Data Analysis and Modelling- Financial Management and Business Data Analytics | CMA Inter Syllabus
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Data analytics is the science of evaluating unprocessed datasets to draw conclusions about the information they contain. It helps us to identify patterns in the raw data and extract useful information from them.
Applications containing machine learning algorithms, simulation, and automated systems may be utilised by data analytics procedures and methodologies. For human usage, the systems and algorithms process unstructured data.
These data are evaluated and used to assist firms in gaining a deeper understanding of their customers, analysing their promotional activities, customising their content, developing content strategies, and creating new products.
Data analytics enables businesses to boost market efficiency and increase profits.
1.1 Process of data analytics
Following are the steps for data analytics:
Step 1:
Criteria for grouping data
Data may be segmented by a variety of parameters, including age, population, income, and sex. The data values might be either numeric or category
Step 2:
Collecting the data
Data may be gathered from several sources, including internet sources, computers, personnel, and community sources.
Step 3:
Organizing the data
After collecting the data, it must be arranged so that it can be analysed. Statistical data can be organised on a spreadsheet or other programme capable of handling statistical data.
Step 4:
Cleaning the data
The data is initially cleansed to verify that there are no duplicates or errors. The document is then examined to ensure that it is comprehensive. Before data is sent to a data analyst for analysis, it is beneficial to rectify or eliminate any errors by cleaning the data.
Step 5:
Adopt the right type of data analytics process:
There are four types of data analytics process:
(i) Descriptive analytics
(ii) Diagnostics analytics
(iii) Predictive analytics
(iv) Prescriptive analytics
We will discuss more on these types of analytics types in section 11.3.
1.2 Benefits of data analytics
Following are the benefits of data analytics:
(i) Improves decision making process
Companies can use the information gained from data analytics to base their decisions, resulting in enhanced outcomes. Using data analytics significantly reduces the amount of guesswork involved in preparing marketing plans, deciding what materials to produce, and more. Using advanced data analytics technologies, you can continuously collect and analyse new data to gain a deeper understanding of changing circumstances.
(ii) Increase in efficiency of operations
Data analytics assists firms in streamlining their processes, conserving resources, and increasing their profitability. When firms have a better understanding of their audience’s demands, they spend less time creating advertising that do not fulfil those needs.
(iii) Improved service to stakeholders
Data analytics gives organisations with a more in-depth understanding of their customers, employees and other stake holders. This enables the company to tailor stakeholders’ experiences to their needs, provide more personalization, and build stronger relationships with them.
Data mining, also known as knowledge discovery in data (KDD), is the extraction of patterns and other useful information from massive data sets. Given the advancement of data warehousing technologies and the expansion of big data, the use of data mining techniques has advanced dramatically over the past two decades, supporting businesses in translating their raw data into meaningful information. Nevertheless, despite the fact that technology is always evolving to manage massive amounts of data, leaders continue to struggle with scalability and automation.
Through smart data analytics, data mining has enhanced corporate decision making. The data mining techniques behind these investigations may be categorised into two primary purposes: describing the target dataset or predicting results using machine learning algorithms. These strategies are used to organise and filter data, bringing to the surface the most relevant information, including fraud detection, user habits, bottlenecks, and even security breaches.
When paired with data analytics and visualisation technologies like as Apache Spark, data mining has never been more accessible and the extraction of valuable insights has never been quicker. Artificial intelligence advancements continue to accelerate adoption across sectors.
2.1 Process of data mining
The process of data mining comprises a series of procedures, from data collecting through visualisation, in order to extract useful information from massive data sets. As stated previously, data mining techniques are utilised to develop descriptions and hypotheses on a specific data set. Through their observations of patterns, relationships, and correlations, data scientists characterise data. In addition to classifying and clustering data using classification and regression techniques, they discover outliers for use cases such as spam identification.
Data mining typically involves four steps: establishing objectives, acquiring and preparing data, implementing data mining techniques, and assessing outcomes.
(i) Setting the business objective:
This might be the most difficult element in the data mining process, yet many organisations spend inadequate effort on it. Together, data scientists and business stakeholders must identify the business challenge, which informs the data queries and parameters for a specific project. Analysts may also need to conduct further study to adequately comprehend the company environment.
(ii) Preparation of data:
Once the scale of the problem has been established, it is simpler for data scientists to determine which collection of data will assist the company in answering crucial questions. Once the pertinent data has been collected, it will be cleansed by eliminating any noise, such as repetitions, missing numbers, and outliers. Based on the dataset, an extra step may be done to minimise the number of dimensions, as an excessive amount of features might slow down any further calculation. Data scientists seek to maintain the most essential predictors to guarantee optimal model accuracy.
(iii) Model building and pattern mining:
Data scientists may study any intriguing relationship between the data, such as frequent patterns, clustering algorithms, or correlations, depending on the sort of research. While high frequency patterns have larger applicability, data variations can often be more fascinating, exposing possible fraud areas.
Depending on the available data, deep learning algorithms may also be utilised to categorise or cluster a data collection. If the input data is marked (i.e. supervised learning), a classification model may be used to categorise data, or a regression may be employed to forecast the probability of a specific assignment. If the dataset is unlabeled (i.e. unsupervised learning), the particular data points in the training set are compared to uncover underlying commonalities, then clustered based on those features.
(iv) Result evaluation and implementation of knowledge:
After aggregating the data, the findings must be analysed and understood. When completing results, they must be valid, original, practical, and comprehensible. When this criterion is satisfied, companies can execute new strategies based on this understanding, therefore attaining their intended goals.
2.2 Techniques of data mining
Using various methods and approaches, data mining transforms vast quantities of data into valuable information. Here are a few of the most prevalent:
(i) Association rules:
An association rule is a rule-based technique for discovering associations between variables inside a given dataset. These methodologies are commonly employed for market basket analysis, enabling businesses to better comprehend the linkages between various items. Understanding client consumption patterns helps organisations to create more effective cross-selling tactics and recommendation engines.
(ii) Neural Networks:
Primarily utilised for deep learning algorithms, neural networks replicate the interconnection of the human brain through layers of nodes to process training data. Every node has inputs, weights, a bias (or threshold), as well as an output. If the output value exceeds a predetermined threshold, the node “fires” and passes data to the subsequent network layer. Neural networks acquire this mapping function by supervised learning and gradient descent, changing based on the loss function. When the cost function is zero or close to it, we may have confidence in the model’s ability to produce the correct answer.
