https://www.linkedin.com/pulse/quality-40-technical-overview-things-you-should-know-when-cachat-yjyhe
Things
you should know when talking with IT!
Accelerating
access time for insights improves decision making.
Summary:
·
Quality
4.0 creates an adaptive data architecture for faster insights, using Data
Lakes, Data Warehouses, Data Hubs, and Data Fabrics
·
Augmented
Self-Service empowers non-technical users with automation and user-friendly
tools for data analytics.
·
Integrated
Performance Excellence aligns technology with business needs and collaboration.
·
Quality
4.0 improves performance by making approved data easily accessible and usable
within privacy and security guidelines.
·
Advanced
data management strategies and AI-powered tools accelerate time-to-insight
across the organization.
Recommendations:
·
Focus
on understanding business skills, needs, and preferences when designing the
logical data architecture.
·
Ensure
strong data governance and quality controls are in place to maintain trust in
the data.
·
Provide
adequate training and support for employees to effectively use the self-service
analytics tools.
·
Regularly
review and update the data architecture to keep pace with evolving business
needs and technology advancements.
Employees need information
at their fingertips to make good and quick decisions in the service of their
customers. They simply can’t wait months or even days for a table, dashboard,
analytic, or report to be created. Employees should be able to ask the computer
questions. Data architectures are adapting, and technologies innovating, to
meet these needs for accelerated business insights from data and analytics. The data and analytics worlds are changing at
a feverish pace!
Technical Overview
Adaptive Data Architectures
Quality 4.0 creates a flexible data architecture
tailored to various employee needs, including advanced analytics, data
exploration, and visual storytelling. This approach significantly speeds up the
time it takes to obtain insights. Modern data architecture, which includes Data
Lakes and Data Warehouses, is crucial here. By effectively integrating and
utilizing these components, the architecture provides faster access to data
insights across different employee groups.
Understanding how Data Lakes and Data Warehouses are used together in
this system is key to grasping how modern data architecture works.
The Data Lake
A Data Lake is a repository where
data is stored in its raw form quickly, with minimal initial processing. It's a
critical element of modern data architecture, especially for advanced
analytics, as it allows access to data even before its full value is
understood. Data scientists use Data Lakes to rapidly apply statistical models
and discover insights and predictions across different data sets without
waiting for comprehensive data modeling and integration. As the system
processes and learns from this data, it feeds refined and validated information
into the Data Warehouse, providing employees with immediate access to reliable
data.
The Data Warehouse
Data Warehouses have been used by organizations for many years but have
often been a source of frustration due to the lengthy time required to prepare
data before it can be used for business purposes. However, in modern data
architectures like Quality 4.0, the process of preparing and using data in Data
Warehouses has become much quicker. New technologies and tools speed up
building and accessing data, and features like automated self-service tools
enhance and quicken the extraction of insights and value from the Data
Warehouse. Thanks to these advancements, employees can now get rapid business
insights from the Data Warehouse. This is largely due to the seamless
integration with Data Lakes, where data scientists can quickly process raw data
for advanced analytics.
The Data Hub
A Data Hub is a new type of data architecture that speeds up how quickly
insights can be accessed within an organization. It acts as a central point
where different data environments, applications, and processes connect. A Data
Hub standardizes and translates data, making it easier to share across the
organization. This setup enables smooth and efficient transfer of high-quality
data. By linking key components like Data Lakes, Data Warehouses, and various
enterprise applications, Data Hubs help ensure that data flows seamlessly
within the organization, thereby accelerating the availability of valuable data
for analysis and operational use.
The Data Fabric
A Data Fabric is an
architecture and set of data services that provide consistent capabilities
across a choice of endpoints spanning hybrid multi-cloud environments.
Essentially, it's a design concept that allows for flexible, resilient
integration of data sources across platforms and business systems. Here are
some key features and purposes of a Data Fabric:
· Integration
of Data Sources: A Data Fabric integrates data from multiple
sources, whether they are on-premises databases, cloud storage, or real-time
data streams. This integration allows data to be accessible and usable across
different organizational environments without needing to replicate data
unnecessarily.
· Data
Management and Governance: It includes tools and technologies for
data governance, quality, security, and privacy. By ensuring that data across
systems is consistent and well-managed, organizations can trust the data's
reliability and compliance with regulations.
· Data
Accessibility and Sharing: Data Fabric facilitates easier access
to data by different stakeholders within the organization, irrespective of
their geographical or organizational location. This makes data-driven
decision-making faster and more efficient.
· Support
for Advanced Analytics and AI: With a unified view and
access to various data sources, Data Fabrics supports advanced analytics
applications and artificial intelligence. AI models can be trained with diverse
datasets that reflect different aspects of the business, enhancing their
accuracy and relevance.
· Automation
and Orchestration: Data Fabrics often include automated
processes to handle data integration, management, and the provisioning of data
services. This reduces the manual effort required and speeds up data workflows.
· Scalability
and Flexibility: Since Data Fabrics are designed to operate
across different environments (including on-premise and multi-cloud setups),
they are inherently scalable and flexible. This allows organizations to expand
their data infrastructure as needed without major rearchitecting.
Augmented Self-Service
Augmented Self-Service is an
approach in data analytics and business intelligence that combines automation
and user-friendly tools to enhance how individuals interact with and utilize
data without requiring deep technical expertise. This concept aims to empower employees
to access, understand, and derive insights from data through intuitive
platforms and automated processes. Here are key aspects of Augmented
Self-Service:
· Empowering
Employees: By reducing dependency on data scientists and IT staff
for generating reports and insights, these tools empower non-technical business
users to make data-driven decisions quickly, enhancing
agility and responsiveness within the organization.
