Deloitte inks tech deal with cyber analytics firm

Deloitte has teamed up with Converging Data in a bid to boost its cyber analytics team and deliver expertise in Big Data, AI and security orchestration …

Deloitte has teamed up with Converging Data in a bid to boost its cyber analytics team and deliver expertise in big data, AI and security orchestration and response (SOAR).

Managing director of Converging Data, Stuart Hirst, is joining Deloitte’s Risk Advisory practice in Sydney, as a partner, along with nine staff.

Converging Data specialises in using data analytics for enterprise level risk and operational management, particularly in the cyber security space.

“Working in the areas of security, operational intelligence, and data analytics, as well as digital and IoT innovation, the Converging Data team will bring its deep domain expertise to help our clients keep pace with the continually evolving technology risk landscape,” said Deloitte’s managing partner for risk advisory, Dennis Krallis

“They will enhance and complement our existing investments in the design, build and running of bespoke Cyber Security Intelligence Operations Centres for clients,” Krallis said.

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The art and science of analyzing Big Data

There has been recent discussion on the existence of several different data gaps across economic, social and political divides – deficits that are left …

There has been recent discussion on the existence of several different data gaps across economic, social and political divides – deficits that are left unaddressed at our own peril. But there is another deficit that has, I would argue, gone relatively unnoticed but is no less important: Canada’s skills gap in data analysis.

If Canada’s data deficit is to be eliminated, more collaborative learning and engagement between data science and the arts is needed. Much of the problem could be addressed by ensuring people have the skills to know not only how to look for data, but how to interpret them.

I am a professor of economics and the Director of the Master of Public Service program at the University of Waterloo, where I have been conducting policy-oriented research using large datasets for over two decades.

Analyzing Big Data

We currently live in the era of Big Data, where massive amounts of information are being collected at an ever-decreasing cost. Every Facebook, Twitter and Instagram post is a data moment that can be archived and become a part of a historical dataset. In an age where governments are making data more open and accessible, there is a significant demand for employees who can aggregate such large information sets in a meaningful manner and deliver key insights.

In response, many undergraduate and graduate programs in Big Data analysis and data science have emerged in universities across the country. These are typically housed in computer science, mathematics, statistics and engineering departments.

A humanities approach to data

From a policy perspective, a key missing ingredient of many of these programs is limited exposure to social science and humanities courses. This might seem puzzling because why should data science programs require courses in the arts?

The social sciences and humanities train students in the behavioural theories that are required to explain trends in data and extract insightful narratives. This allows arts students to be an integral part of any model-building process aimed at predicting human behaviour and choices.

Given the recent controversy on data collection practices by Facebook, data science students would also benefit from data ethics and governance courses. There is a need for students to understand the importance of individual privacy and confidentiality and data protection, which takes priority over actual data analysis.

On the flip side, arts students should also be encouraged to take challenging courses that offer contemporary Big Data analysis methods. These courses could include machine learning, that are not yet common in the social sciences or humanities curriculum.

Datafests and hackathons

What is further required are ground-level scalable ideas that have the potential to bridge the data divide between the sciences and arts. Datafests and hackathons are becoming increasingly common in many university campuses. In these events, students are typically organized into teams and have roughly two days to analyze data and craft a summary of findings or recommendations. There have been many datafests that have been organized in different universities by the American Statistical Association.

However, the emphasis is typically on mining private sector data. In contrast, Canada’s first policy datafest for graduate students in the arts used public datasets. Hosted by the University of Waterloo, in partnership with Innovation, Science and Economic Development Canada, the Government of Ontario and the Royal Bank of Canada, different open datasets were used to analyze data.

Introductory remarks for the University of Waterloo 2019 Datafest.

Datafests demonstrate the expertise of humanities and social sciences students with respect to data analytics. Their creativity, critical thinking and diversity in thinking, which are all key components of these disciplines, are important to developing, researching and analyzing policy issues.

