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3 30022 assessing_yourbusinessanalytics

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  • 1. Assessing Your Business Analytics Initiatives Eight Metrics That Matter WHITE PAPER
  • 2. SAS White Paper Table of Contents Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 The Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Business Analytics Benchmark Study . . . . . . . . . . . . . . . . . . . . . . . . 3 . . Overall Metrics Scores . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Metrics by Industry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Metrics by Organization Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 The Essential 64 Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Details Behind the Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
  • 3. Assessing Your Business Analytics Initiatives Introduction It’s no secret that using analytics to uncover meaningful insights from data is crucial for making fact-based decisions. Now considered mainstream, the business analytics market worldwide is expected to exceed $50 billion by the year 2016.1 Yet when it comes to making analytics work, not all organizations are equal. In fact, despite the transformative power of big data and analytics, many organizations still struggle to wring value from their information. The complexities of dealing with big data, integrating technologies, finding analytical talent and challenging corporate culture are the main pitfalls to the successful use of analytics within organizations. The management of information – including the analytics used to transform it – is an evolutionary process, and organizations are at various levels of this evolution. Those wanting to advance analytics to a new level need to understand their analytics activities across the organization, from both an IT and business perspective. Toward that end, an assessment focusing on eight key analytics metrics can be used to identify strengths and areas for improvement in the analytics life cycle.2 The Metrics The evaluation of an organization’s proficiency across the following eight metrics provides guidance for short- and long-term efforts needed to enhance analytical effectiveness. • Productivity • Governance • Timeliness • ROI • Accuracy • Effectiveness • Empowerment • Maturity Productivity is the efficiency of processes supporting the analytics life cycle across IT and business functions. Companies with a high level of productivity in their analytics activities are characterized primarily by their integration of information technology and capacity, strong data management and continuity of business resources. These organizations invest in the appropriate analytical training for their employees and are able to garner and share insights from complex data sets. Importantly, IT and business priorities in these companies are aligned. 1 IDC, Worldwide Business Analytics Software 2012-2016, Forecast and Vendor Shares, June 2012. Analytics life cycle: An iterative process using data to solve business problems and make informed decisions. The process comprises the following steps: prepare and explore data, develop and deploy models, and monitor results. 2 1
  • 4. SAS White Paper Governance is the overall rigor placed around data and model stewardship. Organizations proficient at governance are marked by consistent monitoring of data and quick corrections to deviations. They adhere to specific and standard methodologies and tools. Between IT and business functions, there are service-level agreements and alignment. Timeliness means more than just speed. It denotes whether analytical value is realized within the available time window. Timeliness in the analytics life cycle is exhibited by organizations that can handle large volumes and varieties of data, quickly get it into a usable state, derive meaningful insights from it and put analytical models into production. Data received from other areas within the organization needs to be assimilated in a timely fashion to make the best decisions and to be able to react to changes in the market quickly. ROI is the value generated from analytics as compared to the cost of providing that value. Organizations that are highly proficient in delivering ROI have analytics-driven culture and operations. These organizations are able to quickly identify the variables that predict outcomes, and they are able to customize their marketing approaches. Upper management advocates the use of analytics, and the organization has the right amount of analytical talent. Product offerings and services are up-to-date with the market. The impact of inaccuracy in the organization is low. Costs in the organization are transparent and well-understood. Cost/benefit