Data Processing & Data Management

1. Introductory words about data management & data processing

Anyone who walks around the world with open eyes will quickly notice that data is of growing importance almost everywhere. The right data management is important in order to know how to address the issue of adequate data processing. Especially in the business sector, there is hardly any activity in which it would not be important to handle data within a certain framework. But what is data anyway? What types of data do we create for downstream data processing? And what is possible if we sustainably data sphere surrender and exploit their full potential, or even try to expand it?

The following article is about a comprehensive deep dive, at the end of which hardly any question about data processing & data management should remain unanswered. In doing so, we are, as it were, addressing the status quo and also taking a look at the future. Our well-established expertise in data processing & data management leads us to want to share our comprehensive knowledge repertoire a bit at this point. However, we also have individual experts for each individual point, who are always available to answer your specific inquiries or inquiries.

1.1. Why is data management so important?

data management is a central aspect of every corporate strategy. More specifically, data management is a strategic point of application where decisions are made that are important for the overall orientation of the company. The main goal of data management is to ensure that data is managed effectively, efficiently and securely in order to sustainably support the needs and goals of the organization or company. Data management is therefore so indispensable in order to maintain an overview, not to be flushed away by a flood of information.

1.2. Corresponding data processing

As part of data management, it is also incredibly important to look at how the respective information should be used in the further process. Data processing is an essential part of overall data management. Data processing requires both a strategic orientation and the infrastructure necessary for implementation.

In the course of this article, we will repeatedly talk about the facts of data processing and data management, because these two aspects are at the center of the data sphere in a business context, according to a thesis that we will be carried away at this point.

In order to correctly start with an explanation of the datafied present tense, we will start at the very beginning: namely with the question “What are we actually talking about when we talk about data? “From there, we embark on a journey through the entire data sphere, i.e. the terrain on which data management and data processing tend to operate.

2. What is data?

We want to start with this question, which at first glance seems rather naive. It will become clear very quickly that the answer will raise a myriad of follow-up questions; the complexity of the initial question will only become apparent as the text progresses. So let's jump right in:

The word date is Latin and literally means “given.” Data is therefore circumstances or their recorded representation. Data differs significantly from facts in their (relative) lack of context, i.e. because they simply attempt to refer to external facts without value. The fact that even data is never completely neutral will of course be discussed in more detail below.

2.1. Digitalization and data

Data forms the basis of digital operations and corresponding interactions. They can be of extremely different types and vary in quality. For example, there are various data types (from image to text, from binary structure to metric scaling) and data structures (e.g. lists, tables, running texts, etc.), which can come from a variety of sources (e.g. sensors, statistical analyses, surveys, etc.) and come into play within equally diverse contexts (e.g. in healthcare, in the financial sector, in traffic, in the dynamics of social networks, in advertising, etc.).

2.2 Types of data

It depends on how you are willing to define if you want to provide information about the types of data. Many experts speak of seven different types of data, including metadata, reference data, company-wide structural data, transaction structure data, inventory data, transaction data and audit data. This categorization makes sense when it comes to mapping data-driven processes in an organization, but we want to go one level deeper and look at what lies at the basis of the data itself, i.e. its immanent way of existence.

Broadly speaking, we want to differentiate between three different types of data:

1. structured data
2. Unstructured data
3. Semi-structured data

What we are dealing with when we tend to squander about the data sphere in this way will be explained below.

2.2.1. structured data

Data that is available in a clearly defined and organized form is referred to as “structured”. Such information is already well-formatted and follows a consistent schema or structure that makes it easy to store, retrieve, and analyze.

2.2.2. Unstructured data

In contrast to the first category, there is unstructured data that does not have a fixed structure or hierarchy. This data is often not organized in an easily analyzable or searchable way; it is, in a sense, “raw”, i.e. it needs to be prepared in order to be used.

2.2.3. Semi-structured data

Semi-structured data lies in the spectrum between structured and unstructured data. In contrast to fully structured data, which already exists in a completely tabular form, and unstructured data, which does not exist in a coherent form, semi-structured data already has a certain structure, but this does not necessarily follow a fixed scheme. This type of data is already easier to search and analyze than unstructured data, but it still offers more flexibility than fully structured data.

