The Big Deal about Big Data. Mike Skinner, CPA CISA CITP HORNE LLP

Transcription

1 The Big Deal about Big Data Mike Skinner, CPA CISA CITP HORNE LLP

2 Mike Skinner, CPA CISA CITP Senior Manager, IT Assurance & Risk Services HORNE LLP Focus areas: IT security & risk assessment IT governance, policies & procedures Application implementation Business intelligence and data analytics IT project & vendor management Information systems audits Services Organization Control (SOC) reports Regulatory compliance Disaster recovery & business continuity Enterprise risk management Internal audit Vendor management Contact Info: Main Direct Mobile [email protected] Professional Affiliations American Institute of Certified Public Accountants International Association of Computer Investigative Specialists Institute of Internal Auditors Information Systems Audit and Control Association Mississippi Society of Certified Public Accountants Tennessee Society of Certified Public Accountants

3 60% of financial institutions in North America believe that big data analytics offers a significant competitive advantage and 90% think that successful big data initiatives will define the winners in the future.

4 Data by the Numbers 1,000 bytes = one kilobyte (kb) 1,000 KB = one megabyte (MB) 1,000 MB = one gigabyte (GB) 1,000 GB = one terabyte (TB) 1,000 TB = one petabyte (PB) 1,000 PB = one exabyte (EB) 1,000 EB = one zettabyte (ZB) 1,000 ZB = one yottabyte (YB)

5 How much data is there? Approximately 90% of all of the world s data was created in the past two years Data is estimated to grow by 650% over the next 5 years and we will reach 40 ZB by 2020 Data in the world doubles every 14 months Each year, the world creates enough data to fill a stack of DVDs that would stretch to the moon and back

6 How much data is there? To put the explosive growth into context, each minute we create: More than 204 million messages Over 2 million Google search queries 48 hours of new YouTube videos 684,000 bits of content shared on Facebook More than 100,000 tweets $272,000 spent on e-commerce

7 What is Big Data? Big Data is a technology buzzword used to describe the massive amount of structured and unstructured data which is so large it is difficult to process using traditional database and software techniques. Refers to technology, not the data itself Operational vs. Analytical

8 Unstructured vs. Structured Data Unstructured Data No pre-defined data model Not organized in a pre-defined manner Typically text heavy May contain dates, numbers and facts Difficult to index and search Structured Data High degree of organization Easy to include in relational databases Easily indexed and searched 80% of data in today s organizations is unstructured

9 Four Vs of Big Data - Volume 40 ZB of data by quintillion bytes of data created each day VOLUME SCALE OF DATA 6 billion people have cell phones Most companies in the US have at least 100 terabytes of data stored

10 Four Vs of BigData - Variety 30 billion pieces of content are shared on Facebook every month Approximately 75 billion credit and debit card transactions in 2013 VARIETY DIFFERENT FORMS OF DATA 420 million wearable, wireless monitoring devices are collecting data 400 million tweets are sent per day by 200 million monthly active users

11 Four Vs of BigData - Velocity NYSE captures 1 TB of trade information during each trading session VELOCITY SPEED OF GENERATION Technology allows us to analyze data while it is generated without ever putting it into databases # 1 By 2016, there will be an estimated 18.9 billion network connections Business intelligence and analytics will be the top priority of CIOs through 2017

12 Four Vs of BigData - Veracity 1 in 3 business leaders don t trust the information they use to make decisions VERACITY UNCERTAINTY OF DATA 27% of respondents in one survey were unsure how much of their data was accurate Poor data quality costs the US economy around $3.1 trillion a year

13 Four Vs of BigData - Value VALUE Having access to tremendous amounts of data is useless unless we can turn it into value.

