Tag: Advanced Corporate AccountingOsmania University B.com Notes

An effective System Development Life Cycle (SDLC) should result in a high quality system that meets customer expectations, reaches completion within time and cost evaluations, and works effectively and efficiently in the current and planned Information Technology infrastructure.

In systems engineering, information systems and software engineering, the software development life cycle (SDLC), also referred to as the application development life-cycle, is a process for planning, creating, testing, and deploying an information system. The systems development life cycle concept applies to a range of hardware and software configurations, as a system can be composed of hardware only, software only, or a combination of both.[2] There are usually six stages in this cycle: requirement analysis, design, development and testing, implementation, documentation, and evaluation.

System Development Life Cycle (SDLC) is a conceptual model which includes policies and procedures for developing or altering systems throughout their life cycles.

SDLC is used by analysts to develop an information system. SDLC includes the following activities:

• Requirements
• Design
• Implementation
• Testing
• Deployment
• Operations
• Maintenance

Phases of SDLC

Systems Development Life Cycle is a systematic approach which explicitly breaks down the work into phases that are required to implement either new or modified Information System.

Feasibility Study or Planning

• Define the problem and scope of existing system.
• Overview the new system and determine its objectives.
• Confirm project feasibility and produce the project Schedule.
• During this phase, threats, constraints, integration and security of system are also considered.
• A feasibility report for the entire project is created at the end of this phase.

Analysis and Specification

• Define the requirements and prototypes for new system.
• Gather, analyze, and validate the information.
• Evaluate the alternatives and prioritize the requirements.
• Examine the information needs of end-user and enhances the system goal.
• A Software Requirement Specification (SRS) document, which specifies the software, hardware, functional, and network requirements of the system is prepared at the end of this phase.

System Design

• Transform the SRS document into logical structure, which contains detailed and complete set of specifications that can be implemented in a programming language.
• Includes the design of application, network, databases, user interfaces, and system interfaces.
• Create a contingency, training, maintenance, and operation plan.
• Review the proposed design. Ensure that the final design must meet the requirements stated in SRS document.
• Finally, prepare a design document which will be used during next phases.

Implementation

• Combine all the modules together into training environment that detects errors and defects.
• Implement the design into source code through coding.
• A test report which contains errors is prepared through test plan that includes test related tasks such as test case generation, testing criteria, and resource allocation for testing.
• Integrate the information system into its environment and install the new system.

Maintenance/Support

• Implement the changes that software might undergo over a period of time, or implement any new requirements after the software is deployed at the customer location.
• Include all the activities such as phone support or physical on-site support for users that is required once the system is installing.
• It also includes handling the residual errors and resolve any issues that may exist in the system even after the testing phase.
• Maintenance and support may be needed for a longer time for large systems and for a short time for smaller systems.

Data visualization is an interdisciplinary field that deals with the graphic representation of data. It is a particularly efficient way of communicating when the data is numerous as for example a time series.

Data visualization is the graphical representation of information and data. By using visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data.

From an academic point of view, this representation can be considered as a mapping between the original data (usually numerical) and graphic elements (for example, lines or points in a chart). The mapping determines how the attributes of these elements vary according to the data. In this light, a bar chart is a mapping of the length of a bar to a magnitude of a variable. Since the graphic design of the mapping can adversely affect the readability of a chart, mapping is a core competency of Data visualization.

Data visualization has its roots in the field of Statistics and is therefore generally considered a branch of Descriptive Statistics. However, because both design skills and statistical and computing skills are required to visualize effectively, it is argued by some authors that it is both an Art and a Science.

Research into how people read and misread various types of visualizations is helping to determine what types and features of visualizations are most understandable and effective in conveying information.

Data visualization can be used for:

• Making data engaging and easily digestible
• Identifying trends and outliers within a set of data
• Telling a story found within the data
• Reinforcing an argument or opinion
• Highlighting the important parts of a set of data

Quantitative messages

Author Stephen Few described eight types of quantitative messages that users may attempt to understand or communicate from a set of data and the associated graphs used to help communicate the message:

Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend.

Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by sales persons (the category, with each sales person a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the sales persons.

Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market.

Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show comparison of the actual versus the reference amount.

