Data Analysis Quiz

As a data analyst, you are on a journey. Think about all the data that is being generated each day and that is available in an organization, from transactional data in a traditional database, telemetry data from services that you use, to signals that you get from different areas like social media. For example, today's retail businesses collect and store massive amounts of data that track the items you browsed and purchased, the pages you've visited on their site, the aisles you purchase products from, your spending habits, and much more. With data and information as the most strategic asset of a business, the underlying challenge that organizations have today is understanding and using their data to positively affect change within the business. Businesses continue to struggle to use their data in a meaningful and productive way, which impacts their ability to act. A retail business should be able to use their vast amounts of data and information in such a way that impacts the business, including: Tracking inventory, Identifying purchase habits, Detecting user trends and patterns, Recommending purchases, Determining price optimizations, Identifying and stopping fraud. Additionally, you might be looking for daily/monthly sale patterns. Common data segments that you might want to examine include day-over-day, week-over-week, and month-over-month so that you can compare how sales have been to where they were in the same week last year, for example. The key to unlocking this data is being able to tell a story with it. In today's highly competitive and fast-paced business world, crafting reports that tell that story is what helps business leaders take action on the data. Business decision makers depend on an accurate story to drive better business decisions. The faster a business can make precise decisions, the more competitive they will be and the better advantage they will have. Without the story, it is difficult to understand what the data is trying to tell you.

However, having data alone is not enough. You need to be able to act on the data to affect change within the business. That action could involve reallocating resources within the business to accommodate a need, or it could be identifying a failing campaign and knowing when to change course. These situations are where telling a story with your data is important. The underlying challenge that businesses face today is understanding and using their data in such a way that impacts their business and ultimately their bottom line. You need to be able to look at the data and facilitate trusted business decisions. Then, you need the ability to look at metrics and clearly understand the meaning behind those metrics. This requirement might seem daunting, but it's a task that you can accomplish. Your first step is to partner with data experts within your organization, such as data engineers and data scientists, to help get the data that you need to tell that story. Ask these experts to participate in that data journey with you.

Your journey of telling a story with data also ties into building that data culture within your organization. While telling the story is important, where that story is told is also crucial, ensuring that the story is told to the right people. Also, make sure that people can discover the story, that they know where to find it, and that it is part of the regular interactions. Data analysis exists to help overcome these challenges and pain points, ultimately assisting businesses in finding insights and uncovering hidden value in troves of data through storytelling. As you read on, you will learn how to use and apply analytical skills to go beyond a single report and help impact and influence your organization by telling stories with data and driving that data culture.

Before data can be used to tell a story, it must be run through a process that makes it usable in the story. Data analysis is the process of identifying, cleaning, transforming, and modeling data to discover meaningful and useful information. The data is then crafted into a story through reports for analysis to support the critical decision-making process.

As the world becomes more data-driven, storytelling through data analysis is becoming a vital component and aspect of large and small businesses. It is the reason that organizations continue to hire data analysts. Data-driven businesses make decisions based on the story that their data tells, and in today's data-driven world, data is not being used to its full potential, a challenge that most businesses face. Data analysis is, and should be, a critical aspect of all organizations to help determine the impact to their business, including evaluating customer sentiment, performing market and product research, and identifying trends or other data insights.

While the process of data analysis focuses on the tasks of cleaning, modeling, and visualizing data, the concept of data analysis and its importance to business should not be understated. To analyze data, core components of analytics are divided into the following categories: Descriptive, Diagnostic, Predictive, Prescriptive, Cognitive.

Descriptive analytics help answer questions about what has happened based on historical data. Descriptive analytics techniques summarize large datasets to describe outcomes to stakeholders. By developing key performance indicators (KPIs), these strategies can help track the success or failure of key objectives. Metrics such as return on investment (ROI) are used in many industries, and specialized metrics are developed to track performance in specific industries. An example of descriptive analytics is generating reports to provide a view of an organization's sales and financial data.

Diagnostic analytics help answer questions about why events happened. Diagnostic analytics techniques supplement basic descriptive analytics, and they use the findings from descriptive analytics to discover the cause of these events. Then, performance indicators are further investigated to discover why these events improved or became worse. Generally, this process occurs in three steps: 1. Identify anomalies in the data. These anomalies might be unexpected changes in a metric or a particular market. 2. Collect data that's related to these anomalies. 3. Use statistical techniques to discover relationships and trends that explain these anomalies.

Predictive analytics help answer questions about what will happen in the future. Predictive analytics techniques use historical data to identify trends and determine if they're likely to recur. Predictive analytical tools provide valuable insight into what might happen in the future. Techniques include a variety of statistical and machine learning techniques such as neural networks, decision trees, and regression.

Prescriptive analytics help answer questions about which actions should be taken to achieve a goal or target. By using insights from prescriptive analytics, organizations can make data-driven decisions. This technique allows businesses to make informed decisions in the face of uncertainty. Prescriptive analytics techniques rely on machine learning as one of the strategies to find patterns in large datasets. By analyzing past decisions and events, organizations can estimate the likelihood of different outcomes.