An accountant today works through software for nearly everything: recording transactions, running payroll, modelling budgets, tracking customers and mining data for patterns. Understanding what each kind of system does, what it costs and where it fails is part of being a trusted adviser, because many business decisions are really decisions about which system to buy or how to use the data it holds.
This topic covers the business uses of information technology (IT), the main systems (databases, accounting software, spreadsheets, enterprise resource planning and customer relationship management), and big data and data analytics. Artificial intelligence is covered in the topic on technology, globalisation and fintech.
In 30 seconds
IT improves efficiency, communication, decisions, customer service, supply chains, financial transactions, recruitment and security, and cuts cost. A database system stores and manages data. Accounting software handles ledgers, receivables and payables, payroll, stock and reports. Spreadsheets calculate, model and chart. Enterprise resource planning (ERP) joins every function in one system. Customer relationship management (CRM) manages customer relationships.
Big data is data too large for ordinary tools, described by volume, velocity, variety, veracity, value and variability, and analysed in stages from collection to visualisation to improve decisions.
Business uses of IT
| Use | How IT helps |
|---|---|
| Operational efficiency and productivity | Automates routine tasks and speeds up work |
| Communication and collaboration | Email, video and shared tools link people anywhere |
| Data management and decision support | Turns the large volume of data a business produces into information for decisions |
| Customer engagement and experience | Personalised, quick service across channels |
| Innovation and competitive advantage | New products, services and ways of working |
| Supply chain and inventory management | Tracks stock and orders in real time |
| Digital financial transactions | Online payments, banking and invoicing |
| E-recruitment and human resources | Online hiring and staff records |
| Security and risk management | Protects systems and data as reliance on them grows |
| Cost reduction and resource optimisation | Savings that outweigh the initial investment over time |
Database systems
A database is an organised collection of data, and a database management system (DBMS) is the software that creates, stores and controls it.
Its functions include defining the data structure and storage, retrieving and changing data through queries, security and authorisation control, enforcing data integrity, backup and recovery, controlling several users at once, managing transactions as logical units of work, keeping a data dictionary, optimising performance, separating the logical view of data from its physical storage, providing administrative tools, and offering links to other programs through interfaces.
Accounting software
Accounting software records and reports the financial activity of a business.
| Core feature | What it does |
|---|---|
| General ledger and subledgers | Updates journals and ledgers automatically |
| Receivables and payables | Tracks money owed to and by the business |
| Payroll | Calculates wages and deductions and pays staff |
| Inventory | Monitors stock levels and reorder points |
| Cash and asset management | Bank reconciliations and fixed asset records |
| Risk and compliance controls | Internal checks, audit trails and user permissions |
| Reporting and analytics | Dashboards and real-time reports |
| Types | Benefits | Limitations |
|---|---|---|
| Billing and invoicing systems. Commercial packages for small firms. Enterprise software inside ERP. Custom-built software. Cloud-based software. Tools using artificial intelligence or robotic process automation | Accuracy through built-in checks. Compliance and tax reporting. Scalability. Access from anywhere in the cloud. Timely information for decisions | A learning curve. Security risks. Subscription and support costs. Dependence on the internet. Off-the-shelf products may not fit |
Spreadsheets
A spreadsheet arranges data in rows and columns and calculates with formulas. Its features are data entry and editing, calculation, sorting and filtering, charts, scenario modelling (such as break-even analysis) and storage of structured data. Businesses use it for budgeting, payroll and invoicing, inventory, data analysis, performance tracking and customer lists. Its advantages are accuracy from formulas, efficiency, flexibility, easy sharing and clear visuals.
