Data is a business gold mine for organizations, yet many companies struggle to unlock its complete potential. Through data, organizations can gain a better understanding of their operations and customers. Acting on the data to make informed business decisions is not necessarily a straightforward process. A data-driven organization can glean deep insights from data to update internal processes and respond directly to market feedback and improve their customer relationships. A data-driven organization can also leverage data to identify to create value for their consumers.
Statista predicts that by 2022, the big data and analytics market will reach 274 billion dollars. Organizations are playing a vital role in the exponential growth of data by utilizing Big Data technologies for analytics to become data-driven. In this article, we will explore how organizations can benefit from data and what challenges they face in the process.
In the world of the cut-throat competitive world, businesses that have discovered the mantra of effective information and data management will only prosper. Though the job is wearisome, it is imperative, thanks to technology that reduces the complexities in information and data management by granting businesses a tool like a dashboard.
Dashboard, a product of high-end technology, helps businesses to gain an in-depth understanding of metrics and crucial information while leveraging the power of decision-making. The effective use of a data analytics and visualization dashboard has a certain impact on business productivity, ROI, and maximum use of in-hand data. However, this can only be accomplished if the at-service dashboard is purpose build and have the right metrics.
The era of data analysis has already arrived. From the state, government, and enterprises to individuals, big data and data analysis have become trends that everyone is familiar with. But you may not have the professional knowledge of data analysis and programming, or you have learned a lot about the theory of data analysis, but you still can't practice it. Here, I will compare the four tools that are most popular with data analysts, Excel, R, Python, and BI, as the basis for getting started with data analysis.
1. Excel
1.1 Usage Scenarios
Data processing work under general office requirements.
Data management and storage of small and medium-sized companies.
Simple statistical analysis for students or teachers (such as analysis of variance, regression analysis, etc.).
Combine Word and PowerPoint to create data analysis reports.
Assistant tool of data analysts.
Production of charts for some business magazines and newspapers (data visualization).
1.2 Advantages
It's easy to get started with Excel.
The learning resources are very rich.
You can do a lot of things with Excel: modeling, visualization, reports, dynamic charts, etc.
It can help you understand the meaning of many operations before further learning other tools (such as Python and R).
1.3 Disadvantages
To fully master Excel, you need to learn VBA, so the difficulty is still very high.
When the amount of data is large, there will be a situation of stuttering.
The Excel data file itself can hold only 1.08 million rows without the aid of other tools, and it's not suitable for processing large-scale data sets.
The built-in statistical analysis is too simple and has little practical value.
Unlike Python, R, and other open source software, there is a charge for the genuine Excel.
2. R
2.1 Usage Scenarios
The functions of R cover almost any area where data is needed. As far as our general data analysis or academic data analysis work is concerned, the things that R can do mainly include the following aspects.
Many times we find that the business data seems good, but the final conversion volume is very low, which indicates that the business conversion rate is problematic. In data analysis, there is a crucial modeling tool that can solve this problem well. It is the funnel analysis model. You probably already know this from my last article Top 5 Methods of Thinking in Data Analysis. Today I will introduce this funnel model in detail.
1. What Is a Funnel Analysis?
The funnel analysis model, in simple terms, is a process in marketing products. And we need to observe the conversion and loss of each step in this process.
In data analysis, data analytical thinking is a framework-based guide. When analyzing problems, we need a lot of skills and tools. Just like in secondary school, you may solve the quadratic equation with the formula method, completing square, extraction of the square root, or the factorization method.
