Deriving Competitive Advantage from Big Data

Deriving Competitive Advantage from Big Data Insights from executives across Australia and New Zealand WHITE PAPER

SAS White Paper Table of Contents Executive Summary.... 1 Finding the Elusive Value in Big Data... 2 Banking and Financial Management... 3 Targeted marketing to increase the number of high-value customers... 3 Credit risk management... 3 Analytics to combat fraud... 3 Communications... 4 Offer optimisation.... 4 Government... 4 Finance and tax offices.... 4 Criminal justice and public safety... 5 Health Care... 5 Predicting and managing health outcomes.... 5 Insurance... 5 Ratemaking... 5 Retail and Manufacturing... 6 Inventory management.... 6 Five Steps to Big Data Project Success... 6 1) Acquire the Right Talent to Mine the Data... 6 2) Quickly Identify the Value in the Data... 7 3) Manage the Data from End to End.... 9 4) Improve the Quality of Your Data... 10 5) Apply Intelligence at the Right Time... 11 So Where Do You Begin?... 12 How to Get Started... 13 Conclusion... 13 Content for this paper, How to Derive Competitive Advantage from Big Data: Five Key Steps to Delivering Big Data Value with High-Performance Analytics, was provided by Vincent Cotte, Product Marketing Manager, SAS Institute Australia.

Deriving Competitive Advantage from Big Data Executive Summary More than ever, executives in information technology and lines of business are working to meet the increasing demands for data that can provide insights to drive competitive advantage for their organisations. From July to December 2012, SAS conducted an industrywide survey with executives across Australia & New Zealand to understand how today s enterprises are managing the big data phenomenon. It s become clear that if you are not looking for the value in big data, your competition is.. Insight from big data holds the key to competitive advantage over the next two years. Respondents were asked to select one of the following (n=153): 80 70 60 50 40 30 20 10 0 Strongly agree Somewhat agree Neutral Somewhat disagree Strongly disagree Did not answer Figure 1: 88 percent of organisations surveyed agreed that big data holds the key to competitive advantage over the next two years. Industry analysts have adopted three V s to describe today s data landscape: volume, variety and velocity. Volume. Organisations today are storing more and more data in disparate systems. Volume discussions have also included how to handle access to large data sources external to the enterprise, such as census information and social conversations. Sensor and machine data, as well as increased transaction data volumes, are also driving increases in data volumes. This growing volume is. placing pressure on existing platforms and systems that store, analyse and produce actionable insights. Variety. An expanding universe of data types and sources has challenged existing relational databases to efficiently store and retrieve information. Especially important is the consideration of insight that can be found in new unstructured and semistructured data sources such as Web logs, blogs, email, online conversations and voice mail. High-performance analytics is for organisations of all sizes. At SAS, we define big data as the junction where volume, velocity and variety of data exceeds an organisation s storage or compute capacity for accurate and timely decision making. Jim Davis Chief Marketing Officer and Senior Vice President, SAS 1

SAS White Paper Velocity. Data flows have accelerated in all directions from operational and transactional systems. Combined with growing expectations from citizens and consumers for instant satisfaction, this is pushing enterprises to bring intelligence to the point of interaction to improve the experience. Reacting quickly enough to deal with velocity is a challenge to most organisations. SAS customers see the need to add a fourth V: Value is critical and holds the key to aligning big data opportunities with business performance.. When is your organisation working on projects that leverage big data? Respondents were asked to select one of the following (n=153): (7) Did not answer (37) Don t know (14) >12 months (34) Project in place (20) Currently piloting (21) Next 6 months (20) 6-12 months Figure 2: Over the next 12 months, 62 percent of organisations will have a big data project in place. In this paper we draw from more than 36 years of experience delivering THE POWER TO KNOW to provide an essential guide on: Finding the elusive value in big data using high-performance analytics. Key challenges for organisations in deriving competitive advantage from big data. Five key steps to success in analysing and deriving value from big data. After reading this paper you will have a clear idea of how to harvest value from. relevant data for more accurate and timely decision making. Finding the Elusive Value in Big Data The explosion of data isn t new, although it has accelerated over the last decade. Things that have changed are the velocity of growth, the diversity of the data and the imperative to make better use of information to transform the business. Along with the expansion in data volume and velocity has come an increase in computing capability, allowing analysts to run complex analytics over large amounts of enterprise data in seconds. and minutes. This approach has drastically reduced the time required to: Identify and apply insight to improve business performance. Produce precise insights with the world s best analytics. Deploy an analytics infrastructure built to scale into the future. 2

