The On-Demand Economy

The On-Demand Economy

• The on-demand economy sees technology firms fulfilling consumer needs via immediate service and product provisioning.
• Proliferation of smartphone-connected consumers, easy purchase flows, and location-based services drive it.
• Key questions involve strategic direction (industry, markets, customers), business models (revenue generation), and business processes (efficiency).

Core Terminology

• A business model generates and sustains revenue for an enterprise.
• A digital business model makes money through digital technology.
• Customer experience (CX) involves creating a digital structure to enable customers to do what they want via their chosen channels.

Business Processes

• Business process improvement consists of inputs (raw materials, data), activities (transforming inputs), and deliverables (products, services).
• Business Process Reengineering involves eight phases: vision, process understanding, redesign, identifying change levers, implementation, operationalization, evaluation, and continuous improvement.

IT Innovation and Mega Trends

• SMAC (Social-Mobile-Analytics-Cloud) integrates cloud, mobile, and social technologies.
• Mega trends shape the future of business, economy, and society.
• Connectivity requires connecting across multiple channels and platforms.
• Cloud services provide computing resources over the Internet, rather than from software.
• Digital resources are no longer dependent on buying or owning.
• Big data and data analytics is commonly high-volume and mostly text data.
• 80%-90% of big data is unstructured data without a predictable format.
• Digitization transforms activities/information into a digital format for electronic collection, storage, searching, and analysis.
• Banks using digitized mortgage applications see cost cuts of 70%.
• A telecomm company created a self-serve, prepaid service to order and activate phones.
• Shoe retailers use in-store inventory systems for item availability.
• Machine-to-machine (M2M) technology enables real-time data sharing from sensor-embedded products via radio signals.
• The Internet of Things (IoT) connects physical things to the internet via sensors.
• M2M and IoT automate business processes, from transportation to healthcare.

Core IT Components

• An information system includes inputs (data, information), processing (programs), outputs (reports), storage (hard drive), and feedback (error reports).
• Data includes hardware, software, people, procedures, and networks.

Data Value Chain

• The data value chain involves talking (assessing knowledge to reverse negative trends), knowledge (reasoning for trends), information (context), and data (raw figures).

Data Processing

• Batch processing collects transactions over time and then updates data.
• Online real-time processing (OLTP) processes each transaction immediately.
• Batch processing is cheaper, but may be inaccurate due to update delays.

IT and Organization

• IT infrastructure and architecture include production, sales, finance, HR, and accounting.
• Enterprise architecture aligns IT with organizational strategy for maximum value.

Measuring Success

• Key Performance Indicators (KPIs) measure business process effectiveness against organizational goals in an easy-to-comprehend format.
• KPIs measure financial, social media, sales, marketing, operations, supply chain, or environmental aspects.
• Examples of KPIs are current ratio, accounts payable turnover, net profit margin, new followers per week, cost per lead, and order status.
• Information management involves IT tools to collect, process, secure, and store data from inconsistent sources.
• Continuous planning, data security, and compliance are critical to information management.

Information Deficiencies

• Deficiencies arise from siloed, inaccessible data, flaws in data collection, poor interfaces, non-standard formats, and changing tracking requirements.
• Information requirements include being understandable, relevant, timely, accurate, and secure.
• Data types include customer, product, procurement, and engineering data.

Data Centers and Cloud Computing

• IT infrastructures include data centers and cloud computing systems.
• Data centers are physical facilities housing network servers for data storage, processing, and management.
• Cloud computing is a model where processing, storage, and software are provided over a network.
• Cloud computing eliminates the need to store data locally.
• Cloud services include Apple iCloud, Google Drive, and Microsoft 365.
• Google, Amazon, SAP, and Oracle offer data computing centers that assist with data storage, app management and increased computing power that can be maintained remotely.

Software-Defined Data Center

• A Software-Defined Data Center (SDDC) integrates infrastructure silos, optimizing resource use and balancing workloads.
• SDDC aims to decrease costs, increase agility, policy compliance and security by deploying, operating, managing, and maintaining applications.
• Data, workload, and virtual LANs are all components of the SDDC.

Cloud Deployment

• Private clouds provision infrastructure for a single organization.
• Community clouds provision infrastructure for specific communities with shared concerns.
• Public clouds provision infrastructure for general use.
• Hybrid clouds combine two or more distinct cloud infrastructures (private, community, or public).

Cloud Service Models

• Cloud Service Models include Software as a Service (SaaS), Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Data as a Service (DaaS).
• SaaS offers end-user apps, Iaas offers resource mangement, PaaS offers block storage and DaaS has data shared across systems

Benefits of Cloud Computing

• On-demand self-service allows consumers to provision computing capabilities unilaterally and have a broad network.

Disadvantages of Cloud Computing

• Down time can occur, and there are security and limited control risks when using cloud services.
• Cloud vendor agreements dictate customer rights.

Database Management

• A database is a relationships, tables and metadata collection.
• A DBMS organizes and administers a database.
• DBMS integrates with data collection systems like TPS, stores data in tables, and is the standard database model.
• Centralized database architecture has better control of data quality and IT security.
• Distributed database architecture allows local/remote access and uses client/server architecture.

Data Manipulation

• Data filtering and profiling processes data efficiently, inspecting for errors.
• Data integrity and maintenance involves correcting and standardizing data.
• Data synchronization integrates data from disparate sources.
• Data security checks data integrity over time.
• Data access provides authorized access in acceptable time.
• A declarative language simplifies data access by specifying what data not how to achieve it.
• Structured Query Language (SQL) is an example of declarative language using SELECT, FROM, and WHERE statements.

Data Lifecycle

• Central to data principles is processing, capturing, formatting, and distributing data in near real-time and justifies investment.
• This ensures data is integrated, processed, analyzed, and formatted in actionable information which leads to investments.
• Online Analytical Processing (OLAP) is in BI software for data discovery activities like reporting, calculations, and forecasting.
• OLAP software performs multidimensional analysis of data.

Data Warehouses and Analytics

• Enterprise data warehouses (EDW) involve transaction, operational databases, ETL processes, and data warehouses.
• Data analytics is a technique that looks at patterns, trends, and relation to improve gian and productivity..
• Big data analytics is suited for data sets too large for traditional processing.
• The Four V's of big data are variety, volume, velocity, and veracity.