Essential Data Analysis

The Competitive Advantage Benefit of Innovative Analytics

Competitive advantage using innovative analytics supports organization improvement and increases the likelihood of success.

Management performance is closely tied to the results of the decisions made by managers. Recent advances in analytics provide better insight into the information that managers rely on when making better decisions. The use of analytics by managers is best enabled when managers have access to tools and methods that are easy to use, apply them to a problem or issue, and make sense of the tasks a manager executes.

Completing Digital Transformation

Innovative analytics use all that digital data you are acquiring. Creating performance, operation and quality advantage provides improved response to customer needs. Public sector organizations provide advantages in quality of service and better support to their citizens. Innovative analytical techniques improve insight for making effective decisions. It is important to know about the analytics available today, and what type of analytics managers really need.

Managers need a good working knowledge of a suite of analytics that provide them with better more reliable insight for decisions. Analysts need this knowledge to provide better recommendations for change as a result of their busines analysis, architecture and application requirements efforts. New tools today provide a core capability for increased quality and productive analysis.

Improved analytics results in the best  corrective action.

New Analytics are Available to Improve Business Analysis Efforts

Improvements in aalytics using artificial intelligence, neural nets, and machine learning are coupled with new algorithms and statistical techniques for rapid and effective insight development. Innovative Analytics are supported by easy-to-use analytic workflows and templates that improve ease of use. A key to management productivity is providing innovative analytic capability.

This course is for managers and analysts seeking to gain current skills in accelerated and improved organization analytics. Awareness of current analytics provides managers and analysts with an advantage in governing the flow of work delivering goods and services to customers.

• Describe the key emerging analytics managers use today.
• Explain the difference between data analysis, data science and data analytics
• Choosing among alternatives for the best decision result
• Explain how ‘easy to use’ analytics can help a manager.
• Applying analytics to identify performance, operation, and quality issues.
• Identify when to use specific analytics for organization performance.
• Know when to position the analytics at process decision points.
• Verify that the analytic is providing management insight.
• Comparing the results of multiple analytics as applied to a decision.
• Using analytics to predict what might happen next.

This professional training session provides a hands-on, skill-oriented working knowledge of the emerging innovative analytic techniques that managers and analysts should consider and use. The learning approach uses discussions, interactive exercises, and group exercises that focus on outcomes that lead to organization success. Participants can apply this learning as soon as they get back to their office.

What techniques will you learn?

Some of the analytics topics covered in this course are Deep Learning, Machine Learning, Neural Nets, Portfolio Ranking, Affinity Recommendation Technique, Correlation, Regression, Ranking with Correlation Matrices, Sentiment and Keyword Analytics, Semantic Comparisons, Semantic Inference, Alignment Analytics, and Impact Analysis.

Where can you use these techniques?

Innovative analytics supports multiple needs of an organization from strategy to operations including such issues as choosing alternatives, process performance, consolidation and context, organization change impact, project portfolio analysis, context analysis of strategies, data, applications and processes, organization alignment, and more.

Who should attend

Managers, Process Analysts, Business Analysts, Managers, Professionals, IT Specialists, IT and Business Architects.

Meet your expert course trainer: Frank Kowalkowski

Frank Kowalkowski is President of Knowledge Consultants, Inc., a firm focusing on business performance, business analytics, data science, business architecture, big data, business intelligence, predictive analytics and statistical techniques. He has over 30 years of line management and consulting experience in a wide variety of industries. He has been involved with many projects both as a user and purveyor of business analytics. He has worked projects in state and federal government dealing with back office operations, legislative compliance and regulatory compliance. He has worked on the federal level with the national defense department, Coast Guard for drug interdiction and other projects. His background includes a number of industries including manufacturing, distribution, supply chain, banking, insurance, financial institutions, health care, pharmaceuticals, oil and gas and chemicals.

More recently Frank has been involved in conducting workshops, professional training sessions and assessments of architecture, data science, governance, compliance, risk and process management efforts. He also develops algorithms for analytics tools particularly semantic algorithms as well as data analysis techniques. He is often a keynote speaker, panel moderator and member at international conferences as well as a conference chair, he has written numerous papers and spoken at conferences on a variety of business subjects. He conducts frequent seminars nationally and internationally on a variety of business management, analytics and information technology topics.