Enterprise resource planning (ERP)
Enterprise resource planning software joins the main business functions in one integrated system with one central database. Its features are integration, real-time processing, automation, customisation, scalability, central data security and reporting.
| Types | Advantages | Disadvantages |
|---|---|---|
| On-premise (hosted by the firm). Cloud-based (hosted remotely). Hybrid (a mix). Open-source (free software, paid support) | Efficiency, better decisions, lower cost, collaboration across departments, scalability, compliance tools, secure central data and better customer service | High implementation cost, a complex process, customisation difficulty, staff resistance, ongoing maintenance, vendor lock-in, data migration problems, and too much complexity for small firms |
Modules include finance, procurement, manufacturing, inventory, order management, warehouse management, supply chain, CRM, professional services automation, workforce management, human resources, e-commerce and marketing automation. Implementation has seven steps: planning (a cross-functional team), evaluation of products, design, development and configuration, testing, deployment, and continuing support.
Customer relationship management (CRM)
CRM is the practices, strategies and technology a business uses to manage and analyse its dealings with customers throughout the customer relationship. It supports sales, marketing and service, and aims to raise satisfaction, loyalty and retention. Its three elements are key customers (finding the high-value ones), knowledge management (acquiring and sharing customer knowledge) and technology.
| Why it matters | Features | Benefits | Demerits |
|---|---|---|---|
| A customer focus, central data, segmentation, better retention and competitive advantage | Data collection, task automation, segmentation, sales and marketing automation, service automation, analytics | Better customer experience, efficiency, better decisions, higher sales, team collaboration, loyalty | High cost, hard to integrate, data privacy concerns, staff resistance, over-reliance on automation |
Measuring CRM
Measures include customer satisfaction scores, retention rate, customer lifetime value, sales metrics, customer acquisition cost, adoption by staff, case resolution time, interaction measures and the net promoter score.
| Measure | Example |
|---|---|
| Retention rate = (customers at end − new customers) ÷ customers at start | 1,000 at the start, 850 at the end including 100 new: (850 − 100) ÷ 1,000 = 75% |
| Customer lifetime value | A customer spends ₦20,000 a year for 4 years: ₦80,000 of revenue |
| Customer acquisition cost | If it costs ₦15,000 to win that customer, lifetime revenue is 80,000 ÷ 15,000 = 5.3 times the cost |
| Net promoter score = % promoters − % detractors | 60% promoters and 15% detractors give 45 |
Big data and data analytics
Big data is data on such a scale that it has to be analysed by computer to reveal patterns, trends and links, especially about human behaviour. Data analytics is the set of techniques for drawing insight from data. It moves a business from decisions based on intuition to decisions based on evidence, with applications in customer targeting, supply chain optimisation and risk management.
| The six Vs | Meaning |
|---|---|
| Volume | The huge amount of data created every second |
| Velocity | The speed at which it arrives |
| Variety | Many formats, structured and unstructured |
| Veracity | Not all data is accurate or trustworthy |
| Value | The goal is to extract useful insight |
| Variability | The same data can mean different things in different contexts |
Stages of analytics
- Data collection from internal and external sources.
- Data storage in scalable systems.
- Cleaning and integration: removing errors and joining sources.
- Processing with processing engines.
- Analysis and modelling with statistical tools and machine learning.
- Visualisation and interpretation so that insights can be communicated.
| Benefits | Challenges |
|---|---|
| Better decisions from current and past data | Data privacy and compliance |
| Lower cost by finding waste | High cost of infrastructure |
| Better insight into customers | Data silos and poor integration between departments |
| Fraud detection and risk management | A shortage of skilled people such as data scientists |
| Real-time operational efficiency | The complexity of implementation without a clear strategy |
| Product and service innovation from unmet needs |
Tools include open-source storage platforms, fast processing engines, NoSQL databases, visualisation tools, the Python and R programming languages and cloud platforms. The future points to artificial intelligence and machine learning, processing data where it is created, real-time analytics, data governance and ethics, and industry-specific solutions.
Worked example: choosing a system
Scenario. A growing distribution company has accounts in one program, stock in spreadsheets and customer details in staff notebooks. Orders are late and the owner cannot see the true profit by customer. Recommend what to do.