Deriving Competitive Advantage from Big Data Below are examples of organisations and industries that are finding the value in big data. Banking and Financial Management Targeted marketing to increase the number of high-value customers Banks today are hoping to profitably grow consumer and business revenues through cross-sell and up-sell techniques that increase wallet share through the number of products per customer and also by creating lasting relationships that prevent attrition. Using high-performance analytics, one bank analysed transactions from more than 7 million of its most profitable customers to design more targeted and relevant marketing campaigns. The result was an additional 80,000 high-value accounts. High-performance analytics is enabling banks to treat each customer interaction as unique, thereby increasing retention, growing revenue and improving profits. How is your marketing department geared to profitably improve the customer experience for each individual consumer or business client? Credit risk management Increased regulatory pressures combined with increases in the amount of transactional data have made the analysis of credit risk exposure very time-consuming and resourceintensive. High-performance analytics can reduce risk calculations for large portfolios from days and weeks to hours and minutes. One global bank cut its loan default probability calculation for more than 10 million loans from 96 hours to four hours. In addition, the credit scoring process for more than 400,000 loans was reduced from three hours to 10 minutes. Timely decisions about defaults help minimise losses and. can identify new growth opportunities within a bank s loan portfolio. Could your credit risk application deliver more profitable and accurate insights? Analytics to combat fraud Analytics can move front-line fraud management from detect-and-defend to preventand-eradicate. While many banks continue the traditional approach of rule-based detection, sampling and post-event notification, Commonwealth Bank of Australia analyses big data to neutralise fraud in real time at the point of transaction*. Commonwealth Bank of Australia reliably monitors data from more than 11 million customers, 31 source systems and 15 million transactions per day to predict the likelihood of fraud activity for any given transaction before it is authorised. The bank. can do this up to 250 times per second across multiple channels and products, receiving answers within 40 milliseconds of the transaction being initiated without transaction sampling. The bank doesn t want to decline nonfraudulent transactions. for valued customers, so the importance of accuracy is crucial to maintaining a positive customer experience. Within months, Commonwealth Bank of Australia experienced. a 95 percent efficiency increase in check fraud detection. Are you doing enough to. protect your customers? *Commonwealth Bank of Australia case study sourced from: Fraud Detection is more than cool by John Geurts, Intelligence Quarterly: Fraud is Everyone s Problem, Journal of Advanced Analytics 3Q 2012. 3

SAS White Paper Communications Offer optimisation To maximise the business value of every customer, communications service providers need to deliver personalised offers that take into consideration all factors that affect the customer experience. High-performance analytics allows operators to quickly create. and execute campaign offers designed to address several competing business objectives, including: Attracting customers to high-revenue smartphones, tablets and. broadband service. Preventing a handful of users from consuming so much bandwidth that service. for other customers is degraded, requiring additional capital expenditure spending. Driving traffic to the least expensive network resources (e.g., from expensive macrocells onto cheaper Wi-Fi and microcells). With millions of customers and hundreds of potential offers, solving the offer assignment problem is complex, especially when considering business constraints, organisation capacity, contact strategies, customer preferences and the likelihood of response. High-performance analytics maximises the potential of these offers by enabling the move away from sampling and assigning customers to broad segments to instead using all data to create more granular and personalised offers based on factors such as reference, context and location. This approach means using more variables, including data from network resources and ecosystem partners. The new environment allows the continuous optimisation of business processes for superior customer experience by running more models of increasing complexity with greater scale and lower operational costs. A large telecommunications provider realised a 30 percent reduction in campaign costs, three times better response rates and four times higher campaign ROI. Government Finance and tax offices Tax offices are often asked to answer questions for government officials (e.g., if you increase income taxes by one point, what is the impact on the budget?). Many revenue statistics departments use high-performance analytics to model and assess the implications of proposed budget options and present their findings. High-performance analytics can be used to improve a city or country s financial status by identifying and prioritising uncollected debt that is owed, as well as identifying new fraudulent tax filings. The more information (both structured and unstructured text data) that can be analysed quickly, the sooner tax organisations can identify fraudulent cases; simulate the effects on a tax code; run scenarios to quantify the impact of changing the macroeconomic factors on the health of the community; and capture information regarding the likelihood of payment through various models when changing tax codes. 4