2.3. Analog vs. digital data

When we talk about data in IT, we often mean completely digitized issues. The most important difference between digital data and its analog equivalents is discretion, i.e. digital data can be addressed individually, unlike analog information, it can be dialed individually and extracted in its entirety from its original context without causing fatal logic errors.

This fact makes digital data appear discrete; digital values are discreet in value.

3. Data Strategy — How to Deal with Data

Data is only as good as the benefits it provides. It could therefore be said that the explicit data strategy is one of pretty much the most important points regarding the entire data sphere. One of the most important aspects of handling data is that of data storage. The question of functional databases and their specifications can be clarified precisely when you know what purpose the information should serve. In any case, it is essential to think about the careful storage (and subsequent evaluation) of the respective (business) data. We therefore want to address this topic first.

3.1. Data storage & data evaluation

The topic of data storage corresponds to that of databases. This is an important prerequisite for successful data processing and adequate data management. Meanwhile, there are various paradigms of data storage, each with their own logic and corresponding strengths and weaknesses. So that the right choice can be made, it is worth taking a brief overview, which we would like to provide below.

One important aspect of databases that should not be neglected is that of data evaluation. This is an important part of general data management and data processing. For companies in particular, it is particularly crucial to focus on choosing the right infrastructure for data processing.

3.1.1 Databases

Similar to how there are different types of data (see above), there are also corresponding storage methods. Broadly speaking, the various data types resonate with the various database types, which can be drawn up in two general directions. On the one hand, there are (classic) relational databases, in which processed information is related to one another, and on the other hand, there are non-relational databases in which different types of data coexist in parallel and in which new patterns sometimes emerge and thus unsuspected potentials can be recovered.

relational databases (SQL, NewSQL)

The approach of a relational database is ideal for dealing with structured data (sets). Databases running along the Structured Query Language (SQL) are built up represent one of the most prominent variants of such data reservoirs. Relational databases are used for various purposes in data management and are particularly suitable for applications that require structured data and complex queries. For example, it involves the classic management of customer or usage data along previously defined parameters. Relational databases provide a clear structure of data and enable complex queries, join operations, and transactions. By using SQL (Structured Query Language), developers and database administrators can efficiently access data, create queries, and manipulate data independently, making them a versatile choice for a wide range of applications. NewSQL refers to a new class of relational databases, which are primarily organized in the face of decentralized (Cloud) networks are important.

Non-relational databases (NoSQL)

Non-relational databases are a suitable container for unstructured data. Especially with regard to artificial intelligence, which is increasingly coming into the limelight, they are an important part of the current data sphere. Non-relational databases, often as NoSQLdatabases, therefore offer advantages in scenarios in which a variable structure of data is required. These are issues that require higher scalability, or when there are no explicit weightings of the data initially. When it comes to specific data processing requirements, such as rough data protection regulations, the use of non-relational databases is also an option.

3.1.2. New paradigms of data management

In addition to the traditional ways of data storage, there are also new approaches to storing and using data in the digital space. At best, these architectures resonate in detail with the company's anticipated target corridor, i.e.: It's about what purpose the respective information should ultimately serve, what circumstances it meets. In the age of emerging artificial intelligence, for example, data must be handled differently than may have been the case in an era of primarily human intervention. Processing information in a human-readable way requires different methods of execution than are the case with algorithmically based actants. In the following, we will therefore present five types of data storage, which have been developed in line with the new options that the digital world is able to deliver.

Data warehouse

A Data warehouse is a central database or data warehouse that is used to store large amounts of structured and often historical data from various sources. It is used to consolidate and organize data for business reporting, analytics, and business intelligence (BI). Data warehouses are designed to efficiently support complex queries and provide a consistent data basis for decision-making processes.

Data Lake

A Data Lake is a large, centralized storage environment that is used to store a wide variety of structured and unstructured data in its raw format. In contrast to a data warehouse, a data lake is less structured and allows data to be stored in its native format. This makes it easier to store large amounts of data and provides flexibility for subsequent data processing and analysis.

Data Mesh

Data Mesh is a data architecture approach that aims to improve scaling and efficient use of data in distributed and decentralized environments. The focus is on the self-service of the teams that produce, own and consume the data. In the data mesh model, the data architecture is viewed as a decentralized ecosystem consisting of interconnected domain data platforms.

