Smooth processes, intelligent predictions, efficiency in everyday tasks – all this is made possible by data analytics. By intelligently analyzing collected data, processes in various areas of the company can be accelerated and problems avoided. In this article, you will learn about the advantages of data analytics and how companies can use the proven methods of data analysis.
Table of contents
Was ist Data Analytics?
Data analytics is a method of examining data from different sources. Analyzing data reveals new information and leads to new conclusions. There are different types of data analysis that can be divided into four categories:
- Descriptive Analytics
This type of data analysis looks at what has already happened and deals with the evaluation of data from the past. This data is collected in data warehouse systems and can be prepared using reports. Descriptive analytics can also be repeated at predefined periods of time. This allows processes in companies to be examined retrospectively in order to make possible deviations visible. - Diagnostic Analytics
The results of diagnostic analytics help to explain causes and effects and answer the question of why something happened. Diagnostic analytics can clarify interactions and identify patterns. This type of data analysis also makes it easier to gain detailed insights into a specific problem. - Predictive Analytics
This data analysis can be used to find out which processes could still happen and helps to predict trends. Various variables are taken into account in order to be able to predict the likely behavior of processes in companies. In addition, the quality of the predictions increases if data quality management is implemented. - Prescriptive Analytics
The fourth type of data analysis goes a step further than predictive analytics and provides recommendations for action to achieve company goals. The results of prescriptive analytics also help to prevent certain events or to respond to them correctly.
Data analytics is already common practice in many companies and is used in many industries such as agriculture, logistics, real estate and telecommunications.
Reasons for data analytics consulting
With data analytics consulting, an initial situation analysis can be carried out and individual challenges and questions can be addressed. Together, a sensible strategy for your company can be developed and all processes that arise during data analysis can be addressed. A data analysis consultant is your personal companion and supports you and your company in every decision. He will examine your data with you, develop use cases and develop ideas on how to get the maximum benefit from your data.
About our data analytics services
What are the benefits of data analytics?
Data analytics enables companies to make processes more efficient and to speed them up. Data analysis driven by big data and small data is a win-win situation for the company and its customers. The information obtained from data analytics enables more precise predictions. For example, a potential disruption can be identified before it occurs.
Data analysis also simplifies planning in supply chain management. Data analytics can be used to determine demand for products or services in advance, so that production capacities and personnel requirements can be adjusted at an early stage.
The use of data analytics is also of great benefit in many other areas of the company, including marketing. In this area, data analytics can be used to identify target groups and tailor publications on social media platforms to the customer. Other areas that benefit from data analytics are finance and customer relationship management.
Big Data vs. Small Data: Data analysis with different data sizes
Data analysis can be carried out with different amounts of data. The two common terms are Big Data and Small Data. They refer to the amounts of data that are worked with. Depending on which of the two methods is used, a different approach is required. In order to understand the difference between the two types of data sets, the typical areas of application of Small Data and Big Data must be considered.
With small data, the focus of the data set is on the end user, usually the customer. This involves information and results that are tangible and actionable. These smaller data sets are also easier for the customer to understand. Small data is particularly helpful for answering specific questions and overcoming specific challenges. In contrast to big data, with small data, the right data can be selected for the respective problem and the information can be processed in a targeted manner.
Big data also offers many advantages, such as the long-term creation of data sets that can be used to make predictions for the future. By analyzing such data sets, important information that is particularly relevant to companies can be gained.
Big data and small data require different approaches to data analysis.
Big data is often referred to as the “three Vs”. These are the terms velocity, volume and variety. This mnemonic explains the three most important aspects that should be considered when building up large amounts of data. The first aspect, “velocity”, is about speed. It states that the speed at which data is collected increases every year. The increasing speed is made possible by faster hardware.
The second aspect deals with the amount of data collected. Even though the actual size of the data in bytes can vary from company to company, large, collected data sets still refer to big data. Variety, the third aspect, envisages a diversity of information collected. In order to analyze the data and gain insights, the collected data must be divided into categories. For a property, for example, a distinction is made between the costs for the company and the purchase price paid by the customer.
Data warehousing as a sub-area of data analytics
The term data warehousing refers to a central database, the analysis of which provides the company with new insights on the basis of which it can make important decisions. A data warehouse collects data from various data sources. This includes data from customer relationship management or billing.
While a warehouse stores physical goods, a data warehouse is used to store data.
The data offers a variety of advantages: By compiling data from multiple sources, a single point of contact is created that the company and algorithms can work with. Above all, this central point of contact allows companies to save time. Incorrect information and incomplete data are identified early on and can be corrected and supplemented accordingly.
A data warehouse also increases the quality of data, which in turn leads to better and more reliable information. By combining and standardizing data from different sources, companies can ensure the quality of their data. The data warehouse simplifies decision-making and thus also improves data analysis processes.
Reasons for data analytics consulting
With data analytics consulting, an initial situation analysis can be carried out and individual challenges and questions can be addressed. Together, a sensible strategy for your company can be developed and all processes that arise during data analysis can be addressed. A data analysis consultant is your personal companion and supports you and your company in every decision. He will examine your data with you, develop use cases and develop ideas on how to get the maximum benefit from your data.
About our data analytics services
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Niklas Ludwig has been Communication Manager at CONET since March 2020 and is responsible for the internal and external communications of the CONET group of companies.
Daniel Keller is Social Media Manager at CONET and is responsible for the strategic and content orientation of the social media channels.
Source: https://www.conet.de/blog/data-analytics/
