Real implementation hurdles are the challenges in big data analytics. These must be taken care of, because if departed, the technology’s failure may happen and might have some not-so-good results. Problems with Big data are about storing and analysis of very large and rapidly growing data. Big Data is very likely to lead to innovation and business growth. However, it also involves a number of problems, these need to be solved. By choosing the right tools and strategies, as well as implementing best practices, these problems are easy to overcome. That is how organizations get the full value from their data at last. With the advances in the field of Big Data, it will be of utmost important to stay educated and proactive with these challenges to keep sustainable competitive advantage.
What is Big Data Analytics?
Big data analytics is utilizing large sets of raw data, trends, patterns, and correlations to make decisions based on the data. These processes leverage traditional statistical analysis methods such as clustering and regression and push them to a more significant scale with newer tools.
Big data analytics tools facilitate effective business and market analysis of structured and unstructured data. The tools to help companies understand customer behaviour are described below. Big data analytics techniques enable firms analyze their data to bring business running speed more smoothly and the quality of decisions higher. These are the techniques used in analytics for big data
Challenges of Big Data Analytics
Big data analytics helps companies find important insights from large and complex sets of information. However, dealing with big data is not simple and presents several challenges. That means businesses will have to solve these problems in order to leverage data and to drive better decisions.
Data Volume
Big data is when you have to process large volumes of information coming from various sources websites, sensors, and so on, as well as apps. Using fast systems and large-capacity storage, massive quantities of data can be managed as though peanuts on an elephant. Organizations will need to possess robust database servers and management tools in order to not only cope with the volume but also maintain their processes intact and retain vital information.
Data Variety
Big data can be expressed in several media, such as text, video and images. The different types and media make it hard for people to identify anything from within rows and rows of columns — let alone analyze them. Different data types require special tools to process them and make them useful for analysis and decision-making.
Data Velocity
Social Media, Online Shopping or live sensors create data at very high speed. This kind of data must be collected and processed in real-time by companies. In the case of analysts, if they are not able to respond in time, they will miss important insights or lose the time to respond. The need for fast tools and systems is inevitable.
Data Quality
Data can also be noisy, that is, data can be erroneous or missing or repeated. Bad results and poor decisions come from bad data. Before being utilized, companies have to clean and check their data. This takes time, but we want to ensure the insights are accurate and actionable.
Data Security
Since so much data is stored online, a lot of data is susceptible to hacking and leaks. Companies will have to take robust security measures such as data encryption and routine audits. Sensitive data leakage can harm customers as well as the trust and reputation of the company.
High Price
To deal with big data, it needs advanced software, powerful machines and highly skilled personnel. All of these things cost money, especially for small businesses. Even keeping the large data for a long time increases cost. It is essential to budget and choose the right tools to compute big data in an efficient way.
Lack of Skilled Professionals
Big data analytics requires professionals who know how to deal with large data sets, tools, and systems. Yet many companies struggle to hire or train the right people. Big data cannot be utilized by businesses without talent. This makes it harder to act and lowers the quality of decisions run on the data.
How Big Data Analysis Works?
Many significant steps are involved in big data analysis. One needs to ask, how these data are represented and what these data are presenting, with each of these data creating more of an organized outcome yielding what is needed to make the okay call of the data. This enables business organizations to process huge amounts of information in a systematic, intelligent way in order to make data-driven decisions.
- Group Data: You will need to add X data to determine how the data will be grouped or used as dichotomise. Data Analysis – It is typewise classified; it can be age, demographic, income, gender, etc., and is quantitative or categorical in nature.
- Data collection: Collecting data from various sources using computers, online platforms, cameras, environmental sensors, or even by human personnel.
- Data Structuring: Once the data is collected, it needs to be structured so that it can be consumed for analysis. and may involve the spreadsheets or applications used to manage statistical data.
- Review of data: The raw data is reviewed to ensure that it is correct. Data cleaning involves searching for and eliminating any repeated entries or mistakes in records. The process also includes correcting records lacking information or incomplete information.By cleaning data, we can reduce all of these errors and inaccuracies that may cause wrong conclusions. This makes data more accurate and dependable for making inferences.