Data Fabric

Data Fabric is a concept in data management that aims to seamlessly integrate, organize, secure, and make data available across various platforms and systems. It is a type of fabric that makes it easier for data to flow within an organization. Data fabric often includes features such as data integration, organization, access, security, and analytics to create a flexible and scalable data infrastructure.

Dataverse

The term Dataverse may mean different things depending on the context. The term is often used to describe a database or data store that supports various types of data and entities. The Microsoft Dataverse, for example, is a Microsoft Power Platform service that enables the storage and management of data for applications. However, the term can also more generally mean a comprehensive data environment that orchestrates universal access.

3.1.3. Cloud computing & edge computing (fog computing)

An issue that must also be considered in connection with the necessary storage of (business-important) data is that of access to storage or the way in which the information enters the respective structures and can also be retrieved from them again. With the paradigm of the no longer very young Cloud-Computing wird gleichzeitig eine Speicherungs- wie auch eine Distributionslogik anvisiert, die einiges zu ändern im Stande war – und die darüber hinaus weiterhin gewaltige Unterschiede zu machen pflegt. Cloud-Lösungen bieten mehrere Vorteile: so bieten sie etwa die notwendige Voraussetzung für eine maximale Skalierbarkeit der unternehmensspezifischen Aktivität, stellen aufgrund ihrer modularen Vertriebsstruktur eine kostengünstige Alternative zu sogenannten On-premise Solutions represent and, thanks to their largely decentralized design, provide much easier accessibility across the limits of the hardware used. Storage and distribution logic is being targeted at the same time, which was able to change a number of things — and which also continues to make huge differences. Cloud solutions offer several advantages: for example, they offer the necessary prerequisites for maximum scalability of company-specific activity and, due to their modular sales structure, provide a cost-effective alternative to so-called

In addition to the logic of the cloud, there is also the approach of Edge computing, or even fog computing, which adopts the metaphor of the cloud and intends to adapt the enforcement logic accordingly. Contrary to cloud logic, which primarily promises a global storage and distribution structure, fog networks are local and temporary phenomena. Their advantage lies primarily in low-latency implementation, which is an important prerequisite for effective synchronization, especially in a world in which a variety of things are equipped with communicative qualities (Internet of Things). Whether you want to talk about edge computing or fog computing depends on the respective perspective. Roughly speaking, the terms are synonymous with each other.

Termin buchen

Let’s get in touch!

Haben Sie Fragen rund um das Thema Datenstrategie? Wir beraten Sie gerne bei einem unverbindlichen Erstgespräch. Für einen Termin Ihrer Wahl, nutzen Sie einfach ganz bequem unser Buchungstool.

Termin buchen

4. Data management & data processing

As we have tried to show so far, data is only as good as the enforcement logic to which it is subjected. In the following section, we would therefore like to address the central importance of such activities, which trade under the term data management.

data management This refers to the planning, execution, and control of all activities related to the collection, organization, storage, processing, distribution, use, and maintenance of data in an organization or company. The primary goal of data management is to ensure that data is managed effectively, efficiently, and securely to support the organization's needs and goals.

As stated at the beginning of this article, data processing refers to the process of collecting, organizing, storing, manipulating, and providing data in various forms and formats. This process can be manual or automated and is critical for generating information that is used to support decisions and complete tasks. Data management and data processing are two closely linked concepts that relate to the efficient use and handling of data in companies. Effective data management is the basis for successful data processing. Well-structured data management ensures that data is of high quality, is easily accessible and meets the diverse needs of users. This in turn facilitates efficient data processing, which depends on data being well organized and prepared.

In order to address the dimension of data management in more detail, we will now look at various aspects that can be considered particularly relevant in its implementation. The aspect of data processing will also be addressed indirectly.

4.1. Data Governance

An important aspect of data management is that of Data Governance. This is a systematic approach to the explicit use of data in a company. It is an explicitly controlling endeavor that is able to set the general direction in which you are inclined to move by analyzing data. Since data sets offer many different options for analysis, systematic data governance is particularly decisive for the war with regard to the efficient use of information.