(iii) Decision tree:
Using classification or regression algorithms, this data mining methodology classifies or predicts likely outcomes based on a collection of decisions. As its name implies, it employs a tree-like representation to depict the potential results of these actions.
(iv) K-nearest neighbour:
K-nearest neighbour, often known as the KNN algorithm, classifies data points depending on their closeness to and correlation with other accessible data. This technique assumes that comparable data points exist in close proximity to one another. Consequently, it attempts to measure the distance between data points, often by Euclidean distance, and then assigns some on the most common category or average.
2.3 Implementation of data mining in Finance and management
The widespread use of data mining techniques by business intelligence and data analytics teams enables them to harvest insights for their organisations and industries.
Utilizing data mining techniques, hidden patterns and future trends and behaviours in financial markets may be predicted. Typically, sophisticated statistical, mathematical, and artificial intelligence approaches are necessary for data mining, particularly for high-frequency financial data. Among the data mining applications are:
(i) Detecting money laundering and other financial crimes:
Money laundering is the illegal conversion of black money to white money. In today’s society, data mining techniques have advanced to the point where they are deemed suitable for detecting money laundering. The data mining methodology provides a mechanism for bank customers to detect or verify the detection of the anti-money laundering impact.
(ii) Prediction of loan repayment and customer credit policy analysis:
Loan Distribution is the core business function of every bank. The loan Prediction system automatically computes the size of the characteristics it employs and examines data pertaining to its size. Consequently, data mining aids in the management of all critical data and massive databases by utilising its models.
(iii) Target marketing:
Together, data mining and marketing work to target a certain market, and they also assist and determine market decisions. With data mining, it is possible to keep earnings, margins, etc. and determine which product is optimal for various types of customers.
(iv) Design and construction of data warehouses:
The business is able to retrieve or move the data into several huge data warehouses, allowing a vast volume of data to be correctly and reliably evaluated with the aid of various data mining methodologies and techniques. It also examines a vast number of transactions.
Businesses utilise analytics to study and evaluate their data, and then translate their discoveries into insights that eventually aid executives, managers, and operational personnel in making more educated and prudent business choices. Descriptive analytics, which examines what has occurred in a firm, diagnostic analytics, which explores why did it occur, predictive analytics, which examines what could occur, and prescriptive analytics, which examines what should occur, are the four most important forms of analytics used by enterprises. While each of these approaches has its own distinct insights, benefits, and drawbacks in their use, when combined, these analytics tools may be an exceptionally valuable asset for a corporation.
It is also essential to examine the privacy principles while utilising data. Public entities and the business sector should consider individual privacy when using data analytics. As more and more firms seek to big data (huge, complex data sets) to raise revenue and enhance corporate efficiency and effectiveness, regulations are becoming increasingly required.
3.1 What are descriptive analytics?
Descriptive analytics is a frequently employed style of data analysis in which historical data is collected, organised, and presented in a readily digestible format. Descriptive analytics focuses exclusively on what has already occurred in an organisation and, unlike other types of analysis, does not utilise its results to draw inferences or make forecasts. Rather, descriptive analytics serves as a basic starting point to inform or prepare data for subsequent analysis.
In general, descriptive analytics is the simplest kind of data analytics, since it employs simple mathematical and statistical methods, such as arithmetic, averages, and percentage changes, rather of the complicated computations required for predictive and prescriptive analytics. With the use of visual tools such as line graphs, pie charts, and bar charts to communicate data, descriptive analytics can and should be readily understood by a broad corporate audience.
3.2 How does descriptive analytics work?
To identify historical data, descriptive analytics employs two fundamental techniques: data aggregation and data mining (also known as data discovery). The process of gathering and organising data into digestible data sets called data aggregation. The extracted patterns, trends, and significance are then presented in an intelligible format.
According to Dan Vesset, the process of descriptive analytics may be broken into five broad steps:
Step 1:
Decide the business metrics: First, measurements are developed to evaluate performance against corporate objectives, such as increasing operational efficiency or revenue. According to Vesset, the effectiveness of descriptive analytics is strongly dependent on KPI governance. ‘Without governance,’ he says, ‘there may be no consensus on the meaning of the data, assuring analytics a minor role in decision-making.’
Step 2:
Identification of data requirement: The data is gathered from sources such as reports and databases. Vesset states that in order to correctly measure against KPIs, businesses must catalogue and arrange the appropriate data sources in order to extract the necessary data and generate metrics depending on the present status of the business.
Step 3:
Preparation and collection of data: Data preparation, which includes publication, transformation, and cleaning, occurs prior to analysis and is a crucial step for ensuring correctness; it is also one of the most timeconsuming tasks for the analyst.
Step 4:
Analysis of data: Utilizing summary statistics, clustering, pattern tracking, and regression analysis, we discover data trends and evaluate performance.
Step 5:
Presentation of data: Lastly, charts and graphs are utilised to portray findings in a manner that nonexperts in analytics may comprehend.
3.3 Information revealed by descriptive analytics:
An organisation uses descriptive analytics regularly in its day-to-day operations. Examples of descriptive analytics that give a historical overview of an organization’s activities include company reports on inventory, workflow, sales, and revenue. These types of reports collect data that can be readily aggregated and utilised to provide snapshots of an organization’s activities.
Social analytics are virtually always a type of descriptive analytics. The number of followers, likes, and posts may be utilised to calculate, for example, the average number of replies per post, page visits, and response time. Facebook and Instagram comments are additional instances of descriptive analytics that may be utilised to better comprehend user sentiments.
However, descriptive analytics does not seek to go beyond the surface data and analysis; extra inquiry falls outside the scope of descriptive analytics, and conclusions and predictions are not derived from descriptive analysis. Nevertheless, this research can show patterns and significance by comparing historical data. An annual income report, for instance, may look financially encouraging until it is compared against the same report from past years, which reveals a declining trend.
3.4 Advantages and disadvantages of descriptive analytics
Due to the fact that descriptive analytics depends just on historical data and basic computations, this technique is easily applicable to day-to-day operations and does not need an in-depth understanding of analytics. This implies that firms may report on performance very quickly and simply and acquire insights that can be utilised to make changes.