analyses for new projects are documented, and there is agreement on the benefits of new initiatives. Investments in IT translate to value. Accuracy pertains to the accuracy of data in the analytics life cycle and the impact it has on effective decision making. Organizations performing well on the accuracy metric continuously focus on finding and rectifying inaccurate data as it gets modified in each step of the analytic life cycle and reused by a variety of applications. They have few costly mistakes in their history because of continuous data quality monitoring. Information is precise, accurate and timely. They have implemented data accuracy processes, and data quality and analytical results are consistent across the organization. Effectiveness is the organization’s ability to overcome challenges and generate value across people, processes, technology, data and culture. Organizations that have mastered effectiveness in analytics are marked by their feedback mechanism for systems and process improvement now and over time. They have reduced the reliance on IT for ad hoc and one-off reports. These organizations receive a high level of value from their technology and have adequate analytical talent to meet their needs. They are using analytics to address key business issues. Importantly, IT and line-of-business requirements are aligned. 2
  • 5. Assessing Your Business Analytics Initiatives Empowerment is the level of self-sufficiency for employees supporting the analytics life cycle across IT and business functions. Organizations that have been successful empowering their employees in the analytics life cycle provide decision makers with access to necessary information. They invest in end-user training on analytics software and provide the appropriate analytical resources. End users are self-sufficient in data exploration and reporting and have the ability to explore data for patterns and insights. A key differentiator for this metric is that employees are independent and empowered to find and solve business problems. Decision making is timely. Maturity is the organization’s analytical competency, consistency and alignment across people, processes, technology, data and culture. Organizations that are mature in their use of analytics have mastered many of the components of the other metrics. They are characterized by the high level of sophistication in their daily analyses. Employees have easy access to statistical consultants. Information is shared across departments. These organizations leverage new and emerging technologies and use data to improve processes. Finally, these mature analytics users have clear priorities and are strategically focused. Business Analytics Benchmark Study These metrics were used in benchmark research with more than 400 US companies, including 30 in-depth company assessments and 375 online surveys conducted among organizations across all industries and company sizes.3 The metrics were derived from a survey of 64 questions, eight for each category, pertaining to an organization’s information management processes and activities. The results of the survey research have been aggregated and the highlights are presented below. Overall Metrics Scores In general, our benchmark research revealed that organizations have not quite reached a high level of proficiency for the key metrics. On a proficiency scale of 1 (low) to 5 (high), the average scores of survey respondents ranged from a low of 2.56 on effectiveness to a high of 3.17 on governance. This suggests that organizations are having the most difficulty overcoming analytical challenges and generating value across people, processes, technology, data and culture, but are somewhat more adept at managing the overall rigor around their data and model management. Timeliness is also an area that received a lower score compared to most other metrics based on our survey results. As organizations continue to address the complexities of more data in a variety of forms, processes in the analytics life cycle can require more resources, which can create bottlenecks. (See Figure 1.) Looking at the balance of these eight metric scores provides information that organizations can use to advance their analytics initiatives. The specific questions that can be used to determine your metric score are discussed below (The Essential 64 Questions). 3 For a breakdown of the survey respondent demographics, see the Appendix. 3