Deriving Competitive Advantage from Big Data Criminal justice and public safety Law enforcement organisations are not only responsible for detecting, preventing, responding to, and solving crimes against people and property in their communities, they are also tasked with a more global approach to policing that challenges resources, skills and personnel limits. With high-performance analytics, law enforcement personnel can more quickly identify and focus on changing criminal dynamics, enabling police to optimally allocate finite, scarce resources to maximise strategic and operational efforts. They are also able to integrate vast amounts of data, both structured and unstructured (social media, text, etc.), to better anticipate and prevent criminal activity. Officer and community safety can be enhanced if investigators can move from a reactive to a more proactive approach. Health Care Predicting and managing health outcomes BioGrid Australia, a health care research provider, aggregates big data from different sources such as hospitals and research institutions. Clinical researchers anywhere can now quickly access secure and reliable data to make more informed decisions on health outcomes. One example is how the Australian government could determine which health projects to fund. The analysis showed the importance of the bowel screening program, indicating that early diagnosis of bowel cancer not only significantly improved the chances of patient survival, but also reduced the cost of treatment per patient. Insurance Ratemaking Product pricing or ratemaking is the process of establishing rates charged by an insurer for accepting the risk. Insurance companies, and specifically actuaries, rely heavily on historical data to predict future behaviour or create premium rates for pricing products. Insurers are using advanced analytical techniques such as generalised linear modelling for pricing and monitoring price efficiency. In the past, actuaries often have relied on using a subset of historical data to run pricing models because the time it takes to prepare the data and run the models has been so time-consuming. To combat these problems, insurers are now turning to high-performance analytics to provide faster processing on the growing volumes of available data. 5

SAS White Paper Retail and Manufacturing Inventory management Retailers and manufacturers with significant volumes of inventory spread across a large variety of locations and distribution levels can benefit from high-performance analytics. Some inventory allocation decisions are time-critical, and significant revenue loss and decreased customer satisfaction can occur by not having the right item in the right place at the right time. While most demand forecast prediction is done on historical shipping data, the availability of point-of-sale (POS) data makes it possible to perform in-memory processing of historical plus time-latency-adjusted POS data on very large data sets. Five Steps to Big Data Project Success 1) Acquire the Right Talent to Mine the Data What key challenges, if any, does your organisation face when trying to make decisions utilising big data? Respondents were asked to tick all that apply (n=153): 70 60 50 40 30 20 10 0 We do not have the right skills Data quality is poor Higher cost of mining data Insufficient storage capacity Inability to meet business time-tomarket demands Unable to analyse unstructured data We do not face any challenges Unknown Figure 3: 41 percent of organisations lack the skills to derive value from big data. Almost half of all organisations surveyed believe that they lack the skills necessary to extract value from big data. Deriving value involves overcoming two challenges. The first challenge is largely technical. Organisations need the right technology and the right data management and warehousing knowledge to manage their expanding information assets in near-real time. While this is still an emerging area and direct experience can be scarce, most organisations are relatively comfortable with the steps necessary to build maturity in this space. Common skills targeted for improvement include awareness and knowledge of how to use technologies such as big data visualisation tools, Hadoop, and other massively parallel storage and processing tools. 6

Deriving Competitive Advantage from Big Data The second is cultural. Organisations need to know how to identify and measure actionable insights from big data. This involves more than just knowledge of algorithms or advanced mathematics; it requires the creation of a culture focused on deriving value from data as well as the mandate to do so. This unique combination of skills is what differentiates a statistician from a skilled data scientist. A skilled data scientist must focus on translating insight into actionable and measurable value, and it is this cultural gap that many organisations struggle with. Key skills that need to be acquired and developed include business acumen, value measurement, innovation and operational analytics. Organisations are investing in business analytics training courses to learn new techniques and capabilities for creating competitive advantage and ensuring the highest return on analytic investments. 2) Quickly Identify the Value in the Data Capabilities No Capability Some Capability Satisfactory High Capability Master Operationalising business intelligence into your customer facing channels and business processes (n=142) Managing the end-to-end information / decision value chain (n=140) Ability to increase decision making accuracy by analysing more data (n=140) Ability to explore, visualise and generate insight into data on-demand (n=141) 15 51 52 18 6 11 51 60 14 4 8 48 53 25 6 13 55 45 21 7 Table 1: 82 percent of organisations have only zero to satisfactory capabilities to collect, analyse and derive value from big data. While the ability to store information is more and more affordable, data management continues to increase in complexity and cost. When investing in more data storage, we must consider its incremental value. Data visualisation tools allow users to quickly discover and identify patterns in the data. Analytical intelligence allows you to identify the strength of relationships between variables. Once you can see the impact that variables have on business metrics such as customer churn, propensity to buy and fraudulent patterns, the value of storing and operationalising the data is easily justified. Leading organisations are combining high-performance data visualisation and predictive analytics to analyse new data sources, creating new views on the fly to accelerate the discovery process and delivering a more flexible and cost-effective approach to identifying and implementing big data value. 7

