BIG DATA: PROMISE, POWER AND PITFALLS NISHANT MEHTA

Agenda Promise Definition Drivers of and for Big Data Increase revenue using Big Data Power Optimize operations and decrease costs Discover new revenue streams Pitfalls Myths surrounding Big Data Technological challenges Other barriers

Big Data can be defined using multiple dimensions Characteristic Opportunity Challenges Volume Detail of view Combine data and scale up Variety Richness of view Tame and combine unstructured data Velocity Timeliness of view Scan, filter and notice Veracity Completeness of view How much is enough? Based on IBM s definition

When did the world become interested in Big Data? The interest in Big Data has spiked in the last couple of years Google Trends query results on Big Data

But why now.? Decreasing costs of data storage and computing power Willingness to spend and allocation of IT budgets Government regulations Competitive landscape Proliferation of social media Rising popularity of mobile devices

Big Data is helping companies drive more revenue from existing customers +33% 12x 5x Revenue Growth Profit Growth Analytics Use Growth seen in organizations with successful implementation of advanced analytics

How can you use Big Data to drive more sales? Better demand management Demand forecasting Customer replenishment Customer segmentation SKU Management Improved customer satisfaction Sentiment analysis Social media analytics Marketing campaigns Interaction management

Big Data is helping companies achieve optimize internal operations and draw more savings +$65,670,000 Annual net income Effect of a 10% data accessibility for the average Fortune 1000 company

How can you use Big Data to reduce costs? Product Development Simulations and real time data feeds Process optimization Fault monitoring Optimizing supply chains Information Security Predict and respond to zero-day threats Protect against state-sponsored attacks Analyze machine and file logs in real-time

Companies can also build alternate revenue streams using Big Data Data Insight More data Data Strategy Data as a Product Data Driven Products

The hype has also given birth to some myths about Big Data All data is equally important. Companies need to prioritize various data streams according to business value. More data is better! But not always! Quality still remains the most important attribute

Big Data adds significant challenges to information management within companies Information Lifecycle Management Traditional Challenges Data Generation Data Storage Data Security Challenges posed by Big Data Data Accessibility Data Reconciliation Data Prioritization Real-time analytics Possible Solutions Flexible data stores Linked Data Metadata management

Winning on Big Data will involve more than just technology Culture Trust data, more than gut Data should drive decision making at all levels Hiring data scientists Cross-collaboration Privacy concerns Data usage Employees versus consultants In-house or outsourced? Frictionless partnership between IT, marketing, sales and operations teams Develop a transparent privacy policy for your customers and employees Driven by real, tangible business goals Formal process for evaluation, enrichment and reprioritization

Summary Promise Definition Drivers of and for Big Data Increase revenue using Big Data Power Optimize operations and decrease costs Discover new revenue streams Pitfalls Myths surrounding Big Data Technological challenges Other barriers