  • 6. SAS White Paper Figure 1: Average benchmark scores on analytics metrics. Metrics by Industry Our benchmark research indicates that industries are at various levels in their proficiency across the eight metrics used to assess their business analytics initiatives. Compared to the average metric scores across all industries, education, professional services, financial services and health care are generally ahead of their counterparts. Interestingly, the education sector lags others in governance, while professional services struggles with analytics ROI, and financial services is below the average on timeliness. Health care, on the other hand, is not as proficient as other industries in their analytics productivity and accuracy. Surprisingly, respondents from the IT and technology industry lag those in other sectors on all but one metric. Manufacturing and retail organizations, according to the survey, are less proficient than other industries on all metrics. (See Table 1.) 4
  • 7. Governance IT and technology Manufacturing Retail/Wholesale All industries Above Industry Average Effectiveness Energy and natural resources Timeliness Communications Maturity Government/Public sector Accuracy Health care Empowerment Financial services 3.13 3.03 3.02 2.95 2.79 2.73 3.17 3.28 3.03 3.08 3.09 3.01 2.74 2.62 3.27 3.09 3.24 3.03 3.02 2.84 2.58 2.67 3.07 3.15 2.95 2.86 2.92 2.68 2.56 3.22 3.04 2.81 2.86 3.02 2.81 2.78 2.52 3.03 3.17 2.96 2.94 2.86 2.64 2.59 2.59 3.30 2.92 2.87 2.87 2.78 2.48 2.56 2.57 2.98 2.92 2.95 2.98 2.86 2.53 2.45 2.32 3.08 3.06 2.77 2.72 2.78 2.76 2.46 2.35 2.85 3.04 3.00 2.87 2.82 2.76 2.48 2.47 3.17 Professional services 3.26 3.33 Education ROI 3.01 Industry Productivity Assessing Your Business Analytics Initiatives 3.09 3.04 2.94 2.94 2.81 2.61 2.56 Below Industry Average At Industry Average Table 1: Metrics by Industry Metrics by Organization Size According to responses provided by our survey participants, it appears that smaller organizations (under $1 billion in revenue) are actually somewhat more proficient in all of the key areas we measured, with the exception of productivity. Although larger organizations have bigger budgets and generally more resources, it makes sense that smaller entities are more agile than their larger counterparts. There are fewer silos, less data and smaller infrastructures to deal with. The biggest difference in the metrics is accuracy. Larger organizations continue to struggle with data management, particularly as big data enters the scene. Getting to the single version of the truth becomes more difficult when dealing with higher volumes of data and disparate sources. (See Figure 2.) 5
  • 8. SAS White Paper Governance 3.15 ROI 3.04 3.25 3.15 3.08 3.10 Productivity Accuracy 2.88 3.06 2.98 2.95 Empowerment 2.85 2.78 Maturity Timeliness 2.58 Effectiveness 2.52 Under $1B 2.69 2.68 $1B and over Figure 2: Metrics by organization size. The Essential 64 Questions The questions asked of survey respondents to assess their business analytics proficiency on these eight metrics address a variety of factors, including processes, infrastructure, people, data and culture. Within a specific organization, gathering responses from a representative group of employees from both the business and IT is critical to developing an accurate picture of the proficiency of the organization on our eight analytics metrics. The differences in perceptions (based on their responses) across various groups within the business are also important when determining action steps to address areas for improvement. Below are the 64 survey questions in descending order based on the average survey response for each question. Note that some questions are worded in the negative, so the metric scores are not simply an average of the ratings for each question. Interestingly, our benchmark research shows that organizations are looking to analytics to improve the way they do business. The top three issues in the questions below relate to the need for the organization to use technology, specifically analytics, to drive better decisions. The details in many of the remaining questions provide guidance on what areas should be addressed to improve the proficiency of analytics initiatives. 6
  • 9. Assessing Your Business Analytics Initiatives Please indicate the extent to which you agree with the following statements: 1 = Strongly Disagree 5 = Strongly Agree Average Level of Agreement There are a significant number of issues at my company that could benefit from better use of analytics. 4.3 We need to get more value out of the current technology we have. 4.2 We need to react faster to market changes or opportunities. 4.1 We have dedicated resources to ensure the continuity of business. 3.8 Resource constraints (people and infrastructure) at my organization make completing work harder than it needs to be. 3.9 My department systematically follows standard methodologies or processes as a practice. 3.7 Employees at my organization are empowered to find and solve problems. 3.7 My organization has analytical data sets that can be used to support multiple initiatives. 