3.5 Examples of descriptive analytics
Descriptive analytics assists organisations in measuring performance to ensure that objectives and goals are reached. And if they are not reached, descriptive analytics can indicate improvement or change areas.Several applications of descriptive analytics include the following:
● Past events, such as sales and operational data or marketing campaigns, are summarised.
● Social media usage and engagement data, such as Instagram or Facebook likes, are examples of such information.
● Reporting general trends
● Compiling survey data
3.6 What is diagnostic analytics?
Diagnostic analytics highlights the tools are employed to question the data, “Why did this occur?” It involves a thorough examination of the data to discover important insights. Descriptive analytics, the first phase in the data analysis process for the majority of businesses, is a straightforward method that records what has already occurred. Diagnostic analytics goes a step further by revealing the rationale behind particular outcomes.
Typical strategies for diagnostic analytics include data discovery, drill-down, data mining, and correlations. Analysts identify the data sources that assists them in interpreting the outcomes during the discovery phase. Drilling down entails concentrating on a specific aspect of the data or widget. Data mining is the automated extraction of information from vast quantities of unstructured data. And identifying consistent connections in the data might assist to pinpoint the investigation’s parameters.
Analysts are responsible for identifying the data sources that would be utilised. Frequently, this requires them to search for trends outside of the organization’s own databases. It may be necessary to include data from external sources in order to find connections and establish causality.
3.7 Advantages of diagnostic analytics
Data plays an increasingly important role in every organisation. Using diagnostic tools helps to make the most of the data by turning it into visuals and insights that can be utilised by everyone. Diagnostic analytics develops solutions that may be used to discover answers to data-related problems and to communicate insights within the organisation.
Diagnostic analytics enables to derive value from the data by asking the relevant questions and doing indepth analyses of the responses. And this demands a platform for BI and analytics that is adaptable, nimble, and configurable.
3.8 Examples of diagnostic analytics
Here are some steps that may be taken to run diagnostic analytics on the internal data, and it may be required to add external information, in order to determine “why” something occurred. Set up the data study by determining what questions are to be answered. This might be an inquiry into the cause of a problem, such as a decreased click>through rate, or a positive development, such as a significant increase in sales during a specific period or season.
After identifying the problem, the analysis may be set up. You may be able to identify a single root cause, or you may require numerous data sets to identify a pattern and establish a link. By fitting a collection of variables to a linear equation, linear regression can assist identify relationships. Remember that the longer you let your data model to collect data, the more precise your results will be. A data model matures like a superb wine does. Next, apply a filter to your findings so that just the most significant factor or two potential factors are included in your report. For example, using the correlative correlations, you should next draw your findings and create a convincing argument for them.
Consider an HR department that wishes to examine the performance of its employees based on quarterly performance levels, absenteeism, and weekly overtime hours. You might establish your data models, utilise Python or R for in-depth examination, and search for correlations in your data.
Cybersecurity is another example of a problem that every organisation should devote resources to. The Cyber Security Team may determine the relationship between the security rating and the number of incidents, as well as assess other objectives, such as the reaction teamwork vs the average time to resolution. The company might utilise these data to design preventative measures for potentially vulnerable regions.
3.9 What is Predictive Analytics?
Predictive analytics, as implied by its name, focuses on forecasting and understanding what might occur in the future, whereas descriptive analytics focuses on previous data. By analysing past data patterns and trends by examining historical data and customer insights, it is possible to predict what may occur in the future and, as a result, many aspects of a business can be informed, such as setting realistic goals, executing effective planning, managing performance goals, and avoiding risks.
3.10 How does Predictive Analytics work?
The foundation of predictive analytics is probability. Using techniques such as data mining, statistical modelling (mathematical relationships between variables to predict outcomes), and machine learning algorithms (classification, regression, and clustering techniques), predictive analytics attempts to predict possible future outcomes and the probability of those events. To create predictions, machine learning algorithms, for instance, utilise current data and make the best feasible assumptions to fill in missing data.
Deep learning is a more recent subfield of machine learning that imitates the building of “human brain networks as layers of nodes that understand a specific process area but are networked together to provide an overall forecast.” Credit scoring utilising social and environmental data and the sorting of digital medical pictures such as X-rays to automated predictions for doctors to use in diagnosing patients are instances of deep learning.
This methodology enables executives and managers to take a more proactive, data-driven approach to corporate planning and decision-making, given that predictive analytics may provide insight into what may occur in the future. Utilizing predictive analytics, businesses may foresee customer behaviour and purchase patterns, as well as discover sales trends. Predictions can also assist in forecasting supply chain, operations, and inventory demand.
3.11 Advantages and disadvantages of Predictive Analytics
Given that predictive analysis is based on probabilities, it can never be absolutely precise, but it may serve as a crucial tool for forecasting probable future occurrences and informing future corporate strategy. Additionally, predictive analytics may enhance several corporate functions, including:
~ Effectiveness, including inventory forecasting
~ Customer service, which may aid a business in gaining a deeper knowledge of who its clients are and what they want so that it can personalise its suggestions, is essential.
~ Detection and prevention of fraud, which can assist businesses in identifying trends and alterations.
~ Risk mitigation, which in the financial sector might entail enhanced applicant screening
This kind of analysis requires the availability of historical data, typically in enormous quantities.
3.12 Example of Predictive Analytics
There are a multitude of ways predictive analytics may be used to foresee probable occurrences and trends across sectors and enterprises. The healthcare business is a major benefactor of predictive analytics, for instance. RMIT University partnered with the Digital Health Cooperative Research Centre in 2019 to develop clinical decision support software for aged care that will reduce emergency hospitalizations and predict patient deterioration by analysing historical data and developing new predictive analytics techniques. The purpose of predictive analytics is to enable senior care professionals, residents, and their families to better prepare for death.
~ E-commerce – anticipating client preferences and proposing items based on previous purchases and search histories
~ Sales – estimating the possibility that a buyer will buy another item or depart the shop.
~ Human resources – identifying employees who are contemplating resigning and urging them to remain.
