Basics of implementing Data Mining, Data Analytics, Machine and Deep Learning and Predictive Analysis for continuous growth of e-businesses?
We usually came across frequently that even Enterprises also got confused in implementation and utilization of Data Mining, Data Analytics, Machine Learning, Deep Learning and Predictive Analysis therefore our CEO, Ahmed Bilal specially dedicated to make a write up for us and our clients on how to actually BEGIN towards implementation with this all from basics, will be addressed in this post with few suggestions and recommendations.
All of our readers are welcome to post comments to share your experiences, queries, suggestions and recommendations.
First and utmost important thing to know exactly WHERE YOUR DATA IS? Even if you know in general it couldn’t be more than 25% of the data actually exist around and in your business because even 50% (estimated) of the IT Leaders and IT Professionals in company reported in a world-wide survey they don’t always know who owns the data. If someone don’t know who owns the data then quality can be imagined and nobody can be accountable then. In fact, even after once you have it for the past and present till date then how would you filter your business related data on continuous and in-time out of 2.5 quintillion bytes of data creating every day around the world? That’s where Machine Learning comes in and let’s start the topic sequentially instead of making post content starts making you feel like what you start boggling about when look at the header image of this post 🙂
Data Mining and Data Analytics:
i. Recognize all your data sources (active/inactive) and centralize them. You can gather the data from (entity wise). For example, registered users, statistics/analytics, inquiries, orders, registrations, logins, sign ups in a month, usage of your mobile and web applications (Read our relevant blog post on this here). Where inquiries alone may be generating from multiple sources like as a result of SMS and E-mail campaigns, through inquiry forms on your website or directly received on your published email addresses etc.
ii. Use one of the tools listed below to actually put in action your gathered data sets above; QlikSense, CrossEngage etc and few useful ones are listed here to not only start observing but recording live responses in timezone wise, gender wise or whatever applicable to your business. To have basic to advance level Analytical Reports with lots of filters to tailor the reports suiting your requirement, aligned with your strategies and priorities.
Machine and Deep Learning:
Deep learning (also known as deep structured learning, hierarchical learning or deep machine learning) is a branch of machine learning based on a set of algorithms that attempt to model high level abstractions in data. Identified algorithms aligned business strategy and industry are then scripted into actual programs by Programmers/Expert Data Scientists using the language and tools of their choice and meant to run continuously and/or on particular events in a system (website/software/mobile application). Research in this area attempts to make better representations and create models to learn these representations from large-scale unlabeled data.
Deep learning is part of a broader family of machine learning methods based on learning representations of data. An observation (e.g., an image) can be represented in many ways such as a vector of intensity values per pixel, or in a more abstract way as a set of edges, regions of particular shape, etc. Applications can be one of them but not limited to: Automatic speech recognition, Customer relationship management, Recommendation systems, Image recognition, Natural language processing, Drug discovery and Toxicology and Biomedical Informatics
Ultimate purpose of setting up scripts (performing Machine and Deep Learning) by Programmers/Expert Data Scientists is to generate Predictive Analytical Reports, which encompasses a variety of statistical techniques from predictive modeling, machine learning, and data mining. That is actually for analyzing current and historical facts to make predictions about future or otherwise unknown events. Applications can be one of them but not limited to: CRM, Cross-sell, Customer retention, Direct marketing and Product prediction.
Required Resources & Tools:
Required resources for the implementation and executing such processes are mainly: Data Scientists, Digital Marketing Managers, SEO Managers, Product Managers / Owners etc where Digital and Technical Project Managers act as a medium in transformation of Raw Data and Analysis into Business Intelligent Reports drafted manually and/or Reports based on Predictive Analytical reports creating automated scripts and tools. FYI, open source and commercial tools are given below and R Language can be used by mid-level and Expert Data Scientist to write automated scripts as per the strategy and aimed goals.
Notable open source predictive analytic tools include:
Notable commercial predictive analytic tools include:
- Alpine Data Labs
- Angoss KnowledgeSTUDIO
- BIRT Analytics
- IBM SPSS Statistics and IBM SPSS Modeler
- KXEN Modeler
- Neural Designer
- Oracle Advanced Analytics
- Predixion Software
- Revolution Analytics
- SAP HANA and SAP BusinessObjects Predictive Analytics
- SAS and SAS Enterprise Miner
Few of the above tools are Industry Specific towards generation of great applications. For example, Watchdog Agent Toolbox has been developed and optimized to add ready-to-use prognostics and health management (PHM). It is developed by IMS Center which uses a patented analysis technique and industry-standard analysis.