SAS White Paper Figure 4: With SAS Visual Analytics, users of all types can easily explore huge amounts of data at unprecedented speeds. 8

Deriving Competitive Advantage from Big Data 3) Manage the Data from End to End Which of the following statements best describes your organisation s approach to data maturity? Respondents were asked to select one of the following (n=153): 50 45 40 35 30 25 20 15 10 5 1 We have a defined data management strategy focusing resources on collecting and analysing valuable data We understand the value of data and are marshaling resources to take better advantage of it We collect a large amount of data but do not consistently maximise the value We collect data but it is severely underutilised We do not prioritise data collection Did not answer Figure 6: 82 percent of organisations don t have a data management strategy to collect and analyse data from existing sources. Delivering insight from data to the key channels of your enterprise in a timely fashion requires management and automation of the data-to-insight value chain. The data governance process needs to address the entire data value chain. This starts by capturing the data from where it enters your organisation (customers, online, machine data, etc.), storing the data, analysing the data and then using that insight to make better decisions. Information technology departments must have the necessary extract, transform and load capabilities. in place to handle not only the size of the data and the speed at which it arrives, but also the ability to manage new types of data coming in. The automation of this process will. help to drive efficiency and bring down the high cost of mining data for insight. 9

SAS White Paper 4) Improve the Quality of Your Data What key challenges, if any, does your organisation face when trying to make decisions utilising big data? Respondents were asked to tick all that apply (n=153): 70 60 50 40 30 20 10 0 We do not have the right skills Data quality is poor Higher cost of mining data Insufficient storage capacity Inability to meet business time-tomarket demands Figure 7: 39 percent of organisations feel that data quality is the biggest barrier to big data value. Unable to analyse unstructured data We do not face any challenges Unknown Data quality is a vital component of making more accurate decisions. The adage of garbage in, garbage out has never been more relevant. The lack of a data quality strategy to ensure that data is accurate will result in failure. Data quality needs to be applied to the data source on entry, during the processing and when it is retrieved.. The reality is that data is never perfect, but analytics provides capabilities to estimate and focus on key variables during analysis to ensure more accurate insight. Beginning with a focus on data quality, improvements can provide a direct effect on. key performance indicators such as customer experience, marketing campaigns. and risk calculations. 10

Deriving Competitive Advantage from Big Data 5) Apply Intelligence at the Right Time How important to your organisation is the ability to apply analytics to make decisions in real time? Respondents were asked to select one of the following (n=154): 80 70 60 50 40 30 20 10 0 Very important Somewhat important Not important Don't know Did not answer Figure 8: 88 percent of organisations recognise the importance of applying analytics to real-time decision making. The impact of improved decision management is highly related to the timeliness,. context and accuracy of information. The application of analytics provides insight about the organisation but to achieve the best effect, that insight must be embedded within the operations of the business. Leading organisations are not just modelling for insights they are embedding those insights into key business applications and customer-facing functions. Some examples are call centres, ATMs, websites, IVR systems, and marketing and sales teams. 11