Role of Big Data Analytics
Currently big data analytics play a significant role in all domains In everything from online retail to healthcare, it empowers organizations to comprehend users, project outcomes, and make better choices. Big Data Analytics is slowly taking charge on our decision-making. It allows teams to discover answers quickly and take actions that can save money, time and resources.
- Data Transfer: Involves collecting data and information from all sources and converting them into one format for safety analysis. Data mining is the most time-consuming step; however, the cost is very high for other actions but an unavoidable step to complete a dataset.
- Data Management: After collecting the data, it must be stored, managed, and readily available. Mining is a data-rich activity that generates lots of information, so we need a database to handle all of this data. SQL (Structured Query Language) is still one of the most widely used tools for database management, especially for querying and analysing data stored in relational databases.
- Statistical Analysis: Data that is collected is analysed statistically to find out trends and patterns. Data is interpreted using statistical modeling and predictions about future trends are made. Data statistics and graphical modelling are often used in open-source programming languages (such as Python) or specialist packages (R).
- Data Representation: The outcome of data analytics has to be represented effectively to the stakeholders. The last step is to report the results in a way that is accessible and understandable to different stakeholders (including decision-makers, analysts, and shareholders). Proper representation of data helps take informed decision making and grow the business.
Relevance to ACCA Syllabus
Big data analytics can be an important area of study for ACCA students as it contributes to different elements in the ACCA examinations such as the ACCA Strategic Business Leader (SBL) and ACCA Audit and Assurance (AA) papers, where the students are required to comprehend how big data analytics help in decision making, risk assessment and audit quality. Nonetheless, insights from big data make it all the more challenging due to the problems of data quality, absence of professionals, and privacy of information. Due to the digital environment they are in, ACCA highlights these risks so that accountants can be responsibly managing them.
Challenges of Big Data Analytics ACCA Questions
Q1: What is the single biggest hurdle you see in applying big data to financial audits?
A) Too many audit staff
B) Data formats such as unstructured and inconsistent.
C) High cost of audit fees
D) Spreadsheet legal limitations
Ans: B) Data formats that are not structured and inconsistent
Q2: There are many key risks with the use of big data analytics into accounting; however, I have selected one of the risks.
A) Reduced speed of reporting
B) Poor visualizations
C) Misuse of data and privacy breaches
D) Excessive paper records
Ans : C) Curtering privacy and misue of data
Q3: All of the following are a skills-based challenge in big data analytics EXCEPT?
A) Limited cloud storage
B) Shortage of skillful analysts professionals
C) Low employee turnover
D) High office rent
Ans: B) Shortage of analytical (analytical) professionals
Q4: Explain why audit planning in ACCA audits must check data reliability before proceeding to analysis
A) To increase client fees
B) To meet local tax laws
C) To make sure the insights drawn from big data are valid.
(D) To encourage reporting offline
Ans: C) In order to obtain valid insights from big Data
Q5: Which paper of ACCA deals with technology and data-related challenges while making a strategy and taking decisions?
A) Taxation
B) Strategic Business Leader
C) Financial Management
D) Performance Management
Ans: B) SBL ( Strategic Business Leader )
Relevance to US CMA Syllabus
Significant data analytics is received within the context of budgeting, forecasting and performance improvement in US CMA Part 1: Financial Planning, Performance and Analytics. CMAs are also taught to watch out for things like data overload, integration challenges, and weak data governance. Awareness of these restrictions allows management accountants to utilize technology more effectively in decision making
Challenges of Big Data Analytics CMA Questions
Q1: What are some common problems that arise with using big data for performance analysis?
A) Lack of Excel software
B) Comprehensive, high-fidelity data from all sources
C) Challenge of integrating data from multiple platforms
D) Too few charts and graphs
Ans: C) Challenging to integrate data from different systems
Q2: What does poor data quality have to do with financial planning?
