Big Data Consulting

Big data simply explained: What is it and why is it important for your company?

“Big data” refers to extremely large amounts of data, which can be both structured and unstructured and come from various data sources. These volumes of data are usually so extensive that traditional data processing tools are unable to effectively store, process, and analyze them.

The sources of big data can be generated in various ways, e.g. from business transactions, social media, sensors, digital images and videos, radio frequency identification (FRID), smartphones, credit and customer cards, assistance devices, surveillance cameras, aircraft and vehicles, Internet protocol recordings and other types of data.

Big data analytics, i.e. the evaluation of big data, enables companies to gain insights that can lead to better decisions and strategic business decisions. For example, it can be useful in detecting fraud, conducting market analysis, predicting customer behavior, and many other applications.

3 V - Volume, Velocity, and Variety

The so-called “3 Vs” are central to big data: Volume, Velocity and Variety, which we will discuss later in the article. Sometimes more Vs are added, such as Veracity and Value. These concepts relate to the amount of data, the speed at which it is created and processed, the variety of data types, the truthfulness of the data, and the benefits they provide.

When does a company need big data?

There are various use cases in which a company needs big data.

1. High data volumes: When a company is dealing with enormous amounts of data that can no longer be processed efficiently with traditional databases and software tools, the use of big data approaches can help.

2. Quick data: When data needs to be collected and analyzed in real time or near real-time, such as financial transactions, social media feeds, or IoT devices, big data can provide a solution.

3. Complex data structures: When companies access a variety of data sources, including structured, semi-structured, and unstructured data, big data can effectively combine and analyze them.

4. Predictive analytics: When companies are looking for trends, patterns, and connections in their data to make forecasts and predictions, big data is often essential.

5. Personalization and customer satisfaction: Big data can help companies create personalized offers, improve customer experience, and increase customer satisfaction by providing insights into customer behavior and preferences.

6. Risk Management and Fraud Prevention: Big data can help to better understand and manage risks and to identify and prevent fraud activities.

What does big data have to do with IoT?

When you find out about big data, you often get IoT — Internet of Things — opposite. And yes, the two concepts are closely linked.

IoT refers to the increasing interconnection of physical devices with the Internet, resulting in an exponential increase in the amount of data generated. These devices, which range from smartphones to home appliances to industrial machines, are constantly generating huge amounts of data about their usage, status, and environment. This data falls into the “big data” category because of its volume, diversity, and speed. By analyzing this big data, companies and organizations can gain valuable insights to improve performance, predict, and prevent problems. For example, big data and IoT are inseparable in the modern digital landscape and together form the basis for many advanced technologies and applications, including AI and machine learning.

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Use cases for using big data consulting

To get an understanding of how big data can be used in companies, we have collected various use cases:

Use of big data in the energy industry and supply

In the energy sector, there are many products that are installed underground or at high altitudes for extended periods of time. These materials should have a particularly long lifespan — if they fail unexpectedly, this can not only mean a loss of sales, but also problems for the population supplied with the energy supply.

With big data and the use of intelligent IoT technology, it is possible to predict when systems need maintenance — this can be planned efficiently.

Using big data in retail and e-commerce

Retail, but also e-commerce retailer can use big data to analyze buying patterns, identify customer segments, and develop personalized marketing strategies. This includes optimising inventory levels and increasing operational efficiency.

Using big data in transport and logistics

With the help of big data, companies can create patterns and trends in complex Logistics and transportation data identify to better manage the flow of goods, reduce delays and improve route planning. For example, real-time data collected by GPS devices or IoT sensors in vehicles or containers allows companies to track the movements of their fleet in real time and react more quickly to disruptions. In addition, historical traffic data can be used to forecast traffic flows and adjust travel routes accordingly.

These examples were just excerpts — the potential uses of big data are huge!

What are the difficulties of using big data?

The use of big data is a challenge for data protection. That is why it is all the more important to carefully plan and implement its use in the company.

Finally, data must be correctly collected, stored, analyzed, and protected.

Big data challenges

Companies must face the following challenges when using big data:

Generating incorrect data

There are serious security concerns with big data, in particular the threat posed by cybercriminals who generate false data and feed it into data lakes to manipulate analytics. This can result in false alarms and missed issues, which can lead to severe operational disruptions, and fraud detection techniques can help address such challenges.

Untrusted mappers

When processing big data in parallel, for example through the MapReduce paradigm, improper access to mapper code can significantly disrupt data processing by foreign or manipulated mappers generating incorrect data. The general security of big data technologies is often inadequate and heavily dependent on perimeter security systems, which makes them an easily vulnerable target in the event of faulty systems.

Cryptographic protection

Despite widespread recognition that encryption is effective protection for sensitive data, this security measure is often neglected in practice, resulting in unprotected sensitive data in the cloud. The main reason is that constantly encrypting and decrypting large amounts of data results in a slowdown that undermines the central advantage of big data — its speed.

