Data Analytics as a method of analysis can help companies to transform their business, their processes and the company as a whole. How the collection of data, its analysis and the insights gained from it are related was already the subject of a previous article. Some data analytics examples are intended to demonstrate the benefits of this three-step process in more concrete terms and show how companies can gain a competitive advantage through this.

The importance of data can be seen, for example, in a Formula 1 car. At first you might think that drivers gain an advantage from the fact that they can drive particularly well, that their car has the better engine or the better aerodynamics. But the decisive basis for victory is laid by something else: the analysis of various sensor data. Every Formula 1 car is equipped with over 150 sensors that collect the most important performance data in real time – fuel efficiency, tire pressure, brake heat, GPS or wind speed. Each sensor is a source of data that can be analyzed during the race, giving the team a competitive edge. For example, the data provides indications as to when the best time for a pit stop is. It can be recorded: The data provided by the sensors is important, but the real added value comes from data analysis.

In a sense, companies are like racing cars: collecting and analyzing the data creates added value that can give companies a competitive advantage over their rivals.

Big Data vs. Data Analytics

During a race a Formula 1 car collects about 3TB of data. After a season with about 20 races this has already added up to 60TB. That is first of all a huge amount of data. But races are not decided by the amount of data itself, but by the questions asked by the mechanics, which have to and can be answered with the help of the data.

First of all, a distinction must be made between Big Data and Data Analytics. Big Data are high-volume, fast and highly variable information stocks (data) that require innovative forms of data collection in order to collect, clean, store and use them. For example, the 3TB collected during a car race are initially Big Data. It is a large amount of different data types, both structured and unstructured. Analysts and data scientists need to process and analyze this type of data using powerful computers and algorithms to identify trends and correlations that can help solve problems.

This type of big data analysis usually leads to greater efficiency at the macro level. To come back to the example of the Formula 1 team Big data analysis allows mechanics and technical experts to determine that the car will always do slower lap times when a certain type of fuel is in the tank. This means that if they change the fuel, the team has a better chance of winning the race.

But what is the difference to Data Analytics? Data Analytics is the process of investigating data by asking a specific question with the goal of finding answers that help to make evidence-based decisions. Data Analytics is therefore used to answer certain questions that relate specifically to a specific business objective. Considered once again with reference to the Formula 1 team, this could be the following question: The team would like to know which is the ideal lap for a pit stop so that it is as efficient as possible and takes as little time as possible. Not only in this example Data Analytics helps to answer such very precise and topic-related questions, which not only lead a Formula 1 team to victory, but also help companies to uncover optimization potential.

Examples how problems could be solved with Data Analytics

Data Analytics uses data – facts – to solve problems, whether the data is analyzed manually or automatically. It therefore starts with a question and only then begins to analyze the available data.

Just like a Formula 1 car with all its sensors, companies produce a wide variety of very different data. Every core operational task, every interaction with a customer or supplier generates valuable data. With appropriate analysis, this data can be used as a fact-oriented basis for corporate decisions and provide new insights that change companies from the ground up. Those companies that apply this in a targeted manner have so far exceeded expectations with their success. McKinsey, for example, has confirmed this in a study. Almost all areas, including operations management and marketing, benefit from this, as does the product itself.

To make this even more concrete, here are some concrete examples that show how companies have changed their business through data.

Example 1: UPS

UPS has equipped more than 10,000 of its vans with sensors to optimise the routes they travel. They started with a simple question: Can we reduce consumption by finding faster routes for our drivers? With the help of the data obtained in this way, it was actually possible to save around 38 million litres of fuel. The drivers also drove a total of about 19.5 million kilometres less per year.

Example 2: T-Mobile

T-Mobile used data analytics to reduce customer turnover. The company used social media data and cross-checked it with its CRM (Customer Relationship Management) software and internal billing to identify loyal customers and launch a personalized campaign tailored to those customers. This enabled T-Mobile to reduce the churn rate by 50 percent.

Example 3: Netflix

Netflix also uses data analytics to plan what content, series, and movies they will produce next. Based on user data, Netflix develops content that is highly likely to be accepted and viewed by users. The 2017 data analysis produced insights into user behavior by country, among other things. This showed which series, for example, were watched particularly frequently in Germany, or that Mexico is the country where most users actually watch Netflix every day. The evaluation of data increased Netflix sales by 36 percent. Even more: Netflix is now one of the guarantors of top 10 series. Just think of the internationally successful hit series “The Crown”, for example.

Using Data Analytics successfully

When a company decides for the first time to set up and use such structures for data analysis, this may seem costly at first. However, such tools can often be used directly with minor adjustments and enable quick results. However, companies should proceed logically: The first step is to define clear objectives. For example, is the goal to reduce costs or to improve customer service? Once the goal has been set, the corresponding questions can be better defined. The next step is to collect the right data in the right places and to create a data set that is suitable for answering the questions asked. Of course, choosing the right tool is also crucial – whether it’s a proprietary tool or an application already on the market.

Once the data analysis has been carried out successfully, the appropriate consequences must of course be drawn. No matter how good an analysis is, it will be of no use if the appropriate steps are not taken afterwards to implement the findings in a strategy. Because only this creates a competitive advantage.

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