Big data analysis is the use of advanced analytical techniques against very large, disparate data sets that include structured, semi-structured, and unstructured data from various sources and of various sizes, ranging from terabytes to zettabytes.

Big data is a term used for datasets that are of a size or type that exceeds the capacity of traditional social databases to collect, monitor, and process the information with little inertia.

Enormous information has at least one of the accompanying qualities: high volume, high speed, or high assortment. Computational thinking (simulated intelligence), wearable, social and the Web of Things ( IoT ) are driving information complexity through new structures and sources of information.

For example, big data comes from sensors, devices, video/sound, systems, logs, value-based applications, web and online networks – much of it is produced continuously and at enormous scale.

Examining massive amounts of information enables experts, specialists and business customers to agree on better and faster decisions, using information that was already locked or unusable.

Organizations can use advanced investigation methods such as content inspection, AI, predictive analytics, data mining, measurements and normal language preparation to gather new experiences from previously undiscovered information sources independently or together with existing business intelligence.

Use cases for big data analytics

Big Data Analysis How it Used In Marketing, Challenges and Risks

Improve customer engagement

Overall organised, semi and unstructured information from the focal points of contact your customer has with the company to increase a 360 degree perspective on your customer’s behavior and inspiration for improved tailored advertising.

The sources of information may include online life, sensors, cell phones, assumptions, and call log information.

Detect and stop fraud

A constant exchange on screen, proactively noticing these strange examples and practices that demonstrate wrong movements. Harnessing the power of vast information along with predictive/predictive auditing and correlating auditable and value-based information helps organizations anticipate and exonerate blackmail.

Driving supply chain efficiency

Gather and examine vast amounts of information to decide how items get to their destination, spotting wasteful aspects and where cost and time can be saved. Sensors, logs and value-based information can help track basic data from storeroom to destination.

How Is Data Analysis Used In Marketing?

In the previous points, application examples for data analysis in marketing were indicated. Data analysis in marketing has become indispensable, especially with increasing digitization. With the help of a proper data analysis, the marketing strategy, the website or a campaign can be successfully evaluated.

In this way, data analysis in marketing has developed into a fundamental and significant success factor that is now indispensable. Therefore, in addition to the classic methods described, other methods were developed that were only tailored to marketing.

Multivariate Data Analysis

In multivariate data analysis, several different parameters are considered in order to identify the relationships and achieve optimization. Usually, this analysis is used based on a usability analysis when evaluating websites.

The data that each user generates when visiting the website is taken into account and used for optimization. This can be, for example, the length of stay or the scrolling behavior of each individual customer.

Cohort Analysis

Cohort analysis involves grouping customers based on actions or events. The criteria for a customer grouping are similar actions at similar times. For example, this can be the first purchase of customers who are close in time.

With the help of the cohort analysis, the user can understand and evaluate the customer life cycle in order to identify optimization opportunities and derive measures.

As a result, for example, the buying pattern or behavioral pattern of a so-called cohort can be recognized and defined, and thus when the next purchase could possibly take place.

Churn Analysis

In the churn analysis, the customer churn rate is calculated. With the churn analysis, factors are analyzed as to why a customer no longer wants to consider the services offered by a company.

A typical example is the cancellation of a subscription. There can be various reasons for termination, such as quality defects, poor customer service or simply undercutting by competitors. The aim of the analysis is to keep the churn rate as low as possible so that there is no major drop in sales.

Churn analysis can be used in combination with analysis methods such as cohort analysis to achieve maximum added value. With the method, customers can be divided into relevant cohort groups in order to determine migration more precisely and counteract it effectively.

The ABC Analysis

The ABC analysis, for example, divides customers according to their importance and value to the company. Accordingly, customers are divided into A, B or C customers depending on their importance.

With this method, the company concentrates on the essentials, so the analysis causes measures to be developed in a resource-saving and customer-oriented manner.

The principle of the ABC analysis was derived from the Pareto principle. This means that 20 percent of customers are responsible for 80 percent of sales. The aim of the ABC analysis is to identify the customers with the highest turnover and to develop individual solutions or measures.