Mining sensitive information

Although perimeter-based security is typically used to protect big data and secures all “inputs and outputs,” the lack of control over internal actions poses a risk that data can be unprotected and exploited for malicious purposes. This problem can be mitigated by implementing additional perimeters and using anonymization techniques that render personal user data harmless by removing identifying information.

Source of data

The origin of the data, or “data provenance,” which documents the source of the data and all manipulations carried out with it, can pose a significant problem with regard to big data, as the associated collection of metadata can be enormous. Security concerns arise when unauthorized changes to metadata result in incorrect records, or when untraceable data sources hinder investigation of security breaches and identification of false data.

The story of big data

Although the phenomenon of big data in its current form is rather new, the history of large data sets began back in the 1960s and 1970s, when the handling of data began, the first data centers were created and the relational database was launched. It wasn't until around 2005 that people became aware of the amount of data that users were generating on platforms such as Facebook and YouTube. Hadoop, an open-source framework specifically designed for storing and analyzing large amounts of data, was also launched during this period. At the same time, the popularity of NoSQL also increased

The rise of open-source frameworks such as Hadoop And later Spark was crucial to the big data boom. These technologies made it easier to process large amounts of data while reducing storage costs. Since then, the volume of big data has increased enormously, and not just due to human users.

The Internet of Things (IoT) has connected a wide variety of devices and objects to the Internet, which are now collecting customer usage data and product performance data. The introduction of machine learning has contributed to even more data.

Big Data: Volume, Velocity, Variety, Veracity, and Value

3-5 “V's” are used to define big data: Volume, Velocity, Variety, Veracity, and Value. We will now look at all “V's”:

Volume — the amount of data

Big data differs from conventional data in its quantity, as the word “big” already suggests. However, there is no guideline that means “a lot” or “big”, but it is about the fact that the amount of data can no longer be easily processed — and this is about the amount of data itself, but also about the software and hardware requirements of processing algorithms.

Velocity — the speed of data

Another characteristic of big data is the velocity or speed with which data is generated or changed. This aspect has changed drastically as a result of new technologies and platforms such as social media or the Internet of Things. Nowadays, data is created, modified, adapted and transmitted in very large quantities, which requires special software to capture and store it.

Variance — not just unstructured data anymore

Variance is another feature of big data that highlights the challenges and opportunities of including different types of data, such as images, videos, or text, in addition to traditional data tables. This diversity places new demands on software, tools, data storage and analysis, but also provides valuable information and insights, prompting companies to analyze these new types of data.

Veracity — Can you trust the data?

The fourth characteristic of big data, Veracity, relates to the origin and quality of the data. The reliability of the data is crucial, as questionable data quality or unclear generation methods can impair the reliability of the analyses.

Value — What is the value of data?

The fifth characteristic of big data, “value,” relates to whether and what value data has. The focus is less on monetary value, but more on content value, i.e. what insights can be gained from the data. It involves checking, among other aspects such as data volume, content and quality, whether the data offers benefits for the company or the organization.

What are the advantages of big data consulting?

Booking big data consulting can solve a wide range of challenges for companies.

After all, one of the main advantages lies in the extensive expertise that consultants bring to this highly specialized and complex area. They have in-depth knowledge in analyzing large amounts of data and are familiar with the latest trends and technologies in this area.

Companies also benefit from the experience that consultants bring with them from a wide range of projects. They have often already overcome similar challenges and can therefore contribute proven solutions and best practices. They can also identify and avoid typical pitfalls and obstacles at an early stage. Their expertise enables companies to effectively use big data, gain valuable knowledge and thus optimize their business processes and increase their competitive advantage.

How do you find the right big data consulting for your company?

Finding the right advice is not that easy. Because the advice must suit you — the employees, the orientation and also the agility.

But in the area of big data consulting, there are many different consultancies with different focal areas. Here, too, it is important for you to recognize exactly where you are: Are employees already well advanced and only need the last “push” for success, or do you need assistance in setting up an entire data strategy?

Of course, both the experience of consultants and consultants are important, but it is also the creativity to find solutions and the ability to integrate into the company.

Why The Data Institute is the strong partner at the side of companies

We are a data-oriented consulting agency specialized in paving the way for product and brand innovation through data-driven analyses. Through the skillful interplay of people and technology, as well as the combination of process and corporate culture, with a focus on data centricity and customer orientation, we established the essential building blocks for corporate success. Our core competence lies in the development of individual data strategies and their implementation.

With our competent team, we offer the full range of skills and resources: From the construction of an extensive data infrastructure and a holistic perspective on customer feedback and requirements, to the collaborative implementation of data-driven insights into specific business models and automated courses of action. We effectively lead smaller data projects to success and steer your comprehensive overall strategy securely to the desired starting point.

We look forward to taking on the market challenges you have raised and to discover together What additional added value data can create for your company.