3.7 It is time-consuming and difficult to get analytical models into production. 3.8 We typically use the results of data analysis to improve our processes. 3.7 Most of our marketing efforts focus on either large customer segments or definitions of group membership rather than customized approaches or microtargeting. 3.5 Any new reporting or changes to existing reports take a long time to develop. 3.6 Work priorities change frequently in my job and are very tactically focused. 3.6 Key stakeholders generally agree on the benefits of new information technology initiatives. 3.5 Our computer systems (hardware, software, network) are able to handle multiple peak periods of usage. 3.5 We are lacking feedback loops to ensure that internal processes become more effective and efficient over time. 3.6 7
  • 10. SAS White Paper Any deviations from established operational norms are slow to be corrected. 3.6 There are specific service-level agreements between IT and business 3.5 areas. Analytics talent is too diluted across the company. There is a disconnect between IT and business or line-of-business requirements. 3.6 Imprecision negatively affects business outcomes and decisions I have to make. 3.6 Upper management strongly advocates or promotes analytics. 3.6 There is a discrepancy in the priorities of the IT department and the needs of the business. 3.6 Accurate information is delivered in a timely manner. 3.4 It takes too long to get data that is in a usable state. 3.5 We use standard processes and tools to compare actual results to goals. 3.4 Our IT group suffers from lost productivity and time due to ad hoc and one-off requests involving different data elements. 3.5 We could benefit from having easier access to statistical consultants for some of the work we do. 3.4 The cost of inaccuracy in our organization is high and has a significant impact on profitability, market share and the ability to meet competitive pressures. 3.6 We often cannot get the internal data we need from other departments, so we find and analyze it ourselves. 3.4 Data security and authorization issues inhibit productivity. 3.3 It takes too long to get meaningful insights from data. 3.4 We are generally able to make good decisions in a timely fashion. 3.4 We have difficulty analyzing data in a timely manner. 3.2 Data quality and data deviations are consistently monitored. 3.1 We typically count on one specific person within our department to do analysis, interpretation and deliver results. 8 3.5 3.1
  • 11. Assessing Your Business Analytics Initiatives I am able to quickly identify key variables that influence or predict business outcomes. 3.2 We have a formal feedback mechanism to help improve processes and systems. 3.1 We are easily able to identify, understand and share insights from complex sets of data. 3.2 There is little statistical sophistication in our daily data analysis. 3.1 A lot of costly business decision mistakes have been made in the past at my organization. 3.4 There is little correlation between the cost of our information technology and the value we receive from it. 3.2 The reasons for internal hardware/software changes are clear. 3.2 The majority of data quality inconsistencies are identified and addressed. 3.0 We are stifled in our decision making due to the volume and variety of data we have. 3.1 We often get inconsistent results when analyzing the same data source. 3.1 Decision making is enabled in a timely fashion. 3.1 My organization does not have self-service capabilities for data reporting or analytics. 3.1 It is difficult to explore our data for patterns or regularities. 2.9 Cost/benefit analysis for new initiatives is well-documented. 2.9 Data from other departments needed for reporting is received in a timely fashion. 3.0 We have the right amount of analytical talent in our organization to address critical business challenges. 2.9 I typically have all the information I need to make effective business decisions. 2.9 We effectively leverage new and emerging technologies to address business challenges. 3.0 End users can quickly explore data and create reports in an ad hoc fashion without relying on experts or IT. 2.8 9
  • 12. SAS White Paper Our current computing infrastructure makes it easy to implement new processes that are similar to existing processes. 2.8 When working on a major project or initiative, we do not usually follow a specific methodology to complete the work. 2.7 End users are adequately trained in the software to analyze data. 2.8 Costs within my organization are transparent and well-understood. 2.8 Our employees get the training they need to leverage analytical software. 2.8 Our product offerings and services have remained relatively unchanged for a long period of time. 2.7 Most of our information technology is integrated well. 2.7 There are currently no processes in place to improve data accuracy. 2.7 We do not have specialized data sets for doing analysis. 2.4 Details Behind the Data As explained above, the survey asked respondents how strongly they agreed with statements related to their organizations’ information management processes and practices. The detail of the questions for each metric is presented in the graphs below. The percentages represent the level of agreement to statements related to each metric. 10
