Automated data analysis: why important and what are the ways to d o it?

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Written By Berry Mathew

Data is one of the most powerful weapons that ever exist. If you have the data, you are the winner. Today more information is generated and consumed than ever before. Data analysis is therefore essential for companies to be able to control their business and the success associated with it. To do this, data must be precisely recorded and analyzed with the help of the best data aggregation and analytics software. Only in this way can multinational companies secure a competitive advantage over the competition. Big data and data mining make it possible to collect unimagined amounts of raw data in the shortest possible time and turn raw data into useful information with the help of software.

Why should computers do data analysis?

Computers offer many advantages to businesses. They work fast and cost less than a human. Human errors can never be completely ruled out. It is not uncommon for people to make incorrect decisions as a result of human misjudgments due to individual likes or dislikes. This problem does not exist with a computer. For important key decisions, it can be crucial to maintain a completely neutral view of things. In order to reduce internal cost, it can be advisable to hand over time-consuming subtasks to the computer. If an algorithm takes over the classification, segmentation, and categorization, employees can take care of the more complex cases.

Data analysis methods

There are 4 methods of data analysis, which are differentiated from the simplest to the most demanding level. The more complex an analysis is the more value and competitive advantages it can bring.

Descriptive Analytics

Descriptive data analysis is about collecting data from the past that helps to answer the question – what happened? For example, a healthcare facility may find out how many patients were hospitalized in the last month. A dealer may find out what the average weekly sales are. A manufacturer may find out how many items were returned in the last month, etc. Descriptive Analytics makes it possible to juggle raw data from multiple data sources in order to gain valuable insights. But these results only show what is wrong and what is right without explaining why it is. For this reason, data-driven companies use descriptive data aggregation and analytics software in combination with other methods.

Diagnostic Analytics

At this stage, historical data can be compared with others to answer the question – why did something happen? With diagnostic analysis, it is possible to clarify causes and effects as well as interactions, to analyze consequences and to identify patterns. Companies choose this method of data analysis to gain in-depth insight into a specific problem. At the same time, a company should have detailed information, because otherwise the data collection has to be carried out individually for each problem, which is very time-consuming. 

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Predictive Analytics

The predictive analysis looks into the future and tries to find out the following – what could or will happen in the future? Based on the results of descriptive and diagnostic analyzes, this method of data analysis makes it possible to determine trends, to identify deviations from normal values ​​at an early stage, and to predict future trends as precisely as possible. Predictive Analytics uses sophisticated algorithms and modern technologies to create future forecasts. 

But while this method has numerous advantages, it is important to understand that forecasts are only estimates, the accuracy of which depends to a large extent on the quality of the data and the extent to which the situation remains stable. Thanks to predictive analytics and its proactive nature, a telecommunications company can identify subscribers who are most likely to reduce their costs and plan. A management team can use cash flow analysis and forecasting to weigh the risks before investing in expanding their business. 

Prescriptive Analytics

Prescriptive analytics is aimed at – what measures must be taken to eliminate or prevent a future problem and to fully exploit the potential of promising trends. This state-of-the-art method of data analysis not only requires historical data but also current information from external data sources, which enables forecasts to be updated continuously. Various advanced tools and technologies such as machine learning, business rules, scenarios, simulation models, and neural networks are used, which makes implementation and management even more complex. For this reason, a company should compare the effort required with the expected added value before using this method of data analysis.

Which model is right?

Current market trends show that more and more companies are choosing predictive and prescriptive analytics. But if you have no idea how and which methods of data analysis make your decisions data-driven, consult with an expert now. Choose the best reputation management software and, if possible, outsource the service at affordable prices. The development of new data analysis methods has brought a multitude of different possibilities to the market. Without a doubt, data is the best way to know what the customer wants today.

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When analyzing data, one thing is very important – what business goal should be achieved? The type of analysis depends on the answer. Every third company already uses big data analyzes for production planning and project management. New and particularly powerful IT solutions are necessary to cope with the volume of data and to transform unstructured data into useful information. Today it is no longer a secret that professional processing of the flood of data gives companies a decisive competitive advantage. With the help of operational business intelligence, companies come to better decisions in their day-to-day business. You can identify difficulties more quickly and take appropriate corrective measures sooner.