At the 2019 University of Waterloo Datafest, most of the research presented was based on open data publicly available from different government websites. The use of open data allows datafests to be low cost ventures with significant returns that result in further awareness of free and easily accessible information. Further, datafests are an experiential education opportunity where students are encouraged to accumulate relevant skills and must work in teams in order to analyze policy issues of contemporary importance.

The seeds are planted in order to produce a critical mass of individuals who are skilled in sophisticated data analytics, can identify data deficits and offer recommendations on how to eliminate them. And this is a key point. The government can try to eliminate the national data deficit by downloading more information on to public websites. However, this will not be efficient or useful if the ability to analyze data and make correct inferences, is not widespread.

Reducing the skills gap

Of course, there should be priority on increased resources to Statistics Canada and to provinces and municipalities, to ensure dedicated offices and personnel who are able to assess data needs across different departments. However, this is a short-term perspective as it does not address the skills gap in data analysis.

A long-term strategy of ensuring a blend of training across different disciplines and encouraging public, private and university partnerships should result in a significantly lower data deficit for Canada by reducing the skills gap in data analytics and encouraging statistical literacy.

Steve Jobs summarized his business strategy by saying: “It is in Apple’s DNA that technology alone is not enough — it’s technology married with liberal arts, married with the humanities, that yields us the results that make our heart sing.” While this was specific to the intersection between technology and the arts, it resonates deeply when one considers how society can further advance by encouraging cooperative learning in data science between the Arts and Sciences.

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Latest Report On Big Data Analytics in Banking Market 2019 published by leading research firm

This report focuses on the global Big Data Analytics in Banking status, future forecast, growth opportunity, key market and key players. The study …

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The Big Data Analytics in Banking report offers a comprehensive evaluation of the market. It does so via in-depth insights, understanding market evolution by tracking historical developments, and analyzing the present scenario and future projections based on optimistic and likely scenarios.

Each research report serves as a repository of analysis and information for every facet of the market,

Download Sample PDF copy of this report

The key players covered in this study

  • IBM
  • Oracle
  • SAP SE
  • Microsoft
  • HP
  • Amazon AWS
  • Google
  • Hitachi Data Systems
  • Tableau
  • New Relic
  • Alation
  • Teradata
  • VMware
  • Splice Machine
  • Splunk Enterprise
  • Alteryx

Market segment by Type, the product can be split into

  • On-Premise
  • Cloud

Market segment by Application, split into

  • Feedback Management
  • Customer Analytics
  • Social Media Analytics
  • Fraud Detection and Management
  • Others

Market segment by Regions/Countries, this report covers

  • United States
  • Europe
  • China
  • Japan
  • South Korea
  • Other Regions

Enquire before purchase@:

Table of contents:

