Business data analysis

1. What is business data analysis?

• Competitive intelligence is the gathering, analyzing, and use of information collected on competitors, customers, and other market factors that contribute to a business's competitive advantage. This data is then used to improve marketing efforts, strategic planning, finance, and operations.
• Financial analysis involves using financial data to assess a company's viability, stability, and profitability. This data is taken from reports and financial statements including balance sheets, income statements, cash flow statements, and statements of shareholders' equity.
• Market research involves a set of techniques used to gather information and better understand a company's target market, including their needs and demographics. This information is then used to determine if a product or service is viable, spot potential gaps in the market, and identify competitors.
• Risk management aims to identify, evaluate, and manage potential threats to a business, including financial uncertainty, legal liabilities, strategic management errors, accidents, and more.
• Strategic analysis is the process of gathering data that helps a company’s management decide on priorities and goals to determine a long-term strategy for the business. The analysis examines a company’s vision, mission statement, values, and external and internal environment to devise this strategy.
• Stakeholder analysis is a process for identifying, prioritizing, and understanding potential stakeholders with the aim of winning their support of a business. This analysis allows a company or team members to determine whose interests should be taken into account when developing and/or implementing a product, policy, or program.

2. What is a business impact analysis?

• Improved business functionality, as any applications and systems being used are identified and updated
• Gaining a better understanding of the nature of the IT and recovery processes
• An increased understanding by departments of their role within the company
• A reduction or removal of costs, such as unnecessary services, software, insurance, maintenance, or licensing costs, based on a new understanding of business needs
• Spotting potential issues or shortfalls in regulatory compliance
• Avoiding potential fines related to not meeting these regulatory requirements

3. Components of business analytics

• Data aggregation: This involves the accumulation, streamlining, and cleaning up of raw data before it can be analyzed. The data is filtered to remove incomplete or duplicated data. Data aggregation is therefore a common method used across any industry taking advantage of data analysis.
• Data mining: This is the process of finding inconsistencies, patterns, and correlations within large data sets to predict outcomes. By sifting through all the “noise” of the data, it is easier to understand what is relevant and then make good use of that information to assess likely outcomes.
• Association and sequence identification: Mostly used in the retail industry, this type of data analysis focuses on consumers performing similar actions at the same time or performing predictable actions sequentially. A book retailer, for example, can use this method to identify which books are often purchased together in the same transaction, and how this might be encouraged through store layout.
• Text mining: Not all data is mathematical, as some may be in the form of textual information from social media sites, blog comments, call center scripts, and other written sources. Text mining is used to identify concepts, patterns, topics, keywords, and other attributes in this data. A company might use this method to review competitor performance, for example, based on what customers are saying about them online.
• Forecasting: By analyzing processes that take place during a particular season or period (such as retail sales over Christmas), future values can be forecasted. This might assist a company in knowing how much of an item to stock over the holiday period so they don’t sell out, based on previous years’ sales data.
• Predictive analytics: Similarly, predictive analytics uses data, statistical algorithms, and machine learning techniques to identify the probability of future outcomes based on historical data. While forecasting predicts values, predictive analytics predicts behavior. The construction industry, for example, might use this method to predict equipment failure, especially in anticipated times of heavy use or seasonal changes.
• Optimization: This is the use of available data to develop and engage simulation techniques, which allow companies to identify best-case scenarios and the next best actions. The retail industry, for example, uses optimization to discover prime opportunity windows for sales, promotions, and new products in the hopes of maximizing profits.
• Data visualization: Large datasets are able to be distilled into visual graphics, such as graphs, to allow for an easy understanding of complex relationships within the data. This allows for faster decision-making, as audiences can instantaneously see trends and the story the data tells. One example might be a fashion retailer compiling its annual sales of jackets and sweaters in a line graph to visually compare the results.

4. Types of business analytics

1. Descriptive analytics tells you what happened in the past. It is the interpretation of historical data to better understand and describe changes that have occurred in a business during a set period of time. This range of historic data is then used to draw comparisons. Common examples include month-over-month sales growth or total revenue per subscriber.
2. Diagnostic analytics helps you understand why something happened in the past. This form of advanced analytics uses processes such as data discovery, data mining, and drill down and drill through to examine data to provide a deeper analysis of a situation and what caused it to happen.
3. Predictive analytics predicts what is most likely to happen in the future. Predictive analytics takes historical data and inputs it into a machine learning model that takes factors such as key trends and patterns into account. The model is then applied to current data to foresee what will happen next.
4. Prescriptive analytics ties in with predictive analytics by recommending actions you can take to affect those outcomes found in the previous step. So not only do you have an idea of what will likely happen in the future, but examining the data also suggests various courses of action and outlines what the potential implications would be for each. Prescriptive analytics are a costly investment, therefore investors need to be confident that the analysis will return substantial benefits.

5. Why is data analysis so important?

• 21% find that using marketing analytics in business is the single most important way to gain a competitive advantage
• 38% of organizations report Big Data analysis as a ‘top five’ issue
• 100% of US marketers feel that data and analytics will play a crucial role in the future

How is data analytics used in business? 

