Understanding data at the fundamental level is critical to building a successful business. Data analysis is the process by which raw data becomes usable knowledge that can be applied in practice. Most companies already analyze certain data, but only modern analysis enables an unprecedented level of knowledge and insight. How far is your company on the way to a data- and analysis-driven culture and what does the next step look like?
More and more companies are increasing their sales by offering customers continuous added value, lowering costs, and optimizing operational processes. But how can you take the right actions at the right time? Data analysis helps your company to gain a significant competitive advantage and to plan strategies in advance. With data analysis, you gain insights from data. Today you can analyze data to make real-time decisions. It is also possible for you to recognize new trends and gain insights that would not have been possible with conventional data processes.
A Case Study For Applied Data Analysis
In recent industry surveys, respondents from five countries voted on the top uses for data analytics. The following aspects were identified as relevant:
- Increase in process and cost efficiency (60 percent)
- Promotion of strategy and change (57 percent)
- Monitor and improve financial performance (52 percent)
The respondents also stated that these factors are supported by cloud computing, big data, and artificial intelligence or machine learning. Cloud computing, in particular, helps to extract data quickly and efficiently. In this way, a company can keep up with a culture in which the customer expects more and more of a product or service.
The Benefits Of Data Analysis
Data analysis helps to monitor and control business processes. Data provide information about which product was successfully sold and why or why a certain product was not sales-generating. Data analysis has been shown to increase sales and enable faster, more informed decisions to be made. It helps to analyze the right target group for a company, to build up a customer base, and ultimately to establish successful customer loyalty. The acquisition of data about previous and future business activities gives companies a decisive competitive advantage.
The first step is to define which data should be used and how. As a rule, a company uses internal data to a large extent, which is supplemented by external data. The data is then divided into groups. Which groups these depend on the business objectives. In the next step, the data is made available for analysis at a central location. This reservoir is also known as a data warehouse (DWH or DW for short). In this technical process, data elements from source databases are compared with the warehouse. All data is mapped to both a source and a destination and, using formulas, is converted into data formats that meet the requirements of the data warehouse.
Correct Analysis Of Different Types Of Data
Newer systems can analyze structured data efficiently, but with conventional or older systems the user reaches his limits since these are not designed to extract information from unstructured data. In order to give the data depth and context, a division between structured and unstructured data is required. You organize structured data in a relational database in such a way that it can be easily processed and edited. Examples of structured data are phone numbers, postcodes, currencies and etc. This data usually reflects the past, which is great for historical analysis. Unstructured data includes things like e-mail, social media posts, article, satellite images, sensor data and etc.This data can be stored in a non-relational database. Unstructured data better reflects the present and can thus contribute to it to forecast future developments.
Once the data is collected, validation occurs to identify and fix data quality issues that can affect the quality of the analysis. In addition to data profiling processes to ensure that the data set is consistent and complete, validation also includes data cleansing processes that help eliminate duplicate information and errors. In the final step, the data can be analyzed using a data visualization tool to reveal hidden correlations, patterns, and trends that can be used to guide business decisions.
Qualitative Data Analysis and Quantitative Data Analysis
Before you methodically evaluate your data, you should first determine which empirical result you want to achieve. In qualitative data analysis, you examine individual cases in detail in order to evaluate them interpretively. Often, open questions are dealt with here. Quantitative data analysis, on the other hand, aims to collect as many results as possible in order to evaluate them statistically. This includes the univariate and multivariate data analyzes.
In quantitative analysis, a distinction is made between univariate data analysis and multivariate data analysis. While you only analyze one variable (characteristic) in a univariate data analysis, you consider two or more variables at the same time (simultaneously) in multivariate data analysis. However, this application only makes sense if there are structural, mutual dependencies between the variables or relationships between the objects (feature carriers).
The 7 Methods Of Data Analysis
Dates can be descriptive or predictive. A company can choose one (or more) of these types based on their own stage of development or their own decision-making processes. However, data-driven organizations that need to make quick decisions are better off using predictive or prescriptive data.
- Descriptive data analysis makes it possible to combine raw data from multiple data sources in order to gain valuable insights into the past. However, the results are not well-founded. It simply determines what is wrong and what is right without explaining why it is so. For this reason, data-driven companies usually use the descriptive analysis of data in combination with other methods.
- Diagnostic data analysis shows what happened in the past and why. It is possible here. Clarify causes and effects, analyze consequences and identify patterns. Companies choose this method to gain in-depth insight into a specific problem. A popular application is the analysis of the success of a product or service. On the basis of well-founded diagnostic data, you can decide whether a product will remain in the range or whether you want to replace it with a new one.
- Predictive data analysis is particularly useful for showing the probability of failure in certain situations. Based on the current data, you predict what will happen in the future. This method makes it possible, based on the results of descriptive and diagnostic analyzes, to determine tendencies and to identify deviations from normal values at an early stage, and predict future trends as precisely as possible. Examples of areas of application for predictive data analysis are customer behavior, device failures, effects of weather on sales, fraud detection, marketing campaign optimization, credit scores and etc.
- Prescriptive data analysis is an increasingly popular method in modern data analysis. This is not just about the current data situation, but also about the exploitation of the potential of the data in connection with the determination of new trends. Example: A company was able to identify some opportunities for repeat purchases in its CRM system using customer analytics and the sales history.
- Data mining is a form of advanced analysis. Unstructured data is turned into useful information such as patterns, correlations, and anomalies.
- Artificial intelligence (AI) and machine learning (ML) are also considered advanced methods of data analysis. AI is a computer’s ability to process information in a human way; B. to understand and answer a question. ML refers to a computer’s ability to program itself. AI and ML are a powerful combination with which you can optimize the data analysis process due to almost complete automation. This includes finding new data sources, structuring data, and suggesting new approaches.
- Big data analysis focuses on extending traditional business intelligence and enabling reporting. These are based on online analyzes of analytical data (OLAP), which enable trend analyzes as well as advanced analyzes such as predictive and prescriptive data analysis. The bigger big data gets, the more tools and techniques are being used online to make the process easier and more efficient.
