What Is Data Acquisition: Key Components & Role in Machine Learning

In 2025, data acquisition continues to drive change. Data acquisition experts influence domains such as finance, healthcare, and technology. They collect, process, and transform raw data into actionable intelligence. The global demand for data acquisition systems is projected to reach $2.25 billion this year.

With broader implementation of data-driven decision-making, its role continues to grow. Collecting data goes beyond simple information gathering. It involves using methods like real-time monitoring, IoT sensors, and API integrations to collect accurate and useful data. This allows companies to improve performance and remain competitive. According to a recent survey, companies using real-time data have improved decision-making by 22%.

Are you curious about what is data acquisition in ML. In this blog, the fundamentals of data acquisition are explained to help you navigate the ever-changing world of data acquisition.

1. What is a Data Acquisition System? Core Components 

A Data Acquisition System (DAS), also referred to as a DAQ system, is a combination of hardware and software components used to measure and collect data from physical or electrical sources. It captures analog signals, such as temperature, pressure, or voltage, through sensors and transducers. These signals are then converted into digital data using analog-to-digital converters (ADCs), allowing the information to be processed, analyzed, and stored by a computer.

Data acquisition systems help ensure more reliable decisions in machine learning algorithms. Let’s explore how they support data quality management in machine learning.

Definition in Modern Machine Learning Context 

In modern machine learning (ML), a Data Acquisition System (DAS) refers to the automated tools and processes used to collect, preprocess, and store raw data required for training ML models. This system ensures efficient collection from diverse sources (e.g., databases, IoT devices, or APIs), transforms raw inputs into structured formats suitable for analysis, and loads them into storage systems like databases or cloud platforms.

Answering “what is data acquisition” involves several steps:

1. Finding Relevant Data

The initial step is finding and retrieving the required business data. Data is sourced from diverse channels such as sensors, APIs, databases, web scraping, IoT devices, or user interactions. This step ensures continuous and scalable acquisition of large datasets.

1. Data Formatting/Preprocessing

Once the data is gathered, it must be translated into a format suitable for analysis. Raw data often contains noise, inconsistencies, or missing values. Preprocessing involves cleaning, normalizing, and transforming the data into a usable format. Techniques include handling missing data, feature extraction, scaling numerical values, and encoding categorical data.

1. Storage

After formatting, the data is loaded into target systems for processing. This may include uploading it to a cloud analytics platform or importing it into a database for further analysis. One of the best ways to learn SQL is by working through a hands-on Database Management System tutorial with real-world examples.

This process is critical. Even the most advanced machine learning models will perform poorly if trained on low quality data. The saying "garbage in, garbage out" applies here, if the input data is biased or flawed, the model’s outputs will be too.

Also, deep learning models require large datasets because they generate features from raw input. Without enough quality data, their outputs may be inconsistent or unreliable.

Key Components in 2025 Systems 

Data acquisition systems today are built on a few principal building blocks that work together to provide efficient storage, processing, and data collection. All the data acquisition system components help to make the data usable, analyzable, and accurate.

1. Sensors

The starting point of any DAQ system is sensors, which measure physical conditions such as pressure, temperature, movement, or light. These devices convert external signals into digital information for further processing. For instance, temperature sensors installed in smart buildings monitor indoor climate conditions and automatically control heating or cooling systems to optimize energy use.

Similarly, motion sensors in security systems monitor activity and provide data in real time, showing their value in scenarios requiring immediate response.

1. Edge Devices

Edge devices process data at the source, where it is generated, instead of sending it to a central server. This reduces latency and enables faster decision-making. Imagine an autonomous vehicle using edge devices to process sensor data in real time, identifying obstacles or road conditions to make split-second navigation decisions.

In manufacturing, edge devices monitor machinery performance in real time, supporting preventive maintenance before breakdowns occur. By placing computation near the point of data creation, edge devices offer speed and responsiveness where it's needed most.

1. APIs (Application Programming Interfaces)

APIs serve as interfaces between different software systems, allowing them to communicate and exchange information seamlessly. In DAQ systems, APIs connect various data sources such as IoT sensors, databases, and cloud services.

