Big Data; from insight to impact
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1 Big Data; from insight to impact Accountex 14 th May 2015 Peter Simons CGMA.org
2 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 2
3 Non-financial assets (intangibles); the most important determinants of value What data do you use to measure & manage your intangibles? 3
4 How to survive and thrive; The new normal requires better information Financial results are an outcome. Performance has to be managed along the value chain. 4
5 Bias in decision making 1. Self Interest not objective 2. Heuristic everybody s happy with it 3. Group Think dissenters not listened to 4. Saliency a good past experience assumed to apply 5. Confirmation credible alternatives not considered 6. Availability only information to hand considered 7. Anchoring historic figures set expectation 8. Halo effect promoter or a link gives confidence Source: Before you make that big decision; Kahneman, Lovallo & Sibony; HBR June
6 Definition of Management Accounting: Communication and use of accounting and management information Trust Influence Preserving value Creating value Relevance Value Sourcing and analysis of accounting and management information Ref: CGMA GMAPs
7 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. Shared Services & BPO 6. New skills needed? 7. Conclusion; what next? 7
8 BI Architecture (conceptual) 8
9 What is Big Data? Big data refers to datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze. (Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute, June 2011) 9 9
10 Big Data? Difficulty in accessing or analysing Big Data Enterprise Data Financial Data Scale and Complexity of Data
11 Emerging view of BI and Big Data Source: Gartner
12 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 12
13 Where would better data be of most value? 13
14 Challenges in harnessing Big Data 14
15 How organisations are using data 15
16 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 16
17 Finance Transformation, the changing role of the management accountant Accounting Analysis Business Overhead Valued 17
18 Big Data the role of finance? A minority (23%) report no role in anlaytics Another minority (21%) claim a leading role A 55% majority say finance partners with others (55% = 37% + 9% + 9%) Interviews agree on a collaborative role
19 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 19
20 The competencies a business needs for Big Data Commercial Data culture Value creation Conformance Performance Data management Analytics Technical
21 Opportunities for management accountants Commercial Data Champion Business Partner Data culture Value creation Conformance Performance Data management Analytics Data Manager Data Scientist Technical
22 The Segregation of accounting & finance: Finance Director Business partnering Globalisation Analytics, ad hoc analysis Routine Analysis Standard reports Transaction processing Skills & technology enables
23 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 23
24 Five traits of the data enabled CFO 1. Have a clear sense of what customers care about most, and ideas about how to track this. 2. Able to identify which data points are useful in understanding what drives the business. 3. Able to embrace new forms of data, and creative ways to incorporate this into business decision-making. 4. Explore new ways to interpret data to better inform management. 5. Comfortable with uncertainty, including the reality that big data may not provide definitive answers. 24
25 Business Model building blocks Value Proposition Key Processes Resources and Relationships Cost base Revenues Value Generated 25
26 From Data to Insight to Impact Lots of complex data Analytical Insight Enterprise data No data Data capture Reports Analysis Commercial Influence Impact Insight Idea
27 Business Partnering Skills Effective business partnering relationships Empathy with business Compelling communication Preparedness to challenge Contribute insights into drivers of cost, risk and value Passion for business Commercial curiosity Professional objectivity CPD Ability to integrate, apply and communicate Business understanding Analysis skills Accounting skills
28 Agenda 1. Context 2. Big Data what s it all about? 3. What does it mean for business? 4. What is the role of finance? 5. New skills needed? 6. Conclusion; what next? 28
29 dunnhumby s advice for smaller business Matthew Keylock, Global Capability Managing Director of Data 1. Start with the data you have. 2. Identify data owners, encourage sharing and look for quick wins for the business as a whole 3. Segment the customer base and continuously review the understanding of trends and behaviours 4. It is fine to buy in 3 rd party data to supplement own records, e.g. for prospecting but not the starting point 5. Implement data projects in phases 29
30 Seven skills finance professionals need (to convert analytical insight to commercial insight and help manage performance through to impact): 1. Strategic understanding of the business 2. Understanding of data sources and analytic techniques 3. Relationship building (IT, Data and Business colleagues) 4. Commercial curiosity; ability to ask the right questions 5. Business acumen; ability to recognise an opportunity 6. Communication skills; present compelling insights 7. Ability to influence and contribute to leadership And/Or Advanced data and analytical skills For those few with ambition to become data scientists. 30
31 cgma.org
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