Closing the Governance Gap - Enabling Governed Self-Service AnalyticsPrivacera
Data democratization and data protection are conflicting forces that both need to be addressed through data governance and security by defining, deploying, and auditing data access control policies. Yet there is a latent “governance gap”: the individuals in the organization accountable for articulating and specifying data policies do not have enough knowledge of the systems to understand how policies are to be implemented, and the technologists who understand the system are not familiar enough with data policy drivers to appropriately define and deploy data protection policies.
This webinar is a must for personnel with an analytics and technology mandate to learn about the root causes of this governance gap and consider ideas for closing the gap.
On-Demand here: https://tdwi.org/webcasts/2021/07/arch-all-closing-the-governance-gap-enabling-governed-self-service-analytics.aspx
Learn about:
- Different roles tasked with managing data policies
- Root causes of the governance gap
- Establishing bridges among the different personas - privacy and compliance teams, data stewards, security teams, IT teams, data users
- Simplifying data policy governance
- Governed self-service analytics and data sharing
- Definitions of data sources and data assets and how to enable delegated policy administration
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
Maturing Your Organization's Information Risk Management StrategyPrivacera
As organizations grow, they face more risks associated with the security and protection of sensitive data. Organizations struggling to navigate the different stages of business need to be sensitive to the increasing maturity necessary to support increasing demands for data governance and information risk management.
Learn about:
▪ Four different stages of the maturity curve
▪ Assessing data sensitivity and classifying data assets
▪ Access controls and data protection
▪ Interpreting policies and determining their impact on information management
▪ Determining the impact of data protection policies on information management practices
▪ Automating policy compliance auditing
▪ Maintaining governance consistency across the hybrid data enterprise
Watch the on-demand webinar here: https://tdwi.org/webcasts/2021/03/arch-all-maturing-your-organizations-information-risk-management-strategy.aspx with TDWI Speaker: David Loshin, President of Knowledge Integrity and guest speaker Bill Brooks, Director of Solutions Engineering, Privacera (www.privacera.com)
Enacting the data subjects access rights for gdpr with data services and data...Jean-Michel Franco
GDPR is more than another regulation to be handled by your back office. As stated by the European Commission, “The primary objective of this new set of rules is to give citizens back control over of their personal data.” And surveys show that European citizens are eager to apply for those new fundamental rights, such as access to information, data portability, and the right to be forgotten. Will you be ready to deliver, or will you be forced to tell your customers that unfortunately, you are not yet ready to respect their rights?
Enacting the GDPR’s Data Subject Access Rights (DSAR) requires practical actions. There’s a mandate for an integrated data governance strategy to establish your data inventory, operationalize controls, foster accountability across teams and ensure compliance, and finally unleash personal data to your customers, employees, visitors, and prospects. Only a strong data governance program on top of a modern, collaborative data hub ensures that you have the policies, standards, and controls in place to enforce compliance.
This presentations outlines the practical steps to deploy governed data services that:
Know your customers and employees with a data inventory
Track and trace data using audit trails and data lineage
Manage and propagate opt-in consent across customer-facing applications
Reconcile and protect your sensitive data in a data hub with automated controls, data stewardship, and data masking
Respect the rights for your data subjects with collaborative data management and portals
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Closing the Governance Gap - Enabling Governed Self-Service AnalyticsPrivacera
Data democratization and data protection are conflicting forces that both need to be addressed through data governance and security by defining, deploying, and auditing data access control policies. Yet there is a latent “governance gap”: the individuals in the organization accountable for articulating and specifying data policies do not have enough knowledge of the systems to understand how policies are to be implemented, and the technologists who understand the system are not familiar enough with data policy drivers to appropriately define and deploy data protection policies.
This webinar is a must for personnel with an analytics and technology mandate to learn about the root causes of this governance gap and consider ideas for closing the gap.
