Module 7

transaction data

booking a hotel, buying tickets, doing groceries

Transaction Processing Systems

systems that record data on fundamental operations within the company; may be connected to government agencies, useful for management, used to support organisations from the inside; what happened with the customer's order? how many items are in stock?

Batch processing

data is kept at temporary storage and processed as a single unit at a time; it enables companies to shift processing to time when computing resources are less busy; ex: money transfer between banks

Online Transaction Processing (OLTP)

data is processed immediately in real-time; current state of the system is always reflected; webshops, hotel and airline booking

Decision Support Systems

serve middle management and non routine decision-making; have analytical capability; what is the impact on production schedule if December sales doubled?

Executive Support Systems

support senior management, non-routine decisions, requiring judgement, evaluation; incorporate data about external events as well as summarised information from internal MIS and DSS; ex: Digital dashboard with real-time view of financial performance

data warehouse

collect and store data from several core transactional systems across the neat organization; data is consolidated, but can't be altered; they provide tools for querying and analysis; serves as organisational memory

data marts

subsets of data stored in a data warehouse; contain highly focused portion of the organisation's data; designed to support work on a specific business challenge by a specific population of users

data lakes

storage of all the data in a non-standardised fashion that may be used later on; ex: Casino with patterns identification and targeting most profitable customers

Online Analytical Processing (OLAP)

transaction-level data stored in relational databases is aggregated and summarised; results of analysis are stored in special databases (data cubes); data cubes structure results across multiple dimensions (space, products, time period)

data mining

the use of specific algorithms to identify hidden patterns in and to fit models to large data sets; more discovery driven; patterns discovered by DM: associations, clusters, sequences, relationships

associations

occurrences linked to a single event

clusters

a group of customers that share certain characteristics and behave in a similar way

relationships

dependences

sequences

time series

support

the fraction of transactions that contain a certain set of items X

Confidence

the fraction of transactions that contain Y among those transaction that contain X

Big Data

data mining techniques help to conduct fast analyses on large data sets; increasing infusion of info technology into products, devices and our daily lives has led to collection of incredible amounts of data; Volume, Velocity, Variety and Veracity

Volume in Big Data

the simple size of a dataset that needs to be processed

Velocity in Big Data

the speed with which new data is generated and needs to be processed

Variety in Big Data

the different formats and features of data that need to be processed (relational databases, documents, photos videos, spatial and temporal aspects)

Veracity in Big Data

the reliability of the data

neural networks

neural networks are usually black box methods, namely extremely hard to quantify the impact of a particular input variable on the outcome

hierarchy of data

bit, byte, field, record, file

problems with traditional file environment

data redundancy; program-data dependence; lack of flexibility; poor security; lack of data sharing and availability

DBMS

Database Management Systems; it enables an organisation to centralise data, manage it efficiently and provide access to the stored data by application programs, acts as interface between application porgams and the data files; it reduces data redundancy

Relational DBMS

represents data as two-dimensional tables -> SQL; operations: Select, Join, Project; tools for accessing and manipulating information also called manipulation language (SQL)

normalisation

the process of creating small, stable yet flexible and adaptive data structures from complex groups of data

blockchain

a distributed database technology that enables firms and organisations to create and verify transactions on a network nearly instantaneously without a central authority

blockchain networks

it drastically reduces the costs of verifying users, validating transactions and the risks of storing and processing transaction information across thousands of firms

Business Intelligence Infrastructure

data warehouse, data marts, Hadoop, in-memory computing; analytical platforms

Hadoop

breaks the big data problem into sub-problems, distributes them among to thousand of inexpensive computer processing nodes and combines to a smaller data set

In-Memory Computing

it relies on computer's main memory RAM, it makes it possible for very large sets of data to reside entirely in memory

Analytical Platforms

include in-memmory systems and NoSQL databses

enterprise applications

systems for linking the enterprise, span functional areas, execute business processes across the firm, include all level of management

4 enterprise applications

enterprise systems; supply chain management, customer relationship management, knowledge management systems

enterprise systems

based on a suite of integrated software modules and common central database; DB collects data from many divisions and departments in a firm and from a large number of key business processes in manufacturing; accounting&finance, sales&marketing and HR

value provided by ERP

increasing operational efficiency; providing firm wide information to help managers make better decisions

Supply Chain Management Systems

answer to the problems of supply chain complexity and scale; bullwhip effect; push-based and pull-based model

bullwhip effect

information about demand for a product gets distorted as it possess from one entity to the next across supply chain; it can be tamed by reducing uncertainties and SCM can help with that

push-based model

schedules based on forecasts of demand

pull-based model

demand driven, build-to-order, customer orders trigger events in the supply chain

SCM provide value by

enabling firms to streamline both their internal and external supply chain processes and provide management with more accurate info about what to produce, store and move; increases sales and reduces costs

Customer Relationship Management Systems

capture and integrate customer data from all over the organisation, consolidate and distribute to various systems and customer touch points (email phone etc.); SFA, customer service, Marketing

SFA

Sales Force Automation; helps sales staff increase productivity by focusing sales efforts on the most profitable customers

customer service in CRM

they increase the effienicency of call centres, help desks and customer support staff

Marketing at CRM

CRM provides capabilities for capturing prospect and customer data

Business Value of CRM

increased customer satisfaction; reduced direct marketing costs; more effective marketing; lower costs for customer acquisition and retention

Types of decisions

unstructured; structured; semistructured

unstructured decisions

decision-maker must provide judgement and evaluation to solve a problem; novel, important and not routine decisions -> Senior Management; long-term goals

structured decisions

repetitive, routine and involve definite procedures for handling them -> operational management; ex: restock inventory

semistructured decisions

only part of a problem has a clear-cut answer provided by an accepted procedure -> Middle Management; design marketing plan or new website

Decision Making Process

Intelligence (Problem discovery) -> Design (Solution discovery) -> Choice (choosing solution) -> Implementation (solution testing)

managerial roles

interpersonal, informational, decisional

interpersonal role

figurehead, leader, liaison; support systems: telepresence, social network, smartphones

informational role

nerve center, disseminator, spokesperson; support systems: MIS, ESS, Texting, email, webinars, social networks

decisional role

entrepreneur, disturbance handler, resource allocator, negotiator; support systems: business intelligence, DSS or none

Business Intelligence

infrastructure for warehousing, integrating, reporting, analysing data that come from the business environment (big data)