SAS White Paper Capabilities No Capability Some Capability Satisfactory High Capability Master Operationalising business intelligence into your customer facing channels and business processes (n=142) Managing the end-to-end information / decision value chain (n=140) Ability to increase decision making accuracy by analysing more data (n=140) Ability to explore, visualise and generate insight into data on-demand (n=141) 15 51 52 18 6 11 51 60 14 4 8 48 53 25 6 13 55 45 21 7 Table 2: 46 percent of organisations struggle to empower their customer-facing channels with business intelligence. So Where Do You Begin? First of all, look for areas where you can bring structure to a key decision-making process that positively affects the customer or citizen. The challenge is how to do this profitably, and that s where business analytics comes in. Here are some examples to jump-start your thinking: Personalised offers at the point of sale. An Australian telecommunications company is making real-time personalised marketing offers to a segment of one, at the point of sale. The relevance in offers has seen a 15 per cent increase in response rates by including detail on behaviour and type of transaction to bring more context and relevance to the offer. Real-time fraud protection. A leading Australian bank uses SAS to prevent fraudulent transactions in real time, protecting 100 percent of credit card transactions for its 11 million customers all of the time. Compare this approach to the bank s competitors, who sample a small set of transactions after the event to detect fraud. It s clear to see where the differentiation is, as their debit card products improve security, reduce financial loss due to fraud and improve the customer experience. This involves the analysis of hundreds of debit card swipes every second with a response time on average of 40 milliseconds. This ensures the bank s customers have higher confidence in security, all delivered using all. of the data all of the time without being obtrusive. Predictive maintenance of assets affecting customer experience. The ability to analyse machine data to search for emerging historical fault patterns (e.g., electricity grids, telecommunication towers, ATMs and production lines) can help prevent downtime and produce a better customer experience. Individual risk-based pricing in insurance. Highly personalised policies can help differentiate you in the market while providing greater profitability in the long term. 12

Deriving Competitive Advantage from Big Data Disruption management in transport and logistics. The ability to understand the ripple effect of an error and re-coordinate different assets and resources enables you to achieve the best possible outcome for the customer and your bottom line. in real time. How to Get Started 1. Identify an area of the business that could benefit from better insights from big data and high-performance analytics. 2. Identify the data sources, locations and business rules that govern that data. 3. Look at how data visualisation and predictive modelling capabilities can help quickly analyse and identify value and understand the impact of the data on business performance. 4. Assess your skills, define the gaps and work to fill those gaps either with new resources or by re-allocating current resources. Conclusion It s clear that most organisations agree that big data does hold the key to competitive advantage. Big data provides the opportunity to gain a complete picture of a complex problem not just a probable solution or a possible way to harness opportunity, but. a precise, evidence-based approach to addressing large issues. However, the executives surveyed for this paper also seem to agree that there are challenges to making the most of their big data, including: Finding the right talent and technologies. Being able to quickly visualise and identify patterns in data. Managing data from the time it enters the system, through storage, analysis. and producing valuable insights. Improving the quality of all data. In fact, most of those surveyed seemed to. feel this is one of their largest barriers to gaining value from big data. Applying intelligence at the right time in other words, embedding insights. directly into operational processes. SAS can help organisations get a handle on big data, and even more importantly, extract meaningful and valuable information from it. Big data exacerbates the need for improving data quality and data governance as well as maintaining security and control across the analytic and decision-making life cycles. SAS offers industry-leading advanced data quality and data management capabilities that enable you to improve the quality of your big data, or any size of data for that matter, and manage it as the strategic, core asset it is. 13

SAS White Paper In-memory data visualisation and analytics provide answers in a fraction of the time traditional tools used to. SAS Visual Analytics enables you to explore large amounts. of data very quickly to identify patterns or trends for further analysis. It is a solution that easily scales to address any size data you may have. Everyone in your organisation can quickly and visually understand the data, and the solution can deliver information to Android tablets and the ipad for decision makers to explore, even when they are. on the go. SAS High-Performance Analytics Server, one of SAS most powerful solutions, enables you to use all of your data (structured and unstructured) to run more models faster, ask more interesting questions and gain better insights so you can act more quickly than your competition. It provides sophisticated modeling capabilities, data mining, text mining, forecasting, and optimisation, along with massively parallel processing, to deliver valuable information at breakthrough speeds. SAS solutions go far beyond query, reporting and descriptive statistics they provide a sophisticated environment of world-class predictive analytics capabilities and optimised decision-making capabilities that can be automated to quickly solve complex, realworld business problems. Analytics truly affects organisations when insights are operationalised and embedded in business processes. This enables more relevant, accurate and timely decision making to improve customer experience and increase. the return on your data and analytics investments. 14

About SAS SAS is the leader in business analytics software and services, and the largest independent vendor in the business intelligence market. Through innovative solutions, SAS helps customers at more than 60,000 sites improve performance and deliver value by making better decisions faster. Since 1976 SAS has been giving customers around the world THE POWER TO KNOW. For more information on SAS Business Analytics software and services, visit sas.com. SAS Australia +61 2 94280428 sas.com/australia SAS New Zealand +64 4 917 6800 sas.com/newzealand SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. indicates USA registration. Other brand and product names are trademarks of their respective companies. Copyright 2013, SAS Institute Inc. All rights reserved. 106321_S99099_0313