Report Overview

1.1 Study Scope

1.2 Key Market Segments

1.3 Players Covered

1.4 Market Analysis by Type

1.4.1 Global Big Data Analytics in Banking Market Size Growth Rate by Type (2013-2025)

1.4.2 On-Premise

1.4.3 Cloud

1.5 Market by Application

1.5.1 Global Big Data Analytics in Banking Market Share by Application (2013-2025)

1.5.2 Feedback Management

1.5.3 Customer Analytics

1.5.4 Social Media Analytics

1.5.5 Fraud Detection and Management

1.5.6 Others

1.6 Study Objectives

1.7 Years Considered

2 Global Growth Trends

2.1 Big Data Analytics in Banking Market Size

2.2 Big Data Analytics in Banking Growth Trends by Regions

2.2.1 Big Data Analytics in Banking Market Size by Regions (2013-2025)

2.2.2 Big Data Analytics in Banking Market Share by Regions (2013-2018)

2.3 Industry Trends

2.3.1 Market Top Trends

2.3.2 Market Drivers

2.3.3 Market Opportunities

3 Market Share by Key Players

3.1 Big Data Analytics in Banking Market Size by Manufacturers

3.1.1 Global Big Data Analytics in Banking Revenue by Manufacturers (2013-2018)

3.1.2 Global Big Data Analytics in Banking Revenue Market Share by Manufacturers (2013-2018)

3.1.3 Global Big Data Analytics in Banking Market Concentration Ratio (CR5 and HHI)

3.2 Big Data Analytics in Banking Key Players Head office and Area Served

3.3 Key Players Big Data Analytics in Banking Product/Solution/Service

3.4 Date of Enter into Big Data Analytics in Banking Market

3.5 Mergers & Acquisitions, Expansion Plans

TOC continued…!

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Databricks Unified Analytics Platform Takes the Stage at Gartner Data & Analytics Summit 2019

The Gartner Data & Analytics Summit will offer a holistic view of current … powered by Apache Spark that provides auto-scaling for big data clusters, …

SAN FRANCISCO–(BUSINESS WIRE)–Mar 13, 2019–Databricks, the leader in Unified Analytics and founded by the original creators of Apache Spark™, will exhibit and participate in several sessions at the Gartner Data & Analytics Summit 2019 taking place March 18 – 21 in Orlando, Florida. On the expo floor, Databricks will showcase its Unified Analytics Platform that has been adopted by over 2,000 organizations to unify data and machine learning initiatives. Databricks will also participate in the first-ever Data Science and Machine Learning Bake-Off, and deliver a session featuring Reynold Xin, a Databricks co-founder and one of the original creators of Apache Spark; it will highlight data science use cases across a range of industries including Financial Services, Media & Entertainment, Healthcare & Life Sciences, Technology, Energy and Retail.

The Gartner Data & Analytics Summit will offer a holistic view of current trends and topics around data management, business intelligence (BI), and analytics, including innovative technologies such as AI, blockchain and IoT. Enterprises attend the Summit to learn how to overcome data and analytics complexities to make game-changing business decisions. The Summit is expected to draw over 3,000 attendees consisting of chief data officers and chief analytics officers, senior IT and business leaders, as well as practitioners.

Conference-goers can visit Databricks at booth #230 within the expo hall or attend the following sessions:

Monday, March 18

Data Science and Machine Learning (DSML) Bake-Off

Sean Owen, Data Scientist

Databricks will participate in the first Data Science and Machine Learning (DSML) Bake-Off taking place on Monday, March 18. This fast-paced session will feature multiple vendors who will demo their platforms, compare capabilities and contrast offerings with each other. The bake-off focuses on showing the diverse nature of DSML platforms but all providing an end-to-end approach to building and managing models.

Tuesday, March 19

Session Talk – Unified Data Teams across The End-to-End Data and Machine Learning Lifecycle

Reynold Xin, Original Creator of Apache Spark and Chief Architect at Databricks

Most organizations today are challenged with data silos, using separate technologies for data processing and machine learning, with limited collaboration between data scientists and engineers. The session will cover a new unified approach that leverages open source technologies (Apache Spark, MLflow, TensorFlow and others) to accelerate your data science initiatives. Unified Analytics combines data processing and machine learning in a single collaborative platform for data science and engineering teams. In this talk, Databricks will also share how customers like Nielsen, Bechtel, Shell, and are building unified teams across the end-to-end data and machine learning lifecycle to accelerate innovation.

“We hear from enterprises across the globe that the end-to-end data and machine learning life cycle continues to be a challenge for data teams looking to achieve AI,” said Xin. “Our session will cover practical approaches that have been leveraged by thousands of organizations to overcome the bottlenecks associated with data silos and disparate technologies.”

Databricks was recently named a Visionary in Gartner’s January 2019 Magic Quadrant for Data Science and Machine Learning Platforms for the second consecutive year. Databricks’ Unified Analytics Platform is a cloud-based platform powered by Apache Spark that provides auto-scaling for big data clusters, performs up to 50x faster than Apache Spark, integrates seamlessly with machine learning frameworks and simplifies productioning data pipelines.

