Ventana Research. Big Data, Analytics & Cloud There s No Free Lunch! David Menninger SVP & Research Director Ventana

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1 1 Big Data, Analytics & Cloud There s No Free Lunch! David Menninger SVP & Research Director Ventana

Conducted Research on Big Data, Analytics and Cloud Data and Analytics in the Cloud Data and Analytics in the Cloud Enabling Access and Integration through Cloud Computing Enabling Access and

2 Conducted Research on Big Data, Analytics and Cloud Data and Analytics in the Cloud Data and Analytics in the Cloud Enabling Access and Integration through Cloud Computing Enabling Access and Integration through Cloud Computing Research Report Copyright Ventana Research 2013 Do Not Redistribute Without Permission Research Report Responses include: Cross section of industries Various size organizations User and management Line of business and IT Worldwide participants Over 200 qualified responses Download Executive Reports (registration req d) at: ventanaresearch.com/bigdataanalyticsexecutivesummary/ ventanaresearch.com/nextgenpredictiveanalyticsexecutivesummary/ ventanaresearch.com/dataandanalyticsinthecloudexecsummary/ Copyright Ventana Research 2013 Do Not Redistribute Without Permission 2

3 David Menninger SVP & Research Director, Ventana Research Covers Analytics, Big Data, Business Intelligence, Information Management and IoT. Over thirty years software industry experience including: o Head of Business Development & Strategy at Pivotal, a division of EMC o Led Product Management & Marketing at Vertica, InforSense, Applix, Oracle s OLAP Division, and IRI Software. o david.menninger@ventanaresearch.com Blog: davidmenninger.ventanaresearch.com Twitter: dmenningervr 3

Importance of Big Data Analytics 80% report

organization Many different opinions on what

different meanings or no agreement -However,

data from all sources rather than just one

Somewhat important 16% Important 33% Not

4 Importance of Big Data Analytics 80% report that big data analytics are important to their organization Many different opinions on what big data analytics means -50% say many different meanings or no agreement -However, 76% say big data analytics is about analyzing data from all sources rather than just one source of data. Somewhat important 16% Important 33% Not important 3% Very important 47% Source: Ventana Research Big Data Analytics Benchmark Research 4

from IT applications Interaction data (e.g.

Text from documents and Web pages 42% 40% 37% 36% 34% 31%

5 What are the Sources of Big Data? Transactional data from applications 60% External sources (e.g., data from Content (e.g., documents, Web Event-centered data 50% 49% 48% Machine data from IT applications Interaction data (e.g., contact or call Survey data Location data Social media text Text from documents and Web pages 42% 40% 37% 36% 34% 31% Statements and Invoices 25% Multimedia (audio, video or images) 18% Weather information Other 3% 9% Source: Ventana Research Big Data Analytics Benchmark Research 5

Why Advanced Analytics Are Important for Big Data Often looking at millions or billions of rows; too big to browse Need more sophisticated techniques Advanced / predictive analytics are the

6 Why Advanced Analytics Are Important for Big Data Often looking at millions or billions of rows; too big to browse Need more sophisticated techniques Advanced / predictive analytics are the most important type of analytics (78% of respondents) Requires detailed data -Mathematically incorrect to use summary data -When you summarize you lose the correlations in individual data points 6

continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics Other related terms and

7 Predictive Analytics, Data Science & Other Analytics Wikipedia definition of Data Science Data science is an interdisciplinary field about processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, which is a continuation of some of the data analysis fields such as statistics, data mining, and predictive analytics Other related terms and techniques include: Machine learning Descriptive analytics Prescriptive analytics Natural language processing Not distinguishing these techniques here 7

8 What are the Business Benefits of These Analytics? Achieved competitive advantage 57% Created new revenue opportunities 50% Increased profitability 46% Improved customer service 39% Realized cost savings from operational efficiencies Increased workforce productivity Increased revenue through cross-sell and up-sell 36% 34% 33% Source: Ventana Research Next-Generation Predictive Analytics Benchmark Research 8

9 Where are These Analytics Applied? Forecasting 37% 56% Marketing analyses Customer service recommendations 46% 42% 41% 43% Product recommendations or offers 35% 44% Logistics analysis 29% 41% Intelligence or surveillance analysis Fraud detection 28% 26% 37% 38% Scientific or clinical research 22% 24% Predicting prices in the supply chain 21% 35% Social network analysis 20% 54% Predicting product development lifecycles 20% 46% Other 21% 23% Predicting mechanical failures 19% 31% Healthcare decisions 15% 26% Current Future 9

There s No Free Lunch: Big Data Analytics is Hard! Only half say users can produce their own analyses Why can t users do their own data science analyses?

