining were growing rapidly with constant pace. It is reported that crowd funders and investors are getting biased towards analytical start-ups, as they are about to acquire majority of market shares in future.