  • 13. Assessing Your Business Analytics Initiatives Governance Overall Score: 3.17 My department systematically follows standard methodologies or processes as a practice. 11% Any deviations from established operational norms are slow to be corrected. 19% Data quality and data deviations are consistently monitored. Our current computing infrastructure makes it easy to implement new processes that are similar to existing processes. When working on a major project or initiative, we do not usually follow a specific methodology to complete the work. Strongly disagree Disagree 16% 59% 44% 18% 15% 59% Neither agree nor disagree 42% 6% 33% 10% 32% 22% 16% 35% 17% 9% 27% 22% 35% 10% 48% 33% 27% 28% 55% 13% 35% 15% 25% 13% 42% 21% 19% The reasons for internal hardware/software changes are clear. 69% 43% 21% 13% 17% 52% 17% There are specific service-level agreements between IT and 7% 16% business areas. We use standard processes and tools to compare actual 6% results to goals. % Agree Agree Strongly agree Figure 3: Metrics for governance. Productivity Overall Score: 3.09 We have dedicated resources to ensure the continuity of business. 7% Our computer systems (hardware, software, network) are able to handle multiple peak periods of usage. 17% Data security and authorization issues inhibit productivity. 5% 27% 7% We do not have specialized data sets for doing analysis. Strongly disagree Disagree 38% 14% 23% Neither agree nor disagree 58% 20% 16% 34% 61% 12% 53% 46% 22% 37% 27% 4% 31% 24% 34% 24% 73% 10% 37% 25% 42% 13% 25% 51% 24% Our employees get the training they need to leverage 6% analytical software. Most of our information technology is integrated well. 48% 18% There is a discrepancy in the priorities of the IT department and the needs of the business. We are easily able to identify, understand and share insights from complex sets of data. % Agree 17% 24% 4% 28% 17% Agree 13% 8% 21% Strongly agree Figure 4: Metrics for productivity. 11
  • 14. SAS White Paper ROI Overall Score: 3.04 % Agree Key stakeholders generally agree on the benefits of new 4% 19% information technology initiatives. Upper management strongly advocates or promotes 5% analytics. The cost of inaccuracy in our organization is high and has a significant impact on profitability, market share and the ability to meet competitive pressures. There is little correlation between the cost of our 4% information technology and the value we receive from it. Cost/benefit analysis for new initiatives is well documented. 13% We have the right amount of analytical talent in our organization to address critical business challenges. 14% 32% Costs within my organization are transparent and well understood. 12% 34% Our product offerings and services have remained relatively unchanged for a long period of time. 11% Strongly disagree Disagree 29% 7% 38% 26% 19% 9% 35% 5% 27% 22% 9% 21% 13% Neither agree nor disagree 42% 31% 21% 46% 54% 15% 27% 21% 33% 25% 29% 25% 61% 69% 28% 31% 23% 19% 16% 45% 16% Agree 32% 30% Strongly agree Figure 5: Metrics for ROI. Empowerment Overall Score: 2.94 Resource constraints (people and infrastructure) at my organization make completing work harder than it needs to be. 11% Employees at my organization are empowered to find and solve problems. My organization does not have self-service capabilities for data reporting or analytics. Decision making is enabled in a timely fashion. 10% 7% 35% 29% 33% I typically have all the information I need to make effective business decisions. 7% End users are adequately trained in the software to analyze data. 8% Disagree Figure 6: Metrics for empowerment. 12 33% 39% Neither agree nor disagree 20% 15% 25% 21% Strongly agree 28% 21% 28% 28% Strongly agree 68% 40% 23% 17% 33% 28% 27% End users can quickly explore data and create reports in an ad hoc fashion without relying on experts or IT. 16% 71% 15% 53% 20% It is difficult to explore our data for patterns or regularities. Strongly disagree % 35% 36% 15% 6% 39% 10% 38% 13% 34% 6% 34% 4% 32%