For example, APIs can fetch real-time weather data from external platforms and combine it with internal logistics information to optimize delivery routes. By creating an integrated ecosystem, APIs help organizations use diverse data sources more effectively for decision-making and analysis.

1. Data Lakes

Data lakes are centralized repositories that store large volumes of both structured and unstructured data in raw format until it is needed. Unlike traditional databases, which require fixed schemas, data lakes can hold a wide variety of data types. This may include customer reviews, transaction records, social media trends, or product sales figures. To efficiently organize and retrieve this diverse information, it's essential to implement proper data modeling for data lakes.

Their flexibility allows for long-term and detailed analysis that supports business innovation and growth. For example, e-commerce companies use data lakes to track customer behavior over time and personalize marketing efforts.

1. Quality Validation Tools

Quality validation tools check that collected data is accurate, consistent, and reliable before it is used for analysis or machine learning. These tools find issues like missing values, outliers, or duplicate entries that could distort results.

In medical applications where patient data comes from different sources, validation tools help ensure that diagnoses and treatment decisions are based on accurate data. By maintaining high data quality, these tools help avoid costly mistakes and improve decision-making.

1. Signal Conditioning

Signal conditioning cleans raw sensor outputs for precise measurement by filtering out noise, amplifying the signals, or changing voltage levels. It is a process that must be done before converting analog signals into digital form. This ensures that the data entering the system is clean and precise. It prepares the signals for accurate analysis and enhances signal quality prior to the main system.

1. Data Logger

A data logger logs and holds processed sensor information over time. It may be a self-contained device or a part of a system. Data loggers find application in many areas such as environmental monitoring or monitoring of industrial equipment to hold history for future reference and analysis. They assist in viewing changes and trends over time.

1. Analog-to-Digital Converter (ADC)

ADCs translate raw continuous analog sensor data into discrete digital numbers readable and usable to computers. It is a central module of DAQ systems that makes it possible for real-world environments such as sound, temperature, or motion, which naturally exist in analog, to be digitally represented. ADCs also serve as a translator bridge between physical signals and digital realms.

1. Interface

The interface is the interlink point between the DAQ system and a controller or computer. It sends the data, communicates with the device, and even supplies power sometimes. A good interface is necessary to allow smooth integration and real-time monitoring, typically through USB, Ethernet, or serial ports. It provides smooth interconnection between the system components.

1. Power Supply

The power source delivers stable electricity to processors, sensors, and other DAQ devices. Whatever grid or battery powered, stable power is a must so the system can be run securely without disruptions in data gathering and processing. Power stability maintains the proper flow of the entire system.

1. Control Unit

The control unit is responsible for managing the functioning of the whole DAQ system. It regulates timing, synchronizing, and triggering processes. As the brain of the system, it makes sure that all components are operating in harmony, reacting to signals accordingly. Also, it coordinates actions according to predetermined guidelines.

1. Software

DAQ software can be programmed, operated, and graphed with cumulative data. It can be bundled with data analysis, dashboard setup, and diagnostic capability. Easy-to-use software has an integral role to interpret meaning from real-time or historical data in making a well-informed decision.

1. Communication Protocols

Communication protocols define the transfer of data between the DAQ system and external devices or networks. Some of the typical protocols are Modbus, MQTT, and TCP/IP. These provide secure data exchange between systems and facilitate easy integration of DAQ systems with IoT or cloud-based systems. They define interactions among systems.

1. Storage

Storage mediums for DAQ systems are memory cards, hard drives, or cloud storage. Both short-term and long-term data are stored within them and analyzed later. The appropriate storage medium depends on the requirements of an application such as capacity, rate of access, and data safety. Proper storage preserves data safely and with ease to be accessed.

1. User Interface

A good user interface facilitates easy interaction between operators and the DAQ system. It allows for system configuration, visualization of data, and performance monitoring using dashboards, buttons, or command lines. An unambiguous interface enhances usability, shortens training time, and provides a better user experience. It facilitates easy control of the system.

1. Calibration and Calibration Standards

Calibration makes sensors and instruments precise by measuring them against known reference standards. Timely calibration minimizes measurement error and data integrity. In regulated environments, following calibration schedules is critical to ensure industry and compliance requirements. It makes measurements precise and in limits.