- Diagnose. The data is scattered across separate tools, which causes delays, errors and no single view of the business.
- Propose ERP. An integrated system with finance, inventory, order management and CRM modules would link orders, stock and accounts in real time. A cloud-based version lowers upfront cost and gives access from anywhere.
- Add CRM. A customer database lets the firm segment customers and track retention and lifetime value.
- Weigh the drawbacks. Implementation cost, data migration, staff resistance and vendor lock-in. Plan the seven steps and train staff.
- Use analytics. Once data is joined, analysis shows profit by customer and product.
Business IT in more detail
Accounting software: how a transaction flows
A credit sale is entered once. The software records revenue and a receivable, updates the stock record, adds to the customer’s statement, and updates the general ledger. When the customer pays, the receipt clears the receivable and updates cash. At month-end, the trial balance and statements are produced automatically. Because each entry is posted once to all affected records, it reduces errors and the time to close the books.
Spreadsheets: what to do and what to watch
Spreadsheets suit budgets, cash forecasts, ratio analysis and what-if models. Risks include formula errors, hard-coded numbers, version confusion, lack of audit trail and unauthorised changes. Good practice includes keeping inputs separate from calculations, labelling clearly, protecting formulas, checking totals and keeping a version history.
ERP: why implementations fail
ERP projects fail when objectives are unclear, top management does not support them, processes are not redesigned before the software is configured, data from old systems is dirty, staff are not trained, or the firm tries to do too much at once. Success needs a cross-functional team, a clear plan, careful data migration, training and a phased go-live.
A cloud ERP lowers the upfront cost but ties the firm to a vendor.
CRM: a measure in practice
A firm starts the year with 1,000 customers, loses 250 and gains 100 new, ending with 850. Retention = (850 − 100) ÷ 1,000 = 75%. If an average customer spends ₦20,000 a year for four years, lifetime revenue is ₦80,000. If it costs ₦15,000 to win each customer, revenue is about 5.3 times the cost. If retention rises to 85%, customers stay longer and lifetime value increases, which is why firms invest in loyalty.
Big data: what each V means for a firm
| V | Challenge for a retailer |
|---|---|
| Volume | Millions of transactions and clicks must be stored and processed |
| Velocity | Sales data arrives continuously and may need real-time action |
| Variety | Sales figures, reviews, images, social media posts |
| Veracity | Duplicate or false data can mislead |
| Value | Only data that supports decisions is worth the cost |
| Variability | The meaning of a post or review depends on context |
Data analytics: types and questions
Descriptive analytics asks what happened (sales last month). Diagnostic asks why (a drop caused by a stock-out). Predictive asks what is likely (next quarter’s demand). Prescriptive asks what to do (the best price). Each builds on the one before.
Exam focus: big data analytics, step by step
Big data refers to extremely large data sets that are analysed by computer to reveal patterns, trends and associations, especially about human behaviour. Data analytics is the set of techniques that draw insight from data. It moves decisions from intuition to evidence.
The six Vs
- Volume. The massive amount of data created every second, for example terabytes of daily retail sales.
- Velocity. The speed at which data arrives, which calls for real-time analysis, for example stock exchange tick data.
- Variety. Many formats: structured (spreadsheets, databases), semi-structured (JSON, XML) and unstructured (video, images, social media comments).
- Veracity. Data can be inaccurate, noisy or inconsistent, so quality must be checked.
- Value. The aim is useful insight that improves business results.
- Variability. The same data can mean different things in different contexts, such as a traffic spike during a promotion.
Six stages of analytics
- Data collection from internal sources (ERP, CRM) and external sources (social media, apps, third-party APIs).
- Data storage in scalable systems such as Hadoop Distributed File System, Amazon S3, Google BigQuery or data lakes.
- Data cleaning and integration: remove duplicates and errors and combine sources into one format.
- Data processing with engines such as Apache Spark, Apache Flink or MapReduce.