Frequency distribution: Shows the number of observations of a particular variable for given interval, such as the number of years in which the stock market return is between intervals such as 0-10%, 11-20%, etc. A histogram, a type of bar chart, may be used for this analysis. A boxplot helps visualize key statistics about the distribution, such as median, quartiles, outliers, etc.

Correlation: Comparison between observations represented by two variables (X,Y) to determine if they tend to move in the same or opposite directions. For example, plotting unemployment (X) and inflation (Y) for a sample of months. A scatter plot is typically used for this message.

Nominal comparison: Comparing categorical subdivisions in no particular order, such as the sales volume by product code. A bar chart may be used for this comparison.

Geographic or geospatial: Comparison of a variable across a map or layout, such as the unemployment rate by state or the number of persons on the various floors of a building. A cartogram is a typical graphic used.[6

Data governance is a term used on both a macro and a micro level. The former is a political concept and forms part of international relations and Internet governance; the latter is a data management concept and forms part of corporate data governance.

Data governance is a collection of processes, roles, policies, standards, and metrics that ensure the effective and efficient use of information in enabling an organization to achieve its goals. It establishes the processes and responsibilities that ensure the quality and security of the data used across a business or organization. Data governance defines who can take what action, upon what data, in what situations, using what methods.

A well-crafted data governance strategy is fundamental for any organization that works with big data, and will explain how your business benefits from consistent, common processes and responsibilities. Business drivers highlight what data needs to be carefully controlled in your data governance strategy and the benefits expected from this effort. This strategy will be the basis of your data governance framework.

Macro level

On the macro level, data governance refers to the governing of cross-border data flows by countries, and hence is more precisely called international data governance. This new[when?] field consists of “norms, principles and rules governing various types of data.”

Micro level

Here the focus is on an individual company. Here data governance is a data management concept concerning the capability that enables an organization to ensure that high data quality exists throughout the complete lifecycle of the data, and data controls are implemented that support business objectives. The key focus areas of data governance include availability, usability, consistency, data integrity and data security and includes establishing processes to ensure effective data management throughout the enterprise such as accountability for the adverse effects of poor data quality and ensuring that the data which an enterprise has can be used by the entire organization.

A data steward is a role that ensures that data governance processes are followed and that guidelines enforced, as well as recommending improvements to data governance processes.

Data governance encompasses the people, processes, and information technology required to create a consistent and proper handling of an organization’s data across the business enterprise. It provides all data management practices with the necessary foundation, strategy, and structure needed to ensure that data is managed as an asset and transformed into meaningful information. Goals may be defined at all levels of the enterprise and doing so may aid in acceptance of processes by those who will use them. Some goals include

• Increasing consistency and confidence in decision making
• Decreasing the risk of regulatory fines
• Improving data security, also defining and verifying the requirements for data distribution policies
• Maximizing the income generation potential of data
• Designating accountability for information quality
• Enable better planning by supervisory staff
• Minimizing or eliminating re-work
• Optimize staff effectiveness
• Establish process performance baselines to enable improvement efforts
• Acknowledge and hold all gain

Benefits of Data Governance

An effective data governance strategy provides many benefits to an organization, including:

A common understanding of data: Data governance provides a consistent view of, and common terminology for, data, while individual business units retain appropriate flexibility.

Improved quality of data: Data governance creates a plan that ensures data accuracy, completeness, and consistency.

Data map: Data governance provides an advanced ability to understand the location of all data related to key entities, which is necessary for data integration. Like a GPS that can represent a physical landscape and help people find their way in unknown landscapes, data governance makes data assets useable and easier to connect with business outcomes.

A 360-degree view of each customer and other business entities: Data governance establishes a framework so an organization can agree on “a single version of the truth” for critical business entities and create an appropriate level of consistency across entities and business activities.

Consistent compliance: Data governance provides a platform for meeting the demands of government regulations, such as the EU General Data Protection Regulation (GDPR), the US HIPAA (Health Insurance Portability and Accountability Act), and industry requirements such as PCI DSS (Payment Card Industry Data Security Standards).

Improved data management: Data governance brings the human dimension into a highly automated, data-driven world. It establishes codes of conduct and best practices in data management, making certain that the concerns and needs beyond traditional data and technology areas including areas such as legal, security, and compliance are addressed consistently.

