Big Data

The advantages of big data for companies

Für Unternehmen liegt in der Auswertung von Big Data der Schlüssel, um ihre Prozesse, Entscheidungen und ihr Geschäftsmodell zu optimieren. So können sie sich einen Wettbewerbsvorteil verschaffen. Big Data ist die Basis für eine datengetriebene Unternehmenssteuerung und Voraussetzung, um neueste Technologien wie künstliche Intelligenz (KI) effektiv einzusetzen.

Big data is therefore sometimes also referred to as the entirety of technologies that are used to analyze large volumes of data.

The 4 Vs of Big Data

The sheer volume of data alone does not qualify it as big data. Gartner developed the 3V concept back in 2001, which was later expanded to 4V. The concept has become established in order to classify big data as such.

1. volume (amount of data)

The main feature of big data is the extreme amount of data. Masses of data are produced every day. Companies often have to manage data in the terabyte range, which pushes traditional technologies to their limits.

2. velocity (speed)

For big data to unfold its value, the data must be generated and processed quickly. In today’s business world, real-time analyses can be crucial, for example to answer customer inquiries immediately, optimize supply chains in real time or manage prices.

3. variety

Big Data umfasst verschiedene Arten von Daten, darunter strukturierte (z. B. Tabellen), semi-strukturierte (z. B. JSON-Dateien) und unstrukturierte Daten (z.B. Videos oder Texte). ERP-Systeme integrieren Daten aus den unterschiedlichsten Quellen und sind damit ein wichtiges Werkzeug für Unternehmen, um maximalen Mehrwert aus ihren Daten zu schöpfen.

4. veracity (accuracy)

Big data is only useful if the quality of the information is high. Inaccurate or incomplete data can lead to incorrect conclusions. Professional data management and the targeted use of analytic tools are crucial to ensure consistency and accuracy of the results.

Newer approaches see additional characteristics that big data must fulfill. Two Vs that are often added:

Variance (fluctuation)

As a rule, big data is dynamic. The results of an analysis vary depending on the point in time, as they reflect seasonal trends, changing customer preferences or market shifts, for example. This is precisely why real-time analyses and predictive analytics are so helpful: they make fluctuations visible and take them into account in their evaluations.

Value

Some data scientists only talk about big data when companies can derive added value from analyzing the data. The focus is on the value-adding analysis of data volumes.

Important data sources for big data

In today’s digitally networked world, companies have access to data from a variety of internal and external sources that provide them with insights into customer behavior, processes and market trends. The importance of the individual sources varies depending on the industry, company size and focus (B2C, B2B, D2C).

Websites and online stores

Companies use tracking and analysis tools to generate data on the usage behavior of their website visitors. They collect insights into their purchasing behavior and shopping baskets. This information creates the foundation for personalized marketing campaigns and conversion optimizations.

Software and apps

Usage data from apps and business software provide insights into customer preferences and process efficiency. Which features are not accepted? Where do processes come to a standstill? The data makes it possible to make precise improvements – without lengthy trial and error.

Social networks

Social media platforms such as LinkedIn, Instagram and TikTok help companies to analyze moods and trends and adapt their brand strategy at an early stage.

Search engines

Keyword tools give companies an insight into which terms users use to find out about certain topics on Google and other search engines. With this knowledge, they can formulate their content in such a way that they achieve greater visibility online among their target group.

Automobiles and traffic

Modern vehicles and traffic systems generate huge amounts of sensor data that are used for traffic management, fleet control or the development of autonomous vehicles.

Wearables

Fitness trackers and smartwatches are a valuable source of data for the health and insurance industry. The devices record health and activity data that companies can use to develop personalized offers.

Robots and IoT devices

Machines in production or smart home devices provide valuable operating data. Companies can use this data to optimize processes or establish new data-based business models that take into account the wear and tear of machines, for example.

AI chatbots

Whether on the website or in internal systems – AI chatbots provide valuable insights into users’ wishes and problems. This information helps to improve support services such as FAQs or documentation in a targeted manner.