4.2. Data consolidation (data qualification)

In order for a company's data to be used correctly, the essential information must first be brought together and, if necessary, a coherent cleansing of the essential information. With the data consolidation Is a company pursuing exactly this goal. As part of this merger, the issue of data qualification also plays a decisive role: Especially when it comes to managing and optimizing data in corporate environments, there is a significant intersection between data consolidation and data qualification. The focus of qualification is on adequately evaluating and improving the quality of data sets. The goal is to ensure that data is accurate, consistent, current, and relevant for its intended use.

4.3. data migration

How to handle the data in the event of a move is just as important as the correct preparation of the data. It is precisely this aspect that the data migration: This is about the proper and professional handling of data (records) in the event of any kind of change, whether this is due to the need to introduce a new format or is required due to a completely new infrastructure. Ultimately, the ability to migrate data should be considered right from the start in order to completely avoid problems. Good data management is characterized by taking into account as many contingencies as possible.

4.4. Data mining

Data mining is an emphatic procedural examination of the dimension of data use. Recognizing patterns and therefore knowing for which purpose the data can best be used is one of the most important functions of this (analytical) process.

Since we have already moved on to data analysis with the topic of data mining, the following section will now continue consecutively. We want to deepen the topic of analytic analysis of data and also touch on the importance of business intelligence and appropriate and professional data integration.

5. Data analysis

Data analysis refers to the process of examining, purifying, transforming, and modeling data to generate useful information, conclusions, and insights. The aim of data analysis is to identify patterns, trends and relationships based on the available data in order to be able to make informed decisions or gain insights into complex phenomena. Although this is actually an important aspect of general data management, it makes sense to look more closely at the issue of coherent analysis, as this should also address the importance of business intelligence and forecasting. For this reason, a separate sub-chapter should be dedicated to the topic of data analysis.

As described above, there are intersections between data mining and meticulous data analysis, but the latter goes beyond mining in its entirety. As part of classic descriptive data analysis, it is primarily about the reliable identification of a respective current situation. Only then will it be possible to think meaningfully about follow-up actions that focus on predictive game types (such as data mining).

In addition to the classic types of data analysis (descriptive and predictive), exploratory data analysis (EDA) also plays an important role from time to time: EDA includes visualizations, hypothesis tests and statistical analyses to discover unknown patterns in data sets and understand them more precisely.

5.1. Business intelligence

One of the most widely used and integral components of a sophisticated data strategy, and in particular one of the corresponding analysis, is in Business Intelligence (BI) to find. The process of data visualization using business intelligence can be described in several steps: First, data extraction and the corresponding integration take place on the other side. This sub-process is particularly relevant because the data to be analyzed must first be aggregated at a central location before it is even possible to subject it to analytical logic. Data modelling is then about emphasizing inherent relationships, creating context and generating connectivity by organising the data in a suitable model. The third step in this series, namely that of data visualization, is decisive: The selected data is visualized clearly. This can include bar and pie charts, heat maps, trend lines, geographic maps, and many other forms of visualization. Depending on the purpose of the data to be analyzed, an adequate visualization can help draw conclusions as efficiently as possible; at best, a well-informed look is sufficient.

In order to give the topic of business intelligence more shape by way of example, let's look at two example cases below. On the one hand, there are Microsoft Power Platform applications, on the other hand, we have comparable products from other companies.

5.1.1. Power BI & Power Automate (Microsoft Power Platform)

One of the most interesting environments in terms of business intelligence is the Microsoft Power Platform, which primarily with its products Power BI and Power Automate Can shine. Power BI is primarily used to visualize and analyze data and transform it into meaningful reports and dashboards. The platform is aimed at companies and professionals who want to use their data effectively to make well-founded decisions. Power BI is suitable for a wide range of industries and use cases, from financial analysis and sales reports to operational dashboards and annual reports. Over the years, it has become one of the most popular business intelligence solutions used by a wide range of companies worldwide.

5.1.2. Tableau (and others)

The fact that there are various providers on the market, each offering their own business intelligence solutions, is just as much a part of the truth as the fact that Microsoft is one of the most important players in this area with Power BI. In order to also address competing products, the following is now an example of tableau be received.