  • 15. Assessing Your Business Analytics Initiatives Accuracy Overall Score: 2.94 % Most of our marketing efforts focus on either large customer segments or definitions of group membership 5% 18% rather than customized approaches or micro-targeting. Imprecision negatively affects business outcomes and decisions I have to make. 15% 10% 28% Accurate information is delivered in a timely manner. 5% 15% I am able to quickly identify key variables that influence or 6% predict business outcomes. A lot of costly business decision mistakes have been made in the past at my organization. The majority of data quality inconsistencies are identified and addressed. 23% There are currently no processes in place to improve data accuracy. Strongly disagree Disagree 17% 32% 57% 9% 29% 33% 11% 12% 46% 37% 39% We often get inconsistent results when analyzing the same 6% data source. 59% 45% 25% 16% 62% 19% 40% 24% 9% 19% 43% 36% 22% 43% 5% 26% 20% Neither agree nor disagree 15% 13% 19% Agree 8% 44% 41% 39% 27% Strongly agree Figure 7: Metrics for accuracy. Maturity Overall Score: 2.81 We typically use the results of data analysis to improve our processes. 16% % Work priorities change frequently in my job and are very tactically focused. We often cannot get the internal data we need from other 4% departments, so we find and analyze it ourselves. We could benefit from having easier access to statistical consultants for some of the work we do. Strongly disagree Disagree 64% 38% 22% 60% 21% 59% 18% 21% 10% 38% 16% 54% 43% 15% 29% 25% Neither agree nor disagree 14% 31% Agree 11% 54% 37% 24% 28% 13% 38% 21% 16% We have difficulty analyzing data in a timely manner. We effectively leverage new and emerging technologies to address business challenges. 21% 20% Analytics talent is too diluted across the company. 4% 19% There is little statistical sophistication in our daily data analysis. 43% 18% 14% 51% 25% 20% 28% 7% 45% 35% Strongly agree Figure 8: Metrics for maturity. 13
  • 16. SAS White Paper Timeliness Overall Score: 2.61 We need to react faster to market changes or opportunities. % Agree It is time consuming and difficult to get analytical models into production. 14% Any new reporting or changes to existing reports take a long time to develop. 16% 21% It takes too long to get meaningful insights from data. 4% 23% Strongly disagree Disagree 55% 20% 52% 9% 50% 12% 40% 8% 36% 41% 30% 28% 28% 27% Data from other departments needed for reporting is 6% received in a timely fashion. 20% 32% 21% We are generally able to make good decisions in a timely 3% 17% fashion. 62% 35% 18% 67% 22% 40% 20% 80% 26% 41% 17% It takes too long to get data that is in a usable state. 5% We are stifled in our decision making due to the volume 6% and variety of data we have. 34% 46% 16% 28% 27% 31% Neither agree nor disagree Agree Strongly agree Figure 9: Metrics for timeliness. Effectiveness Overall Score: 2.56 There are a significant number of issues at my company 6% 6% that could benefit from better use of analytics. We need to get more value out of the current technology we have. We are lacking feedback loops to ensure that internal 4% 13% processes become more effective and efficient over time. Our IT group suffers from lost productivity and time due to ad hoc and one-off requests involving different data elements. We typically count on one specific person within our department to do analysis, interpretation and deliver results. We have a formal feedback mechanism to help improve processes and systems. Strongly disagree Disagree Figure 10: Metrics for effectiveness. 14 9% 25% 23% Neither agree nor disagree 19% 35% 25% 18% 24% 35% 23% 31% 15% 36% 22% Agree 67% 21% 40% 24% 84% 30% 37% 15% 88% 37% 47% 16% 13% 50% 38% 16% My organization has analytical data sets that can be used to 5% 13% support multiple initiatives. There is a disconnect between IT and business or line-ofbusiness requirements. % Agree 15% 10% Strongly agree 61% 59% 54% 46% 46%
  • 17. Assessing Your Business Analytics Initiatives Appendix What was the approximate gross annual revenue in US$ for your organization in 2012? Less than $100 million 9% $100 million to $499 million 32% SMBs 11% (under $1B) $5 billion or more 44% $500 million to $999 million 12% 68% Enterprise $1 billion to $4.9 billion 24% What is your organization's primary industry? Professional services Manufacturing Retail/Wholesale 2% 7% 9% IT and technology 5% Other industries 9% Communications, entertainment, media and publishing 5% Health care, pharmaceuticals and biotechnology 18% Education 10% Government/Public sector 9% Financial services 22% Energy and natural resources 4% 15
  • 18. 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 65,000 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 Institute Inc. World Headquarters   +1 919 677 8000 To contact your local SAS office, please visit: sas.com/offices 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. 106494_S106492_0613

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