~ IT security – detecting potential security vulnerabilities requiring more investigation
~ Healthcare – anticipating staffing and resource requirements
3.13 What is prescriptive analytics?
Descriptive analytics describes what has occurred, diagnostic analytics explore why it occurred, predictive analytics describes what could occur, and prescriptive analytics describes what should be done. This approach is the fourth, final, and most sophisticated step of the business analysis process, and it is the one that urges firms to action by assisting executives, managers, and operational personnel in making the most informed decisions possible based on the available data.
3.14 How does the prescriptive analytics work?
Prescriptive analytics goes one step farther than descriptive and predictive analysis by advising the best potential business actions. This is the most sophisticated step of the business analytics process, needing significantly more specialised analytics expertise to execute; as a result, it is rarely utilised in daily company operations.
A multitude of approaches and tools – such as rules, statistics, and machine learning algorithms – may be used to accessible data, including internal data (from within the business) and external data, in order to produce predictions and recommendations (such as data derived from social media). The capabilities of machine learning dwarf those of a human attempting to attain the same outcomes.
The widespread misconception is that predictive analytics and machine learning are same. While predictive analytics uses historical data and statistical techniques to make predictions about the future, machine learning, a subset of artificial intelligence, refers to a computer system’s ability to understand large and often enormous amounts of data without explicit instructions, and to adapt and become increasingly intelligent as a result.
Predictive analytics predicts what, when, and, most importantly, why something may occur. After analysing the potential repercussions of each choice alternative, suggestions may be made regarding which options would best capitalise on future opportunities or reduce future hazards. Prescriptive analytics predicts future outcomes and, by doing so, enables decision-makers to assess the potential consequences for each future outcome before making a choice.
Effectively conducted prescriptive analytics may have a significant impact on corporate strategy and decision making to enhance production, customer experience, and business success.
3.15 Advantages and disadvantages of prescriptive analytics
When utilised correctly, prescriptive analytics gives important insights for making the most optimal data-driven decisions to optimise corporate performance. Nonetheless, similar to predictive analytics, this technique requires enormous volumes of data to deliver effective findings, which is not always the case. In addition, the machine learning techniques frequently used in this study cannot consistently account for all external variables. On the other hand, machine learning significantly minimises the likelihood of human mistake.
3.16 Examples of prescriptive analytics
GPS technology is a frequent prescriptive analytics tool since it gives recommended routes to the user’s intended destination based on factors such as travel time and road closures. In this scenario, prescriptive analysis “optimises a goal that analyses the distances between your origin and destination and prescribes the ideal path with the least distance.”
Further prescriptive analysis applications include the following:
~ Oil and manufacturing – monitoring price fluctuations.
~ Manufacturing – enhancing equipment administration, maintenance, cost modelling, production, and storage.
~ Healthcare – enhancing patient care and healthcare administration by analysing readmission rates and the cost-effectiveness of operations.
~ Insurance – evaluating customer risk in terms of price and premium information.
~ Pharmaceutical research – determining the optimal testing methods and patient populations for clinical trials.
4.1 Extensible Markup Language (XML)
XML is a file format and markup language for storing, transferring, and recreating arbitrary data. It specifies a set of standards for encoding texts in a format that is understandable by both humans and machines. XML is defined by the 1998 XML 1.0 Specification of the World Wide Web Consortium and numerous other related specifications, which are all free open standards.
XML’s design objectives stress Internet usability, universality, and simplicity. It is a textual data format with significant support for many human languages via Unicode. Although XML’s architecture is centred on texts, the language is commonly used to express arbitrary data structures, such as those employed by web services.
Several schema systems exist to help in the design of XML-based languages, and numerous application programming interfaces (APIs) have been developed by programmers to facilitate the processing of XML data.
Serialization, or storing, sending, and rebuilding arbitrary data, is the primary function of XML. In order for two dissimilar systems to share data, they must agree on a file format. XML normalises this procedure. XML is comparable to a universal language for describing information.
As a markup language, XML labels, categorises, and arranges information systematically.
The data structure is represented by XML tags, which also contain information. The information included within the tags is encoded according to the XML standard. A supplementary XML schema (XSD) defines the required metadata for reading and verifying XML. This is likewise known as the canonical schema. A “well-formed” XML document complies to fundamental XML principles, whereas a “valid” document adheres to its schema.
IETF RFC 7303 (which supersedes the previous RFC 3023) specifies the criteria for constructing media types for use in XML messages. It specifies the application/xml and text/xml media types. They are utilised for transferring unmodified XML files without revealing their intrinsic meanings. RFC 7303 also suggests that media types for XML-based languages end in +xml, such as image/svg+xml for SVG.
RFC 3470, commonly known as IETF BCP 70, provides further recommendations for the use of XML in a networked setting. This document covers many elements of building and implementing an XML-based language.
4.2 Application of XML
XML is now widely utilised for the exchange of data via the Internet. There have been hundreds of document formats created using XML syntax, including RSS, Atom, Office Open XML, OpenDocument, SVG, and XHTML. XML is also the foundational language for communication protocols like SOAP and XMPP. It is the message interchange format for the programming approach Asynchronous JavaScript and XML (AJAX).
Numerous industrial data standards, including Health Level 7, OpenTravel Alliance, FpML, MISMO, and National Information Exchange Model, are founded on XML and the extensive capabilities of the XML schema definition. Darwin Information Typing Architecture is an XML industry data standard in publishing. Numerous publication formats rely heavily on XML as their basis.
4.3 Extensible Business Reporting Language (XBRL)
XBRL is a data description language that facilitates the interchange of standard, comprehensible corporate data. It is based on XML and enables the automated interchange and trustworthy extraction of financial data across all software types and advanced technology, including Internet.
XBRL allows organisations to arrange data using tags. When a piece of data is labelled as “revenue,” for instance, XBRL enabled applications know that it pertains to revenue. It conforms to a fixed definition of income and may appropriately utilise it. The integrity of the data is safeguarded by norms that have been already accepted. In addition, XBRL offers expanded contextual information on the precise data content of financial documents. For example, when a a monetary amount is stated. XBRL tags may designate the data as “currency” or “accounts” within a report.
With XBRL, a business, a person, or another software programme may quickly produce a variety of output formats and reports based on a financial statement.
5.1 Cloud computing
Simply described, cloud computing is the delivery of a variety of services through the Internet, or “the cloud.” It involves storing and accessing data via distant servers as opposed to local hard drives and private datacenters.