14 Big Data Adoption Levels

15 Winning with Big Data Acquire new customers Improve credit risk estimation Maximize Lead Generation Potential Apply publicly obtained unstructured data (e.g., Facebook and Twitter) to improve process of choosing and targeting customers Advanced analytics increased the conversion of prospects by 7 times at a leading European bank Transitioned from analyzing internal-only data to internal and external data Drive Share of Wallet Increase revenue through predictive analysis Create and use customer scorecards to predict outcomes of various customer or product groups 10x increase in sales and 200% growth in conversion rate for e-payment product at a leading European bank Used 1.5 million data sources across 40 variables

16 Winning with Big Data Limit Customer Attrition Improve customer satisfaction Nurture customer relations and loyalty programs to gain valuable insight into customer purchasing trends and improving communication with customers Mid-sized European bank analyzed 2 million customers across 200+ variables Developed automated scorecards and multiple logistic regression models and decision trees Efforts led to the identification of cancellation risks and helped avoid outflow of EUR 30M

17 Winning with Big Data Fraud Detection, Prevention and Security Ability to identify and mitigate fraud quickly by monitoring customer usage and spending data Clickstream Sensor data Log files Geospatial mobile data

18 Big Data Use Cases MasterCard Incorporated 1.8 billion customers Analyzes customer spending behaviors Provides insights to retailers in benchmarking reports Morgan Stanley Uses real-time wire analytics to detect problems in its applications U.S. Bank Analytics enabled a single customer view across multiple channels and improved the bank s lead conversion rate by over 100%.

19 Big Data Use Cases Rabobank Dutch multinational banking and financial services company EUR 759B Ranked 10 th World s Safest Banks by Global Finance Analyzed criminal activity at ATMs to determine factors that increased risk of being victimized Bank of America Big Data emphasis on integrating customers with internal operations Understanding customers across all channels Presenting consistent, appealing offers to well-defined customer segments

20 Big Data Use Cases Deutsche Bank Big Data plans were held back due to legacy infrastructure Mainframes and databases Data warehouses built over the past 3 decades difficult to unravel Struggling with what to do with traditional system given significant investments made in the past

21 Big Data Roadblocks Data silos Talent gap Big Data seen as just another IT project Value proposition until Big Data strategy fully implemented Privacy concerns

22 Big Data Security Handling large volumes of intelligence also means protecting sensitive information that would damage the enterprise if leaked: Private information such as credit card numbers, bank account numbers or personally identifiable information (PII) such as Social Security numbers Strategic information like intellectual property, customer analytics or business plans Performance information including sales figures, financial metrics and customer metrics used to make critical decisions New or revised IT policies required

23 Big Data Roadmap Level of Maturity Beginner Novice Expert Culture Preliminary analytics strategy, little buy-in from leadership Analytics used to understand issues, developing data based strategies Full executive sponsorship of data analytics Capabilities Limited reporting and analytics capability with dispersed talent Defined process for recruiting analytics talent with budgets for analytics training Analytics collaboration, strategic external analytics partnerships Data No defined data infrastructure, informal and dispersed data Data available for existing and potential customers, most data still unstructured and internal Internal, external and social media data is merged to build integrated and structured datasets Technology Poor data governance, basic reporting using spreadsheet based tools Some statistical and forecasting tools in use Established, robust data management for structured and unstructured data sets Analytics Activities Random targeting of customers using basic product eligibility criteria Basic profiling of customers with customized analysis Analyzing customer behavior across channels to predict interest and developing personalized products and services

24 Big Data Roadmap 1. Identify business requirements for data analytics based on strategic goals. 2. Identify and train Big Data Champions in IT and various business units. 3. Clearly outline your data sources and align those resources to meeting their stakeholder needs. 4. Consistently assess the value of data against the associated costs of storage and retrieval, considering compliance, privacy and regulatory concerns. 5. Maintain a scalable budget for infrastructure and database analysis tools. 6. Create data sources and analytics, beginning with those that bring the highest value to the organization (increase customer retention, reduce customer acquisition risk, reduce fraud). 7. Maintain a repeatable process for acquisition and usage of data.

25 Questions?