Basically, Tableau is also a powerful BI and data visualization platform that was developed to transform data into meaningful and interactive reports and dashboards. It remains to be noted that Tableau has a wider range of data visualization options, but this also results in a steeper learning curve. Ergo: Tableau is far less user-friendly than the competition from Microsoft. In addition, interested users face higher costs. In contrast to Power BI, which is already included in Office 365 as a basic version, Tableau has corresponding additional costs.

5.2. Big data analytics

What should also not be ignored when it comes to data analysis is the topic big data. Anyone who deals with large amounts of data needs a strategy that is suitable for processing the sheer mass of information that is delivered every second.

Big data analytics generally refers to a variety of processes of collecting, storing, processing, and analyzing large amounts of structured and unstructured data to extract valuable insights, patterns, and trends. It comprises advanced technologies and analytical methods that would often be too extensive or complex for traditional data processing systems. Accordingly, some computing capacity is required to approach the topic of big data analytics. It can play a decisive role in overall success if the business model is based on appropriate data sets.

6. Automated data processing

With the increasing amount of data to be processed, there is also a call for appropriate organization mechanisms. Algorithms help to keep track of the veritable jumble of data and sometimes (semi-) autonomously make initial prescriptions. As informed users, we must be prepared to work with algorithmically based agencies. In this way, we permanently prevent being reduced to the role of powerless spectators, but keep the reins of action in our hands. The approach goes beyond the analysis, as discussed in the previous section. It also and especially relates to new paradigms of storage and general use in data-driven environments. Issues such as data protection play just as important a role as preventing situational short circuits. For this purpose, the emphatically interdisciplinary research field of data science exists.

6.1. Big Data & Data Science

Data science is an interdisciplinary field of research that uses advanced techniques from statistics, computer science and machine learning to gain valuable insights from large and complex amounts of data. The aim is to analyze data, identify patterns, make forecasts and create a basis for decision-making for various industries. Data science plays a crucial role in overcoming the challenges of information overload and makes it possible to transform data into a strategic resource. This fact is particularly relevant with regard to big data: When ever larger amounts of data are available, which are sometimes updated every second, then it is essential to dedicate yourself to researching efficient use. Such an undertaking can develop both along classical canonical paths, but it can also take on a speculative and exploratory form; depending on the level of innovation or business model, there is even a suitable mix of R&D (Research & Development).

An area closely related to the topic of Big Data & Data Science is that of artificial intelligence (AI). Scientific findings are particularly relevant when it comes to the corresponding learning processes. In the following, we would therefore like to approach the specific design of such procedures.

6.1.1. Machine learning

What the current state of AI is primarily associated with is that Machine learning. The approximation of machine training processes to socio-mental models of conditioning is paramount in all of this: Instead of explicit programming, corresponding computer systems are trained with various data in order to then recognize patterns, make predictions and solve problems on the basis of them.

6.1.2. Deep learning

With the so-called Deep learning It is a specific subspecies of machine learning. Deep learning is based on feedback from artificial neural networks (KNNs), which make it possible to recognize significant patterns and corresponding features ever more precisely. Since deep learning is an iterative and highly flexible process, there are deviations that are due to ever more precise analysis. However, it is important to handle such a mechanism with care and to thoroughly review the initial data to avoid serious errors in subsequent analysis.

6.1.3. Federated Learning

Like deep learning, so is that Federated Learning a special process that can and must be located in the machine learning environment. Instead of having the training or learning process take place on a central server, it is outsourced in connection with federated learning and is executed using decentrally arranged (end) devices and/or computing resources. Federated learning is particularly used when data protection concerns are involved, meaning that it must be secure as a matter of priority.

6.1.4. Swarm Learning

Another type of machine learning consists in the so-called Swarm Learning. As the name suggests, this is a learning process that is massively influenced by the concept of (animal) swarm behavior in nature. In this context, it is primarily about the collaborative training of a model of various individually selectable agents. Swarm learning has some similarities with federated learning, but unlike this, the individual training actants remain as individuals who only cooperate for the explicit purpose of training in the context of swarm learning.

As we have tried to show in this section, there are various variants of machine learning, each of which follows their own, different logics in order to benefit the goal of data-driven learning of a special model that is to be used in the context of AI.

