What does a data-driven application mean

What is data analysis? Definition, methods and application

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.

What is a data analysis?

With data analysis, you gain insights from data. You can extract, transform and centralize them to discover and analyze patterns, relationships, trends, correlations and anomalies or to validate a theory or hypothesis. In the past, data was analyzed to make future decisions; 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: n from. 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

  • A data analysis helps to monitor and control business processes. Data provide information about which product was sold successfully and why, or why a certain product did not generate sales.
  • Data analysis has been shown to increase sales and enable faster, more informed decisions to be made.
  • Data analyzes help 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.

Correct handling of data

To take advantage of data analytics effectively, data must first be mapped and filtered.

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 subdivided into groups. Which groups these are depends 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 subdivision between structured and unstructured Data. You organize structured data in a relational database in such a way that it can be easily processed and edited.

examples for structured data are:

  • Phone numbers
  • Postcodes
  • Currencies

This data usually reflects the past, which is great for historical analysis.

Unstructured data include things like:

  • Email, social media posts
  • items
  • Satellite images
  • Sensor data

This data can be stored in a non-relational database such as NoSQL. Unstructured data better reflects the present and can therefore help to forecast future developments.

As soon as the data has been recorded, it comes to Validationto identify and resolve 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 accessed using a Data visualization tools can be analyzed 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 the qualitative data analysis examine individual cases in detail in order to evaluate them interpretively. Often, open questions are dealt with here.
  • The 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 the multivariate data analyzes.

What is the difference between univariate and multivariate data analysis?

In quantitative analysis, a distinction is made between the univariate data analysis and one 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 a 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. Organizations that are not data-driven or reactively make decisions can rely on descriptive analytics for reporting purposes. However, data-driven organizations that need to make quick decisions are better off using predictive or prescriptive data.

What is descriptive data analysis?

Descriptive data analysis provides information about what happened in the past. It is the most common method of data analysis offered with traditional technology. Examples of areas of application for descriptive data analysis are:

  • Stocks
  • Production numbers
  • Average expenses per customer and annual changes in sales

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.

What is Diagnostic Data Analysis?

The 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.

What is predictive data analysis?

Predictive data analysis are 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 to 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

What is prescriptive data analysis?

The prescriptive data analysis is an increasingly popular method in modern data analysis. This is not just about the current data situation, but also about exploiting 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.

What is data mining?

Data mining is a form of advanced analysis. Unstructured data is turned into useful information such as patterns, correlations, and anomalies. Data mining helps find the proverbial needle in the haystack.

What is AI or machine learning in terms of data analytics?

Artificial Intelligence (AI) and Machine Learning (ML) are also considered to be advanced methods of data analysis. AI is the ability of a computer to process information in a human way, e.g. 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 an almost complete automation. This includes finding new data sources, structuring data, and suggesting new approaches.

What is text mining?

Text mining is another form of advanced data analysis. It supports natural language processing, i.e. the ability of a computer to read texts or hear languages ​​(NLP). AI systems regularly search the web for new information and scan and adapt texts from documents or online books to obtain information.

What is Big Data Analytics?

Enterprise big data analytics 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. Big data analysis is usually carried out in a cloud, since large amounts of data can be stored there at reasonable costs.

There are a number of analytical tools in the cloud, such as Hadoop and NoSQL, that can be used to quickly store, structure, and retrieve big data. Hadoop is an open source platform. It is free and designed to run on standard hardware. The best thing to do here is to use inexpensive desktop workstations or simple server hardware that can run scaled-down database environments, rather than more expensive server devices that require regular updates.

Data analytics and the cloud: driving your business forward

Data analytics, including big data analytics, help companies grow. A company looking to turn its data into actionable insights can take full advantage of processes based on data analysis.

What advantages does a company expect from applied data analysis?

  • Improved Processes
  • Faster decision making
  • Higher productivity
  • Clearer insights into the use of products and services
  • Help with the development of new products and services
  • Advanced real-time analytics

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