Next-generation business analytics

Transcription

1 Next-generation business analytics Lou Celi President Oxford Economics April 10, 2014

2 About Oxford Economics Oxford Economics is a world leader in global forecasting and quantitative analysis. Our worldwide client base comprises over 850 international corporations, financial institutions, government organizations and universities. Founded in 1981 as a joint venture with Oxford University, Oxford Economics is now a leading independent economic consultancy. Our link to Oxford University is still present today through our management board, empirical research approach and access to Oxford scholars. Headquartered in Oxford, with offices around the world, we employ more than 150 people, including 90 economists, and a network of 500 contributing researchers. The rigor of our analysis, caliber of staff and affiliation with Oxford University make us a trusted resource for decision makers.

3 Measuring the value of analytics: our methodology To quantify the economic value that companies achieve through analytics, Oxford Economics and Cognizant conducted a survey of 300 senior executives. Sample: Industries: Insurance, banking/ financial services, life sciences, healthcare, communications/ information/ media/ entertainment, technology, consumer goods/retail, and manufacturing. Size: Revenues in excess of $500 million. Location: US, UK, Germany or France. Analytical output: A survey model estimating the economic value that respondents attained from the use of analytics. An industry model assessing the ROI on analytics achieved by companies within key industries.

4 Big data is getting much bigger We are in the analytics era, one in which insight and foresight create huge competitive advantages. Data is everywhere in business: Inside and outside of the company End-to-end in corporate processes Embedded in products and services Surrounding customers and suppliers Constantly changing in real-time 2.5 quintillion bytes created each day (2.5 quintillion pennies would circle the earth 5 times!) 60% growth in data annually 35 zettabytes of data generated annually by 2020 (1 zettabyte = 1,000,000,000,000,000,000,000 data bytes) Big Data has been with us forever. The big change is we are getting data from more stuff. Randy Krotowski, VP of Global Information, Caterpillar Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

5 The new economics of meaning-making Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

6 Meaning Makers expect the largest gains Meaning Makers: Data Explorers: Data Collectors: Apply analytics across multiple business functions Use analytics across select business functions Just starting to adopt analytics Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

7 The ROI of business analytics Estimated percentage impact of business analytics on revenue and costs over the last financial year Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

8 Meaning makers across industries Logistics Analytics helped UPS eliminate 85 million miles per year from drivers routes and reduce fuel consumption by 8 million gallons. For UPS, shaving one mile per driver per day is worth $50 million in annual savings. Entertainment Netflix s ability to marshal its sophisticated knowledge of customer likes helped the firm design House of Cards, a successful political thriller starring Kevin Spacey. Manufacturing Ford Motor built tools for fleet managers to remotely monitor engine performance and created smart systems to help dealers determine the optimal car models for their lots. Ford received the 2013 INFORMS Prize for their analytical initiatives. Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

9 Incorporating analytics into business processes Meaning makers embed analytics in all that they do from setting strategy and making products to driving sales and managing risks. More than 60% of study respondents say that they currently add business value through analytics for sales, financial management, business operations, overall revenue growth and strategy-setting. More than half of the surveyed executives are adding value through more effective risk analysis, cost control, product development and supply chain management. Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

10 From insight to foresight Growth in predictive analysis, today vs. in 24 months In today s uncertain global environment, where disruptive change can come suddenly, companies are turning increasingly to predictive modeling and forecasting. Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

11 Analytics challenges Given the economic benefits of analytics, why aren t more companies moving rapidly to build their capabilities? As with any major business shift, there are a variety of organizational hurdles, such as the lack of talent and management support. Significance of obstacles in deploying analytics Uncertain ROI Lack of talent Business-IT Gap 33.0% 32.3% 32.3% To overcome these obstacles, companies are actively recruiting data specialists from inside and outside their industry, developing analytical training programs, using new software, and hiring outside consultants. Source: The Value of Signal (and the Cost of Noise), Cognizant and Oxford Economics, 2014

12 Our analytical approach: a case study

13 Finding and gathering global data The first step in our analytical process is to identify and gather the most reliable current and historical data. Supranational IMF/World Bank, UN, Bank of International Settlement, International Labor Organization, etc. Local Eurostat, UK National Statistics Regional Accounts, US Census Bureau by State, etc. Our team of data specialists work with our economists to pull data from a wide range of public and private sources. We also rely on specialist data aggregators such as Haver Analytics, Thomson Reuters and FactSet to help us collect and deliver data. National For the US for example: BEA, BLS, US Treasury, Federal Reserve, etc. OXFORD ECONOMICS Private Fitch, Moody s, S&P, ISM, Markit, LMC Automotive, etc.