6.2. distributed computing

distributed computing is a computing paradigm that, through its emphatic decentralization, contributes to how AI and big data analyses work. It is particularly related to the Internet of Things, ubiquitous computing and/or Industry 4.0 particularly valuable and provides the opportunity to respond flexibly and in line with needs to arise problems. Distributed computing allows tasks to be processed in parallel on different computers. This improves overall performance as multiple parts of a task can be processed at the same time. Especially in times when the amount of data that needs to be handled is constantly growing, the application of a corresponding paradigm is absolutely necessary.

7. Data and artificial intelligence

From the topic of big data and the corresponding use to sharpen certain data models, we now come to the star of the proverbial show: It is of course — how could it be otherwise — about artificial intelligence (AI). The connection between data sphere and AI is extremely simple: If a technical system is to draw conclusions from environmental data on its own, it requires an initial understanding, which also consists of a lot of exemplary data. The larger the amount of data used for such training, the more adequate the system's response, or even its prediction, is ultimately. Heuristically, we differentiate between two types of artificial intelligence: namely between weak and strong AI. After a first section, in which we want to address these two exemplary issues, we will then talk about various AI tools and their ideal use.

7.1. Weak AI

First things first: all current attempts at AI are ultimately located in the area of weak AI. What does that mean now, weak AI? The adjective weak does not refer to skills in a specific area, it is much more about the fact that such mechanisms are highly specialized and are by no means suitable as all-rounders. Weak AI can actually be pretty powerful! Weak AI is fully focused on performing targeted and limited functions. It functions more as a tool than as an autonomous entity with its own motivations.

7.2. Strong AI

What is often referred to as strong AI is associated with a highly speculative endeavor that would give a technical system the same (or even more pronounced) cognitive abilities as a human being. Strong AI is characterized above all by the fact that it does not need any help from a social being in order to take action. It is therefore primarily an aspect that is negotiated in (dystopian) science fiction and represents an increasingly technocratic environment. As remote as the vision of strong AI may be, the efforts that big tech companies such as Google, Amazon and Meta are putting into its actual implementation are real.

What should concern us more in the following is the use of weak AI in the form of helpful tools that are able to change work routines sustainably.

7.3. AI tools and areas of application

In the recent past, the introduction of now well-known generative AI tools has created a lot of confusion. We will present the generative software examples from OpenAI, Google and other smaller players as well as the equally widespread, albeit less effective, examples of predictive AI. The areas of application for AI are as diverse as they are largely undetermined: from marketing to project management to software development, there are various tools that promise their respective users greater convenience and process-oriented security. Many of these tools (especially those equipped with generative AI) invite you to experiment, which can lighten up everyday work.

7.3.1. Generative AI

Generative AI tools are based primarily on frameworks such as GPT (General Pretrained Transformer), NLP (Natural Language Processing) and related LLMs (Large Language Models) on. They are therefore all trained to understand spoken language in depth, which shows a general trend: No special programming knowledge is required to use AI tools. It is enough to be able to clearly express your own project in order to initiate generative AI.

ChatGPT (OpenAI)

One of the most well-known tools that uses AI is ChatGPT, which was developed by OpenAI. This is a text generator that handles a completed data set. It builds on the progress of previous GPT models and is known for generating relatively long and coherent texts. It is important to note, however, that ChatGPT is not a fully conscious or understanding model; it generates answers based on statistical patterns and particular contextual information, which, however, is explicitly not up-to-date on a daily basis.

Midjourney (Midjourney Institute, SF)

Midjourney is a generative AI that can create images from text descriptions. It is a proprietary program developed by the eponymous research institute from San Francisco, California, USA. Midjourney is an impressive tool that can be used for a wide range of applications. It can be used to create art, illustrations, graphics, and even for scientific purposes.

Co-pilot (GitHub x OpenAI)

Copilot is an AI tool developed jointly by GitHub and OpenAI that acts as an extension to various integrated development environments (IDEs). It is designed to help developers write code by automatically generating suggestions for lines and blocks of code. GitHub Copilot is based on OpenAI's GPT-3.5 architecture and uses machine learning to make contextual and syntactically correct code suggestions.