He is the author of a 1996 book on Enterprise Analysis. His most recent publications are a featured chapter in the business book “Digital Transformation: Using BPM You Already Own.” for publication in 2017. His chapter is titled “Improve, Automate, Digitize”, he also has a chapter in the business architecture book titled ‘Business and Dynamic Change’ June, 2015 and a chapter on semantic process analytics in the book Passports to Success in BPM published in 2014 all are available on Amazon.

Knowledge Consultants. Inc. (KCI)

Knowledge Consultants, Inc. is a professional services firm founded in 1984. KCI provides consulting and professional education services. With over 50 courses taught worldwide, KCI provides the opportunity to develop core strengths in the following certification areas:

• Process Management
• IT Management
• Business Performance Management
• Business Analysis
• Analytical Techniques for Business
• Business and IT Architecture

KCI has expanded its training and consulting efforts internationally into Europe, Southeast Asia and the Middle East. KCI has an outstanding list of current and past clients including many of the Fortune’s 100 companies.

Consulting focuses on the key areas of Business Performance Management, Process Management, Business and IT Architecture, Business Analysis, Using Analytic Techniques for Performance Improvement and IT Management.

Day 1 Theme: Key Use of Data Analysis Today

The hot topics today are digital transformation, neural nets, generative AI, and machine learning. All of these are a part of data analysis. Digital transformation requires thinking about analysis and analytics that leverage all the digital data collected. Tools that support and enable analytics help with the everyday decisions all organizations make. The focus today is applying analysis with emerging analytics to key decisions.

Section 1 – What is Data Analysis?

• The many types of data perspectives today
• Data Analysis, Data Science, Data Analytics
• Types of data we deal with today
  • Objective, Subjective, video, audio, objects etc.
• The need for innovative algorithms.
• Data Analysis and digital twins
• Video and Discussion: Analytics, Alternatives and Decisions

Section 2 – Data Analysis and Problem Solving

• Problem solving and opportunities in the organization
• Business analysis and problem resolution
• Performance, Operations, Quality, Strategy, Alignment
• Today – Performance analysis using indicators
  • Indicators about how well you are doing.
• Operational where is the opportunity
• Example: Analytics used in Balanced Scorecard Strategy Maps
• Exercise: What Analytic Opportunities do you Have?

Section 3 – Data Analysis and Analytical Insight

• The things you make decisions
• Alternatives, capabilities, strategies, projects etc.
• A driver diagram explains the forces on a decision
• Using driver diagrams for property analysis
• Influence diagrams, properties, and decisions
• Issues with decision trees
• Video and discussion: The value of decision trees

Day 2 Theme: Business Statistics and Machine Learning

Machine learning showed up early on in data analysis with some familiar business statistics. Correlation and related regression methods have been implemented in many tools and are easily done in Excel. Correlation and regression are early machine learning efforts using historical data and statistical methods for diagnosis and prediction. These core business statistics provide considerable insight into the movement of financial and operational performance indicators in an organization.

Section 4 – Forecasting Using Correlation and Regression for Prediction

• Correlation as an analysis method
• Regression is a form of machine learning.
• The regression idea – What a manager chooses to look at
• How is this different from correlation?
• Preparing data for regression analysis
• Example: Sales trend over time analysis

Section 5 – Correlation Matrices

• What are they?
• How are they useful?
• Interpreting a correlation matrix.
• Issues and advantages of correlation
• Tracking correlation over time.
• Video and Discussion: Understanding Correlation.

Section 6 – Affinity and Sensitivity Analysis

• Methods of sensitivity analysis
• What is a Tornado diagram?
• Affinity analysis – measuring the strength of a relationship
• Using affinity for recommendation.
• Example demo – Amazon approach people who bought this book also bought that book
• Exercise: For what would you use a recommendation engine?