- Data analysis and modelling with statistics and machine learning: clustering, regression, neural networks and natural language processing.
- Visualisation and interpretation through dashboards and tools such as Tableau and Power BI.
Tools and technologies
Hadoop is an open-source platform for storing and processing large data sets in a distributed way, using MapReduce. Apache Spark is a fast engine for real-time analytics and machine learning. NoSQL databases such as MongoDB and Cassandra hold large volumes of unstructured or semi-structured data. Visualisation tools include Tableau, Power BI and QlikView. Python and R are used for data work and SQL for querying relational databases.
Cloud platforms (AWS, Microsoft Azure, Google Cloud) provide scalable storage and processing.
Benefits and challenges
- Benefits: improved decision-making, cost reduction, enhanced customer insights, fraud detection and risk management, operational efficiency, and product and service innovation.
- Challenges: data privacy and compliance (GDPR and Nigeria’s data protection rules), the high cost of infrastructure especially for SMEs, data silos and integration problems, a shortage of skilled talent, and complexity of implementation without a clear strategy or executive buy-in.
In accounting and auditing, big data supports automated risk assessment, continuous auditing and anomaly detection in transactions.
Common mistakes in the exam
- Describing ERP as only accounting software. It joins every function.
- Listing benefits without the limitations, or the reverse.
- Confusing CRM, which manages customers, with ERP, which integrates the whole business.
- Naming the Vs of big data without explaining them.
- Forgetting that cleaning and integration comes before analysis.
- Ignoring privacy and compliance when discussing data analytics.
Quick revision
- Ten uses of IT. Database: stores and controls data.
- Accounting software: ledgers, receivables and payables, payroll, stock, controls, reports.
- ERP: integrated, one database, four types, seven implementation steps.
- CRM: key customers, knowledge, technology. Measures: retention, lifetime value, acquisition cost, net promoter score.
- Big data: six Vs, six stages, six benefits, five challenges.
Practice questions
These are original questions written in the style of the Institute of Chartered Accountants of Nigeria (ICAN) exam. Try each one before you open the answer.
Section A style: multiple choice
Question 1. Which system integrates finance, procurement, manufacturing, inventory and other functions in one database?
- A spreadsheet
- An enterprise resource planning system
- A simple billing program
- A social media page
Show the answer
B. ERP joins the business functions in one integrated system.
Question 2. The ‘velocity’ of big data refers to:
- The amount of data
- The speed at which data flows in
- The accuracy of data
- The different formats of data
Show the answer
B. Volume is amount, veracity is accuracy and variety is format.
Question 3. In a data analytics process, which stage comes immediately before analysis and modelling?
- Data collection
- Data cleaning and integration, and processing
- Visualisation
- Data storage
Show the answer
B. Data is cleaned, joined and processed before it is analysed. Visualisation comes last.
Question 4. Which of the following is a measure of CRM effectiveness?
- Customer lifetime value
- Gross profit margin only
- Server speed
- Number of spreadsheets
Show the answer
A. Others include retention rate, acquisition cost and the net promoter score.
Question 5. A disadvantage of ERP systems is:
- Real-time reporting
- High implementation costs
- Integration of departments
- Centralised data security
Show the answer
B. The others are advantages.
Question 6. A firm has 1,000 customers at the start of a year and 850 at the end, including 100 new ones. The retention rate is:
- 85%
- 75%
- 65%
- 90%
Show the answer
B. 75%. (850 − 100) ÷ 1,000 = 0.75.
Question 7. A firm has 1,000 customers at the start of a year and 900 at the end, including 150 new ones. Retention is:
- 90%
- 75%
- 85%
- 65%
Show the answer
B. (900 − 150) ÷ 1,000 = 75%.
Question 8. Which type of analytics asks ‘what is likely to happen?’