Before the advent of cloud computing, businesses had to acquire and operate their own servers to suit their demands. This necessitated the purchase of sufficient server capacity to minimise the risk of downtime and disruptions and to meet peak traffic volumes. Consequently, significant quantities of server space were unused for the most of the time. Today’s cloud service providers enable businesses to lessen their reliance on costly onsite servers, maintenance staff, and other IT resources
(i) Private cloud:
Private cloud offers a cloud environment that is exclusive to a single corporate organisation, with physical components housed on-premises or in a vendor’s datacenter. This solution gives a high level of control due to the fact that the private cloud is available to just one enterprise. In a virtualized environment, the benefits include a customizable architecture, enhanced security procedures, and the capacity to expand computer resources as needed. In many instances, a business maintains a private cloud infrastructure on-premises and provides cloud computing services to internal users over the intranet. In other cases, the company engages with a third-party cloud service provider to host and operate its servers off-site.
(ii) Public cloud:
The public cloud stores and manages access to data and applications through the internet. It is fully virtualized, enabling an environment in which shared resources may be utilised as necessary. Because these resources are offered through the web, the public cloud deployment model enables enterprises to grow with more ease; the option to pay for cloud services on an as-needed basis is a significant benefit over local servers. Additionally, public cloud service providers use rigorous security measures to prevent unauthorised access to user data by other tenants.
(iii) Hybrid cloud:
Hybrid cloud blends private and public cloud models, enabling enterprises to exploit the benefits of shared resources while leveraging their existing IT infrastructure for mission-critical security needs. The hybrid cloud architecture enables businesses to store sensitive data on-premises and access it through apps hosted in the public cloud. In order to comply with privacy rules, an organisation may, for instance, keep sensitive user data in a private cloud and execute resource-intensive computations in a public cloud.
5.2 Business Intelligence:
Business intelligence includes business analytics, data mining, data visualisation, data tools and infrastructure, and best practises to assist businesses in making choices that are more data-driven. When you have a complete picture of your organization’s data and utilise it to drive change, remove inefficiencies, and swiftly adjust to market or supply changes, you have contemporary business intelligence. Modern BI systems promote adaptable selfservice analysis, controlled data on dependable platforms, empowered business users, and rapid insight delivery.
Traditional Business Intelligence, complete with capitalization, originated in the 1960s as a method for disseminating information across enterprises. Alongside computer models for decision making, the phrase “Business Intelligence” was coined in 1989. Before becoming a distinct product from BI teams with IT-dependent service solutions, these programmes evolved to transform data into insights.
# BI Methods:
Company intelligence is a broad word that encompasses the procedures and methods of gathering, storing, and evaluating data from business operations or activities in order to maximise performance. All of these factors combine to provide a full perspective of a firm, enabling individuals to make better, proactive decisions. In recent years, business intelligence has expanded to incorporate more procedures and activities designed to enhance performance. These procedures consist of:
(i) Data mining: Large datasets may be mined for patterns using databases, analytics, and machine learning (ML).
(ii) Reporting: The dissemination of data analysis to stakeholders in order for them to form conclusions and make decisions.
(iii) Performance metrics and benchmarking: Comparing current performance data to previous performance data in order to measure performance versus objectives, generally utilising customised dashboards.
(iv) Descriptive analytics: Utilizing basic data analysis to determine what transpired
(v) Querying: BI extracts responses from data sets in response to data-specific queries.
(vi) Statistical analysis: Taking the results of descriptive analytics and use statistics to further explore the data, such as how and why this pattern occurred.
(vii) Data Visualization: Data consumption is facilitated by transforming data analysis into visual representations such as charts, graphs, and histograms.
(viii) Visual Analysis: Exploring data using visual storytelling to share findings in real-time and maintain the flow of analysis.
(ix) Data Preparation: Multiple data source compilation, dimension and measurement identification, and data analysis preparation.
5.3 Artificial Intelligence (AI)
John McCarthy of Stanford University defined artificial intelligence as, “ It is the science and engineering of making intelligent machines, especially intelligent computer programs. It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable.”
However, decades prior to this description, Alan Turing’s landmark paper “Computing Machinery and Intelligence” marked the genesis of the artificial intelligence discourse. Turing, commonly referred to as the “father of computer science,” poses the question “Can machines think?” in this article. From there, he proposes the now-famous “Turing Test,” in which a human interrogator attempts to differentiate between a machine and a human written answer. Although this test has been subjected to considerable examination since its publication, it remains an essential aspect of the history of artificial intelligence and a continuing philosophical thought that employs principles from linguistics.
Stuart Russell and Peter Norvig then published ‘Artificial Intelligence: A Modern Approach’, which has since become one of the most influential AI textbooks. In it, they discuss four alternative aims or definitions of artificial intelligence, which distinguish computer systems based on reasoning and thinking vs. acting:
~ Human approach:
● Systems that think like humans
● Systems that act like humans
~ Ideal approach:
● Systems that think rationally
● Systems that act rationally
Artificial intelligence is, in its simplest form, a topic that combines computer science and substantial datasets to allow problem-solving. In addition, it includes the subfields of machine learning and deep learning, which are commonly associated with artificial intelligence. These fields consist of AI algorithms that aim to develop expert systems that make predictions or classifications based on input data.
As expected with any new developing technology on the market, AI development is still surrounded by a great deal of hype. According to Gartner’s hype cycle, self-driving vehicles and personal assistants follow “a normal evolution of innovation, from overenthusiasm through disillusionment to an ultimate grasp of the innovation’s importance and position in a market or area.” According to Lex Fridman’s 2019 MIT lecture, we are at the top of inflated expectations and nearing the trough of disillusionment.AI has several applications in the area of financial services
Types of Artificial Intelligence – Weak AI vs. Strong AI
Weak AI, also known as Narrow AI or Artificial Narrow Intelligence (ANI), is AI that has been trained and honed to do particular tasks. Most of the AI that surrounds us today is powered by weak AI. This form of artificial intelligence is anything but feeble; it allows sophisticated applications such as Apple’s Siri, Amazon’s Alexa, IBM Watson, and driverless cars, among others.