Data policies and procedures

A data governance policy is a documented set of guidelines for ensuring that an organization’s data and information assets are managed consistently and used properly. Such guidelines typically include individual policies for data quality, access, security, privacy and usage, as well as roles and responsibilities for implementing those policies and monitoring compliance with them.

A data governance policy should articulate the principles, practices and standards that organizational leaders have determined necessary to ensure the organization has high-quality data and that its data assets are protected. The policy-forming group, called a data governance committee or data governance council, is primarily made up of business executives and other data owners.

The policy document this group creates clearly defines the data governance structure for the executive team, managers and line workers to follow in their daily operations.

A data governance policy formally outlines how data processing and management should be carried out to ensure organizational data is accurate, accessible, consistent and protected. The policy also establishes who is responsible for information under various circumstances and specifies what procedures should be used to manage it. In addition, it can incorporate risk management and data ethics principles to reduce potential business problems from the use of data.

Data governance is not a new concept by any stretch of the imagination, but it has come into sharp focus as the world’s data footprint continues to grow exponentially. Today, organizations not only must adhere to strict data policies and regulations (i.e. Sarbanes-Oxley Act, Basil Accord, HIPAA, Government agencies, GDPR), but they’re also looking to build a data governance strategy to better manage and properly safeguard their data as a valuable organizational asset.

Forces:

Maintenance

Nobody likes to talk about maintenance because, much like data governance, it’s not new. However, it still has its place, marking the difference between being organized or unorganized, between saving time and resources or wasting them and losing opportunities. All existing data in an organization’s infrastructure must be maintained; the more you have, the more of an effort it is to maintain it.

Risk Beyond Regulation

In addition to policy risk and regulations which mandate companies to safeguard certain data in a specific way, organizations are now facing the risk that their most valuable possession, their data, isn’t being properly handled. Access rights may be too lenient, there might be no data lineage, or they simply don’t know what exists in their infrastructure. With customer data being the most valuable asset for successful targeted marketing campaigns, it’s clear these three types of risks can have real repercussions.

Some incremental steps to get your process on the right track:

• Determine the senior leaders you trust with creating/updating your policy. Generally, this will include at least a senior leader from IT, business, and management, sharing knowledge from different areas of expertise.
• The data governance team should assess all areas of operational risk with respect to the data and come up with a plan for using your existing data.
• Determine the plan and implementation strategy with the operational risks clearly communicated and addressed. If you’re implementing a new policy, I highly recommend determining how you can automate the entire process. This should also include a plan for maintaining all systems and their data.
• Implement the changes and put your governance strategy into practice.
• Re-assess and change course, if needed.

Life cycle of data

The lifecycle of data starts with a researcher or a team creating a concept for a study, and the data for that study is then collected once a study concept is established. After data is obtained, it is prepared for distribution to be archived and used by other researchers at a future stage. When data enters the distribution point of the life cycle, it is contained in a location where other researchers can then discover it.

The five stages are as follows:

• Obtaining the Data: This stage involves using technical knowledge like MySQL to process and generate the data. It can even be in simpler file formats such as Microsoft Excel. Some examples like Python and R even directly import the datasets into a data science program.
• Scrubbing the Data: This stage involves cleaning raw data to retain only the relevant part of the processed data. The noise is also scrubbed off, and the data is refined, converted, and consolidated.
• Exploring the Data: This stage consists of examining the generated data. The data and its properties are inspected since different data types demand specific treatments. Descriptive statistics are then computed to extract the features and test the significant variables.
• Modelling the Data: The dataset is refined further, and only the essential components are kept. Only relevant values are kept and tested to predict accurate results.
• Interpreting the Data: At this stage, the final product is interpreted for the client or business to analyze if it meets the requirement or answers a business question. The insights are shared with everyone, and the results of the final stage are visualized.

Requirements

Data Generation and Understanding: The available data which can be used and the data which needs to be generated is analyzed and discussed. This is one of the fundamental data science life cycle steps as it deals with understanding the data requirement and gathering the data.

Data Preparation: This part of the process deals with preparing the raw data by cutting out the noise and irrelevant information. This is a time-consuming process because it deals with the cleaning and fine-tuning of data from datasets that are relevant and won’t lead to the corruption of the model.