A) It raises interest rates
B) It undermines the validity of financial projections
C) It improves tax savings
D) It reduces the inventory turnover
Ans: B) It undermines the authenticity of fiscal projections
Q3: A CMA analyst should ensure that data that is used for analytics are:
A) Encrypted at all times
B) Only from one department
And all three are clean, consistent, duty, and reliable.
D) Reviewed by auditors only
Answer: (C) Clean, consistent, and reliable
Q4: CMAs face the human capital aspect as a challenge in the big data.
A) Staff do not understand basic accounting principles
B) The staff lack proficiency in analytical tools
C) Multiple staff using same software
D) Use of paper-based ledgers
Ans: B) Staff have difficulty using analytical tools
Q5: In which CMA part, big data analytics and its related challenges are included?
A) Part 1
B) Part 2
C) Ethics Module
D) Not covered in CMA
Ans: A) Part 1
Relevance to CFA Syllabus
The importance of big data is highlighted in several places throughout the CFA curriculum under Quantitative Methods and Portfolio Management. Big data can enhance modelling and accuracy, but issues like data privacy, interpretability, overfitting, and technology limitations need to be resolved. To understand in detail how to identify and manage these risks while investing based on big data, a CFA charter holder is the best choice.
Challenges of Big Data Analytics CFA Questions
Q1: What is one danger of using machine learning extensively to make financial predictions?
A) Manual errors
B) Adding unnecessary complexity to the model by means of overfitting on past data
C) Shorter time horizons
D) Higher fixed costs
Ans: B) Fitting the model too closely to historical data
Q2. Why data governance matters in financial analytics
A) It elevates fund performance
B) It makes ethics training less necessary
C) It validates the integrity of data and compliance
D) Discounters portfolio risk
Ans: C) To ensure data integrity and compliance
Q3: As an investment analyst, one of the big technical challenges in big data is
A) Lack of formulas
B) Large, complex datasets are a challenge to manage
C) Use of accounting ratios
D) Portfolio concentration
Ans: B) Poor scalability when handling many different datasets
I am trained on data until Oct 2023.
A) Better insights
B) Sounder investment advice
C) False conclusions and bad decisions
D) Reduced operational costs
Ans: C) Misleading conclusions and bad decisions
Q5: Primary topic covering big data challenges for CFA Level I candidates?
A) Economics
B) Quantitative Methods
C) Derivatives
D) Ethics and Standards
Ans: B) Quantitative Methods
Relevance to US CPA Syllabus
Big data and analytics already influence the Business Environment and Concepts (BEC) and Audit (AUD) portions of the US CPA examination. CPAs should become familiar with the opportunities and limitations of big data, such as cybersecurity threats, inconsistent data and compliance risks. CPAs are equipped to analyze data integrity and validate that big data analytics enable accurate reporting and audit quality.
Challenges of Big Data Analytics CPA Questions
Q1: A challenge that auditors are facing in the use of big data tools is:
A) Lack of paper documents
B) Mining useful information from big databases
C) Manual transaction testing
D) Fewer documentation requirements
Ans: B) Mining useful information from big databases
Q2: Why is it important to have data privacy while using big data?
A) It slows down processes
B) It helps avoid data breaches and protects against unauthorized access
C) Staff involvement is reduced
D) It helps with tax returns
Ans: B) This protects against unauthorized access and data breaches.
Q3: In CPA audit procedures, inconsistent data can lead the CPAs to
A) Better assurance
B) Faster reporting
C) Misrepresentations and invalid audit findings
D) Higher earnings
Ans: C Material misstatements and unreliable results of audit
Q4: Where in the CPA exam would we find data analytics risk, audit, and compliance issues?
A) REG
B) BEC
C) FAR
D) Ethics
Ans: B) BEC
Q5: All of these challenges could be considered to contribute to one of four types of challenges when it comes to the use of big data.
A) Use of standard GAAP
B) Efficient training on even large datasets is not possible
C) Open financial disclosures
D) Journal entry matching
Ans: A) Use of standard GAAP