Artificial General Intelligence (AGI) and Artificial Super Intelligence (AIS) comprise strong AI (ASI). Artificial general intelligence (AGI), sometimes known as general artificial intelligence (AI), is a hypothetical kind of artificial intelligence in which a machine possesses human-level intellect, a self-aware consciousness, and the ability to solve problems, learn, and plan for the future. Superintelligence, also known as Artificial Super Intelligence (ASI), would transcend the intelligence and capabilities of the human brain. Despite the fact that strong AI is yet totally theoretical and has no practical applications, this does not preclude AI researchers from studying its development. In the meanwhile, the finest instances of ASI may come from science fiction, such as HAL from 2001: A Space Odyssey, a superhuman, rogue computer aide.
Deep Learning vs. Machine Learning
Given that deep learning and machine learning are frequently used interchangeably, it is important to note the distinctions between the two. As stated previously, both deep learning and machine learning are subfields of artificial intelligence; nonetheless, deep learning is a subfield of machine learning.
Neural networks truly constitute deep learning. “Deep” in deep learning refers to a neural network with more than three layers, which includes inputs and outputs, and may be termed a deep learning method. Typically, this is depicted by the following diagram
Deep learning and machine learning differ in how their respective algorithms learn. Deep learning automates a significant portion of the feature extraction step, reducing the need for manual human involvement and enabling the usage of bigger data sets. Deep learning may be thought of as “scalable machine learning,” as Lex Fridman stated in the aforementioned MIT presentation. Classical or “non-deep” machine learning requires more human interaction to learn. Human specialists develop the hierarchy of characteristics in order to comprehend the distinctions between data inputs, which often requires more structured data to learn.
Deep machine learning can utilise labelled datasets, also known as supervised learning, to educate its algorithm, although a labelled dataset is not required. It is capable of ingesting unstructured data in its raw form (e.g., text and photos) and can automatically establish the hierarchy of characteristics that differentiate certain data categories from one another. It does not require human interaction to interpret data, unlike machine learning, allowing us to scale machine learning in more exciting ways.
5.4 Robotic Process Automation:
With RPA, software users develop software robots or “bots” that are capable of learning, simulating, and executing rules-based business processes. By studying human digital behaviours, RPA automation enables users to construct bots. Give your bots instructions, then let them to complete the task. Robotic Process Automation software bots can communicate with any application or system in the same manner that humans can, with the exception that RPA bots can function continuously, around-the-clock, and with 100 percent accuracy and dependability.
Robotic Process Automation bots possess a digital skill set that exceeds that of humans. Consider RPA bots to be a Digital Workforce capable of interacting with any system or application. Bots may copy-paste, scrape site data, do computations, access and transfer files, analyse emails, log into programmes, connect to APIs, and extract unstructured data, among other tasks. Due to the adaptability of bots to any interface or workflow, there is no need to modify existing corporate systems, apps, or processes in order to automate.
RPA bots are simple to configure, utilise, and distribute. You will be able to configure RPA bots if you know how to record video on a mobile device. Moving files around at work is as simple as pressing record, play, and stop buttons and utilising drag-and-drop. RPA bots may be scheduled, copied, altered, and shared to conduct enterprisewide business operations.
Benefits of RPA
(i) Higher productivity
(ii) Higher accuracy
(iii) Saving of cost
(iv) Integration across platforms
(v) Better customer experience
(vi) Harnessing AI
(vii) Scalability
5.5 Machine learning
Machine learning (ML) is a branch of study devoted to understanding and developing systems that “learn,” or ways that use data to improve performance on a set of tasks. Considered a component of artificial intelligence. In order to generate predictions or conclusions without being explicitly taught to do so, machine learning algorithms construct a model based on training data and sample data. In applications such as medicine, email filtering, speech recognition, and computer vision, when it is difficult or impractical to create traditional algorithms to do the required tasks, machine learning techniques are utilised.
The premise underlying learning algorithms is that tactics, algorithms, and conclusions that performed well in the past are likely to continue to perform well in the future. These deductions may be clear, such as “because the sun has risen every morning for the past 10,000 days, it will likely rise again tomorrow.” They can be nuanced, as in “X% of families include geographically distinct species with colour variations; thus, there is a Y% possibility that unknown black swans exist.”
Programs that are capable of machine learning can complete tasks without being expressly designed to do so. It includes computers learning from available data in order to do certain jobs. For basic jobs handed to computers, it is feasible to build algorithms that instruct the machine on how to perform all steps necessary to solve the problem at hand; no learning is required on the side of the computer. For complex jobs, it might be difficult for a person to manually build the necessary algorithms. In reality, it may be more efficient to assist the computer in developing its own algorithm as opposed to having human programmers describe each step.
The field of machine learning involves a variety of methods to educate computers to perform jobs for which there is no optimal solution. In situations when there are a large number of viable replies, one strategy is to classify some of the correct answers as legitimate. This information may subsequently be utilised to train the computer’s algorithm(s) for determining accurate replies.
Approaches towards machine learning
On the basis of the type of “signal” or “feedback” provided to the learning system, machine learning systems are generally categorised into five major categories:
(i) Supervised learning
Supervised learning algorithms construct a mathematical model of a data set that includes both the inputs and expected outcomes. The data consists of a collection of training examples and is known as training data. Each training example consists of one or more inputs and the expected output, sometimes referred to as a supervisory signal. Each training example in the mathematical model is represented by an array or vector, sometimes known as a feature vector, and the training data is represented by a matrix. By optimising an objective function iteratively, supervised learning algorithms discover a function that may be used to predict the output associated with fresh inputs. A function that is optimum will enable the algorithm to find the proper output for inputs that were not included in the training data. It is claimed that an algorithm has “learned” to do a task if it improves its outputs or predictions over time. Active learning, classification, and regression are examples of supervised-learning algorithms.
Classification algorithms are used when the outputs are limited to a certain set of values, whereas regression techniques are used when the outputs may take on any value within a given range. For a classification algorithm that filters incoming emails, for instance, the input would be an incoming email and the output would be the folder name in which to file the email.
Similarity learning is a subfield of supervised machine learning that is closely connected to regression and classification, but its objective is to learn from examples by employing a similarity function that quantifies how similar or related two items are. It has uses in ranking, recommendation systems, monitoring visual identities, face verification, and speaker verification.