Modeling of Project: The project is modeled, and different variations are tried out before deciding upon the final one with statistical and analytical means.

Evaluation of Model: This stage deals with finding out if the model is good enough before deployment. It is checked if the model can tackle a business problem or serve the business requirement.

Deployment of Model and Communication: The model is deployed and monitored. Basic communication is done regarding the model in regards to optimization and maintenance.

Enterprise performance management (EPM) is a field of business performance management which considers the visibility of operations in a closed-loop model across all facets of the enterprise. Specific to financial activities in the office of the chief financial officer, EPM also supports financial planning and analysis (FP&A). Corporate performance management (CPM) is a synonym for enterprise performance management. Gartner has officially retired the concept of CPM and reclassified into “financial planning and analysis (FP&A)” and “financial close” to reflect two significant trends increased focus on planning, and the emergence of a new category of solutions supporting the management of the financial close.

There are several domains in the EPM field which are driven by corporate initiatives, academic research, and commercial approaches. These include:

• Strategy formulation
• Business planning and forecasting
• Financial management
• Supply chain effectiveness

Strategy formulation

Strategy formulation refers to activities of an organization which determine the direction of its agenda. This is generally constructed of the mission, vision, and strategic goals and objectives of an organization. Once the direction is established, an organization monitors its progress against those activities and takes corrective actions to reach a particular target state.

While execution is the key to any operational objective, the strategy formulation surrounding why execution should occur and the context by which execution should be performed is also important. In recent years, organizations embed formal approaches to risk management to address market opportunities that organizations pursue. In this way strategy is aligned, performance is predictable, and executives can make better business decisions.

Executives live in a financially driven environment, where operational processes are traditionally a means of organizing resources inside the company and its value chain and employees are the responsible actors to execute those processes. The strategy gap that some industry watchdogs have noted is real and growing. Innovative technologies provide one approach to collapsing this gap and allowing corporate strategic outcomes to be fully realized and risk management programs to be fully described.

The first two steps of the closed-loop EPM process model involve developing the strategy and then to translate the strategy into particular actions that the organization can undertake. Strategy development as a subset of strategy formulation represents the articulation of the key components of strategy: mission, vision, strategic goals, and strategic objectives. There are several approaches to strategy development which may be considered. However, this may occur the executive leadership of the organization approve the strategy and typically review this strategy every 3–5 years based upon a 10–20-year horizon. Some business and national cultures may consider a longer-range strategy horizon.

Strategy translation then takes the often-obscure direction and statements defined in the first step and considers how to make these directions actionable. In the case of strategic goals, these are lofty targets given generally 3–5 years to achieve. Strategic objectives then identify specific progress against goals in a given time period. For example, “product ABC will achieve a market share of x% over the next two fiscal years.” would be a strategic objective. Key performance indicators assigned to these goals are determined which can monitor the organization progress towards achieving goals and objectives.

Business planning and forecasting

Business planning and forecasting refers to the set of activities where business is planned against the strategy and what forecast activities or results of the organization may occur from operational execution during a particular time period. This discipline corresponds to the third and sixth steps of the closed-loop EPM process model. Financial forecasts are a forecast of how a business will perform financially over, say, the year ahead.

Preparing forecasts will help a business to assess its likely sales income, costs, external financing needs and profitability. Financial forecasts are essential if a business needs to raise money from a third party, such as a bank. But they also provide businesses with the means to monitor performance on, say, a monthly basis and thereby exercise effective financial control – arguably the second most important management function in running a business.

Financial management

Financial management refers to the set business processes done to close the financial records of an organization at the end of a period in an accurate and timely fashion according to a generally accepted basis of accounting, including the financial statement presentation of results to both internal and external stakeholders, as well as providing appropriate explanations and insights to the nature of the financial results and all supporting documentation.

Supply chain effectiveness

Supply chain effectiveness refers to capabilities to not only manage an enterprise supply chain, but also to provide transparency to all parts of the value chain. This includes the ability to see the sales pipeline and create demand plans organized with suppliers to fulfill those demand plans. Another area of key focus is sales and operations planning (S&OP).

A modern EPM solution enables you to understand how, when, and where to adjust to disruptions.