Customer Value Analysis

The customer value analysis identifies the value of the individual customer for the company. Based on the ABC analysis, the customer value analysis takes into account the sales of a customer and considers this in relation to the expenses.

In addition, factors such as customer retention, loyalty, reference potential and deposit surplus play an important role in the analysis. In addition, various other factors can be taken into account, depending on how customer value is viewed.

RFM Analysis

The RFM analysis is based on the three key figures recency, frequency and monetary in German topicality, frequency and turnover. With the help of these key figures, a point method is set up to divide the customers into segments and target groups.

A property or value is then assigned to each segment or group. These values can vary. Usually, values such as profitability and loyalty are included.

Next Best Offer

The “ Next Best Offer ” method is often used in combination with predictive data analysis, especially since Next best offer, as the name suggests, is based on the next offer in the future. As a result, this method is used to identify the next best offer for the customer to boost sales and customer retention.

With this approach, certain patterns can be identified in advance, such as the question of which product in which price category the customer could buy next. As a result, the offer can be tailored and personalized to one customer.

As with predictive data analysis, methods such as machine learning can be used to identify the next best offer early on.

What Are The Challenges And Risks Of Data Analysis?

The data analysis is characterized by high dimensionality and large sample sizes. These two traits bring with them three unique challenges:

High dimensionality brings false correlations and random homogeneity.
High dimensionality combined with large sample size leads to problems such as high computational costs and algorithmic instability.
The massive samples in big data are typically aggregated from multiple sources at different points in time using different technologies. This leads to problems with heterogeneity and statistical bias and requires the development of adaptive methods.

Nevertheless, data analysis brings new possibilities for the digital world. Data analysis has many advantages and immense potential to make a company competitive.

On the other hand, the massive sample size and high dimensionality of big data pose unique computational and statistical challenges, including necessary scalability and storage constraints, spurious correlations, random endogeneity, and measurement errors.

These challenges are different and require new computational and statistical paradigms. Furthermore, the exogenous assumptions in most big data statistical methods are not validatable due to random endogeneity. They can lead to incorrect statistical conclusions and, consequently, incorrect scientific conclusions.

Another dimension concerns the technology: big data or data analysis is not only large and complex, but also requires innovative technology for analysis and processing, such as cloud data management.

Data analysis reflects the challenges of data that is too large, too unstructured and too fast-moving to be managed using traditional methods. The tools available to deal with the volume, speed, and variety of big data have improved greatly in recent years.

However, these technologies require a skill new to most IT departments as they work hard to integrate all relevant internal and external data sources. For competent personnel, learning in working life must become part of everyday life and further training in working life must be strengthened.

In our dynamic and short-lived digital world, knowledge will become outdated faster and faster, which is why new skills and competences will be required on an ongoing basis. As a result, it comes with a high cost.

What Are The Goals and Opportunities Of Data Analysis?

Data analysis has two main goals: first, to develop effective methods that can predict future events, and second, to understand the relationships between features.

Due to the large sample size, two further goals result from the data analysis: heterogeneity and similarities across different subpopulations. In other words, data analysis promises that:

exploring the data of each subpopulation that is not possible with traditional methods.
extract important common traits across many subpopulations, even when there is large individual variation.

As a result, clearly defined risk areas or questions should be answered in advance with the data analysis using the available data. In addition, the data analysis indicates deviations, outliers, cluster risks or rule violations.

More precisely, the aim of data analysis is to collect the maximum amount of information in order to fully exploit the potential of the data and thus generate added value.

Conclusion: How Valuable Is Big Data Analysis?

With the help of big data analysis, connections and future events can be identified. Information obtained from data analysis can therefore have a significant impact on business decisions and the development of measures.

In view of digitization but also global developments, companies can suddenly be confronted with major challenges. In addition, the amount and complexity of data is constantly growing, which entails additional IT systems or solutions.

Accordingly, a quick and complete analysis of the data is often not possible. For this reason, corporations should use technologies and methods to question the existing data.

Nevertheless, despite innovative technologies, there is still a certain susceptibility to error and manipulation, which is why it is all the more important to identify and minimize business risks at an early stage.

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