Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

The amount of data is exploding, driving the move to analytics for business value extraction

Stacy Novack, Distinguished MI Professional, Manager, Market Development - Software SolutionsCraig Doyle, Senior Advisor, Analytics BU, Market Development, IBMBill Chamberlin, Distinguished Market Intelligence Professional, MD&I HorizonWatch February 15, 2017

Analytics Trend Report, 2017

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

About This Trend Report

15Feb2017

Purpose: The slides provide an overview on the Analytics trend

Content: Summary information about the Analytics marketplace, including trends drivers, spending trends, industry business cases, and adoption challenges. Also included are links to additional resources.

How To Use This Report: This report is best read/studied and used as a learning document. You may want to view the slides in slideshow mode so you can easily follow the links

Available on Slideshare: This presentation (and other Trend Reports for 2017) will be available publically on Slideshare at http://www.slideshare.net/horizonwatching

Please Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

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Analytics Trend Report, 2017 (External Version)

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

The amount of data is exploding, driving the move to analytics for business value extraction

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Key Insights

IoT will drive demand for new-age analytics solutions. Internet of Things (IoT) will create massive amounts of data that will drive demand for streaming analytics and AI led analysis

Open Source and emerging technologies. Whether Spark, Hadoop or emerging database technologies there are increasingly important alternatives to traditional analytics capabilities.

Growth in unstructured data. Large amounts of unstructured data will drive demand for capabilities such as streaming analytics and data lakes.

Adoption of Self Service Analytics. Enabling enterprise users to reduce complexity of big data from data gathering to visualization is a key requirement and will gain traction in 2017

Data integration. Data quality, integration, and preparation capabilities will be increasingly important to effectively address trends such as Cloud, machine learning, data discovery, 3rd party data sources

Business leaders influence. Business leaders are focused on the challenges posed by the huge increase in data. They have an increasingly significant influence over the direction of technology investment in the enterprise

“Deriving insights from contextual customer data from mobile and other internet-of-things (IoT) devices will become mainstream in 2017.” Forrester: Predictions 2017: Artificial Intelligence Will Drive The Insights Revolution

IBM (blog) Big data and analytics trends in 2017

15Feb2017 Analytics Trend Report, 2017 (External Version)

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Trends such as IoT, AI and Cloud are driving data analytics investmentsIoT and digital systems of engagement drive requirements for new analytics capabilities. IoT has resulted in massive amounts of exchange data being generated every second, which have necessitated use of big data and analytics to efficiently create, store, retrieve and analyze it.

Increased interest in streaming analytics. IoT devices coupled with open source technology, low cost storage infrastructure, bandwidth and smart sensors, have resulted in generation of massive amounts of data which has thereby resulted in rise of streaming analytics. A combination of analytics with machine leaning would enable enterprises to unlock key business insights and accordingly create better products and services

Machine Learning simplifies predictive analytics. Ability to automate the complexity of predictive analytics, leading to use cases being understood by end users and not just data scientists results in it being a key trend in 2017 . In addition, machine learning is a key driver behind growth of Spark (in-memory data processing framework).

Adoption of the cloud delivery model continues to impact the market for analytics solutions. As barriers and adoption challenges to cloud platforms are overcome, analytics and data-as a service solutions are becoming increasingly popular. A growing number of new business intelligence use cases along with increased self-service and easy access on mobile devices are motivating companies to expand analytics solutions and services to more employees.

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Market Trends

15Feb2017 Analytics Trend Report, 2017 (External Version)

Forbes Driving Value By Monetizing Data From The Internet Of Things

“Data, and more importantly analytics, are changing the way we see our machines, our processes and our operations. Analytics can identify patterns in the data, model behaviors of equipment, and predict failures based on a variety of variables that exist in manufacturing”. IBM via Forbes How Cognitive Computing And The IoT Can Transform Manufacturing To Please Customers

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Cognitive computing based analytics will be used to create high growth insights-driven businesses

Cognitive computing provides business users with faster decision making insights. Cognitive computing based analytics will drive faster business decisions in marketing, eCommerce, product management, and other areas of the business by helping close the gap from insights to action. Through the use of cognitive interfaces in complex systems - advanced analytics and machine learning technology vendors are already embedding components of cognitive computing capabilities into their solutions

Enterprise software is being embedded with cognitive computing techniques. Analytics applications have traditionally relied on hard-coded or rules-based approaches. This is changing as the use of various machine learning techniques, natural language processing, knowledge graph, and other related analytics are being incorporated into enterprise software.