Streamline account reconciliation: Account reconciliation is the number one reason for nondata-related delays in the financial close. EPM enables you to efficiently manage and improve global account reconciliation by exploiting automation and comprehensively addressing the security and risk typically associated with this process.

Optimize the financial close: In a changing regulatory environment, you need to adapt quickly to new requirements and deliver faster, more accurate insights to all stakeholders. EPM helps you streamline the financial close and report with confidence and insight.

Drive accurate and agile integrated plans: The digital economy demands more than spreadsheets and department-oriented planning processes. Truly effective planning should seamlessly connect your entire organization for a better vision. With EPM you can align planning across the enterprise, so that you can develop agile forecasts for all lines of business and respond faster and more effectively to change.

Align tax reporting with corporate financial reporting: Changing tax laws are causing global organizations to plan and manage their tax affairs very differently than they have to-date. EPM supports effective tax reporting by connecting the processes, data, and metadata that tax and finance share, such as financial planning, financial close, and regulatory reporting.

Manage and drive profitability: To survive in uncertain times, you must be able to manage and drive profitability. EPM helps you gain insight into dimensions of cost and profitability to determine where to invest limited resources.

Satisfy all your reporting requirements: No matter how many reporting standards you have to comply with, you want to be sure that the data you provide in your reports is accurate, complete, and the most current information available. EPM reduces the need for multiple reporting systems.

Manage change with enterprise data management: Whether you’re migrating applications to the cloud, managing applications in a hybrid environment, or spearheading major business and financial transformation, an enterprise data management platform provides data accuracy and integrity with the alignment of your data and master data.

Residual income is income that one continues to receive after the completion of the income-producing work. Examples of residual income include royalties, rental/real estate income, interest and dividend income, and income from the ongoing sale of consumer goods (such as music, digital art, or books), among others. In corporate finance, residual income can be used as a measure of corporate performance, whereby a company’s management team evaluates the income generated after paying all relevant costs of capital. Alternatively, in personal finance, residual income can be defined as either the income received after substantially all of the work has been completed, or as the income left over after paying all personal debts and obligations.

Residual income is not a GAAP concept. It is an internal financial assessment technique to help scale the relative success or failure of specific business activities. It adjusts income for a presumed cost of capital (or other threshold rate of return). Although there are many variations of the residual income calculations, the general approach is portrayed by the following formula:

Residual Income = Operating Income – (Operating Assets X Cost of Capital)

Residual Income (RI) = Net profits – Equity Charge

Equity Charge = Total Equity × Cost of Equity Capital

Types of Residual Income

Equity Valuation

In equity valuation, residual income represents an economic earnings stream and valuation method for estimating the intrinsic value of a company’s common stock. The residual income valuation model values a company as the sum of book value and the present value of expected future residual income. Residual income attempts to measure economic profit, which is the profit remaining after the deduction of opportunity costs for all sources of capital.

Residual income is calculated as net income less a charge for the cost of capital. The charge is known as the equity charge and is calculated as the value of equity capital multiplied by the cost of equity or the required rate of return on equity. Given the opportunity cost of equity, a company can have positive net income but negative residual income.

Corporate Finance

Managerial accounting defines residual income in a corporate setting as the amount of leftover operating profit after paying all costs of capital used to generate the revenues. It is also considered the company’s net operating income or the amount of profit that exceeds its required rate of return. Residual income is typically used to assess the performance of a capital investment, team, department, or business unit.

The calculation of residual income is as follows:

Residual income = operating income – (minimum required return x operating assets).

Personal Finance

In personal finance, residual income is known as disposable income. The residual income calculation occurs monthly after paying all monthly debts. As a result, residual income often becomes an essential component of securing a loan.

A lending institution assesses the amount of residual income remaining after paying other debts each month. The greater the amount of residual income, the more likely the lender is to approve the loan. Adequate levels of residual income establish that the borrower can sufficiently cover the monthly loan payment.

Advantages of the Residual Income Method

The residual income method can be used in both performance appraisals of a project, division, and business valuations. It considers the future cash flows in the present value term, which is the most liked approach in investment appraisals.