10 There s No Free Lunch: Big Data Analytics is Hard! Only half say users can produce their own analyses Why can t users do their own data science analyses? 79% say users don t have enough skills 66% say users don t understand the mathematics Lack of skilled resources is the major reason for dissatisfaction 10

analytics 62% cite preparing and accessing data as the biggest challenge in predictive analytics Most challenging

11 There s No Free Lunch, Big Data Analytics is Hard! (cont d) Need data engineers Nearly half are not satisfied with the integration of information for big data analytics 62% cite preparing and accessing data as the biggest challenge in predictive analytics Most challenging part of big data analytics process is quality and consistency of data Business skills (domain knowledge) also critical to big data analytics Need to bring all these skills together 11

12 Hadoop is Hard! Only 1 in 6 (16%) have Hadoop skills Organizations seem to prefer SQL: o Nearly 6 out of ten (57%) have SQL Skills o Over half (52%) plan to use SQL to implement their Hadoop Analyses o Four out of ten plan to use Hive ODBC (SQL) What if they could use an RDBMS? 12

functions which are not your differentiators Creates more agility

13 Organizations want an Easy Button Cloud + SQL is the IT equivalent of an easy button Doesn t solve all the problems, but it offloads functions which are not your differentiators Creates more agility Allows you to focus on the core value your organization provides 13

More and More Sources of Data are in the Cloud Cloud-based business applications 61%

, Facebook, 48% Internet information sources 42% Government sources (e.g., U.S.

14 More and More Sources of Data are in the Cloud Cloud-based business applications 61% Social media (e.g., Facebook, 48% Internet information sources 42% Government sources (e.g., U.S. census) 33% Market sources (e.g., Nielsen, Data brokers (e.g., Acxiom, 27% 29% Other Web applications Sensor data from devices and Location sources (e.g., TomTom) 17% 16% 19% Weather sources 9% Wearable computing (e.g., smart watches) Other 4% 3% Source: Ventana Research Data And Analytics In The Cloud Benchmark Research 14

15 Trends in Cloud-based analytics It s No Longer Exotic! Not yet; we intend to use it, but do not know when. 31% Not yet, but we will begin to use it within 12 months. 19% Yes, we have used it for more than one year. 18% Yes, we began to use it within the last 12 months., 30% 15

Big Data is Also Moving to the Cloud Managing big data systems can be a challenge Four out of ten plan to manage majority of their big data in the cloud within 12 months 86% expect to manage majority

16 Big Data is Also Moving to the Cloud Managing big data systems can be a challenge Four out of ten plan to manage majority of their big data in the cloud within 12 months 86% expect to manage majority of their big data in the cloud eventually Big data technologies can serve as consolidation point e.g. Data lake Big data technologies help deal with new types of data e.g. Social media, machine data, audio/visual 16

17 People want to use familiar tools BI tools: More than half want to access big data analytics through their existing BI tools & Spreadsheets Analytics: o More than half want to integrate BI

17 17 People want to use familiar tools BI tools: More than half want to access big data analytics through their existing BI tools & Spreadsheets Analytics: o More than half want to integrate BI tools with predictive analytics capabilities o Almost half would like to integrate predictive analytics into business applications SQL: Almost three quarters are using RDBMS to manage Big Data

cloud analytics important to 3 or more departments Sales Customer service Accounting

18 Use Cloud-based Analytics Across Departments We see cloud-based analytics used in all functions Analytics and/or business Marketing 34% 54% Most common: -Analytics and BI are rated the most important -Marketing, sales and customer service next Nearly half (47%) say cloud analytics important to 3 or more departments Sales Customer service Accounting Finance (e.g., planning, Product information or content Supply chain management Human capital management Commerce No cloud-based application 31% 30% 23% 23% 20% 16% 16% 13% 7% Other 3% 18

19 Conclusions/Wrap up Big data is important and valuable it is also hard Leverage the cloud to make your big data life easier Leverage SQL to make your big data life easier Look for opportunities across all departments in your organization 19

Ventana Research Big Data and Information Management Research in 2017

Ventana Research Big Data and Information Research in 2017 Setting the annual expertise and topic agenda David Menninger SVP Research blog.ventanaresearch.com @ventanaresearch In/ventanaresearch @dmenningervr