Smart Data: How companies can make better decisions

Florian Zyprian

Rapidly increasing data volumes are a challenge of the digital age for companies: How can you efficiently identify and evaluate relevant information? In this context, smart data - also known as intelligent data - plays an important role. Your company can use it to evaluate data automatically so that you have all valuable information available at a glance - and thus make the right decisions.

We explain how smart data works, what the difference is to big data, and what areas of application there are for smart data. And: We show you how your company can benefit from sophisticated data management.

12 percent revenue loss due to bad data: Importance of careful data curation

On average, bad data costs companies 12 percent of their revenue. And, bad data impacts the bottom line for 88 percent of all (U.S.) companies. That's what a study of the research department of Experian Data Quality discovered. In practice, this means that companies can only make sound decisions with carefully curated data.

Smart Data Definition

smart data

Smart Data refers to data that is intelligently collected, analyzed and used to generate valuable insights and recommendations for action.

In the dance of data's flow,
Big Data has much to show,
AI sifts and sorts to know,
Smart Data's the star of the show.

Unknown artist

By using modern technologies such as artificial intelligence and machine learning, companies can effectively use this data to make informed decisions, increase their efficiency and tap into new business potential. This means that unlike raw data, often used synonymously with Big Data or data from the Data Lake, smart data has already been manually or automatically consolidated by modern technology, checked for quality or even made particularly easy to access through meaningful analyses.

How does Smart Data work?

The process of mining smart data refers to the process of transforming large amounts of data into useful, actionable information through a series of methods including data collection, data cleansing, data preparation, and data analysis. Each step uses a combination of techniques and tools, including but not limited to algorithms, to maximize the value of the data. The result is timely and accurate insights that help a wide range of industries optimize processes and decisions.

Smart Data vs. Big Data

Big Data and Smart Data are often used interchangeably. However, there are significant differences between the two concepts. Big Data describes huge amounts of data that companies collect on a daily basis - both structured as well as unstructured. Big Data therefore describes data that has not yet been consolidated, analyzed and evaluated.

From Big Data to Smart Data, it's a comprehensive process: Smart Data is Big Data that is related to the business goals of

  • semantically placed in a relevant context,
  • evaluated and structured according to quality and benefit and
  • has been tested for safety.

In other words, Intelligent Data is useful, high-quality and secured data.

A key difference between Smart Data and Big Data is the way data is presented. Big Data is often referred to as a "data lake". This is because it is difficult for companies to find valuable, detailed information in a flood of data. Intelligent Data, on the other hand, presents information in a clear and easy-to-understand form. This makes business processes transparent. Smart Data Integration therefore offers data-driven companies in particular the opportunity to uncover unused potential.

Smart Data vs. Advanced Analytics

Just like Big Data, Advanced Analytics is often mistakenly used synonymously with Smart Data. Smart data aims to efficiently gain actionable insights. Data processing techniques such as data mining, data cleansing and data visualization are used for this purpose. They extract the most important information from large data sets.

Advanced Analytics, on the other hand, focuses on the use of complex statistical models and algorithms to analyze data and gain insights.

Techniques such as predictive modeling and natural language processing are used. The goal of advanced analytics is to identify patterns and relationships in the data that companies can use to predict future events or optimize business processes. Thus, just like Intelligent Data, Advanced Analytics is a way of processing data.

Benefits of Smart Data

Smart Data Access offers numerous benefits to data-driven businesses, enabling them to operate more efficiently and thus generate more revenue:

Improved decision making

With Smart Data Services, companies can access relevant detailed information in real time, enabling them to make informed decisions. They can thus gain competitive advantages.

Process automation

Smart Data Systems enable companies to automate business processes - and thus make them more efficient. The technology primarily automates manual processes carried out by employees. The result: faster availability of error-free data.

Improved data quality

The intelligent evaluation and structuring of data produces a high Smart Data Quality that is (virtually) error-free. All data is always correct, complete and up-to-date.

Better data analysis

Smart Data analytics offer data-driven organizations the ability to scrutinize their vast amounts of data more accurately and better aligned with their business goals.

Easier access to data

With Intelligent Data Integration, companies can access relevant data without having to manually collect, structure, contextualize and analyze it. This makes accessing relevant data easy. This saves time and resources.

Transforming Data into Smart Data in 6 Steps

Transforming data into smart data is a six-step process that requires careful planning and execution. Each of these steps helps transform raw data into actionable, meaningful, and detailed information:

  1. Data acquisition

    This can be done through various means, including sensors, online interactions, transaction records, and more. The data collected can be structured (as in databases) or unstructured (such as text, images, or video). For more information, see our post Data Capture.

  2. Data consolidation

    Individual data sources are connected with each other and the data is thus condensed. This creates a uniform picture of the data. This step is not always necessary in some cases - depending on the type and volume of data.

  3. Data quality analysis

    Meaningful analyses of data quality ensure that all data are correct and complete - and thus meet a high standard. This phase aims to identify and fix errors, inconsistencies and repetitions in the data. Data cleansing tools can include automated scripts, specialized software or manual review. This is the only way companies will have reliable and meaningful data later on.