Its benefits closely resemble those arising from the dividend discount model or discounted cash flow models in present value terms. Some benefits of the Residual Income method in performance appraisals and business valuations include:

• It appraises the project net income in terms of present value, the residual income then equals the net worth of the shareholders.
• It focuses on the economic profits of the business rather than operating profits.
• The Residual Income method includes the book value of stocks in addition to the future stock price appreciation.
• Residual income utilizes readily available data from the business financial statements.
• It offers an adequate performance measure in terms of shareholders’ expectations, often profit generating businesses may not be generating enough economic profits for the shareholders.

Disadvantages of the Residual Income Method

• As with any theoretical performance appraisal method, the residual income method also offers some limitations:
• The Residual Income model also uses the cost of equity and cost of capital from The Income statement, both of which are assumption based measure.
• The use of book value for stock appraisals or asset valuation in project appraisal may offer invalid forecasts, as the book value of asset may not be accurate as market or intrinsic values.
• The residual income calculations begin with the book value of stocks or the net profits from the Income statement, these accounting entries can be manipulated by managers to show positive results.
• Residual income discards the long-term gains arising with cash flows in the later stages of the project.

Bad Timing

Though the least common of these five challenges, some new investors go into the market right before a financial downfall. This has caused investors to lose money before making any! However, this risk can easily be mitigated by rupee-cost averaging, a strategy where you invest into the market bit by bit and mitigate larger fluctuations in the value in your portfolio over a long period.

Over-Diversification

This challenge is almost always self-inflicted. Many new investors feel they need to invest a bit in everything to shield themselves from risk. However, over-diversification can significantly stunt your portfolio’s growth. It is often best to pick 2-3 options to invest the majority of your portfolio in.

Unknown Risks

New investors may not know about the hidden risks in many seemingly simple investment strategies. This can cause their portfolios to take large hits early on in the process. To combat this pitfall, it’s important to be as informed as possible. Make sure to be familiar with the risks involved with margin, leverage, options, futures, etc., before considering them as an investment option.

Information Overload

Many people looking to get involved with the stock market google around to discover the basics and quickly find themselves overwhelmed by the sheer amount of seemingly complex and even contradictory advice on the internet. Luckily, many of the most reliable trading strategies used by successful investors are quite timeless. New investors may find it easier to avoid the noise and use books as a resource to get started.

Limited Capital

One of the biggest challenges that new investors face is having limited capital available to invest. This is only compounded when certain financial instruments are too expensive. However, these issues can often be solved by looking into “partial shares.”

Not Getting Help

It’s risky to start investing without any outside help. Especially when you’re getting started, you should be using some form of investment advising, whether it’s automated or live. This will give you added assurance that you’ll see a return on your money.

Roadmap

Portfolio diversification

Asset allocation and portfolio diversification go hand in hand. Portfolio diversification is the process of selecting a variety of investments within each asset class to help reduce investment risk. Diversification across asset classes may also help lessen the impact of major market swings on your portfolio.

Rupee-cost averaging

Rupee-cost averaging is a disciplined investment strategy that can help smooth out the effects of market fluctuations in your portfolio.

With this approach, you apply a specific Rupee amount toward the purchase of stocks, bonds and/or mutual funds on a regular basis. As a result, you purchase more shares when prices are low and fewer shares when prices are high. Over time, the average cost of your shares will usually be lower than the average price of those shares. And because this strategy is systematic, it can help you avoid making emotional investment decisions.

Asset allocation

Appropriate asset allocation refers to the way you weight the investments in your portfolio to try to meet a specific objective and it may be the single most important factor in the success of your portfolio.

For instance, if your goal is to pursue growth, and you’re willing to take on market risk to reach that goal, you may decide to place as much as 80% of your assets in stocks and as little as 20% in bonds. Before you decide how you’ll divide the asset classes in your portfolio, make sure you know your investment timeframe and the possible risks and rewards of each asset class.

Pay off high interest credit card debt.

There is no investment strategy anywhere that pays off as well as, or with less risk than, merely paying off all high interest debt you may have. If you owe money on high interest credit cards, the wisest thing you can do under any market conditions is to pay off the balance in full as quickly as possible.

Create and maintain an emergency fund.

Most smart investors put enough money in a savings product to cover an emergency, like sudden unemployment. Some make sure they have up to six months of their income in savings so that they know it will absolutely be there for them when they need it.