- Descriptive
- Diagnostic
- Predictive
- Prescriptive
Show the answer
C. Prediction uses past data to forecast.
Question 9. A common reason ERP projects fail is:
- Strong top management support
- Dirty data and poor staff training
- A phased go-live
- Clear objectives
Show the answer
B. Data and training are common problems.
Question 10. A spreadsheet risk is:
- Formula errors and lack of audit trail
- Perfect accuracy
- No cost
- No sharing
Show the answer
A. Good practice reduces the risk.
Section B style: written questions
Question 11 (10 marks). Explain five business uses of information technology.
Show the marking guide
Award 2 marks for each: 1 for naming the use and 1 for explaining how IT helps. Any five of the ten uses.
Question 12 (20 marks).
(a) Explain the benefits and limitations of accounting software. (8 marks)
(b) Describe the seven steps in implementing an ERP system. (7 marks)
(c) Explain the six Vs of big data. (5 marks)
Show the marking guide
(a) 8 marks. Four benefits and four limitations, 1 mark each, with a brief explanation: accuracy, compliance, scalability, accessibility, decision support; learning curve, security, cost, internet dependence, customisation limits.
(b) 7 marks. Planning, evaluation, design, development, testing, deployment and support, 1 mark each.
(c) 5 marks. Volume, velocity, variety, veracity, value and variability, 1 mark for each of the first five explained, with the sixth for completeness.
A further written question
Question 13 (10 marks). Explain the benefits and limitations of ERP systems.
Model answers
Show the model answer to Question 11
Five business uses of information technology. Operational efficiency: IT automates routine tasks, such as payroll, speeding up work and reducing errors. Communication and collaboration: email, video and shared tools link staff across places. Data management and decision support: IT turns large volumes of data into information for decisions. Customer service: systems give quick, personalised service across channels. Supply chain and inventory management: IT tracks stock and orders in real time.
Others include digital financial transactions, e-recruitment, security and cost reduction.
Show the model answer to Question 12
(a) Accounting software. Benefits: accuracy through built-in checks; automatic compliance and tax reporting; scalability as the business grows; access from anywhere for cloud versions; and timely information for decisions. Limitations: a learning curve; security risks; subscription and support costs; dependence on the internet; and off-the-shelf products may not fit the business.
(b) Seven steps in implementing an ERP system. Planning: set objectives and form a cross-functional team. Evaluation: compare vendors and products. Design: map processes and requirements. Development and configuration: set up and customise the system and migrate data. Testing: check that it works as designed. Deployment: go live, often in phases. Support: train users, fix problems and maintain the system.
(c) The six Vs. Volume: the huge amount of data. Velocity: the speed at which it arrives. Variety: its many formats. Veracity: its accuracy and trustworthiness. Value: the useful insight that can be drawn. Variability: the way the same data can mean different things in different contexts.
Show the model answer to Question 13
Enterprise resource planning (ERP) joins the main business functions in one integrated system with a central database. Benefits: it integrates departments, so information is shared and consistent; it works in real time; it automates routine processes and reduces cost; it improves reporting and decisions; it supports compliance and control; and it scales as the business grows.
Limitations: implementation is costly and complex; customisation can be difficult; staff may resist change; it needs training and continuing maintenance; the firm may become dependent on one vendor; and data migration can be a problem. For small firms it may be too complex.
Pathfinder on business uses of IT
These are the ICAN Pathfinder questions that test this topic. ICAN owns the questions and the suggested solutions, so we point you to them and do not copy them. Open your Pathfinder, attempt each question under exam conditions, then check your answer against the notes above.
Tested the database system and the spreadsheet application. Revise the database and spreadsheet sections.
Revise first: Database systems
None of the diets mapped so far has a written question on this topic, so practise it through the multiple-choice questions. Check Section A of every diet for items on accounting software, ERP, CRM and big data.
Revise first: Exam focus: big data analytics, step by step
Source: ICAN Pathfinder, Business Environment. Diets covered: May Diet, 2021 to May Diet, 2026. November Diet, 2024 is not yet mapped.
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