(ii) Unsupervised learning
Unsupervised learning approaches utilise a dataset comprising just inputs to identify data structure, such as grouping and clustering. Therefore, the algorithms are taught using unlabeled, unclassified, and uncategorized test data. Unsupervised learning algorithms identify similarities in the data and respond based on the presence or absence of such similarities in each new data set. In statistics, density estimation, such as calculating the probability density function, is a fundamental application of unsupervised learning. Despite the fact that unsupervised learning encompasses additional disciplines, such as data feature summary and explanation.
Cluster analysis is the process of assigning a set of data to subsets (called clusters) so that observations within the same cluster are similar based on one or more preset criteria, while observations obtained from other clusters are different. Different clustering approaches necessitate varying assumptions regarding the structure of the data, which is frequently characterised by a similarity metric and evaluated, for example, by internal compactness, or the similarity between members of the same cluster, and separation, the difference between clusters. Other methods rely on estimated graph density and connectivity.
(iii) Semi supervised learning
Semi-supervised learning is intermediate between unsupervised learning (without labelled training data) and supervised learning (with completely labelled training data). Many machine-learning researchers have discovered that when unlabeled data is combined with a tiny quantity of labelled data, there is a significant gain in learning accuracy.
In poorly supervised learning, the training labels are noisy, restricted, or inaccurate; yet, these labels are frequently less expensive to acquire, resulting in larger effective training sets.
(iv) Reinforcement learning
Reinforcement learning is a subfield of machine learning concerned with determining how software agents should operate in a given environment so as to maximise a certain concept of cumulative reward. Due to the field’s generic nature, it is explored in several different fields, including game theory, control theory, operations research, information theory, simulation-based optimization, multi-agent systems, swarm intelligence, statistics, and genetic algorithms. The environment is generally represented as a Markov decision process in machine learning (MDP). Many methods for reinforcement learning employ dynamic programming techniques. Reinforcement learning techniques do not need prior knowledge of an accurate mathematical model of the MDP and are employed when exact models are not practicable. Autonomous cars and learning to play a game against a human opponent both employ reinforcement learning algorithms.
(v) Dimensionality reduction
Dimensionality reduction is the process of acquiring a set of major variables in order to reduce the number of random variables under consideration. In other words, it is the process of lowering the size of the feature set, which is also referred to as the “number of features.” The majority of dimensionality reduction strategies may be categorised as either deletion or extraction of features. Principal component analysis is a well-known technique for dimensionality reduction (PCA). PCA includes transforming data with more dimensions (e.g.,3D) to a smaller space (e.g., 2D). This results in a decreased data dimension (2D as opposed to 3D), while retaining the original variables in the model and without altering the data. Numerous dimensionality reduction strategies assume that high-dimensional data sets reside along low-dimensional manifolds, leading to the fields of manifold learning and manifold regularisation.
In artificial intelligence, there are two schools of thought: data-driven and model-driven. The data-driven strategy focuses on enhancing data quality and data governance in order to enhance the performance of a particular problem statement. In contrast, the model-driven method attempts to increase performance by developing new models and algorithmic manipulations (or upgrades). In a perfect world, these should go hand in hand, but in fact, model-driven techniques have advanced far more than data-driven ones. In terms of data governance, data management, data quality handling, and general awareness, there is still much room for improvement.
Recent work on Covid-19 serves as an illustration in this perspective. While the globe was struggling from the epidemic, several AI-related projects emerged. Whether it’s recognising Covid-19 from a CT scan, X-ray, or other medical imaging, estimating the course of the disease, or even projecting the overall number of fatalities, artificial intelligence is essential. On the one hand, this extensive effort around the globe has increased our understanding of the illness and, in certain locations, assisted clinical personnel in their work with vast populations. However, only few of the vast quantity of work was judged suitable for any actual implementation procedure, such as in the healthcare industry. Primarily data quality difficulties are responsible for this deficiency in practicality. Numerous projects and studies utilised duplicate photos from different sources. Even still, training data are notably lacking in external validation and demographic information. The majority of these studies would fail a systematic review and fail to reveal biases. Consequently, the quoted performance cannot be applied to real-world scenarios.
A crucial feature of Data science to keep in mind is that poor data will never result in superior performance, regardless of how strong your model is. Real-world applications require an understanding of systematic data collection, management, and consumption for a Data Science project. Only then can society reap the rewards of the ‘wonderful AI’
Solved Case 1
Arjun joined as an instructor in a higher learning institution. His responsibility is to teach data analysis to students. He is particularly interested in teaching analytics and model building. Arjun was preparing a teaching plan for the new upcoming batch.
What elements do you think, he should incorporate into the plan.
Teaching note - outline for solution:
The instructor may explain first the utility of data analytics from the perspective of business organizations. He may explain how data analytics may translate their discoveries into insights that eventually aid executives, managers, and operational personnel in making more educated and prudent business choices.
He may further explain the four forms of data analytics:
(i) Descriptive analytics
(ii) Diagnostic analytics
(iii) Predictive analytics
(iv) Prescriptive analytics
The instructor should explain each of the terms along with their appropriateness in using under real-life problem situations.
The advantages and disadvantages of using each of the methods should also be discussed thoroughly.
A. Theoretical Questions:
1. Following are the benefits of data analytics
(a) Improves decision making process
(b) Increase in efficiency of operations
(c) Improved service to stakeholders
(d) All of the above
Answer:- D. All of the above
Following are the benefits of data analytics:
(i) Improves decision making process
Companies can use the information gained from data analytics to base their decisions, resulting in enhanced outcomes. Using data analytics significantly reduces the amount of guesswork involved in preparing marketing plans, deciding what materials to produce, and more. Using advanced data analytics technologies, you can continuously collect and analyse new data to gain a deeper understanding of changing circumstances.
(ii) Increase in efficiency of operations
Data analytics assists firms in streamlining their processes, conserving resources, and increasing their profitability. When firms have a better understanding of their audience’s demands, they spend less time creating advertising that do not fulfil those needs.
(iii) Improved service to stakeholders
Data analytics gives organisations with a more in-depth understanding of their customers, employees and other stake holders. This enables the company to tailor stakeholders’ experiences to their needs, provide more personalization, and build stronger relationships with them.