• Predicting customer trends and behaviors to identify breakdowns in the customer acquisition path, improve conversion rates, and increase the customer’s lifetime value
• Developing innovative new products and services by analyzing data to understand the desires and needs of the business’s target audience
• Determining the ROI of marketing efforts by examining data surrounding engagement rates, sales, and more
• Identifying trends and patterns that inform decision-making, drive optimal operational performance, and cut costs by accessing information contained in log, machine, and sensor data
• Tailoring customer service to audiences’ needs to provide more personalization and build stronger relationships by assessing data to reveal information about customers’ communications preferences, interests, concerns, and more

How does data analysis improve decision-making? 

1. Improved decision making. We touched on this previously, but improved decision making is perhaps the most vital advantage to come from data analysis. After all, important business decisions are rarely made based on a hunch. Data analysis is helping companies make smarter decisions that lead to higher productivity and more efficient operations. This is why Big Data has become adopted by more companies in recent times, with the demand increasing from just 17% to 59% in as little as the last three years.
2. Improved collaboration and information sharing. Sharing vital information, details, stats, or insights across departments creates a more efficient organization. When each department is receiving data in real-time, they are constantly reviewing, analyzing, and interpreting it to improve their work and make better decisions for the company. According to research, executive management, operations, and sales teams are the main drivers of business intelligence in organizations.
3. Improved customer satisfaction/retention. Did you know that companies that fully utilize their customer behaviors to make decisions outperform their competition by a whopping 85%? Customer and social analysis are considered the second most important Big Data analytics use case due to two key reasons: it greatly assists businesses in improving both their customer satisfaction and retention, as they learn the most effective ways to serve their target audience.

6. What is a business analyst?

• Analyzing how a business uses technology and what its goals are in relation to those systems or software
• Identifying problems within a business, for example, by using data modeling techniques
• Communicating with senior management in organizations to find out what they hope to achieve with specific projects or practices
• Formulating ways for businesses to improve their processes, based on previous research
• Persuading internal and external stakeholders about the benefits of new processes, strategies, technology, or projects
• Overseeing the implementation of new technology and systems
• Running workshops and training sessions in relation to their finding and proposed solutions

• Improve efficiency within a company’s processes
• Reduce wasted time and resources
• Identify and implement solutions
• Meet project deadlines
• Accurately document the necessary requirements of a project

• A thorough understanding of their company’s industry
• Communication and interpersonal skills
• Time management and organizational skills
• Problem-solving skills
• Analytical skills
• Leadership and management skills
• An understanding of project management techniques and computing systems

• Management analyst: works closely with each department within a company to identify which business processes can be improved, find solutions to enhance efficiency, and communicate recommendations to management
• Data analyst/scientist: uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data. This data is then used to determine how to leverage insights for their organization
• Business intelligence analyst: primarily analyzes data with the purpose of identifying areas where an organization can improve. They also use this information to guide decisions for higher profitability and to gain an advantage in their organization’s market.
• Big data analytics specialist: defines Big Data from an ever-increasing amount of sources to uncover hidden patterns, spot opportunities, and create insights to give a company a competitive edge.
• Operations research analyst: develops solutions for complex business problems through data analytics, mathematical problem-solving, and other advanced techniques. Their aim is to improve the company’s existing processes and to help them be more cost-effective.
• Market research analyst: focuses on the marketing data of their organization and uses these insights to evaluate product desirability and identify potential customers and price points to boost profits.

7. What is a business analysis process model?

Step 1: Collect information

• The PESTLE method, which evaluates the impact of six crucial external forces, including political, economic, social, technological, legal, and ethical concerns
• Porter’s Five Forces framework, which gathers information about forces including competitors in the industry, buyers, suppliers, substitutes, and new entrants in the market
• A SWOT analysis determines information about a company or project including strengths, weaknesses, opportunities, and threats

Step 2: Identify stakeholders

• Owners
• Trustees
• Shareholders
• Managers
• Employees
• Customers

Step 3: Outline business objectives

Step 4: Narrow down your options

• Impact analysis can help in finding out the actions that can influence the project
• Risk analysis can assist with finding potential hazards or dangers that accompany a particular action
• Cost-benefit analysis refers to the evaluation of costs related to a particular option, as well as the benefits it might offer

Step 5: Focus on the scope definition 

Step 6: Develop the business analyst delivery plan

Step 7: Define project requirements

• Business requirements: including statements of goals, objectives, and requirements
• Stakeholder requirements: including their project needs, desires, and what outcomes they expect
• Solution requirements: describing the characteristics that a product or service must have to meet the needs of the stakeholders and the business itself
• Transition requirements: defining what is needed from an organization to successfully move from its current state to its desired state with the new product or service

Step 8: Support project implementation

• Seeking feedback from the development team
• Updating requirements for project implementation
• Collaborating with quality assurance specialists
• Managing project adjustments requested by the business owner/stakeholders
• Ensuring user acceptance so the project is completed effectively

Step 9: Evaluate the value added by the project

Conclusion