14 Ensuring data consistency To ensure data consistency across multiple locations, our economics team uses a series of methods to codify and standardize data: Adjust for seasonality Create common base years Re-index data Set comparable ratios Identify sources of consistent data Where there are data gaps, our economists provide data estimates and extrapolations. We include derived series, along with the original official data in our models, particularly for emerging markets, where data can be inconsistent.

15 Creating a global econometric model The Global Economic Model was developed in the early 1980s by leading economists from Oxford Economics and Oxford University. John Walker, today s Chairman of Oxford Economics, worked together with Sir David Hendry, Dr. John Muellbauer, and other early pioneers of econometric thinking. Over the past 30 years, Oxford s global model has gone through continuous improvements. For example, after the global 2008/09 recession, we extended the model s coverage of interest rates, balance sheet variables, credit ratings, and feedback effects. Over 100 organizations use our model for forecasting, stress testing and scenario analysis. It has also been used as a teaching tool at several universities. The model design is flexible so that it can be used in conjunction with other models. At Oxford Economics, it sits at the heart of our suite of industry, city and economic impact models.

16 Economic drivers behind the model The Oxford model is an eclectic model that replicates the inner workings of the global economy: Keynesian in the short run Monetarist in the long run So increased demand will lead to higher output and employment initially. But eventually that raises wages and prices. As interest rates rise, demand falls again. In the long-run, output and employment are determined by supply side factors, such as investment.

17 Comprehensive geographic coverage The Model covers 47 economies (including the Eurozone) in detail, including many emerging markets, and provides headline forecasts for another 30 countries. The remaining countries are covered in 6 blocs : OPEC, Eastern Europe, Africa, Latin America, rest of OECD, rest of World.

18 Linking distinct country models Trade volumes World trade for each country is a weighted average of the growth in total goods imports (excluding oil) of all other countries. The weights are thus the relevant coefficients in the trade matrix. Competitiveness IMF relative unit labour costs where available. Relative prices elsewhere. Trade prices Interest rates and exchange rates Commodity prices Oil depends on supply/demand balance Metals on industrial growth Capital flows project under way to model bilateral capital flows for major blocs.

19 The equations behind the model The basic principle behind the Oxford model is that economic trends and policies work in combination to drive the growth of an economy. This can be expressed as a formula: Y = C + I + G + NX Y = C = I = G = NX = GDP Consumption (function of income, wealth, prices and interest rates) Investment (function of confidence and interest rates) Government spending (function of revenue and interest rates) Net exports = exports imports Our economists input initial assumptions into our model equations. Some variables, such as monetary policy, are exogenous. The model then runs through millions of calculations to solve across all country models to arrive at an equilibrium level.

20 Scenario modelling

21 Easy to use Windows based software

22 Getting started To enter the model simply click Run Model

23 Model screen for starting analysis Black numbers show historical values Red number is our forecast

24 Select country or region Our list of country in the drop down box here

25 Select variable from series list Our list of variables in the drop down box here

26 Open architecture enables you to review and change equations Source information Variable equation Variables directly dependent If the variable is endogenous or exogenous

27 Changing the economic assumptions: oil price shock Import from a spreadsheet Can specify change over time period Simply overwrite

28 We can add $50 to the oil price

29 The chart shows the new trend line vs the baseline New forecast Oxford forecast

30 By clicking solve you can run the model Click Solve to create economic output based on a higher oil price

31 Our software runs multiple iterations to solve the model

32 Viewing the results Our data workstation enables you to view different scenarios

33 Slow down from higher prices in the US (net energy importer) Higher oil prices lead to a slowdown in consumption and higher imports As wages adjust to higher prices, a recovery over baseline is seen in the longer term

34 Growth rise in Russia (a net energy exporter) Higher value of exports leads to a surge in growth in the short term But an ensuing global slowdown eventually takes its toll on Russia

35 Macro matters

36 Macro trends can have a major impact on business Waves of macro change A survey of 300 manufacturing executives around the world showed that external trends, such as economic and technology shifts, are significant concerns for industrial firms. Global macro trends can have serious implications for all aspects of your business from customer spending and operating costs to competitive positioning and risk management. Source: Manufacturing Transformation, Oxford Economics and PTC

37 Analyzing macro trends can be challenging Global uncertainty and complexity, combined with a dearth of reliable data, make it difficult to monitor and predict macro trends across countries, industries and urban areas. More than ever, companies need more advanced quantitative tools, deeper forecast data and more timely insights that will help them track external macro trends and assess their future impacts on business strategies. Oxford Economics has developed the skills and tools to track, analyze and forecast macro trends and gauge their impact on business and government.

