www.pwc.com.au Big Data Unlocking Opportunities February 2014
The world today Big Data February 2014 2
Welcome What is Big Data? How do you start? How can you leverage Big Data? Big Data February 2014 3
What is Big Data 13 February 2014
Big Data Fact vs Fiction Fiction is only used for unstructured data is only used for customer analytics is only needed for massive data sets is only available from the open-source community is a replacement for my current BI platform Fact technology can cope with a wide variety structured and unstructured data provides an architectural blueprint for storing and analysing large amounts of diverse data can be complimentary to your existing Business Intelligence investments can be used to solve end to end business issues to truly transform your business 5
Our point of view Executives know that they now operate in a data rich and data complex market; Big Data for business analytics will become non-negotiable. Data is an asset and should be treated as an Enterprise asset to leverage the capabilities of any organisation. Data needs to be business led to solve real business issues, with analytics and technology as key enablers. Data enables organisations to deliver more value for their customers and more growth to their shareholders at a faster pace than ever before. Data requires organisations to invest in new set of skills. These skills are scarce, but can be developed. 6
The term Big Data The term Big Data encompasses large data sets that are rapidly expanding due to the speed and volume in which information can now be stored and produced. 1, Capitalizing on the promise of Big Data: How a buzzword morphed into a lasting trend that will transform the way you do business, January 2013, www.pwc.com/us/bigdata. 7
Big Data goes beyond traditional data management Volume Velocity Variety February Big 2014 Data 8
What is the landscape of Big Data organisations? has analysed the market and observed three types of organisations Organisation Type What are they achieving? Who are they? Digital Enabled Organisation Data is the core of the organisation. Data drives strategy, marketing, product & decision making. Bricks & Mortar Manufacturing How to leverage data to optimize operations & better service the customer and the market. The In Between Heavy digital front end with a solid manufacturing base. Building extensions to their existing business models to leverage data and drive strategy. February Big 2014 Data 9
Why does Big Data matter? Big Data techniques allow organisations to analyse data for patterns more quickly and at a much lower cost, as opposed to more traditional business intelligence systems. New model Source the data, at times pulling directly from the systems of record. Convert the data to a format for analysis. Extract value from data analysis. Old model Source the data from systems of record into a data warehouse. Convert the data to a format for analysis. Extract value from data analysis. Time 10
Who is offering Big Data solutions? 11
How can you leverage Big Data 13 February 2014
How are organisations transforming Big Data to Big Insights? Customer Data Monetisation Operational Efficiency Risk Management 13
Real life examples 14
So who is doing what? Even though many organisations are aware of the potentials of Big Data, very few organisations have it on their roadmap and only few have started investing on it. Figure: Big Data Investment by Region Source: Gartner (September 2013) February Big 2014 Data 15
How do you start 13 February 2014
Should I be interested in Big Data and Data Analytics? Reports without insight Competitive pressure Significant transformation Investment in new technologies Growing digital capability KPI refresh Customer centric use case IT capex/opex reduction Cyber attack Unclear data strategy Fragmented Centres of Excellence 17
Where do I begin? Does your organisation currently: Collect the data you need? Analyse what you need? Discard what you do not need? Distribute what adds value? If the answers to these questions are yes, then all systems are go for launch. It s time to start your engines. 1. Determine if Big Data is the right answer for you 2. Design and establish an organisation 3. Establish a business case evaluation 4. Pilot, assess, and operationalise 5. Evaluate and improve 18
How do I build the Big Data organisation? It is critical that the Big Data organisation is led by an Executive Council, has a core solution team and is governed by strong guiding principles. Example: Big Data organisation Innovation partners Data innovation executive Council Office of the Corporate Data Officer Executive Council Functional CIO/CTO Business partners Vendors Service providers Data innovation council/steering committee Data innovation leadership Data innovation core team Data hygienists Data explorers Business solution architects Data scientists Campaign experts Pool of specialists (Leveraged from the organisation s different areas as needed) Data innovation advisory board Marketing Legal Product Finance IT delivery Business sponsor Testing Compliance Roles that might already be aligned with the Big Data innovation initiative, as they should be. Roles that need to be aligned with the Big Data innovation initiative if they are not already. Source: Five roles you need on your Big Data team http://blogs.hbr.org/2013/07/five-roles-you-need-on-your-bi/ 19
What are the new career opportunities? By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with 2 deep analytical skills 1 Companies need to spend time upfront to identify the kinds of roles they need to make the Big Data machine run. While different companies will have different talent needs, here are five important roles to staff your advanced analytics bureau: Data Hygienists Data Explorers Business Solution Architects Data Scientists Campaign Experts 1. According to a report published in 2011 by McKinsey & Co. 2. Source: Five roles you need on your Big Data team http://blogs.hbr.org/2013/07/five-roles-you-need-on-your-bi/ 20
I want to be a Data Scientist where do I start? 1. Fundamentals 2. Statistics 3. Programming 4. Machine Learning 5. Text Mining/Natural Language Processing 6. Visualisation 7. Big Data 8. Data Ingestion 9. Data Munging 10. Toolbox Source: More information can be found at http://nirvacana.com/thoughts/becoming-a-data-scientist/ 21
Thank you Sheetal Patole Director Australia Office: +61 (2) 8266 3977 Mobile: 0414257516 Fax: +61 (2) 8286 3977 sheetal.patole@au.pwc.com To learn more visit us at: http://www.pwc.com.au/analytics/ February Big 2014 Data 22
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