  4. Data aggregation

    Data aggregation brings the data into a uniform structure. This involves arranging the data in such a way that it can be easily analyzed. Data aggregation sets the course for efficient processing of large amounts of data.

  5. Data evaluation

    Data analysis analyzes the data to identify patterns. This is where algorithms often come into play to identify patterns, relationships and trends in the data. Methods can range from simple statistical analysis to complex machine learning algorithms. It enables insights to be gained from the data from which companies can derive recommendations for action.

  6. Data delivery

    Data provisioning, as the name implies, provides data in a format that organizations can easily use and leverage for their business goals, such as interfacing a Data Factory.

smart data applications

Smart Data applications

Smart Data is being used in various applications to help companies make better decisions:

EAI - Enterprise Application Integration

Intelligent Data is used in data-driven organizations to create valuable insights. These insights can then be used to inform different applications and systems to optimize data sharing and automate processes.

AI - The use of artificial intelligence or a rule engine

Smart data, resulting from big data analysis, plays a critical role in AI or rule-based decision systems. It not only enables automatic pattern identification and prediction, but also provides valuable insights that are used to train AI or specify rule engines. Using intelligent data for processes helps companies optimize their business processes and improve their products and services.


Smart data is used in automation for accelerated and error-minimized process handling. To this end, the technology provides meaningful, contextual and directly applicable information. This enables automated systems to make better decisions in less time. In particular, the pre-processing and validation of data ensures that systems can identify and eliminate sources of error at an early stage. The result: higher process efficiency and quality.

smart data use cases

Smart Data in Practice

In practice, smart data services are primarily used in conjunction with AI to make large amounts of digital data accessible. However, intelligent data is also used in combination with physical sensors, for example in the energy sector ("smart energy"). For example, machines have sensors that collect data and are analyzed via AI. In this way, companies can monitor machines and plants, optimize their maintenance and minimize downtime. Another smart data example is the use of curated data in logistics to make supply chains more efficient.

Smart data in different industries

The following examples show how smart data can be used in different industries:

Public health

Predictive analytics for patient health: Using smart data, healthcare providers can predict health risks more effectively and accurately. This makes early intervention and preventive care possible. By analyzing a patient's past health data, lifestyle factors, genetic data, etc., smart data can help identify patterns or risk factors that may indicate a future health problem. This is more efficient than Big Data because it filters out irrelevant information and focuses on personalized and highly relevant data.

Financial sector

Fraud detection and risk management: In finance, smart data enables more efficient real-time fraud detection by examining transaction patterns. By examining specific types of transactions, locations, times, etc., financial institutions can more effectively detect anomalies and stop fraudulent activity. Big Data can contain all transactions, but Smart Data can highlight those that are potentially suspicious.

Insurance industry

Pricing and risk assessment: In the insurance industry, smart data can lead to more accurate pricing and risk assessment. For example, using telematics data from cars, insurance companies can more accurately assess a driver's behavior and risk than using traditional factors alone. This smart data can provide a more nuanced view of a driver's risk than simply looking at Big Data such as age or accident frequency.

Public Sector

Intelligent urban planning and management: In the public sector smart data can help plan and manage smart cities. Using data from traffic patterns, utility usage, environmental sensors, etc., local governments can manage resources more efficiently and plan for growth. Compared to Big Data, smart data provides more accurate insight into urban dynamics in real time, enabling better decision making and delivery of public services.

Smart Data Providers and Smart Data Software

In the field of smart data, there are some lesser-known but specialized companies that impress with their niche expertise:


Tamr offers a patented software platform that automates the organization and synthesis of enterprise data. This platform uses machine learning algorithms to help companies transform their Big Data into intelligent data for better decision making.

Webtunix AI

Webtunix AI provides various data services such as Big Data analytics, data science, machine learning and artificial intelligence. The company helps organizations interpret their unstructured data and transform it into useful, informed data.


Siren's analytics platform integrates various data tools into one solution, including search, business intelligence, Big Data, link analysis and knowledge representation. This platform is designed for investigative intelligence and large-scale networked data situations.


Maana is a knowledge platform that leverages AI and semantic search to facilitate knowledge discovery, increase profitability, and transform industrial data into insights. Its patented knowledge graphs accelerate the extraction of valuable information from data silos and promote informed decision making.


Konfuzio is a software developed by the Helm & Nagel GmbH that uses artificial intelligence to identify, extract and understand text from a wide range of documents. It converts them into intelligent data, making it an adaptable and multifunctional tool for smart data. It is used for a variety of tasks. These range from automating manual data entry to improving document workflows. The software can handle both complex and unstructured documents.


What is Smart Data?

Smart Data - also called Intelligent Data - collects large amounts of data from various sources and organizes and analyzes it to gain valuable insights. Unlike Big Data, Intelligent Data is a specific selection of data that is relevant for a particular purpose.

How does Smart Data work?

Smart data is generated through the use of algorithms and analytical technologies. Smart data prepares relevant data in such a way that companies can quickly and easily gain meaningful insights and thus make profound business decisions.

What is the difference between Smart Data and Big Data?

While Big Data describes enormous amounts of data collected from various sources, Smart Data describes the specific selection of data that is relevant for a particular purpose. Smart Data is therefore a subset of Big Data that has been selected for a specific analysis.

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