· Automation
of Analytical Processes: The automation of many of the data
processes that typically require specialist knowledge, such as data
preparation, analysis, and the generation of insights. For example, these tools
might automatically clean and transform data, identify patterns, and even
suggest areas for deeper analysis.
· User-Friendly
Interfaces: Highly intuitive interfaces that allow users
to interact with data using natural language queries or simple drag-and-drop
operations. This reduces the learning curve and opens up data analytics to a
broader range of users within an organization.
· Conversational
Analytics: Conversational interfaces (talk to the computer), such
as chatbots or virtual assistants, that understand and respond to user queries
in natural language. This makes it easier for users to ask questions and
receive insights as if they were having a conversation with a data analyst.
· Data
Visualization and Storytelling: Dynamic and intelligent
visualizations that adjust according to the data being analyzed. They help in
telling a story with data by linking various data points in a logical flow that
makes sense to business users, aiding in better understanding and decision-making.
· Contextual
and Predictive Insights: Leveraging machine learning and AI,
augmented self-service tools can provide predictive analytics and contextual
insights directly to users. They can suggest new areas of investigation or
automatically highlight anomalies and trends without users specifically
searching for them.
Integrated Performance Excellence™ - People, Process, Technology
Accelerating access time for
insights requires implementing technologies that align with business skills and
desires, establishing processes to improve business collaboration, and
enlisting the business in driving value from data assets. Building a robust and powerful logical data
architecture is key for Quality 4.0.
The logical data
architecture view is concerned with the design of the data structures and
relationships between them, without getting into the specifics of physical
storage details. It models data in a way that is comprehensible to business
stakeholders, focusing on what data is held and how it is interconnected. This view helps in understanding the
organization’s data in terms of business entities and their relationships,
independent of physical considerations. It’s crucial for data governance and
data modeling.
Physical data architectures
are built from the technology up and lack a focus on employee needs and wants.
Scalability, redundancy, and performance are all valid and noble goals for a
data architecture, but in a vacuum, they alone don’t typically deliver optimal
business value.
Understanding business
skills, needs, desires, and preferences is critical in designing a logical data
architecture that will enable organizations to accelerate access time for
insights to improve decision making. Organizational success with Quality 4.0
requires commitment and collaboration from the entire organization. Integrated
Process Excellence™ (IPE)* provides
organizations a specific, detailed, “How-To” framework.
*IPE emphasizes the
importance of focusing on the process rather than just the results. It outlines a six step approach, which
include creating a positive environment, identifying key variables, developing
process worksheets, communicating the process, controlling the process, and
improving the process. The recording
also mentions the types of cause-and-effect analysis (FMEA vs SMEA), the
importance of understanding process vs. results, and the need for a combination
of urgency on the process and patience in the results when the IPE framework.
Impact on Quality 4.0
Quality 4.0 enables significant performance improvements by making
approved data sets easily accessible. This system allows data to be found,
evaluated for quality, and contextualized for business needs, ensuring it can
be safely used within set privacy and security guidelines. Users can also rate
the usability of data, access data shared by others, or contribute data they
find useful. This framework facilitates quick and simple access to valuable
data, enhancing understanding and usage among employees.
Quality 4.0 uses advanced data management strategies in distributed
systems to meet the needs for data speed, quality, and compliance across hybrid
and multi-cloud environments. This approach is key for businesses to
efficiently use their data for gaining a competitive edge. The above architecture
components, working in complementary coordination, all help to reduce the
time-to-insight and value of organizational (and external) data and analytics.
Quality 4.0 offers an automated and conversational way to
access data insights on mobile devices, tailored to individual user needs and
delivered directly to them. This includes using AI for natural language
queries, dynamic and smart visualizations. With Quality 4.0, all employees
receive data contextualized for their specific business needs, enhanced by AI
that learns and adapts. This speeds up their ability to access insights and
make decisions, minimizing the time they spend sorting through data to find
relevant information. Quality 4.0 also helps uncover insights that might
otherwise be missed.
Quality 4.0 incorporates advanced data science tools that
streamline the data usage process. These tools include pre-built machine
learning models accessible through Automated Machine Learning (AutoML), which
quickly determines the most suitable models for datasets and scenarios. AutoML will automate many data preparation
tasks, such as classifying data attributes and mapping data intelligently,
making the process faster and less dependent on expert data scientists.
Yes, I know there is a
spelling error in the last graphic. AI tools are powerful. All the graphics in this article were created
by AI. So, regarding the spelling
error, IA must get better, just like a child learning to spell.
Plan for success. Have a bias for action.
Any feedback
is greatly appreciated. If you need any help with your Quality 4.0 strategy, I
provide services to provide guidance and strategic planning.
John Cachat
Integrated Process Excellence Data Architect
jmc@peproso.com
Related Material:
FOR IMMEDIATE RELEASE - Looking for Company interested in
developing State-of-the-Art Quality Cost 4.0 Software Tool
PeProSo Quality Cost 4.0
From Theory to Deployment White Paper Mar 2024
https://drive.google.com/file/d/1r4EeeOYG3An8vULRaL1tQRdMTqIdL50v/view?usp=drive_link
PeProSo Quality 4.0 Don't
Feel Overwhelmed Feel Motivated White Paper Mar 2024
https://drive.google.com/file/d/1iSsIZ9QXaoYBDEAaLE-bsLsRoiPjOYfC/view?usp=drive_link
Recording - ASQ QMD PeProSo
Quality 4.0 Don't feel overwhelmed. Feel motivated Mar 2024
https://www.youtube.com/watch?v=Tev6nikU5OU
Recording - Integrated Process Excellence (IPE ) Apr 17 2024
https://www.youtube.com/watch?v=4MxA5Onr-ds&t=1s
John Cachat Background
Summary
https://www.linkedin.com/pulse/john-cachats-journey-quality-tale-innovation-john-m-cachat/