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Market Trends

“The availability of very large data sets is one of the reasons Deep Learning, a sub-set of artificial intelligence (AI), has recently emerged as the hottest tech trend.” Forbes 6 Predictions For The $203 Billion Big Data Analytics Market

15Feb2017 Analytics Trend Report, 2017 (External Version)

IBM: Analytics: Dawn of the cognitive era

“Over the next few years, enterprises of all sizes, globally, will have access to a new generation of intelligent software tools and application that will automate some decision making and business processes and augment the human work involved in other processes.“ Dan Vesset, group VP, Analytics and Information Management research. IDC FutureScape: Worldwide Analytics, Cognitive/AI, and Big Data 2017 Predictions

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Alternatives to traditional analytics capabilities and technology are increasingly importantNew workloads driving investigation in alternative data repositories. Inability of traditional relational databases to scale beyond resources of a single server or handle unstructured big data workloads has resulted in a transition from RDBMS to unstructured data stores such as Hadoop and NoSQL.

Wider Adoption of Hadoop. More enterprises will take to Hadoop for storing large chunks of data and running analytics to derive valuable information. The ability to provide low cost secure storage along with use of in-memory processing frameworks such as Spark would result in being a key alternative to expensive disk based investments.

Benefits of Spark increasingly compelling In-memory computations coupled with ability to process large scale data 100 times faster than MapReduce are key advantages Spark offers. In addition, parallel processing, quick application development in Java, Scala, Python and support for SQL queries, machine and unstructured data are other advantages. It is anticipated that in coming years, Spark might overtake MapReduce as the default data processing engine for Hadoop

Drivers of Data Lake adoption are evolving. For organizations that have experience with big data and the Hadoop platform, data lakes are the next step as they’ll become the ingest point for raw data. This would be significant as it does away with transferring data into structured form (excel sheets), helps keep it accessible all the time and provides for inexpensive storage. The long term focus will then be on securing access while automating cataloguing and ingest from various sources.

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Market Trends

“The data platforms and analytics sector has changed considerably in recent years, starting at the bottom up with the emergence of new data platforms. As those continue to emerge, we are witnessing greater impact at the data management and analytics layers as enterprises evolve their strategies to take greater advantage of the increased data processing and analytics capabilities available to them.” 451 Research: 2017 Trends in Data Platforms and Analytics

15Feb2017 Analytics Trend Report, 2017 (External Version)

ComputerWorld: Big data and business intelligence trends 2017

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Self-Service analytics remain a top need Emergence of data and source agnostic tools. Convergence of IoT, cloud and big data has resulted in enterprises seeking analytical tools that can capture data from multiple sources (Hadoop clusters to NoSQL databases) and platforms (on premise and the cloud), combine the different data types, visualize and analyze this data thereby deriving valuable insights and justify the investment

Continued demand for intuitive visualization and self-service analytics. Data discovery and self-service BI will continue to be important in 2017. Self-service BI has been in demand as more organizations look to work with ‘easy to use’ and intuitive interfaces and IT departments have not delivered satisfactory results. Data discovery and visualization, as well as predictive analytics, are among the typical functions users want to consume in a self-service mode.

More focus on data preparation capabilities. Self-serve applications such as Tableau are becoming popular as they significantly reduce time to analyze data. Enabling data access through self-service analytics at reduced time and lesser complexity while dealing with structured and unstructured data was a key requirement in 2016. In 2017 there will be increased focus on data preparation capabilities. Business users want to reduce the time spent in preparing complex data for analysis, something that’s very important when dealing with a variety of data types and formats.

