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Business intelligence (BI) & Advanced Analytics


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There are lots of buzzwords around Analytics. In this article I am showing clear cut differences between Business Intelligence (BI) and various Advanced Analytics.

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Business intelligence (BI) & Advanced Analytics

  1. 1. Business Intelligence (BI) & Advanced Analytics There are lots of buzzwords around Analytics. In this article I am showing clear cut differences between Business Intelligence (BI) and various Advanced Analytics. Basically, analytics refers to our ability to collect and use data to generate insights that inform fact-based decision-making. Data can arrive at your organization from various sources. It includes everything from your sales records, customer database, feedback, social media channels, marketing list, email archives and any data gleaned from monitoring or measuring aspects of your operations. Business Intelligence (BI): Traditionally focuses on using a consistent set of metrics to measure past performance and guide business planning. Business Intelligence consists of querying, reporting, OLAP (online analytical processing), and can answer questions including “what happened,” “how many,” and “how often.”, etc. Advanced Analytics : Goes beyond Business Intelligence by using sophisticated modeling techniques to predict future events or discover patterns which cannot be detected otherwise. Advanced Analytics can answer questions including “why is this happening,” “what if these trends continue,” “what will happen next” (prediction), “what is the best that can happen” (optimization).
  2. 2. There are 3 types of Advanced Analytics. 1) Descriptive Analytics, 2) Predictive Analytics & 3) Prescriptive Analytics. 1) Descriptive Analytics : The simplest form of analytics, one that allows you to condense big data into smaller, more useful nuggets of information. The purpose of descriptive analytics is to summarize what happened. For example, number of posts, mentions, fans, followers, page views, kudos, +1s, check-ins, pins, etc. There are literally thousands of these metrics, it's pointless to list them, but they are all just simple event counters. More than 60% of business analytics, most notably social analytics are descriptive. 2) Predictive Analytics : It utilizes a variety of statistical, modeling, data mining, and machine learning techniques to study recent and historical data, thereby allowing analysts to make predictions about the future. No analytics can tell you what exactly will happen in future, it can only forecast what might happen in the future, because all predictive analytics are probabilistic in nature. You basically take data that you have to predict data you don't have. Sentiment analysis, for instance, is a common type of predictive analytics. In this case, the model computes the score, whether it's a positive or negative sentiment.
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  4. 4. Delivery Manager for series of Programs, working for three different continents, projects with diverse work cultures, projects executed remotely and different organizational models with leading teams in the range of 600+ resources directly with $160 million contracts. Varied onsite, offsite and offshore experience for domestic and international clients like Reliance Jio Infocomm (India), Reliance Communications (RCOM), OneNetwork (USA), Macys (USA), Kohls (USA), Department Of Work & Pension (Manchester, UK), TD Canada Trust (Toronto, Canada), Deutsche Bank (Manila, Philippines), Globe Telecom(Manila, Philippines), Vodafone Essar (India), PSS Medical Inc. (USA), British Telecom (UK), GE Health Care (USA), Abbott (USA), MCI (UK), Accenture (USA), SalesTech (USA), Dell (USA), Cisco (USA), Fujitsu(USA), IOCL(India), HPCL(India), BPCL(India) & ONGC(India). Industry / Verticals worked : eCommerce, CRM, Telecom, Business Intelligence (BI), Social, Mobility, Analytics & Cloud (SMAC), Digital Transformation, Digital Marketing, Finance, Retail, Hospitality, Oil & Gas / Energy, Manufacturing, Public Sector, Government, & New Product Development. Key Skills : Delivery Management, Program Management, Presales, Account Management, Account Mining, Business Development, Program Finance Management (P & L responsibility), Client Liaison, Establish and maintain Excellent Relationships with Clients to build Trust, Respect and high Client Satisfaction, Handle Escalations, Risks, Issues & Dependencies, Accountable for SLA Compliance, Quality of Service and Review service Delivery Performance. Plan and implement business strategy in accounts contributing to exceeding the Client’s expectations. Project Initiation, Value add service to Clients, Contract Management, Contract Review, Managing Stakeholders, Project Processes setup, Resource Management, Mentoring & Coaching to team. Contact Information : Email : / Blog : LinkedIn : Twitter : YouTube :