survey and analysis of Big Data use in large mid market organizations around the world. Research report available upon request.
An exclusive survey and report on: Midmarket companies moving fast toward big data reliance Larry Marion Spring 2014 Presented to Prepared by Midmarket interest in Big Data and data analysis rivals that of enterprise firms. Budgets are rising, with IT and sales and marketing taking the lead. Agenda Introductions Survey Objectives Methodology Demographics Universal agreement on Big Datas importance Improving quality currently the biggest benefit Data volumes, budget limitations top Big Data challenges Strong ties between business and IT critical to success Best practices Next stepsreport themes 2 Team background 3 Triangle Publishing Services Co. Inc. (TPSC) is a leading provider of content about information technology for business and technology publications and vendors. It has produced hundreds of research reports, web sites, feature articles, case studies and other forms of content. Triangle consists of a team of 60 business and technology journalists, designers, audio and video experts around the world. Beacon Technology Partners LLC helps leading companies better understand the needs of their customers and prospects. The company's client base includes both business-to-business and business-to-consumer companies in other sectors. It offers both quantitative and qualitative research methods, depending on the client's need to know for their strategic decision-making as well as additional research on brand positioning, communications architecture, customer satisfaction, pricing strategy, market segmentation, marketing effectiveness and employee engagement. Survey Objectives Understand business drivers and anticipated results for Big Data initiatives in midmarket companies. Document technical and business challenges the midmarket faces with Big Data. Explore tools and technologies midmarket firms need to implement Big Data projects, and lessons learned. 4 Methodology Triangle Publishing Services Co. posted a 15- question questionnaire on a website accessible only to executives in midmarket companies familiar with Big Data Survey conducted over 4 days in November, 2013 300 responses received 5 Commentary: +/- 5.5% margin of error We only highlight data that clearly have statistical significance , i.e., exceed 6 percentage point deltas Executive Summary 80% agree that they need Big Data 41% have one or more big data projects in place; another 55% are starting one Budgets on the rise Biggest drivers of success: IT/Business collaboration, proper skills, and performance management Biggest causes of failure: Lack of IT/Business cooperation, lack of tools and skills Most effective suppliers: Best of breed and integrated full service providers Most influential in Big Data projects: IT closely followed by sales/marketing 6 Overview Biggest and MostKey Highlights Commentary: Improving quality of products/services a bigger driver than cost cutting Sentiment analysis and social media not yet important Wide variety of data types, data volumes and budgets are big challenges today Respondent Profile Functional Areas 7 Title 300 Respondents 67% 33% IT Involvement Business 50%50% C-Level or VP Director or Manager Responsibilities Application performance management, including cloud- based apps IT security systemsIT governance and policies, including budgeting 55% 47% 42% Growing adoption 8 Commentary: Over half of midmarket companies are just getting started 55% in Asia have one or more projects, followed by 41% in NA, only 26% EMEA EMEAs relative paucity of Big Data activity is a recurring theme through the results Question 5. Please select the ONE best response below that most accurately describes whether your organization currently has a Big Data initiative in place. 55%41% 4% My organization is just getting started with a Big Data project. My organization has one or more Big Data initiative(s) in place. My organization has no Big Data initiative in place but has discussed implementing such a program in the foreseeable future. Picking the low hanging fruit comes first Question 1: How important is Big Data to meeting the following strategic or tactical goals in relation to meeting the business goals of your organization? (% responding very important) Commentary: Note prominence of tactical, near-term goals Weakness of understand constituent sentiments implies Big Data analysis of social media not yet a strong use case among mid-market companies 9 Improve quality of our products and services Obtain better and deeper understanding of customer needs Identify and take advantage of business opportunities Improve quality and speed of decision making Quickly respond to competitive threats or other inputs Improve effectiveness of our marketing programs Predict future trends that may imperil business goals Reduce operating expenditures Better understand constituent sentiments 51% 51% 50% 45% 44% 44% 44% 43% 41% 38% Enable managers to have a better understanding of the profitability - and profit potential of each customer, product and line of business Real-time processing, predictive analytics are most valuable tools 10 Question 15: How valuable are each of the following tools or technologies to help your organization optimize its Big Data initiative(s), now and in two years? Commentary: Real-time processing not surprising due to trend toward more timely analysis Data cleansing, data dashboards, visualization see significant uptick in two years Financial Services (64% answering extremely valuable) values real-time processing the highest of all industries right now In two years, Manufacturing ranks data dashboards the highest at 64% 57% 51% 56% 61% 58% 60% 49% 50% 53% 56% 58% 60% % Responding "Extremely Valuable Now" % Responding "Extremely Valuable in Two Years" Real-time processing of data and analytics Predictive analytics Data visualization to convert processed data into actionable insights Use of cloud computing to provide anytime, anywhere data and applications access at lower cost Data aggregation that spans multiple databases, including Big Data platforms such as Hadoop Data dashboards (desktop self-service data integration) Big Data proves its worth once deployed 11 Question 16: What impact, if any, has your organizations Big Data initiative(s) had on improving decision making? Comparing answers of respondents in development vs. production 89% 49% 10% 42% 2% 9% Big Data system in production BD initiative in development but not yet in production Improved decision making Not yet improved decision making Not sure Commentary: Of the 123 mid market in production with at least one Big Data system, overwhelming endorsement of benefit How Big Data Was Successful 12 Question: How well do you currently perform this task? Respondents saying very well without BD vs. with BD 23% 20% 27% 32% 23% 44% 40% 46% 49% 50% Quickly sense and respond to competitive threats or other inputs Reduce capital or operating costs Understand customer needs Improve product quality Quality and speed of our decision making In production In development Commentary: Larger the customer, the more satisfied they are with Big Data. Manufacturing reports higher satisfaction in most Big Data areas. Consistent gap of 10-20 points between improvement and considerable improvement in all areas. Data volumes, budget limitations top Big Data challenges Question 3. Which of the following are among the biggest challenges facing your organization in using data and analytics tools to achieve its business goals? (% responding) Commentary: Top two concerns related to data and infrastructure, big areas for IT Concerns over sheer volume of data point to need for scalable tools Number One, with unusual consistency across geographies, was, wide variety of new data types and structures C level almost as aware of it (35%) as director manager (40%) and business (33%) not too far behind IT (43%) Number Two is sheer volume of data slows processing, cited by 30%-40% of respondents across all geographies, and between 31% and 36% of both business and IT respondents 13 24% 25% 25% 25% 26% 27% 29% 32% 34% 40%Wide variety of new data types and structures Determining what data (both structured and unstructured, and internal and external) to use for different business decisions Getting business units to share information across organizational silos Inaccurate data Understanding where in your company we should focus our Big Data investments Not enough trained staff to analyze the data Analytics tools are lacking and many potential users do not have access Lack of easy-to-use, cost-effective data cleansing tools Sheer volume of data slows processing Budget limitations to improve our data analysis capabilities Strong ties between business and IT a path to success 14 Question 9: In general, what are the top three reasons why, in your view, Big Data or data analytics projects succeed. Please select up to three reasons Commentary: Note, top two reasons for success and failure are organizational speak to business/IT alignment Importance of link between data analytics/performance management especially strong for those with experience, rising from 17% to 41% While not in the top three, busines