The more data an organization accumulates, the harder it becomes to extract real value from it. Because of the fragmented nature of storing information in isolated silos, modern companies’ business processes are like builders trying to construct a skyscraper out of materials stored in thousands of different warehouses. The excess of information not only makes it difficult to access legally relevant information, but also slows down decision-making: every step has to be repeatedly checked and confirmed.

Each task or process is hard-wired to a separate table or database, and data exchange between systems requires complex integrations. Errors and inconsistencies in one system can cause chain failures in others. Incorrect values, late updates, and duplicate information force employees to spend significant time manually reconciling and reconciling data. As a result, the organization spends more time dealing with the consequences of fragmentation than developing and optimizing processes

This problem is universal: some companies continue to struggle with chaos, while others find a solution in integration – moving information flows into a centralized storage system. Think of it as one big table where you can store any entities related to tasks, projects and objects. Instead of dozens of disparate tables and formats, a single cohesive repository appears (Fig. 2.2-2), allowing:

minimize data loss;

eliminate the need for constant harmonization of information;

improve data availability and quality;

simplify analytical processing and machine learning

Bringing data to a single standard means that regardless of the source, information is converted into a unified and machine-readable format. Such organization of data allows to check its integrity, analyze it in real time and promptly use it for making managerial decisions.

The concept of integrated storage systems and their application in analytics and machine learning will be discussed in more detail in the chapter “Big Data Storage and Machine Learning”. The topics of data modeling and structuring will be covered in detail in the chapters “Transforming data into a structured form” and “How standards change the game: from random files to an elaborate data model”.

Once the data has been structured and merged, the next logical step is to validate it. With a single integrated repository, this process is greatly simplified: no more multiple inconsistent schemas, duplicate structures and complex relationships between tables. All information is aligned to a single data model, eliminating internal inconsistencies and speeding up the validation process. Validating and ensuring data quality are cornerstone aspects of all business processes, and we will explore them in more detail in the relevant chapters of the book.

At the final stage, the data are grouped, filtered and analyzed. Various functions are applied to them: aggregation (addition, multiplication), calculations between tables, columns or rows (Fig.‎ 2.2-4). Working with data becomes a sequence of steps: collection, structuring, validation, transformation, analytical processing and offloading to final applications where the information is used to solve practical problems. We will talk more about building such scenarios, automating steps and building processing flows in the chapters on ETL -processes and data pipeline approach.

Thus, digital transformation is not just about simplifying the handling of information. It is about eliminating excessive complexity in data management, moving from chaos to predictability, from multiple systems to a manageable process. The lower the complexity of the architecture, the less code is required to support it. And in the future, code as such may disappear altogether, giving way to intelligent agents that independently analyze, systematize and transform data.

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