About the Gartner Data & Analytics Summit2019

Data and analytics leaders are fueling digital transformation, creating monetization opportunities, improving the customer experience and reshaping industries. The Gartner Data & Analytics Summit 2019 provides the tools to build on the fundamentals of data management, business intelligence (BI), and analytics; harness innovative technologies such as artificial intelligence (AI), blockchain and the Internet of Things (IoT); and accelerate the shift toward a data-driven culture to lead the way to better business outcomes.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, express or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

About Databricks

Databricks’ mission is to accelerate innovation for its customers by unifying Data Science, Engineering and Business. Founded by the original creators of Apache Spark, Databricks provides a Unified Analytics Platform for data science teams to collaborate with data engineering and lines of business to build data products. Users achieve faster time-to-value with Databricks by creating analytic workflows that go from ETL and interactive exploration to production. The company also makes it easier for its users to focus on their data by providing a fully managed, scalable, and secure cloud infrastructure that reduces operational complexity and total cost of ownership. Databricks has secured investments from Andreessen Horowitz, Coatue Management, Microsoft, New Enterprise Associates (NEA), Battery Ventures, Green Bay Ventures, and Geodesic, among others, and has a global customer base that includes Viacom, Shell and HP.

Apache, Apache Spark and Spark are trademarks of the Apache Software Foundation.

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CONTACT: Kristalle Cooks

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SOURCE: Databricks

Copyright Business Wire 2019.

PUB: 03/13/2019 04:00 PM/DISC: 03/13/2019 04:00 PM

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11.84% CAGR Growth by 2023 in the Application Performance Management (APM) Market by …

… B Funding for Innovation in APM for Big Data. This funding was led by GGV Capital, with Microsoft Ventures and Menlo Ventures also participating.

Market Analysis:

North America to hold Major Market Share

The increasing number of applications, along with the easy availability of SaaS solutions on monthly rental basis, is greatly helping the incorporation of these systems and increasing the consumer awareness in the market. Furthermore, due to the early adoption of technology, the region is witnessing increased advanced systems and integrated solutions demand.

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The APM systems enabling performance enhancements by analyzing peak time performance, architectural, and networking issues are expanding into business front analytics, accelerating the market growth. The current systems are able to analyze user groups and consumer journey through interface and its translation into business. Further, such capabilities have helped their incorporation into business environments, as it reduces time and resources consumed in performing corrective measures. Thus, real time monitoring and predictive maintenance is necessary for a company driving the market. The direct software solutions are appealing to large organizations, which have the capabilities of hosting these systems on their own at large-scale. So, the Software segment is expected to hold a significant market share.

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Market Segments:

APM Market is segmented by:

1. Solution Type: Software, Services

2. Deployment Type: On-Premise, Cloud, Hybrid

3. Access Type: Web, Mobile

4. End-user: BFSI, E-commerce, Manufacturing, Healthcare, Retail, IT and Telecommunications, Media and Entertainment, Academics, Government, Others

5. Geography: North America, South America, Europe, Asia Pacific, Middle East & Africa

Key Developments in the Market

•March 2018 – Pepperdata announced Application Spotlight. This self-service portal is expected to enable Big Data application developers to generate application-specific recommendations to improve application performance, highlight applications that need attention, automatically identify bottlenecks, and alert on duration, failure conditions, and resource usage.

•January 2018 – Unravel Data secured USD 15 million in Series B Funding for Innovation in APM for Big Data. This funding was led by GGV Capital, with Microsoft Ventures and Menlo Ventures also participating.

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We at Market Prognosis believe in giving a crystal clear view of market dynamics for achieving success in today’s complex and competitive marketplace through our quantitative & qualitative research methods.

We help our clients identify the best market insights and analysis required for their business thus enabling them to take strategic and intelligent decision.

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