On-Demand here: https://tdwi.org/webcasts/2021/07/arch-all-closing-the-governance-gap-enabling-governed-self-service-analytics.aspx
Learn about:
- Different roles tasked with managing data policies
- Root causes of the governance gap
- Establishing bridges among the different personas - privacy and compliance teams, data stewards, security teams, IT teams, data users
- Simplifying data policy governance
- Governed self-service analytics and data sharing
- Definitions of data sources and data assets and how to enable delegated policy administration
The Economic Value of Data: A New Revenue Stream for Global CustodiansCognizant
Global custodians' big data offers myriad opportunities for generating value from analytics solutions; we explore various paths and offer three use cases to illustrate. Data aggregation, risk management, digital experience, operational agility and cross-selling are all covered.
Maturing Your Organization's Information Risk Management StrategyPrivacera
As organizations grow, they face more risks associated with the security and protection of sensitive data. Organizations struggling to navigate the different stages of business need to be sensitive to the increasing maturity necessary to support increasing demands for data governance and information risk management.
Learn about:
▪ Four different stages of the maturity curve
▪ Assessing data sensitivity and classifying data assets
▪ Access controls and data protection
▪ Interpreting policies and determining their impact on information management
▪ Determining the impact of data protection policies on information management practices
▪ Automating policy compliance auditing
▪ Maintaining governance consistency across the hybrid data enterprise
Watch the on-demand webinar here: https://tdwi.org/webcasts/2021/03/arch-all-maturing-your-organizations-information-risk-management-strategy.aspx with TDWI Speaker: David Loshin, President of Knowledge Integrity and guest speaker Bill Brooks, Director of Solutions Engineering, Privacera (www.privacera.com)
Enacting the data subjects access rights for gdpr with data services and data...Jean-Michel Franco
GDPR is more than another regulation to be handled by your back office. As stated by the European Commission, “The primary objective of this new set of rules is to give citizens back control over of their personal data.” And surveys show that European citizens are eager to apply for those new fundamental rights, such as access to information, data portability, and the right to be forgotten. Will you be ready to deliver, or will you be forced to tell your customers that unfortunately, you are not yet ready to respect their rights?
Enacting the GDPR’s Data Subject Access Rights (DSAR) requires practical actions. There’s a mandate for an integrated data governance strategy to establish your data inventory, operationalize controls, foster accountability across teams and ensure compliance, and finally unleash personal data to your customers, employees, visitors, and prospects. Only a strong data governance program on top of a modern, collaborative data hub ensures that you have the policies, standards, and controls in place to enforce compliance.
This presentations outlines the practical steps to deploy governed data services that:
Know your customers and employees with a data inventory
Track and trace data using audit trails and data lineage
Manage and propagate opt-in consent across customer-facing applications
Reconcile and protect your sensitive data in a data hub with automated controls, data stewardship, and data masking
Respect the rights for your data subjects with collaborative data management and portals
Originally Published: Jan 21, 2015
The size and complexity of data make it difficult for companies to unlock the true value of their data. IBM Information Integration Governance can improve data quality, protect sensitive data, and reduce cost and risk. Free up your resources and get more out of your data.
Data protection and privacy regulations such as the EU’s General Data Protection Regulation (GDPR), the California Consumer Privacy Act (CCPA), and Singapore’s Personal Data Protection Act (PDPA) have been major drivers for data governance initiatives and the emergence of data catalog solutions. Organizations have an ever-increasing appetite to leverage their data for business advantage, either through internal collaboration, data sharing across ecosystems, direct commercialization, or as the basis for AI-driven business decision-making. This requires data governance and especially data asset catalog solutions to step up once again and enable data-driven businesses to leverage their data responsibly, ethically, compliantly, and accountably.
This presentation explores how data catalog has become a key technology enabler in overcoming these challenges.
Slides: Applying Artificial Intelligence (AI) in All the Right Places in the ...DATAVERSITY
Data and Analytics are fundamental to digital transformation, yet many companies are still under-utilizing them. To go full throttle, AI and automation technologies can be added across the full spectrum of your data journey to truly re-imagine processes and business models.
Join Information Builders for this webinar on how AI:
• Augments your traditional business intelligence and analytics systems
• Minimizes manual inefficiencies with the way data is generated, collected, cleansed, and organized
• Helps you realize substantial performance gains with use cases such as churn forecasting, predictive maintenance, supply chain planning, risk mitigation, and more
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
MIT ICIQ 2017 Keynote: Data Governance and Data Capitalization in the Big Dat...Pieter De Leenheer
We live in the age of abundant data. Through technology, more data is available, and the processing of that data easier and cheaper than ever before. But to realize the true value of this wealth of data, data leaders must rethink our assumptions, processes, and approaches to managing, governing, and stewarding that data. And to succeed, they must deliver credible, coherent, and trustworthy data into the hands of everyone who can use it.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
Data is everywhere, and delivering trustable data to anyone who needs it has become a challenge. But innovative technologies come to the rescue: through smart semantics, metadata management, auto-profiling, faceted search and collaborative data curation there is a way to establish a Wikipedia like approach for your data. Find out how Talend will help you to operationalize more data faster and increase data usage for everyone with an Enterprise Data Catalog
Data governance and data quality are often described as two sides of the same coin. Data governance provides a data framework relevant to business needs, and data quality provides visibility into the health of the data. If you only have a data governance tool, you’re missing half the picture.
Trillium Discovery seamlessly integrates with Collibra for a complete, closed-loop data governance solution. Build your data quality rules in Collibra, and they are automatically passed to Trillium for data quality processing. The data quality results and metrics are then passed back to Collibra – allowing data stewards and business users to see the health of the data right within their Collibra dashboard.
View this webinar on-demand to see how you can leverage this integration in your organization to readily build, apply, and execute business rules based on data governance policies within Collibra.
3 Steps to Turning CCPA & Data Privacy into Personalized Customer ExperiencesJean-Michel Franco
Your company’s success lies in your capacity to keep your customers’ trust while offering them a personalized experience. With the right Data Privacy framework and technology for your data governance project you will maintain compliance and prosper.
CCPA isn’t the first privacy regulation to impact virtually every organization that does business in the United States – it’s simply the one starting in 2020. As these regulations continue to expand and change, what if there was a way to turn compliance into your advantage? Attend this session and learn how a strong, carefully considered data governance program can help you stay ahead of new regulations like CCPA, and also enhance customer experiences with trusted data.
Learn how a 3-step approach can help you:
Ensure regulatory compliance at scale
Deliver advanced analytics with trusted data
Enable customer personalization for more accurate business insights targeted offers, and behavioral knowledge
Data Standardization with Web Data Integration PromptCloud
Before analyzing data aggregated from multiple sources, it is essential to first standardize the datasets. At PromptCloud, we put special emphasis on this process and understand that as a web crawling company, our solution must enable our clients to integrate data efficiently.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Multi-Tenancy in Data Lakes are on the rise. When looking at multi-tenancy from the lens of data governance, a lot is changing the landscape, and the way we have been operating with respect to the governance model probably needs a rethink. It is time to think of Governance and its various entities as a first-class citizen in data architecture and bake it as part of the platform. We will look at the various aspects of governance, extending to accommodate the growing compliance and regulatory requirements and suggestive architectural approaches to realize the same.
Slides: Applying Artificial Intelligence (AI) in All the Right Places in the ...DATAVERSITY
Data and Analytics are fundamental to digital transformation, yet many companies are still under-utilizing them. To go full throttle, AI and automation technologies can be added across the full spectrum of your data journey to truly re-imagine processes and business models.
Join Information Builders for this webinar on how AI:
• Augments your traditional business intelligence and analytics systems
• Minimizes manual inefficiencies with the way data is generated, collected, cleansed, and organized
• Helps you realize substantial performance gains with use cases such as churn forecasting, predictive maintenance, supply chain planning, risk mitigation, and more
Strategy how to change Big Data into useful information and win the business/candidacy, and Big Problem into Big Opportunity in the information exposure era.
MIT ICIQ 2017 Keynote: Data Governance and Data Capitalization in the Big Dat...Pieter De Leenheer
We live in the age of abundant data. Through technology, more data is available, and the processing of that data easier and cheaper than ever before. But to realize the true value of this wealth of data, data leaders must rethink our assumptions, processes, and approaches to managing, governing, and stewarding that data. And to succeed, they must deliver credible, coherent, and trustworthy data into the hands of everyone who can use it.
Mohanbir Sawhney, Robert R. McCormick Tribune Foundation Clinical Professor of Technology Kellogg School of Management, Northwestern University presents at the 2012 Big Analytics Roadshow.
Companies are drinking from a fire hydrant of data that is too big, moving too fast and is too diverse to be analyzed by conventional database systems. Big Data is like a giant gold mine with large quantities of ore that is difficult to extract. To get value out of Big Data, enterprises need a new mindset and a new set of tools. They also need to know how to extract actionable insights from Big Data that can lead to competitive advantage. The Big Story of Big Data is not what Big Data is, but what it means for business value and competitive advantage.... read more: http://www.biganalytics2012.com/sessions.html#mohan_sawhney
Data is everywhere, and delivering trustable data to anyone who needs it has become a challenge. But innovative technologies come to the rescue: through smart semantics, metadata management, auto-profiling, faceted search and collaborative data curation there is a way to establish a Wikipedia like approach for your data. Find out how Talend will help you to operationalize more data faster and increase data usage for everyone with an Enterprise Data Catalog
Data governance and data quality are often described as two sides of the same coin. Data governance provides a data framework relevant to business needs, and data quality provides visibility into the health of the data. If you only have a data governance tool, you’re missing half the picture.
Trillium Discovery seamlessly integrates with Collibra for a complete, closed-loop data governance solution. Build your data quality rules in Collibra, and they are automatically passed to Trillium for data quality processing. The data quality results and metrics are then passed back to Collibra – allowing data stewards and business users to see the health of the data right within their Collibra dashboard.
View this webinar on-demand to see how you can leverage this integration in your organization to readily build, apply, and execute business rules based on data governance policies within Collibra.
3 Steps to Turning CCPA & Data Privacy into Personalized Customer ExperiencesJean-Michel Franco
Your company’s success lies in your capacity to keep your customers’ trust while offering them a personalized experience. With the right Data Privacy framework and technology for your data governance project you will maintain compliance and prosper.
CCPA isn’t the first privacy regulation to impact virtually every organization that does business in the United States – it’s simply the one starting in 2020. As these regulations continue to expand and change, what if there was a way to turn compliance into your advantage? Attend this session and learn how a strong, carefully considered data governance program can help you stay ahead of new regulations like CCPA, and also enhance customer experiences with trusted data.
Learn how a 3-step approach can help you:
Ensure regulatory compliance at scale
Deliver advanced analytics with trusted data
Enable customer personalization for more accurate business insights targeted offers, and behavioral knowledge
Data Standardization with Web Data Integration PromptCloud
Before analyzing data aggregated from multiple sources, it is essential to first standardize the datasets. At PromptCloud, we put special emphasis on this process and understand that as a web crawling company, our solution must enable our clients to integrate data efficiently.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
My keynote speech at the ISACA IIA Belgium software watch day in October 2014 in Brussels on the value of big data and data analytics for auditors and other assurance professionals
Real-time Data is Changing the Face of the Insurance IndustryDataWorks Summit
The insurance industry was founded on data and yet, new data sources and the “speed” of data are entirely changing how the industry conducts its business. Real-time data used to be a foreign term for insurers but in the digital and connected world it has a significant impact on how the industry engages with customers, manages relationships, conducts core operations of risk assessments and manages claims.
Predictive analytics is the minimum table stakes to remain competitive. Preventive analytics and machine learning are on the rise to the extent they are called out and considered critical success factors in an insurance company’s business strategy. The question is, how do you prepare the organization and adjust the mindset of a business to use real-time data to better serve customers whether individuals or companies?
During this interactive session insurance industry leaders will discuss a variety of topics, including:
· how business data strategies are changing
· filling the skills gap
· value of open data sources and incorporating machine learning
In an age where the insurer must be founded on machine learning and advanced analytics, you’ll hear from the leaders who have a grasp on the opportunities, as well as how to avoid and/or prepare for the bumps along the way
Speakers for this Session:
1. Cindy Maike
2. Denise Rogers
3. Naresh Mudunuru
Multi-Tenancy in Data Lakes are on the rise. When looking at multi-tenancy from the lens of data governance, a lot is changing the landscape, and the way we have been operating with respect to the governance model probably needs a rethink. It is time to think of Governance and its various entities as a first-class citizen in data architecture and bake it as part of the platform. We will look at the various aspects of governance, extending to accommodate the growing compliance and regulatory requirements and suggestive architectural approaches to realize the same.
Top 15 Predictions about Data Analytics and AI for Decision MakersCygnet Infotech
Data Analytics and Artificial Intelligence are transforming businesses and societies in general. Know about how valuable they are for CXOs and other Decision Makers.
5 Digital Transformation Resolutions CIOs Need to Keep in 2021CompunnelDigital1
The global pandemic has changed the trajectory and velocity of digital transformation and will likely continue to do so until 2021. These are top 5 trends that CIOs cannot afford to miss out in 2021.
Top data science and AI trends to watch out for in 2021 | AIM & AnalytixLabsSrishti Deoras
The annual data science and AI trends report by Analytics India Magazine aims to highlight the top trends that will define the industry each year. This report, which has been developed in association with AnalytixLabs, covers the trends that will shape the year 2021.
Digital Transformation & Cloud ProfitabilityGui Carvalhal
A quick view about Digital Transformation and what's happening with Industries across the globe.
A guidance to IT Channel to accelerate Cloud Profitability with valuable resources for download.
HPE Building Intelligent & Autonomous Datacenters for the Data EconomySamarn Pannue
Automation is becoming a critical need IDC’s Enterprise Datacenter survey 2018 shows that 45% of the respondents are demanding more shared, agile and flexible resources in the digital transformation era. As a result, IDC sees software defined
and automation becoming the top infrastructure priority for the CIO.
AI will also play a huge role in enabling datacenter automation with enhanced self-learning and predictive capabilities to continuously stay ahead of the problems that cause datacenter failure.
Top 8 digital transformation trends shaping 2021run_frictionless
In a world that’s increasingly dependent on digital, IT’s role is more critical than ever. To meet rising demands, organizations are accelerating their digital transformation. This report identifies the top 8 technology trends that will face CIOs, IT leaders, and organizations in their digital transformation journey in 2021.
https://runfrictionless.com/b2b-white-paper-service/
What's in store for Big Data in 2015? Will the 'Internet of Things' fuel the Industrial Internet? Will Big Data get Cloudy? Check out the top five Big Data predictions for 2015 according to Quentin Gallivan, CEO, Pentah0
Our report will provide a look into the technology landscape of the future, including:
- Importance of AI in enabling innovation
- Catalysts of future innovations
- Top technology trends in 2023-2024
- Main benefits of AI adoption
- Steps to prepare for future disruptions.
Download your free copy now and implement the key findings to improve your business.
GoodData: Introducing Insights as a Service (White Paper)Jessica Legg
Crafted and copywrote a new white paper announcing new GoodData product features and positioning as the first entrant in the Insights-as-a-Service category. Led design and development applying new branding.
Summary: BI is entering a new era, an era where purchasing decisions are being led by business units and managers, instead of corporate systems and IT. Learn more about this fundamental market shift and the benefits Insights as a Service can offer your business in this white paper.
Crafted and copywrote a new white paper announcing new GoodData product features and positioning as the first entrant in the Insights-as-a-Service category. Led design and development applying new branding.
Summary: BI is entering a new era, an era where purchasing decisions are being led by business units and managers, instead of corporate systems and IT. Learn more about this fundamental market shift and the benefits Insights as a Service can offer your business in this white paper.
Similar to Evtm 281 07_bi2015_infographic_r2h (20)
