Data & Analytics

What is data & analytics?

Data & analytics refers to the systematic collection, processing, analysis and visualization of data with the aim of gaining usable insights for business decisions. In a world in which companies generate huge amounts of data every day, the ability to understand this data and make it usable has become a decisive competitive advantage.

Data & analytics encompasses a broad spectrum of methods and technologies: from the simple evaluation of sales figures to complex machine learning models and AI-supported forecasting systems that automatically support business decisions. The common denominator is always the same: data should not only be collected, but also understood and used.

prodot has been developing customized data & analytics solutions for SMEs and corporations for over 25 years. With more than 80 IT experts and a modern, AI-ready technology stack, we help companies turn their data into real added value.

Why data & analytics is indispensable today

Many companies are sitting on a huge treasure trove of data that they are not exploiting. Data is scattered across different systems, is inconsistent, difficult to access or is simply not analyzed. As a result, decisions are made on the basis of gut feeling or outdated reports instead of up-to-date, reliable data.

Companies that use data and analytics consistently benefit in several areas:

• Decisions are made faster and on a better basis

• Inefficiencies in processes become visible and can be rectified in a targeted manner

• Customer behavior is better understood and offers can be better targeted

• Risks are identified at an early stage before they become problems

• New business models are created on the basis of data that previously remained unused

The decisive step is to no longer view data as a by-product of business activity, but as a strategic asset that is actively managed.

The basics: data infrastructure as a prerequisite

Before data can be analyzed, a solid infrastructure is required. Data must be collected, merged, cleansed and stored in a form that enables efficient analysis. Without this foundation, data & analytics remains a promise that cannot be kept.

The most important building blocks of a modern data infrastructure are

• Data Warehouse & Data Lake: central storage locations for structured and unstructured data from different sources that can be accessed by analysis and reporting tools.

• ETL processes: Extract, Transform, Load describes the process of extracting data from different source systems, converting it into a standardized format and loading it into the central database.

• Data integration: ERP systems, CRM solutions, IoT platforms, web analytics and many other sources must be seamlessly connected to create a complete picture.

• Data consistency and data quality: Poor data leads to incorrect findings. Processes to ensure data quality are therefore not an optional extra, but a basic requirement.

prodot supports companies in setting up this data infrastructure: from the architectural decision to the technical implementation and the integration of all relevant data sources.

Business intelligence: making data visible

Business Intelligence, or BI for short, is the area of Data & Analytics that deals with making data accessible and understandable for decision-makers. Dashboards, reports and visualizations translate complex data sets into clear, actionable information.

Well-designed business intelligence fulfills several requirements:

• It is up-to-date: decision-makers see today's status, not last week's.

• It is relevant: Each user group sees the key figures that are important for their tasks.

• It is understandable: complex correlations are presented visually without simplifying the essentials.

• It is interactive: users can filter, drill down and create their own evaluations.

Modern BI platforms such as Microsoft Power BI make it possible to meet these requirements even without in-depth technical knowledge. prodot develops and implements BI solutions that are tailored to the specific key figures and processes of a company and ensures that the data basis on which the dashboards are built is correct.

Advanced analytics: diving deeper into the data

While business intelligence primarily describes what has happened, advanced analytics goes one step further. It answers the questions of why something happened, what will happen next and what should be done.

Advanced analytics comprises a range of methods that are used depending on the question being asked:

• Descriptive analysis: summarizing and visualizing historical data to identify patterns and trends.

• Diagnostic analysis: investigating the causes behind observed developments, for example why a product is selling less well in certain regions.

• Predictive analysis: Using statistical models and machine learning to predict future developments, such as demand patterns, failure probabilities or customer churn.

• Prescriptive analysis: Recommendations for specific measures based on the predictions, for example optimal stock levels or ideal maintenance times.

The further you progress along this scale, the greater the potential added value, and the more technical and methodological expertise is required. prodot accompanies companies on this journey and develops advanced analytics solutions that match the existing data, the existing systems and the specific business objectives.

AI and machine learning as the core of modern data & analytics

Artificial intelligence and machine learning have fundamentally changed data & analytics. Tasks that used to take weeks of manual analysis are now completed by algorithms in seconds. Patterns in data that would be unrecognizable to humans are reliably identified by AI models.

This results in specific fields of application for companies. AI models recognize unusual patterns in transaction, production or sensor data and automatically trigger alarms or follow-up processes. Failure probabilities are calculated on the basis of machine data and maintenance measures are planned as required instead of according to rigid time intervals. Machine learning models analyze customer behavior and identify churn risks before the customer cancels. Precise demand forecasts for sales, stock levels and resource requirements reduce overstocks and bottlenecks at the same time. And natural language processing is used to automatically classify, summarize and evaluate texts, customer reviews, support requests and documents.

prodot develops AI and machine learning solutions that do not end up as isolated experiments, but are integrated into ongoing operations. Our technology stack adapts to our customers' system landscape and includes machine learning, Azure AI Services and Microsoft Fabric, so that AI functions are scalable, maintainable and embedded in existing systems.

Microsoft Fabric: The modern foundation for data & analytics

Microsoft Fabric is Microsoft's comprehensive platform for data & analytics and combines data integration, data engineering, data warehousing, data science and business intelligence in a single, seamlessly integrated environment. For companies that already rely on Microsoft technologies, Fabric is a particularly attractive basis for their data & analytics strategy.

The key benefits of Microsoft Fabric at a glance:

• Unified platform for all data and analytics workloads without media disruptions between different tools

• Seamless integration with Microsoft 365, Power BI, Azure and existing data systems

• OneLake as a central data store that makes all of a company's data available in one place

• AI functions are deeply integrated, including co-pilot functionalities that enable data analysis using natural language

• Scalable, cloud-native architecture that keeps pace with growing data requirements

prodot is an experienced implementation partner for Microsoft Fabric and supports companies from the initial architecture design through the migration of existing data pipelines to productive operation.

Data governance: managing data reliably and securely

Data & analytics can only develop its full potential if the foundation is right: reliable, consistent and securely managed data. Data governance refers to the framework of processes, guidelines and responsibilities that ensures that data in the company is handled correctly, completely and in accordance with the rules.

Specifically, data governance covers several areas. Data owners are defined who are responsible for the quality of certain data areas. Standardized definitions for key figures and data terms ensure that everyone in the company is talking about the same things. Data quality processes with automatic validation and cleansing ensure that analyses are based on a reliable foundation. Data access regulations ensure that sensitive data can only be viewed by authorized persons. And compliance with legal requirements such as the GDPR is ensured by clear rules for the processing of personal data.

Without data governance, problems will arise sooner or later: Reports provide contradictory figures, data silos emerge anew and trust in analyses dwindles. prodot supports companies in setting up pragmatic data governance structures that fit the size and complexity of the organization.