Gemini (Google/DeepMind)

Gemini is a generative AI tool developed by Google's subsidiary DeepMind and is based on the language models LamDA and PalM 2 previously released by Google. Gemini was released on December 6, 2023 and is commonly understood as Google's answer to OpenAI's GPT framework. In contrast to the competition, Google's alternative is able to access up-to-date data and generate various outputs in the same application: from images to text to musical set pieces, everything is included. Gemini's multimodal functionality helps users generate the most accurate output possible after initial input.multimodale Funktionsweise von Gemini hilft Nutzer:innen dabei, nach initialem Input den jeweils möglichst passgenauesten Output zu generieren.

7.3.2. Predictive AI

In addition to AI tools, which intervene autonomously, almost quasi-magically, in the creative process, there are also tools that approach the use of data in a no less intelligent, albeit more discreet way. We're talking about predictive AI, i.e. systems that analyze data streams in detail in order to discover patterns and trends and are thus fully beneficial to an informed prediction. There are many software tools that fulfill this function, but they are often not classified as belonging to the field of general AI.

Azure Machine Learning (Microsoft)

One of the first examples of predictive AI is found in Microsoft Azure Machine Learning. This is a comprehensive platform that represents the entire process of machine learning, from data preparation to training models, to managing or predicting results, including appropriate contextualization. Azure Machine Learning enables companies to drive the development of models to predict future events or to classify data. This can be used, for example, to forecast sales figures, to identify fraud in financial transactions or to classify images in medical applications.

DataRobot

DataRobot is an automated machine learning (AutoML) platform that helps companies build machine learning models quickly and efficiently. The platform enables users to develop complex models even without extensive knowledge in the area of machine learning, train them successively and deliver them consistently to create reliable forecasts.

In contrast to Azure Machine Learning, DataRobot places a strong focus on automating the entire machine learning process. It enables users to automatically create, train and evaluate appropriate models. The whole thing works even without extensive knowledge in the area of data science.

Scikit-learn

scikit-learn is an open-source library for machine learning in the Python programming language. This library provides simple and efficient tools for data analysis and machine learning and is an essential part of the Python ecosystem in artificial intelligence (AI) and data science. In addition to a wide range of algorithms for classification, regression, clustering and dimensional reduction of essential data, scikit-learn offers in particular a user-friendly API that makes the application of machine learning immensely easier. All in all, scikit-learn is a popular choice for data scientists, researchers, and developers who want to use machine learning in conjunction with Python.

Termin buchen

Let’s get in touch!

Haben Sie Fragen rund um das Thema Künstliche Intelligenz? Wir beraten Sie gerne bei einem unverbindlichen Erstgespräch. Für einen Termin Ihrer Wahl, nutzen Sie einfach ganz bequem unser Buchungstool.

Termin buchen

8. Challenges & issues related to data

Now that we have focused primarily on use cases and opportunities that come with dealing with data, we now want to address the problems and situational challenges that arise around the subject area of data processing and data management. This section is particularly important because careful handling of business-important data is important in order to be able to rely on decisions made on the basis of it on the one hand and not to squander social trust on the other hand.

8.1. Data protection

One of the most obvious challenges that affects data-driven processes differently is that of data protection! In Germany and Europe in particular, policies such as these, which frame the handling of data accordingly, are particularly rigid and the rights to personal privacy are extremely high. It is therefore particularly important to obtain a comprehensive overview of the relevant directives and to translate their requirements into consecutive measures.

8.1.1. Statutory privacy policies

In Europe, the important guidelines regarding data protection are adopted at EU level and must finally be translated into national law. It is then necessary to comply with the relevant laws. For example, the GDPR is based on such a procedure. In the recent past, in particular, the EU NIS 2 Directive caused a stir. It is the second version of the NIS Directive and builds on the first directive from 2016. NIS 2 was proposed by the European Commission on December 16, 2020 and is part of the EU's wider efforts to improve digital security. The Directive was published in the EU Official Journal on 27.12.2022 and came into force on 16.01.2023. The EU NIS 2 Directive is part of the European Union's wider efforts to strengthen digital security and increase resilience against cyber threats. It should ensure that essential service providers and digital service providers take appropriate measures to ensure the security of their networks and information systems. Both data protection concerns and the anticipation of the emergency of a possible Cyber attacks on critical infrastructure.

Since technical options are constantly evolving, it is important to always stay up to date in terms of safety technology. The more valuable data becomes as a resource for economic success, the more important its thorough protection becomes.