Big data: examples for corporate use

Big data analytics has enormous potential for companies. Statistical evaluations are no longer carried out selectively. Instead, data volumes can be analyzed comprehensively. Big data tools find correlations and patterns in hundreds of thousands of data points that human analysts often fail to recognize or only recognize with a much greater investment of time. This makes the applications ideal for efficiently improving decisions and processes in many areas of a company – and thus contributing to the future viability of an organization.

1. production: efficiency through data optimization

In production, a big data analysis tool can help to coordinate and at least partially automate processes.

One example is predictive maintenance. Sensors on machines continuously collect and analyze data such as temperature, vibrations or running times. If there are signs of possible defects, the system provides an indication so that repairs can be carried out early and major damage can be avoided. At the same time, the service life of the machines can be extended in this way, enabling companies to achieve significant cost benefits.

Other use cases for big data in production include analyzing product quality as well as monitoring and automating warehouse management. This enables cost-efficient and fast production.

2. marketing: personalization through customer data

In marketing, for example, big data analyses enable the creation of anonymized customer profiles. Companies gain a detailed understanding of their customers and can make personalized offers and recommendations . When used correctly, the effectiveness of advertising campaigns also benefits.

For example, an online retailer uses big data analytics to identify seasonal trends and individual preferences. It then offers its customers personalized discount campaigns. The result: the conversion rate increases and customer loyalty is strengthened.

3. finance: risk management and fraud detection

Finance departments can identify anomalies in data streams by analyzing accounting and transaction data. These can be atypical payments or duplicate invoices. Thanks to the analysis, anomalies can be investigated at an early stage and potentially greater damage caused by fraud can be averted. Companies can also use big data analyses to uncover gaps in internal control systems so that fraud can be prevented from the outset.

Alongside fraud prevention, AI-supported forecasts are the most important use case for big data in finance. By analyzing historical data and market movements, the systems are able to reliably predict future developments. On this basis, companies can better assess their financial risks and seize previously untapped market opportunities.

4. logistics: optimization of supply chains

Welches Produkt ist wann wo angekommen? Big Data und Echtzeitanalysen revolutionieren die Logistik. Die Supply Chain wird plötzlich vollständig transparent. Verzögerungen und Störungen können klar zugeordnet und zügig behoben werden. 

One example: by analysing traffic data, weather conditions and stock levels, companies can optimize their routes for short delivery times and low emissions. Both benefit their cost balance.

Big data technologies at a glance

If you want to store and analyze big data, you need powerful technologies – in all areas of IT. From hosting and databases to interface technologies and analytics software.

Hadoop: The basis for distributed data processing

Apache Hadoop is a key technology for processing big data. The open source framework stores large amounts of data on multiple servers. This enables parallel processing, which is significantly faster than a central computer. Hadoop is particularly useful when data from different sources, both structured and unstructured, needs to be processed.

NoSQL databases: flexibility in data storage

In contrast to traditional relational databases, NoSQL databases such as MongoDB or Cassandra are specially designed for big data. They offer a high degree of flexibility as they are not tied to rigid table structures. This makes them ideal for storing unstructured data such as text documents or sensor data. They are also highly scalable, which makes them indispensable for data-intensive applications.

In-memory computing: speed in real time

In-memory computing is a key technology for companies that want to analyze data in real time. Data is stored in memory instead of on hard disks, which significantly increases processing speed. This technology is often used in areas such as predictive analytics, where milliseconds count when making data-based decisions.

Big data analysis tools and ERP integration

In addition to these basic technologies, big data analysis tools are also used. Well-known applications of this type include Tableau, Microsoft Power BI and Qlik. These tools make it possible to visualize data evaluations and make them easy to understand.

Analytic tools are particularly effective when they are linked to the ERP system. This allows data evaluations to be enriched with master data, for example, which facilitates value-adding interpretation. In addition, automated follow-up actions can be triggered in processes when certain values are reached.

Many modern ERP systems offer interfaces to big data solutions for easy integration.

Typical challenges and solutions

Although big data offers enormous opportunities, it also brings with it a number of challenges. Companies should take these into account in order to exploit the full potential of the technology.

