Seeking Foresights with Big Data Analytics

//Seeking Foresights with Big Data Analytics

Seeking Foresights with Big Data Analytics

Today, best run companies are constantly seeking data driven answers to critical business questions such as:-

  • Why are our customers leaving us?
  • How many customers have switched to competition
  • What is the value of a ‘tweet’ or a ‘like’?
  • What is the impact on the new product launch on my market share
  • What products are our customers most likely to buy?
  • What is the best way to communicate with our customers?
  • Are our investments in customer service paying off?
  • What is the optimal price for my product right now?
  • Which retailers and distributors are growing for me?
  • How can we continually increase the value of customer interactions?

However, most businesses today have data for different functions such as logistics, marketing, finance stored in silo databases and processing the data from source to data visualization tools are often costly, slow and exclusive to few key decision makers.

Compounding this issue is the fact that businesses are no longer satisfied with just formatted relational databases but are seeking to amalgamate external data sources such as online social media, blogs, audio, and video, images, streaming data, machine data or other smart devices for competitive business advantage. Companies that move quickly to capitalize on the potential of Big Data will often gain “first mover” advantage, enabling them to innovate in ways that are difficult to replicate.

The increasing rate of data volumes, velocities and varieties have defined the concept of Big Data, and more importantly a new opportunity to better transform insights to foresight’s.

Today, organizations are turning to Big Data Analytics to seek foresights to help them address fundamental business decisions such as:

  • How can we use large data sets to understand our customers’ channel preferences and behavior?
  • How can we analyze the shopping behavior of our loan applicants, so we may be able to assess risk more accurately than a credit score does?
  • How can we ensure that we know our customer enough to grow the business with them?
  • How can we quickly spot trends for fast decision making?
  • How could we monitor the response to our products and competitor products in real time across social media channels?
  • How can we derive decisions and focused actions from large volumes of data?


One of the biggest challenges which Big Data Analytics addresses is the ability to manage, analyze, visualize, and extract useful information from large, diverse, distributed and heterogeneous data sets.

With Big Data Analytics, companies can now analyze ALL of their available data including external unstructured data, rather than a subset, to gain a holistic view to solve complex business problems and gain rapid insights that unlock tremendous business value to deliver intelligence in the moment. Businesses today are using Big Data to advance from traditional business intelligence which looks at past events to predictive and prescriptive analytics that helps anticipate what will happen and when it will happen to make predictions and suggest decision options.

Today, businesses build Big Data Analytics model for predicting outcomes real-time like: – Which segments of customers are most likely to churn in the next month? What is likely to happen to our supply chain if there is a sudden spike in customer demand? Whom should I target for up-sell and cross-sell effectiveness?

To help predict future business impacts, businesses turn to the simulation capability of big data analytics for prediction of What-If scenarios such as; What if we introduce a new product into the market, how are our competitors likely to react? What if we change our pricing strategy, how will that impact our customer loyalty and market penetration?

Organizations that are at advance levels of analytics maturity cycle are using Big Data Analytics as a competitive advantage for higher business optimizations decisions. Businesses are often using Big Data Analytics to answer questions such as; How can we achieve the best loading plan for supply chain?; What are the best projects to invest in to maximize our profits minimize our cash outflow in the next 24 months? Understanding when it is the optimum time to contact customers to maximize up selling or cross-sales opportunity? How can we use data from sensors that track the performance, wear and tear of machinery critical to our production process, so we could proactively schedule critical maintenance to minimize disruption and cost?

The underlying power of Big Data Analytics stems in its ability to now handle 3 V’s of Big Data, volume, velocity and variety.

Volume – which relates to the size of the data set, now enables new ways to process huge amounts of data generated hourly, by cutting it into small “bite sized” pieces and processing it with mathematical algorithms leveraging massive parallelism. For example, retailers now develop 360-degree view of customer, by combining huge volumes of insights from products, pricing, customer, locations across all product categories and brand to personalize offers, pricing and promotions based on individual preferences and geographical location. Leveraging Big Data Analytics, an Asia Pacific bank analyzed a portfolio of 30 million complex cash flow instruments across 50,000 different scenarios in less than eight hours.

Variety – which relates to the structure of the data, now enables new ways of ingesting, handling and processing data which can be originated from multiple structure types including Geospatial data, 3D data, Audio and Video, Unstructured Text including log files and social platforms such as Twitter and Facebook. A leading supermarket chain turned to Big Data Analytics for proactive maintenance to determine when its machines were due for service, and to reduce energy costs by analyzing 70 million machine data points originating from its sensors.

Velocity – This relates to the rate at which data are generated and the speed at which it should be used, analyzed and acted upon. Credit card fraud detection is a good example where millions of transactions are checked for unusual patterns in almost real time. Another common usage in by online retailers, who can now compile user’s history of every click and interaction, past buying patterns and recommend additional related purchases real time.

Businesses globally are now directing more attention to reap the bulk of the volume, variety and velocity benefits of Big Data Analytics. In the next post, I will deep dive into industry use cases and better understand how Big Data Analytics is transforming the way companies do business.

By | 2017-09-08T22:10:04+08:00 July 31st, 2012|Categories: AI Blog|Tags: , |0 Comments

About the Author:

Being an Innovation Evangelist, Harphajan is a well-established expert in leveraging Artificial Intelligence and Data Science

Leave A Comment

Leading the charge towards AI

Leading the charge towards General Purpose AI with Reinforcement Learning as a first step that can survive in a variety of environments(instead of being tied to certain rules or models)

Innovating with AI

Building new equation for future AI innovation, one in which business disrupts and spurs a great leap forward