Day 3 Theme: Analytic Ensembles

Today the emphasis is changing to extensive use of artificial intelligence neural nets to help diagnose and predict factors of significance to managers. Artificial intelligence (AI) augments and replaces human intelligence where it makes sense to do so. As such, AI works with both knowledge and data. A manager can use combinations of analytics such as neural nets, machine learning and statistics that provide the best practice in addressing solutions or evaluating an opportunity. Cross checking or enhancing results is one of the goals of applying combinations of analytics.

Section 7 – Composite Ranking

• Traditional separate ranking and analysis
• One, Two and three variables as separate rankings
• The 4- box for interpretation of the three variables
• The technique of composite ranking -more than three variables
• An example: A composite ranking of more than three variables
• Exercise: For what would you use composite ranking?

Section 8 – Using Linear Neural Nets – Which Criteria has the most Influence?

• Linear neural nets – a key business analysis tool
• Using a neural net to analyze process properties.
• Identifying the property of greatest influence
• Issue of bias in neural nets
• Example: Neural net influence on process performance
• Demo and Discussion: Where can you use ensemble analytics

Section 9 –The Ensemble Analytic for Ranking Portfolios

• Portfolio analysis
• What is an  ensemble analytic?
• A composite ranking
• A Correlation matrix for ranking
• A neural net ranking approach
• The 4 – box for assessing results for a portfolio
• Exercise: For what would you use an ensemble?

Day 4: Theme: Innovative Data and Analytics Uses

Achieving a high-performance organization requires advanced analytic techniques for understanding the impact of innovative analytics. Three techniques are dominant in the emerging analytics space, context analysis, semantic analysis using structured text, and property analysis . New analytic techniques are needed to take advantage of that rich source of data. These analytics use structured text and the properties of organizational components.

Section 10 – Emerging Semantic Analytics

• Internal Sentiment Analysis – What do your employees think?
• External Sentiment Analysis – What do customers think?
• Keyword analysis – what is the emphasis of a document.
• Comparative semantic analysis – Consolidating processes
• Demo and Discussion –Sentiment Analysis of Documents

Section 11 – Context analysis – Understanding the structure of the organization:

• Strategic/Landscape context
  • Assessing the external environment context
  • Assessing the strategic direction context
  • Aligning the external environment with strategies
• Operational context
  • Data,  Application and process context
• Exercise – Applying context analysis

Section 12 – Organization components and Property analysis

• What is property analysis
  • Organization components/domains
  • Component/domain properties
  • Objective and subjective Properties
• Property analytics
  • DNA diagrams relating properties
  • Capability assessment with properties
• Demo and Discussion – Applying property analysis

Day 5 Theme: Generative AI and Data Analysis

Every day operational processes handle large volumes of unstructured data. Data that is not quantitative and nicely organized into matrices or time series that you can use well known analysis and analytic techniques to support decisions. Handling the unstructured data has been a big problem until recently. The advent of generative AI has provided a useful tool in extracting significant parts of unstructured data for process support.

Section 13 – Deep Learning and Data Analysis?

•  What is Deep learning?
•  Deep Learning vs linear Neural Nets
• LLM, activation algorithms – ‘Attention is all you need’
• Issues with scaling up neural nets to deep learning
  • Costs, size, complexity, scope, impact, skills, risk
• Video and Discussion: Applying Deep Learning to organization use

Section 14 – The Generative AI Concept

• Unstructured data types for inputs to organization operations
  • Text, Forms, Pictures, Video, Audio, Objects and more
• Generative AI components
• Foundation Models
• Large Language Models
• Generative Neural Nets
• Good Uses of Generative AI today
• Summarization, Text generation, Research, Process augmentation, Marketing
• Data Issues with generative AI
  • Too much, Not enough Incomplete, Inaccurate, etc.
• Video and Discussion: – Can you trust Generative AI?

Section 15 – Custom/Corporate generative AI

• Sources of Material for a custom  LLM
• Policies, procedures, memos, emails, reports, studies etc.
• Marketing material, competitor brochures, web sites and so on.
• External data sources
• Creating a custom LLM
• Video and Discussion: Generative AI and text summarization

Course Questions and Wrap