Data protection and ethical issues

In many cases, big data contains sensitive data about customers or employees. Companies must therefore comply with the high legal data protection requirements. Otherwise, there is a risk of legal consequences, financial damage and loss of reputation if breaches of the law become known.

Irrespective of the legal assessment, companies should address ethical issues: After all, personalized measures and automated decisions based on algorithms always harbour the risk of discrimination and bias.

Recommendations:

• Integrate data security measures, such as encryption, access controls and anonymization, into big data processes from the outset
• Einhaltung gesetzlicher Vorgaben wie der DSGVO für den Schutz sensibler Daten und den langfristigen Erfolg von Big Data Analytics
• Development of an ethical mission statement that gives employees confidence in handling data

Data quality and management

Big data is only as valuable as the quality of the underlying data. Incorrect or incomplete data can lead to incorrect analysis results and subsequently to poor decisions. This is where data management comes into play: companies need to ensure that their data is consistent, up-to-date and correct. ERP systems can help by consolidating data from different sources and monitoring its quality.

Recommendations:

• Introduction of a data governance framework that defines clear standards for data collection, validation and maintenance
• Use of ERP systems to consolidate data from different sources
• Regular data checks and AI-based monitoring that automatically corrects errors

Cost and knowledge barriers

The implementation of big data analytics is often associated with high costs. In addition to high-performance hardware and software, companies also need specialists with the relevant expertise to use the technologies effectively. It can be particularly challenging for small and medium-sized companies to provide these resources.

Recommendations:

• Use of cloud-based big data platforms that are flexibly scalable and offer cost-effective entry-level rates
• Employee training and collaboration with external experts to build up the necessary knowledge internally
• Investment in user-friendly big data analysis tools that can be used without in-depth technical expertise

Also interesting: Knowledge silo risk – How companies secure critical knowledge

Connection to ERP systems

ERP systems can alleviate many challenges in dealing with big data by serving as a central platform for data management. By networking with big data analytics, operational data is structured, monitored and prepared for analysis. This not only improves data quality, but also reduces technical hurdles through standardized interfaces.

Recommendations:

• Networking ERP systems with big data tools to seamlessly integrate operational data into analyses
• Use of APIs and middleware to connect existing systems and thus avoid data silos

Delve deeper: Big Data and ERP – Why the combination makes more out of your data

Future prospects: Big data analytics is becoming a key strategic discipline

The future of big data will be characterized by new technologies, growing data volumes and an ever greater integration of artificial intelligence.

We are in a transition from pure data analysis to intelligent analysis systems that learn independently and recognize patterns in data. Machine learning and deep learning are increasingly being integrated into big data tools. This allows complex data, such as streaming data, to be predicted more precisely and analyzed in real time.

Edge computing technologies, in which data is processed directly at the source instead of first being sent to central servers, will become increasingly important. This is particularly relevant for IoT devices that generate huge amounts of data, for example in production or transportation. Edge computing reduces latency times and therefore enables faster data analysis and decisions – a decisive advantage for applications such as autonomous driving or Industry 4.0.

Data from social media, videos and audio files now account for a large proportion of global data generation. The management of this unstructured data is becoming increasingly important. New AI algorithms and special tools are helping to extract valuable information from large volumes of data for corporate management.

Data-based work will be the standard in companies in the future – with and without AI. So far, we are still in the early stages of this development. But in the next few years, low-code and no-code platforms will ensure that big data analytics can be carried out without in-depth technical knowledge. This will open up the possibility for organizations to use data analytics comprehensively.

Big data is no longer a niche topic. It is a key strategic technology. All managers and employees should have a basic understanding of it (data literacy) in order to perform their tasks competently and data-driven in the future and thus contribute to the growth of their company.

Interesting facts from the blog:

https://www.applus-erp.de/wissen/unternehmensentwicklung/blog-synergie-erp-und-data-warehouse/

https://www.applus-erp.de/wissen/erp-technologie/blog-big-data-und-erp-worin-besteht-der-zusammenhang/