On-Demand Economy: Strategic Questions and Terminology

• On-demand economy entails economic activities fulfilled by tech entities via immediate provisioning of products/services.
• Key drivers include smartphone use, secure transactions, and location services.

Strategic Questions

• Strategic direction includes determining what "we" do and "our" direction, pinpointing target demographics, and strategy.
• Business model involves how "we" operate, generate revenue, and establish brand identity.
• Business processes include producers and technologies efficiency.

Terminology

• Business model defines how revenue is created and sustained.
• Digital business model refers to making money with digital technology.
• Customer experience (CX) builds the digital infrastructure, supporting customer actions on any channel.
• Business process improvement includes inputs (raw materials, data), transforming activities, and deliverables (plans, products, and services).

Business Process Reengineering: Eight Phases

• Develop vision & objectives.
• Understand existing processes.
• Identify process for redesign.
• Identify change levers.
• Implement new process.
• Make new process operational.
• Evaluate new process.
• Perform continuous improvement.
• IT innovation and disruption is represented by Social-Mobile-Analytics and Cloud (SMAC).
• It entails model integration of cloud, mobile, and social tech where social networks facilitate connections.

Disruption: Mega Trends

• forces shaping business, economy, and society futures.
• Connectivity emphasizes connecting on various channels/platforms.
• Cloud services involves computing resources over the Internet on demand.
• Digital resources no longer require ownership.

Data Analytics

• Big data analytics is defined by high-volume and textual data, with unstructured data dominating (80-90%).
• Digitization transforms activities/info into analyzable digital formats.
• Digitization by banks and telecommunication cut costs and enable new services.
• Inventory systems provide up-to-date item status.
• Machine-to-machine (M2M) uses sensors for real-time data via radio.
• The Internet of Things (IoT) connects physical objects to the internet via sensors.

Information Systems

• Information systems inputs consist of data, information, knowledge, and instructions.
• Processing involves programs and hardware.
• Output includes reports and calculations.
• Storage includes hard drives and servers.
• Feedback uses error reports and performance metrics.
• Core elements include hardware, software, people, procedures, and network.

Data Value Chain

• Talking assesses knowledge for policies to reverse downturns.
• . Knowledge uses information to determine consistent downward trend.
• Information answers who, what, where, when.
• Raw data.
• Batch processes updates data stores after collecting transactions over time.
• Online transaction processing (OLTP) does real-time translation.
• IT infrastructure and architecture includes production, sales, finance and HR etc.
• Enterprise architecture focus is to maximize value.

Measuring Success

• Key Performance Indicators (KPIs) evaluate business process effectiveness in goals.
• KPIs measure financial, social media, sales, supply chain, and environmental data.
• KPI sample include current ratio, payable turnover, profit margin, followers per week, cost per lead and order status.
• Information management uses tools to consolidate secure data from sources.

Information Deficiencies

• Data silos trap data, flawed collection, poor interfaces, data formats etc.
• Information must be understandable, relevant, timely, accurate and secure.
• Data types include customer/product, procurement, order, parts, engineering, and logistics.
• Data and cloud computing systems are IT infrastructures and systems.
• Data centers are physical facilities with many network servers.
• Cloud computing delivers processing, storage, and software over a network.
• It eliminates direct data storage in devices, offering services like Apple iCloud and Microsoft 365.

Cloud Services

• Amazon and Google offer cloud service.
• SAP and Oracle provide applications over the Internet.
• Software-Defined Data Centers (SDDC) facilitate infrastructure integration.
• SDDC optimizes resources, balances workloads, and drives operational effectiveness.
• SDDC aims to cut costs, increase security, and streamline application management.
• VL is the cloud deployment.
• Private clouds are for a single organization's use.
• Community clouds are for specific groups with shared interests.
• Public clouds are for public use, run by various entities like academic and government.
• Hybrid clouds use a combination of clouds that remain unique.

Cloud Service Models

• Cloud service models includes are diverse.
• Software as a Service (SaaS).
• Infrastructure as a Service (IaaS).
• Platform as a Service (PaaS).
• Data as a service (DaaS) shares data among clouds.
• Benefits include self-service provisioning and broad network access.

Cloud Computing: Disadvantages

• Downtime arise from maintenance, failures, disasters, and intrusions.
• Security measures align with standards, data storage presents risks.
• Infrastructure is provider-managed, customer control limited.
• Vendor agreements outline rights in Cloud Service Agreements (CSA).

Database Management Systems

• Database is collection of tables, relationships, and metadata.
• DBMS organizes structured data.
• DBMS is a program to organize databases.
• Integrate with TPS, data can be accessed and managed.
• Data is stored similarly to spreadsheets.
• Centralized architecture enhances data quality and IT security.

Database Architecture

• Distributed database architecture enables remote access via client/server structure.
• Data filtering efficiently processes data, looks out inconsistencies, and incomplete information.
• Data integrity ensures correct, standard, and consistent.
• Synchronization integrates data from sources.
• Security checks data over time.

Data Access

• Data requires authorized access to data.
• Centralized database architecture ensures better IT security.
• SQL provides access using declarative code.
• It simplifies data access, define specific data without detailing how to obtain it.
• SQL is declarative language.
• Data must be stored, processed, formatted, and distributed the principle of data in context.
• Online Analytical Processing (OLAP) is included in business intelligence software.
• OLAP support multidimensional analysis, modeling on a range of business data.
• Data is transaction and operation, uses ETL operations.

Data Discovery

• Analytics reveals patterns, trends, and human behavior relation.
• Big data cannot be analysed by traditional processing.
• Analytic environment includes big data unstructured sources.
• Structured and unstructured data are analyzed.
• Access speed to reports distinguishes effectiveness.
• Validating data and extracting insights are key factors.