38 Analytical tools to understand macro trends Oxford Economics has developed a range of techniques for analyzing global macro trends: Trend monitoring Market and product sizing and forecasting Economic impact analysis Scenario framing and modeling Benchmarking and indexing Risk assessment and stress testing Infographics and data visualization Risk analysis techniques now or soon to be used according to a PwC survey: 83% 82% 74% 64% Risk rating system Identification and forecasting of emerging risks Risk dashboard/visualization Scenario planning Horizon scanning or early 62% warning indicators Stress-testing or reverse 53% stress-testing Source: Risk in Review, Oxford Economics and PwC, 2014

39 Analytical best practices

40 How a US conglomerate tracks macro trends every month Monitoring key developments Oxford worked together with a US conglomerate to develop a monthly economic indicators dashboard. This report gives thorough economic and industry analysis on all regions and key countries for their core business groups. The company uses this information for near-term internal planning as well as to support quarterly sales calls and meetings with current and potential investors. +

41 Leading sales indicators for an alcoholic beverages company Developing leading indicators Oxford identified the top five indicators that most closely predict future sales conditions for a European beverages company. A custom model applies weights to each factor, producing a 6-month view so that the company can adjust their strategy. Microsoft Excel Worksheet

42 Hypertherm s sales forecasting model Correlating sales to economic trends For Hypertherm, a US precision tools manufacturer, Oxford produces sales forecasts for 9 product lines across 5 regions. These forecasts and circulated to their business units to inform sales and production planning. An excel-based model using Oxford s macro and industry forecasts generates product sales projections for Hypertherm s products. The model was created by correlating Hypertherm s sales with key economic drivers. The tool allows input of alternative assumptions and comparison with the central forecast. A detailed forecast report outlines the macro and sectoral background and traces out the implications for the forecast of Hypertherm s products and the risks they face. +

43 Economic impact analysis for Rolls Royce Measuring total business impact We worked with Rolls Royce to provide evidence showing the value of their business to the UK government. To measure the full value that Rolls Royce generates, we analyzed four types of impacts: Direct -- employment, tax revenue and other direct economic activity. Indirect -- employment and economic activity supported via the supply chain. Induced -- economic activity by the expenditure of those employed. Catalytic -- improved productivity and performance in other parts the economy.

44 Sales and production forecasts and scenarios for the automotive sector Sales and production for light vehicles In conjunction with our partner, LMC Automotive, we provide forecasts for global sales of light vehicles (up to 6 tons). Along with our baseline forecast, we also predict production and sales levels under four timely and pertinent alternative economic scenarios, such as a China shadow banking crisis, or a slow down in emerging markets. The tool provides seven-year light vehicle production forecasts by manufacturer, make, model, platform and assembly.

45 Indexing labor supply and demand for Hays Global skills index We developed an index for Hays, a recruitment firm, to show the efficiency of skilled labor around the world. The index identifies imbalances between the supply and demand of skilled labor. For each market, we ranked seven key categories and combined them into a country scorecard: Education flexibility Labor market participation Labor market flexibility Talent mismatch Overall wage pressure Wage pressure in high-skill industries Wage pressure in high-skill occupations + Adobe Acrobat Document +

46 An index of key metrics for the Worldwide Web Foundation Web impact index Oxford Economics helped to develop a pioneering Web Index for Tim Berners-Lee s Worldwide Web Foundation. + This is an interactive tool for measuring the Web s growth, utility, and impact on people and nations. Oxford Economics relied on its global network of economists and analysts to provide input into the 80 country models behind the ranking tool.

47 Strategic workforce planning for a consumer goods firm Talent gap analysis In these times of business transformation and demographic change, companies need to assess the impact of their long-term strategies on talent needs. + To help a well-known consumer goods firm analyze trends in Europe, Oxford Economics compared internal talent segmentation and forecasted trends against expected shifts in the supply and demographics of talent in 8 key countries.

48 Infographics and data visualization Infographics, dashboards and data workstations Providing at-a-glance analysis of key trends and their impacts. +

49 Interactive infographic Interactive analysis A clickable dashboard created for SAP to show survey data on the cloud and the connectivity revolution. An engaging interface to draw in the user. +

50 Conclusion

51 Calls to action Every company is different, so there is no single right answer, but there are several clear mandates for companies to succeed at this inflection point. Act now to become a Meaning Maker. In today s new business environment, companies will need to leverage analytics fully to drive performance and stay relevant. Reimagine work at the process level. The imperative to use big data is too broad to be meaningful. Instead, focus on improving specific business processes. Don t overlook small data problems. Start by exploring your internal and external data. Do you have what you need? Is it reliable and consistent? Are you deriving right analysis? Look to gain foresight, not just insights. Meaning makers do just find insights in data trends, they use analytics to anticipate future market trends that will affect their business. Build a business analytics ecosystem. With analytical skills in high demand and short supply, it is not easy to find the right talent. Don t plan to go it alone, build an ecosystem of suppliers/consultants to support in-house teams.

52 Lou Celi President Americas P: