We live in an era where customer experience trumps product features and functions. How do you exceed customer’s expectations every time they interact with your organization? By leveraging more information and applying insights you have learned over time. Turning data-driven power into delightful experiences will give you the advantages required to succeed in today’s climate of one-click shopping and crowd-sourced feedback. Whether you are a retailer, a banker, a care provider, or a policy maker, your organization must harness the power of growing data volumes, data types, and data sources to foster experiences that matter.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Semantic 'Radar' Steers Users to Insights in the Data LakeCognizant
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Big Data is Here for Financial Services White PaperExperian
Conquering Big Data Challenges
Financial institutions have invested in Big Data for many years, and new advances in technology infrastructure have opened the door for leveraging data in ways that can make an even greater impact on your business.
Learn how Big Data challenges are easier to overcome and how to find opportunities in your existing data and scale for the future.
DATA VIRTUALIZATION FOR DECISION MAKING IN BIG DATAijseajournal
Data analytics and Business Intelligence (BI) are essential components of decision support technologies that gather and analyze data for faster and better strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence which helps in making future predictions whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools whose main objective is to enable interactive access to real-time data, manipulation of data and provide business organizations with appropriate analysis. Business Intelligence solutions leverage software and services to collect and transform raw data into useful information that enable more informed and quality business decisions regarding customers, market competitors, internal operations and so on. Data needs to be integrated from disparate sources in order to derive valuable insights. Extract-Transform-Load (ETL), which are traditionally employed by organizations help in extracting data from different sources, transforming and aggregating and finally loading large volume of data into warehouses. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL often serve unique and complementary purposes in performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.
Virtual Data Steward: Data Management 3.0CrowdFlower
Every company that is serious about data governance needs data stewards. Data stewards connect business information requirements and processes with information technology capabilities. This function is essential to bridging data management policies and standards to day-to-day operational practices.
Decision-Making: The New Frontier for AutomationCognizant
Decision process automation is a forward-looking, practical strategy to improve enterprise operations, enabling faster responses to rapidly changing conditions and identifying options for action based on a more complete exploration of potential outcomes.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Learn the basics of business intelligence, including common terms, how to implement solutions, and what it can do for your company. For even more insight into how project management can benefit your work, visit: http://bit.ly/GuideToBI
To find a custom business intelligence solution that fits the specific needs of your work, visit: http://bit.ly/GetBI1
Real time responses to events will be feasible when enterprises are designed to be maneuverable and their flow of activity is not disrupted by a breakdown in any one component in the chain of business processes that enable the completion of an activity.
The business models across industries around the world are becoming Customer Centric. Recent studies show that “knowing” customers based on internal as well as external data is one of the top priorities of business leaders. On the other hand various surveys also reveal that customers do not mind to share their semi-personal data for the benefit of differentiated service. In that context, the 360 degree view of customer – which was once thought to be a business process, master data management, data integration and data warehouse / business intelligence related problem has now entered into the whole new big world of BIG data including integration with unstructured data sources. Impact of big data on Customer Master Data Management is spread across - from Integration and linkage of unstructured or semi-structured data with structured master data that is maintained within enterprise; to analyze and visualization of the same to generate useful insight about the customers. There are various patterns to handle the challenges across the steps i.e. acquire, link, manage, analyze and distribute the enhanced customer data for differentiated product or services.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
Virtual Data Steward: Data Management 3.0CrowdFlower
Every company that is serious about data governance needs data stewards. Data stewards connect business information requirements and processes with information technology capabilities. This function is essential to bridging data management policies and standards to day-to-day operational practices.
Decision-Making: The New Frontier for AutomationCognizant
Decision process automation is a forward-looking, practical strategy to improve enterprise operations, enabling faster responses to rapidly changing conditions and identifying options for action based on a more complete exploration of potential outcomes.
Few decades ago, Managers relied on their instincts to take business decisions. They could afford to make mistakes and learn from it. Today, the scope for learning from mistakes is very minimal. Instincts should be backed by data to minimise mistakes.
Technological advancements, in addition to opening new channels of communication with customers, have also enabled organizations to collect vital information about their businesses with customers. But, have these organizations fully leveraged this data?
Today, Organizations make use of data for business decisions, but the data is not close enough to the customer to reap maximum benefit. In many cases, importance is not given to the granularity of data. The probability of “customer centric” decisions being right could be high, if the top management makes better use of the end user customer data (such as point of sale data, voice of customer, social media buzz etc.) to devise business strategies.
Learn the basics of business intelligence, including common terms, how to implement solutions, and what it can do for your company. For even more insight into how project management can benefit your work, visit: http://bit.ly/GuideToBI
To find a custom business intelligence solution that fits the specific needs of your work, visit: http://bit.ly/GetBI1
Real time responses to events will be feasible when enterprises are designed to be maneuverable and their flow of activity is not disrupted by a breakdown in any one component in the chain of business processes that enable the completion of an activity.
The business models across industries around the world are becoming Customer Centric. Recent studies show that “knowing” customers based on internal as well as external data is one of the top priorities of business leaders. On the other hand various surveys also reveal that customers do not mind to share their semi-personal data for the benefit of differentiated service. In that context, the 360 degree view of customer – which was once thought to be a business process, master data management, data integration and data warehouse / business intelligence related problem has now entered into the whole new big world of BIG data including integration with unstructured data sources. Impact of big data on Customer Master Data Management is spread across - from Integration and linkage of unstructured or semi-structured data with structured master data that is maintained within enterprise; to analyze and visualization of the same to generate useful insight about the customers. There are various patterns to handle the challenges across the steps i.e. acquire, link, manage, analyze and distribute the enhanced customer data for differentiated product or services.
Top 10 guidelines for deploying modern data architecture for the data driven ...LindaWatson19
Enterprises are facing a new revolution, powered by the rapid adoption of data analytics with modern technologies like machine learning and artificial intelligence (A).
Semantic 'Radar' Steers Users to Insights in the Data LakeThomas Kelly, PMP
By infusing information with intelligence, users can discover meaning in the digital data that envelops people, organizations, processes, products and things.
NFRASTRUCTURE MODERNIZATION REVIEW
Analyze the issues
Hardware
Over-running volume of data is a problem that should be addressed by data management and storage management. Data is being constantly collected but poorly analyzed which leads to excessive amounts of data occupying storage and delay in operations which inevitably affect production, sales and profits. If this remains unresolved, current data may have to be moved to external storage and recovered if needed. There is also the risk of data not being encoded into computers and thus will remain in manual state. This can be a case of redundant or extraneous data that is not yet cleaned and normalized by operations managers with the guidance of IT. This situation is known as data overload where companies actually use only a fraction of the data they capture and store. Many companies simply hoard data to make sure that they are readily available when they are needed. This negatively impacts the Corporation when assessing data relevance, accuracies and timeliness (Marr, 2016).
Software
The Largo Corporation (LC) seems to running on an enterprise resource planning system that is probably as long as 20 years old. Initially, LC has had success with the old system because they were able to establish themselves in various industries such as healthcare, media, government, etc. But due to various concerns, the Corporation is currently running on an outdated system because it is unable to provide services that keeps the Corporation a float. The LC is losing revenue and customers. Complete data without analysis is invaluable because, no information and insights can be produced that will support decisions. Customer data should lead to the best marketing and sales campaigns. The Corporation needs to recognize its weaknesses and implement changes to their software by incorporating funding for a new system that is reliable, secure, and has the ability to run on integrated systems; all of which will streamline data organization and analysis for the enterprise. (Rouse, n.d).
Network/Telecommunications
The network that was built in the 1980’s has become slow and unreliable affecting business operations. The problems caused by the old network are; lack of integration and communication between departments affecting the work flow, supply vs. demand, and inability to analyze data to carry out these operations. The Corporation should have taken into consideration the growth of the company by expanding and upgrading their networks along with their services. They should also take into consideration the number of departments, the number of users and their skill level, storage and bandwidth, and budget (Rasmussen, 2011). The current network does not allow employees to connect on their mobile devices which restricts flexibility and places limitations on productivity and portability.
Management
The responses of both IT and the business group are both juxtaposed against e ...
Now companies are in the middle of a renovation that forces them to be analytics-driven to
continue being competitive. Data analysis provides a complete insight about their business. It
also gives noteworthy advantages over their competitors. Analytics-driven insights compel
businesses to take action on service innovation, enhance client experience, detect irregularities in
process and provide extra time for product or service marketing. To work on analytics driven
activities, companies require to gather, analyse and store information from all possible sources.
Companies should bring appropriate tools and workflows in practice to analyse data rapidly and
unceasingly. They should obtain insight from data analysis result and make changes in their
business process and practice on the basis of gained result. It would help to be more agile than
their previous process and function.
To Become a Data-Driven Enterprise, Data Democratization is EssentialCognizant
To optimise enterprise knowledge, organizations need a modern platform that enables data to be more easily shared, interpreted and capitalized on by internal decision makers and by business partners across the extended value chain.
How In-memory Computing Drives IT SimplificationSAP Technology
Discover how the in-memory technology of SAP HANA can reduce complexity and simplify the IT landscape to foster real-time results, innovation and lower costs.
Data Virtualization, a Strategic IT Investment to Build Modern Enterprise Dat...Denodo
This content was presented during the Smart Data Summit Dubai 2015 in the UAE on May 25, 2015, by Jesus Barrasa, Senior Solutions Architect at Denodo Technologies.
In the era of Big Data, IoT, Cloud and Social Media, Information Architects are forced to rethink how to tackle data management and integration in the enterprise. Traditional approaches based on data replication and rigid information models lack the flexibility to deal with this new hybrid reality. New data sources and an increasing variety of consuming applications, like mobile apps and SaaS, add more complexity to the problem of delivering the right data, in the right format, and at the right time to the business. Data Virtualization emerges in this new scenario as the key enabler of agile, maintainable and future-proof data architectures.
Analyst Webinar: Discover how a logical data fabric helps organizations avoid...Denodo
Watch full webinar here: https://bit.ly/3zVUXWp
In this webinar, we’ll be tackling the question of where our data is and how we can avoid it falling into a black hole.
We’ll examine how data blackholes and silos come to be and the challenges these pose to organisations. We will also look at the impact of data silos as organisations adopt more complex multi-cloud setups. Finally, we will discuss the opportunities a logical data fabric poses to assist organisations to avoid data silos and manage data in a centrally governed and controlled environment.
Join us and Barc’s Jacqueline Bloemen on this webinar to get the answer and further insights on how to better avoid falling into a #datablackhole. Hope to see you connected!
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
2. We live in an era where customer experience trumps product features and functions. How do you exceed
customer’s expectations every time they interact with your organization? By leveraging more information
and applying insights you have learned over time. Turning data-driven power into delightful experiences will
give you the advantages required to succeed in today’s climate of one-click shopping and crowd-sourced
feedback. Whether you are a retailer, a banker, a care provider, or a policy maker, your organization must
harness the power of growing data volumes, data types, and data sources to foster experiences that matter.
Introduction
76 percent of organizations believe that untimely data has inhibited
business opportunities.1
3. or most enterprises, gaining a complete view of user experiences and context requires the modernization of legacy and disparate
technologies, many of which have been designed to support siloed applications/functions in individual departments. The difference
between modern and legacy platforms lies in their capabilities.
Holistically support the digital
transformation brought on by massive
cloud, mobile, and social adoption while
simplifying development and support
requirements.
Support both transaction processing and
analytics in real-time.
Empower organizations to deliver modern,
data-rich applications amongst legacy
applications, support on-prem and cloud
deployment options, mitigate risk
between open source and
enterprise-grade software.
Featuring new market data from IDC, this eBook will highlight the motivating factors driving the evolution of the modern data platform
and explain how organizations can benefit from a simplified, yet more powerful, data infrastructure.
MODERN DATA PLATFORMS
F
4. The digital evolution is driving the push toward the modern enterprise, where transactions and analytics
complement one another to derive new, actionable insights before opportunities are lost. To meet these
rapidly evolving market and consumer demands, organizations must accelerate their path to innovation.
According to the IDC study of more than 500 organizations across the globe, speeding innovation was the top
business priority for more than two-thirds (33.9 percent) of respondents, with streamlining operations (31.9
percent) coming in as the close second.2
Chapter 1: Business Drivers
5. DATA LATENCY.
Making a decision based on live data requires the ability to
perform analytical queries with transactional data in real-time.
Most companies, however, are basing decisions on data that is
anywhere from 10 minutes to two hours, or even days, old.
These latencies make it impossible for organizations to
capitalize on real-time and near real-time business
opportunities. It is not surprising, then, that 76 percent of
respondents reported that the inability to analyze current
data inhibits their ability to take advantage of business
opportunities and 54 percent claimed that it also inhibits their
ability to improve operational efficiency.3
6. THE ROLE OF TRANSACTIONAL AND
ANALYTIC DATA PROCESSING IN THE
ENTERPRISE.
Today’s enterprise often consists of two separate data
processing arms: Transactions and Analytics. Understanding
the role and requirements of each arm enables organizations
to better understand their limitations and opportunities when it
comes to processing and analyzing data to positively impact
the business.
Transactions typically involve the processing of data in relation
to regular operations conducted by the business and are
optimized for write, not query, speed. Analytics are optimized
for query performance and provide organizations with insights
based on specific questions.
7. THE PATH TO REAL-TIME ANALYTICS GOES
THROUGH ETL (EXTRACT/TRANSFORM/LOAD).
Data often needs to move from transactional systems to analytics, increasing
complexity and latency that slows the business down and can lead to missed
opportunities. Transactional data processing is often limited in its ability to
quickly perform analytic queries, while analytics data processing depends on
first moving and pre-processing data from transactional systems, making it
impossible to deliver valuable real-time insights.
According to the IDC study, 86.5 percent of organizations use ETL to move at
least 25 percent of all enterprise data between transactional and analytical
systems. And nearly two-thirds (63.9 percent) of data moved via ETL is at
least five days old by the time it reaches an analytics database.4
This is a
critical obstacle for most organizations that want to deliver the right customer
experience in the moment.
EXTRACT
TRANSFORM
LOAD
8. CURRENT DATA INFRASTRUCTURE
CHALLENGES FOR APPLICATION
DEVELOPERS.
Delivering the ultimate customer experience demands high
performance and quick response times. But, this is independent
of the many sources streaming data into the application or the
type of mixed workloads the application requires (processing
large volumes of transactions while executing complex
predictive analytical algorithms). Adding to the complexity is
the need to support more data types (structured, unstructured,
etc.), larger data sets, and an accelerated path from analysis to
action introduced by mobile users, IoT/sensor data, and fickle /
constantly emerging trends.
9. NEW DATA TYPES
✓✓ Relational
✓✓ Internet of Things
✓✓ Streaming sources
✓✓ Sensor data
✓✓ Document
✓✓ Key value
✓✓ Video/audio/image
✓✓ Object
✓✓ Geospatial
Often, to support the different types of data and applications
required, companies utilize several database systems across the
organization, which means the data is saved and stored in
disparate places and formats. Each database may be unique to
the application, data, and workload type.
More than 60 percent of respondents to a recent
IDC survey reported having more than five
analytical databases, and more than 30 percent
have more than 10.5
The majority of respondents have more than five
production transactional databases, while 25
percent had more than 10.6
Companies now have the challenge of
harnessing that data and determining how to
extract value from it by applying it to business
operations – making sense of all the data by
tying all the sources to an individual customer,
patient, citizen, investor, etc.
MULTIPLE DATABASES INCREASE COMPLEXITY
10. By combining analytic and transactional data processing,
including a range of data types in support of digital
transformation, a modern data platform lies at the heart of a
data-driven business.
A data management platform is a centralized computing
system for collecting, integrating, managing, and analyzing
large sets of structured and unstructured data from
disparate sources at massive scale (distributed as well as
single server) and can support multiple use case scenarios
and workloads (transaction processing and analytics) with
native data and application interoperability.
There are three central pillars to a modern data
platform.
It must support all data types and
workloads in a single architecture.
It must incorporate database management,
interoperability, and analytics.
It must be reliable and provide high
throughput and low latency.
Chapter 2: What is a modern data
platform?
11. Why do companies need a consolidated data platform? Because disparate systems create a disconnect between
insight and action, resulting in a delay in the feedback loop that drives the ultimate customer experience. A
consolidated data platform helps companies achieve their core (IT-related) business objectives, while simplifying
architecture, reducing cost, speeding innovation, and streamlining operations.
CONNECTING INSIGHT AND ACTION
Managing multiple databases is complex, expensive, and
introduces latency issues. As data itself grows more complex,
deploying a unique database and data integration system for
each business need creates unnecessary complexity. It means
tactical decisions cannot be supported as long as data is
segregated into transactional and analytical databases. Most
users need a broad variety of data type support that goes well
beyond what native relational database management systems
(RDBMS) provide. Lastly, the rising costs of database
management is cost prohibitive to many organizations. The
maintenance of many databases leads to excessive cost and
complexity in the data center.
12. How do you define the ultimate experience for your customers, partners,
and stakeholders? What data do you need to ensure all of the information
can be accessed for both guiding a decision and executing intelligent,
data-driven actions?
To answer these questions, you must first identify your organization’s data
infrastructure needs. This includes understanding where your data resides,
how often and by what means it is accessed, and how it is to be analyzed.
Companies no longer need to choose between data from different sources
and having real-time access to information through robust analytics. They
can access data how and when they want and have the ability to make the
data actionable in real-time. Modern data platforms can unlock your data
and transform your business.
• Where is your data located?
• How often is it accessed? And by
what applications?
• How/where is new data “routed”
into your organization?
• What data is being analyzed?
Current? Data lakes, etc.?
• Where are the biggest gaps in your
data infrastructure?
Chapter 3: Delivering ultimate
data-driven experiences
Answering these questions
will help you identify your
organization’s data
infrastructure needs.