Strategies For Setting Up Your Organisation For Success With Big Data. Kevin Long Business Development Director Teradata

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1 Strategies For Setting Up Your Organisation For Success With Big Data Kevin Long Business Development Director Teradata

2 Agenda Developing a big data strategy and plan that is aligned with your organisation s overall business goals today and in the future Exploring critical success factors for developing and delivering your big data strategy Looking at the human dimension: do you have the right technical, analytical and governance skills to get the most out of big data? Identifying potential constraints - what are the hurdles that need to be overcome for the strategy to be achieved in full? Translating your big data strategy into implementation - developing a roadmap with set milestones 2 1/4/12 Footer

3 Strategy: technology is the easy(er) bit people and their expectations are hard 3 1/4/12 Footer

4 The Big Data Gap 1.Speed of insight 2.Scaling up to handle all data 3.Do More Analytics 4.Silos of Data, Technology & Skills 4 1/4/12 Footer

5 Expectations and Realities 55% 66% collection and analysis of data underpins strategy and decision making big data management not viewed strategically at senior levels 5 1/4/12 Footer

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7 Big Data Vision What does big data really mean to your organisation What is your immediate objective What do you have today What are you missing Can you buy it / hire it / borrow it Can you work around it Collaboration: data users tools Hypothesis testing: experiment not pilot 7 1/4/12 Footer

8 Big Data Questions existing questions in existing businesses, with a focus on improved efficiency and operations new business questions in existing businesses, with a focus on opportunities for growth 8 1/4/12 Footer new questions in new businesses, with the goal of reshaping the competitive landscape

9 Reshaping Competition Customer Product Ecosystem 9 1/4/12 Footer

10 Big Data : Creating Value information transparency: usable at much higher frequency more accurate and detailed performance increased collection of data: exposes variability and performance conduct controlled experiments: better management decisions shift from basic low-frequency forecasting to high-frequency nowcasting to adjust their business levers just in time. ever-narrower segmentation of customers more precisely tailored products or services sophisticated analytics: substantially improves decision-making improve development of the next generation of products sensors embedded in products to create innovative after-sales service offerings 10 1/4/12 Footer

11 Big Data : Big Decisions Point Big data can complicate big decisions Good decisions come from clean data Big data provides little insight Business needs consistent strategies Counter-Point Big data can drive big decisions Good decisions come from sound analysis It s the little things that matter Business needs pliable strategies 11 1/4/12 Footer

12 Start with Objectives A Business Vision Big Data Scope Illustrative questions Be clear and concise and very specific About projects Hypothesis Identify Stakeholders Vision > Roadmap Technology avoid huge projects Experimentation which data is the right data which analysis is the right analysis Defined measures / KPIs Identify, Understand and Assess Shortfalls 12 1/4/12 Footer

13 People & Skills Data is so widely available and so strategically important that the scarce thing is the knowledge to extract wisdom from it. Hal Varian Chief Economist, Google 13 1/4/12 Footer

14 The Elusive Data Scientist Academic qualification Practical experience Communication skills Customer focus 14 1/4/12 Footer

15 The Elusive Data Scientist What is the question? understanding (and articulate) an organisation s questions, problems, or strategic challenges and translate them into the design of one or more data analysis Better to have an approximate answer to the right question than a precise answer to the wrong question. John Tukey 15 1/4/12 Footer

16 What makes a Data Scientist? General skills include: excellent analytical capabilities machine learning data mining statistics maths algorithm development writing coding data visualisation understanding multi-dimensional database design and implementation Specific skills include: Technologies to handle big data 16 1/4/12 Footer

17 What makes a Data Scientist Hadoop and related technologies MapReduce NoSQl databases MSc in Data Science. Knowledge of languages such as SQL MDX R Functional and OOP languages such as Erlang and Java General characteristics include: Insatiable curiosity Interdisciplinary interests Excellent communication skills 17 1/4/12 Footer

18 Critical Success Factors Clear business objectives Data awareness : e.g. quality Staff readiness IT infrastructure Set the right KPIs for Big Data Begin with small, manageable projects Ask the right questions 18 1/4/12 Footer

19 Conclusions: part 1 A 360 degree view won t exist for big data Focus on Key business drivers that need quantification Think about costs and opportunities And opportunity costs Smaller targeted projects Big Data doesn t begin with data; It starts with clearly articulated problems and opportunities more data does not guarantee better decisions but the right data properly analysed and acted upon often does 19 1/4/12 Footer

20 Conclusions: part 2 Agility is key: Recognise and be alert for change Coexistence with existing analytics Experiment don t prevaricate Get started quickly Exploration drives discovery Turn insight into action Express insight for Execs Show success or learning / repeat Communicate widely Big doesn t mean big - sandbox 20 1/4/12 Footer

21 Strategies For Setting Up Your Organisation For Success With Big Data Kevin Long Business Development Director Teradata