2. Following are the techniques of data mining
(a) Association rules
(b) Neural network
(c) Decision tree
(d) All of the above
Answer:- D. All of the above
Using various methods and approaches, data mining transforms vast quantities of data into valuable information. Here are a few of the most prevalent:
(i) Association rules
(ii) Neural Networks
(iii) Decision tree
(iv) K-nearest neighbour
3. XML is the abbreviated form of
(a) Extensible mark-up language
(b) Extended mark-up language
(c) Extendable mark-up language
(d) Extensive mark-up language
Answer:- A. Extensible mark-up language
XML stands for eXtensible Mark-up Language.
4. XBRL is the abbreviated form of
(a) eXtensible Business Reporting Language
(b) eXtensive Business Reporting Language
(c) eXtended Business Reporting Language
(d) eXtensive Business Reporting Language
Answer:- A. eXtensible Business Reporting Language
XBRL is the abbreviated form of eXtensible Business Reporting Language.
5. Following are the types of cloud computing
(a) Private cloud
(b) Public cloud
(c) Hybrid cloud
(d) All of the above
Answer:- D. All of the above
There are three deployment options for cloud computing: private cloud, public cloud, and hybrid cloud.
(i) Private cloud:
Private cloud offers a cloud environment that is exclusive to a single corporate organisation, with physical components housed on-premises or in a vendor’s datacenter. This solution gives a high level of control due to the fact that the private cloud is available to just one enterprise.
In a virtualized environment, the benefits include a customizable architecture, enhanced security procedures, and the capacity to expand computer resources as needed. In many instances, a business maintains a private cloud infrastructure on-premises and provides cloud computing services to internal users over the intranet. In other cases, the company engages with a third-party cloud service provider to host and operate its servers off-site.
(ii) Public cloud:
The public cloud stores and manages access to data and applications through the internet. It is fully virtualized, enabling an environment in which shared resources may be utilised as necessary.
Because these resources are offered through the web, the public cloud deployment model enables enterprises to grow with more ease; the option to pay for cloud services on an as-needed basis is a significant benefit over local servers. Additionally, public cloud service providers use rigorous security measures to prevent unauthorised access to user data by other tenants.
(iii) Hybrid cloud:
Hybrid cloud blends private and public cloud models, enabling enterprises to exploit the benefits of shared resources while leveraging their existing IT infrastructure for mission-critical security needs. The hybrid cloud architecture enables businesses to store sensitive data on-premises and access it through apps hosted in the public cloud.
In order to comply with privacy rules, an organisation may, for instance, keep sensitive user data in a private cloud and execute resource-intensive computations in a public cloud.
1. Decision tree classifies or predicts likely outcomes based on a collection of decisions. Answer:- True
2. K-nearest neighbour, often known as the KNN algorithm, classifies data points depending on their closeness to and correlation with other accessible data. Answer:-True
3. Utilizing data mining techniques, hidden patterns and future trends and behaviours in financial markets may be predicted. Answer:- True
4. Social analytics are virtually always a type of descriptive analytics. Answer:- True
5. Diagnostic analytics highlights the tools are employed to question the data, “Why did this occur?” Answer:- True
1. Data analytics helps us to identify patterns in the raw data and extract useful information from them.
2. Through smart Data analytics, data mining has enhanced corporate decision making.
3. Data mining techniques are utilised to develop descriptions and hypotheses on a specific data set.
4. Data mining typically involves Four steps.
5. Primarily utilised for deep learning algorithms, neural network replicate the interconnection of the human brain through layers of nodes to process training data.
1. What are descriptive analytics?
Answer:-
2. Define diagnostic analytics.
Answer:-
3. What is the difference between descriptive analytics and prescriptive analytics?
Answer:-
4. Discuss the advantages and disadvantages of prescriptive analytics.
Answer:-
5. How does the prescriptive analytics work?
Answer:-
1. Discuss the different steps in the process of data analytics.
Answer:-
2. Discuss the benefits of data analytics
Answer:-
3. Define data mining. Discuss the various steps in data mining.
Answer:-
4. Discuss the various techniques of data mining.
Answer:-
5. Discuss various applications of data mining techniques in finance and accounting.
Answer:-
References:
● Davy Cielen, Arno D B Meysman, and Mohamed Ali. Introducing Data Science. Manning Publications Co USA
● Cathy O’Neil, Rachell Schutt. Doing data science. O’Reilley
● Joel Grus. Data science from scratch. O’Reilley
● https://corporatefinanceinstitute.com
● https://www.ibm.com
● https://studyonline.unsw.edu.au
● https://www.tableau.com
● https://www.automationanywhere.com
● John McCarthy, What is artificial intelligence? http://www-formal.stanford.edu/jmc/
● Understanding the XML Standard for Business Reporting and Finance. White paper. Software AG
Ruchika Saboo
An All India Ranker (AIR 7 - CA Finals, AIR 43 - CA Inter), she is one of those teachers who just loved studying as a student. Aims to bring the same drive in her students.
Ruchika Ma'am has been a meritorious student throughout her student life. She is one of those who did not study from exam point of view or out of fear but because of the fact that she JUST LOVED STUDYING. When she says - love what you study, it has a deeper meaning.
She believes - "When you study, you get wise, you obtain knowledge. A knowledge that helps you in real life, in solving problems, finding opportunities. Implement what you study". She has a huge affinity for the Law Subject in particular and always encourages student to - "STUDY FROM THE BARE ACT, MAKE YOUR OWN INTERPRETATIONS". A rare practice that you will find in her video lectures as well.
She specializes in theory subjects - Law and Auditing.
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A Story teller, passionate for simplifying complexities, techie. Perfectionist by heart, he is the founder of - Konceptca.
Yash Sir (As students call him fondly) is not a teacher per se. He is a story teller who specializes in simplifying things, connecting the dots and building a story behind everything he teaches. A firm believer of Real Teaching, according to him - "Real Teaching is not teaching standard methods but giving the power to students to develop his own methods".
He cleared his CA Finals in May 2011 and has been into teaching since. He started teaching CA, CS, 11th, 12th, B.Com, M.Com students in an offline mode until 2016 when Konceptca was launched. One of the pioneers in Online Education, he believes in providing a learning experience which is NEAT, SMOOTH and AFFORDABLE.
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