Chapter 6 Introduction to financial information Flashcards
1.1 Financial information
Data consists of numbers, letters, raw facts, events, and transactions which have been recorded but not yet processed into a form which is suitable for use by management. Information is data which has been processed in such a way that it is meaningful to the person who receives it. Data + meaning = information.
1.2 Uses of information
The following are purposes are information:
- Planning: such as production manager with monitoring future production levels and a buyer using sourcing material
- Controlling: such as a managing director viewing actual market share verses budget or distribution manager viewing actual costs verses standards and governments seeing how much tax an entity should pay
- Recording transactions: such as an accountant with ledger accounting and a sales ledger clerk with customer balances
- Performance management: such as board of directors seeing how performance is against expectation and shareholders with performance versus share price
- Decision making such as a buyer deciding which supplier to use and a project team deciding which machine to invest in
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1.3 Qualities of good information
The qualities are accurate, complete (all information provided), cost-effective (benefit is more than the cost of producing the information), user-targeted (understandable), relevant, authoritative (highest quality for purpose), timely and easy to use.
1.4 Sources of data
Internal data sources include accounting records, HR and payroll records, machine logs/computer systems, timesheets and communicate to and from staff.
External information can include formally gathered marketing research (on new trends, R+D, tax and accounting specialists with new legislation/standards and a legal specialist). Informally gathered information can be gathered from newspapers, interest, meetings with external business colleagues, etc.
2.1 Information processing
Effective processing of information includes include completeness, accurate, timeliness, inalterability (data cannot be tampered with by unauthorised persons), verifiability (clear audit trial from data source to information) and assessability (information produced can be challenged, ensuring quality of system).
2.2 Transaction processing systems
These are systems which perform and record routine transactions. For example, finance and accounting systems (such as sales and purchase ledgers, nominal ledger, and management accounts), HR systems (payroll, personnel files, and training records), manufacturing and production systems (purchasing orders, production schedules and stock control) and sales and marketing systems (market research, pricing records and sales management records).
2.3 Management information systems
These are systems which produce information allowing managers to make effective decisions. Examples include:
- Executive information systems (EIS) or executive support systems: a database which pools data from internal and external sources, providing easy to use information to help make strategic decisions
- Decision support systems: combine data and analytical models or data analysis tools to support decision making
- Expert system: allows users to benefit from expert knowledge, they consist of a database holding specialised data and rules about what to do in, or how to interpret, given a set of circumstances
- Knowledge work systems: facilitate the creation and integration of new knowledge into an organisation
- Office automatic systems: systems which increase the productivity of data and information workers
3.1 Information security
Data security issues need to be assessed by availability, confidentiality, integrity, authenticity (data comes from bona fide sources), non-repudiation (users trust the information produced and the system producing it) and authorisation (system changes only made by staff accountable for those changes).
3.2 Cyber-security
Procedures need to be in place to protect information systems from damage, disruption, or other loss. There can be physical access controls such as locks, alarm systems and PIN numbers. There can be security and integrity controls which input and output controls.
Input controls can be control totals (e.g., batch of invoices), range checks (e.g., hours on timesheet), authorisation of source documents/data and password controls over input of data. Output controls can include control totals (e.g., batch of invoices versus batch total) and follow up or error/exception reports (e.g., large overtime payments).
There also needs to be back-up and archiving of data (disaster recovery plans and regular back-up of data), personnel controls (careful recruitment and selection and training of staff) and segregation of duties controls (different people responsible for generation of data, processing of data, recording data and physical asset custody such as inventory and cash).
4.1 Developments – big data
Datasets whose size is beyond the ability of typical database software to capture, store, manage and analyse. The key features of big data are described as:
- Volume: considers the amount of data fed into the organisation
- Variety: considers the various formats of data received
- Velocity: considers the speed that data is fed into the organisation
- Veracity: considers the reliability of the data being received
Importance of big data: potential to achieve competitive advantage, huge array of new data sources (social media and interest), exponential growth in computing power and storage capacity and new avenues of knowledge creation such as crowd sourcing and open source software.
The risks of big data include storage (systems must be reviewed and upgraded to cope with data and processing required), skills (need to recruit and retain the right staff), data dependency (data led decisions leads to risk should be data be weak, erroneous or corrupted) and overload (too much information can make businesses lose sight of key data and slow down the decision making process).
4.2 The internet of things
Significant amount of source of data due to the internet. Internet connected devices continually collect and exchange data. For example, home automation (lighting, heating and security systems enabled through smart technology and home assistants) and vehicles and transport (connected cars and live traffic monitoring).
4.3 Data science and analytics
Data science deals with collecting, preparing, managing, interpreting, and visualising large and complex datasets. This is a scientific approach applying maths and statistical ideas and computer tools to process big data. The importance of big data has led to increased demand for employees with data science skills.
Data scientists extracted value through data analytics. This can be used in decision making (real-time information allows better decisions), customer analysis (market segmentation and customisation can occur from having greater insight into customer needs), innovation and risk management (can assist with identification and quantification of risk).
4.4 Intelligent systems
Computer based systems which interest data can derive new information and identify strategies and behaviours built upon the result of their analysis.
- Artificial intelligence: computers perform tasks thought to require human intelligence such as learning and reasoning
- Machine learning: these algorithms detect patterns, learn how to make their own decisions, and adapt over time
- Automation: control and monitor activities without human interference. Intelligent systems and artificial intelligence facilitate the process of automation.
4.5 Cloud computing
This provides business with computing resources such as software and storage using a network of remote servers hosted on the interest. Common features of cloud computing systems are they can be private or public (public systems sell services to anyone, private system is a proprietary closed system for a limited set of users), they are elastic so users can use as little or as much as they need, and the service is fully managed by the provider in the case of public systems.
Cloud accounting is one application of cloud computing. Accountancy software is provided in the cloud by a service provider rather than a local server.
4.6 Distributed ledger technology
(Or blockchain) allows people or organisations to trust a shared record of events. The key features are:
- A distributed ledger has no central storage and is shared by all parties who require access, each having an identical copy
- Additional stages in a transaction are verified by consensus algorithms
- The lack of central administrator is a main advantage of the technology, preventing one party from dominating or corrupting the process
- Security is provided by cryptographic keys and clear signatures of all transactions