8.1.2. Rights of (private) persons and obligations of companies

One aspect that must be considered primarily in the context of the omnipresence of data in public spaces is the rights to privacy of the subjects to whom the data tend to refer and the emphatic protection of the same. The rights of (private) individuals in this regard correlate with the obligations of companies and/or organizations whose business model is largely based on the use of relevant data. The rights granted to individuals include, for example, the right to information, the right to correction, the right to delete (be forgotten) or even the classic right of objection. In addition to these general examples, there are also some specific rights, which are sometimes regulated separately and individually.

In line with these rights, there are also corresponding obligations of companies, in particular due diligence and transparency obligations, which relate in each case to lawful enforcement and the appropriate correct processing of the managed data. In case of doubt, companies must therefore be able to disclose the respective logics by which the information is processed. Companies are also required to ensure that the personal data they process is accurate and up to date. It is the responsibility to correct incorrect or outdated data. An important principle for collecting data is also purpose limitation, i.e. the clear accountability of the collection and use of a respective data set must be ensured.

What can happen when data is handled carelessly, or the maintenance of databases is neglected, is the creation or occurrence of so-called dirty data, or dirty data. We will try to clarify what this is all about in the following section.

8.2. Dirty Data & Data Bias

Data provide the purported opportunity to draw objective conclusions. But this is not a matter of course. How fragile the connection between analog reality and representative data sphere is reflected very well in the circumstances of Dirty Data or read from the data bias. Both terms revolve around the same core: namely around the fact that there is no objective data! Each piece of information carries traces of its respective survey context and can therefore be manipulated or may be subject to misguided interpretation. Where dirty data is more about an unintentional distortion in the data material, which can have various reasons, the topic of data bias focuses on the pre- and (unconscious) value judgments written into the data material, which can lead to overcontrol through (positive) feedback and thus contribute to cementing existing inequities. This risk exists in particular when AI or ML systems are trained on the basis of such compromised data (sets). In this particular case, this is also referred to as an algorithmic bias. The topic of various biases therefore pays more attention to the use of data, whereas dirty data addresses the inherent qualities of data (sets). Ultimately, however, both concepts are similar, as the two problematic constellations equally point to the need to exercise caution when it comes to an apology of the primate of the data sphere.

8.3 Data discrimination

One potential risk arising from the problems briefly discussed in the previous section is that of data discrimination. This term and the associated concept refers to situations in which data or data-based systems tend to have discriminatory or unequal effects on specific groups of people or individuals. This discrimination can take various forms and occurs primarily in the areas of data analysis, machine learning, artificial intelligence (AI) and automated decision-making.

9th wrap-up: The potential of data in the economy of the future

After looking at the past and present of data in previous sections, it is now time to take a look ahead and, of course, incorporate what has been said so far.

As far as the future of a data-driven economy is concerned, it can be said that the current trend of acceleration appears to continue to develop exponentially. It will therefore become even more important to deal with storage structures and corresponding utilization mechanisms so that business-important data can be separated from data that only appears as an accessory. Establishing regular routines for the purpose of data consolidation is therefore already urgently required today, but will be essential in the future. In order to remain in control of an impending flood of data, it is also advisable to compare your own objectives and the measures selected for this purpose at regular intervals.

The future of the data-driven economy generally promises continued growth and technological innovation. The continuous (further) development of data-based technologies and the corresponding analysis methods will play a central role in no matter which sector.

Another component that will be increasingly emphasized is the communicative qualities of”Things“: That Internet of Things (IoT) ensures that new technological standards and protocols are introduced, which gradually make it possible to use data efficiently on the spot. Edge computing joins the existing cloud logic and ensures that low-latency communication can take place, which involves the full integration of”Things“guaranteed in the process of value creation. All in all, it can be stated that the importance of data administration and corresponding data management will continue to grow. The progress in hardware capacities and the corresponding process level is also focusing on the data sphere with all its specifications and idiosyncratic procedures, which promise ever more efficient implementation. Anyone who wants to continue doing business safely and, above all, successfully in the future is particularly well advised to address the importance of data and the assessment of potential potential. Regardless of whether (digital) data is now the core of the company, or is simply processed in the background: traffic will get stronger — and we can hardly wait!

‍