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Market Trends

“One of the biggest impediments to accurate analytics is data preparation. This long and complex process can take so much time that there’s barely any left for analyzing the data after it’s ready. And yet, without data preparation, the results from analysis just aren’t reliable. ” IBM: Overcoming the challenge of self-service data access and preparation in business analytics

15Feb2017 Analytics Trend Report, 2017 (External Version)

BI-Survey: Top Business Intelligence Trends 2017: 

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Big data and analytics create complex security, data management, cost and organizational change issues

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Business case/ROI. Enterprises want improved productivity, revenue growth and TCO metrics in the short term which is hard to deliver from big data standpoint considering most of the firms are in their initial stages of implementing it. As per a survey, ~25% of the firms were able to witness ROI through BD&A tool implementationSecurity. Security of data is a critical factor in the success of Analytics projects and must be addressed from the start of any implementation. Data Management. Top seven reasons analytics solutions failed to meet customer needs relate to data integration, cleansing, management, storage and access. Vendors must provide these capabilities, particularly as customers seek to increase utilization from external data sources. Lack of standards and interoperability. Seamless connectivity between various devices through a common data format is a key requirement and a challenge for rise of big data. Developing a common standard allowing the extraction of data across various systems is a key requirementFlexible and agile Analytics infrastructure. Given high IT infrastructure costs and a shortage of internal resources to support deployment, many companies are evaluating new approaches for emerging needsShortage of skilled staff. Data scientists and skilled analysts are difficult to attract and retain which has resulted into high labor costs and desire for improved usability

CIO Insight: Big Data's Biggest Challenges

Adoption Challenges

“The challenges we face in data analytics are not technology-related but skills-related—for we all have difficulty keeping up with the pace of technological change.” Jen UnderwoodFounder, Impact Analytix, LLC: 10 reasons to be excited about data analytics in 2017

15Feb2017 Analytics Trend Report, 2017 (External Version)

Forbes: How Data Complexity Is Changing the Face of Business Analytics

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Selected IBM Resources and Links

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IBM Analytics website Industry Technology Business

IBM Analytics Learn Center IBM Marketplace IBM Watson Analytics IBM blog platform

The Big Data & Analytics Hub Software: Cognos /

Business Intelligence / Data Warehousing / Customer Analytics / Predictive Analytics / Risk Analytics

Global Services: Big Data & Analytics Consulting

DeveloperWorks: Big data and analytics

IBM Analytics website

Important Links

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IBM: The Rise of the DataEconomy: Driving Value through Internet of Things Data Monetization

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Selected Analyst Websites and Resources

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Forrester: Business Intelligence Playbook /  Analytics (search)

Frost & Sullivan: Analytics (search)

Gartner: Business Intelligence (Portal) / Analytics (search) / Analytics Blog Posts (search)

IDC: Big Data and Analytics (Portal) / Analytics Research / Analytics Blog Posts

International Institute for Analytics - http://iianalytics.com/

TBR: BI and Analytics

Gartner: Gartner’s Data & Analytics Excellence Awards

IDC: Big Data and Analytics

Important Links

15Feb2017 Analytics Trend Report, 2017 (External Version)

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

Selected Media Websites and Resources

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CIO.com: Business Intelligence, Analytics (search)

ComputerWorld: Business Intelligence / Analytics (search)

eWeek: Big Data Analytics Project Center / Analytics (search) / Business Intelligence (search)

Forbes:  Data Driven / Analytics (search) / Business Intelligence (search)

Harvard Business Review:  Analytics InformationWeek: Big Data Analytics /

Business Intelligence (search)

InfoWorld: Analytics (search) / Business Intelligence (search)

MIT Sloan:  Analytics & Strategy

MIT Sloan

eWeek: Big Data Analytics Project Center

Important Links

15Feb2017 Analytics Trend Report, 2017 (External Version)

“The creation and consumption of data continues to grow by leaps and bounds and with it the investment in big data analytics hardware, software, and services and in data scientists and their continuing education.” Forbes 6 Predictions For The $203 Billion Big Data Analytics Market

Note: This report is based on internal IBM analysis and is not meant to be a statement of direction by IBM nor is IBM committing to any particular technology or solution.

More Insights on Technology Trends are Available

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Other slide decks in this 2017 Trend Report series have been posted to Slideshare

You are also invited to check out the following IBM websites and resources– IBM Academy of Technology– IBM Institute for Business Value– IBM Research and Research News and 5 in 5– IBM’s THINK blog– IBM Think Academy on YouTube

15Feb2017 Analytics Trend Report, 2017 (External Version)