1. Real-time Clock (RTC)

An RTC gives exact timing to all measurement points, and it ensures synchronized data collection on devices. This becomes particularly significant in high-precision applications, including seismic surveillance or monetary trading frameworks. Real-time timing allows correlating events on data and facilitates analysis of fine granularity. It supplies accurate time information to every measurement.

1. Triggering Mechanism

Trigger mechanisms initiate data gathering on the basis of events or thresholds, i.e., sharp temperature increase or mechanical oscillation. It provides efficient data acquisition by targeting crucial periods, reducing storage, and making the system more responsive to changes in the real world. It provides recording of data only when needed.

1. Data Compression

Compressed data preserves information that was gathered without sacrificing necessary information. It is useful with respect to remote systems or those with bandwidth or storage limitations. Effective compression facilitates speedy data transfer and simple long-term storage without reducing data quality.

Role of Edge Computing & IoT 

Edge computing in data acquisition is a decentralized computing system that processes data at the location where it is generated instead of relying on central cloud servers. This setup improves bandwidth efficiency, enables faster real-time decision-making, and significantly reduces latency. It is a strong solution in today’s data-heavy environment. By bringing processing and storage closer to the source, response time is reduced and operational efficiency improves.

The combination of edge computing and the Internet of Things (IoT) has transformed the way data acquisition systems (DAS) work. Through this integration, real-time processing at the source of data is possible, which is especially helpful in sectors where timing can lead to major resource and cost savings. Some of the key sectors are listed below:

1. In Healthcare

In the medical field, IoT devices such as wearable health monitors track vital signs like heart rate and oxygen levels in real time. These readings are collected while the patient is active, and edge computing processes the data instantly.

For example, if a patient’s heart rate suddenly increases, the system can alert healthcare staff immediately. This supports patient safety and improves care by allowing quicker response to health changes.

1. In Agriculture

Agriculture has also seen major gains from this integration. IoT sensors placed in fields track soil moisture and weather conditions in real time. Edge devices process the data locally and suggest the best irrigation schedules based on current environmental conditions.

This helps avoid water waste and boosts crop yield. Real-time feedback enables more sustainable farming and better decision-making for productivity.

1. In Manufacturing

Smart factories use edge computing in data acquisition to monitor equipment performance in real time. On-site analytics allow companies to apply predictive maintenance, reducing equipment downtime and extending machine life.

For instance, if sensors detect wear on a part, the edge system can trigger a maintenance alert before failure occurs. This lowers unexpected repair costs and improves overall efficiency.

1. In Warehousing and Transportation

Warehousing and transportation rely on speed, accuracy, and coordination. Edge computing and IoT together support real-time processing that helps improve storage use, fleet tracking, and system response.

1. Real-time Fleet Management

Edge computing enables real-time fleet tracking. It allows vehicle location monitoring, route adjustments, and fuel usage tracking. For example, trailer sensors send updates to edge systems, which process the data immediately.

This helps logistics teams reroute vehicles based on traffic or delays, improving delivery time and cutting operating costs.

1. Inventory Optimization

In warehouses, IoT sensors track how products are stored and moved. Edge computing processes this data on site to improve space use and reduce storage expenses.

For instance, when a sensor detects low stock of an item, it can trigger an automatic reorder without delay.

1. Predictive Maintenance

Edge computing is used to predict failures in transport vehicles and warehouse equipment. Real-time sensor data is analyzed locally to identify issues early.

If a delivery truck’s engine overheats, edge devices can alert the maintenance team before major damage occurs. This prevents delays and extends asset lifespan.

1. Enhanced Security

Edge systems also strengthen security in logistics. Decentralized processing of video feeds and sensors allows for faster response. If an IoT camera detects an intruder, the edge system can alert security staff right away. Quick action helps protect goods and maintain safe operations.

Also Read: The Future of IoT: 15 Applications, Challenges, and Best Practices for 2025

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2. Why Data Acquisition Matters for ML Projects 

Data acquisition forms the base of machine learning (ML) projects. It provides the raw material that algorithms need to learn and improve. Collecting data supports the delivery of meaningful results.

Without quality data, even the most advanced ML training datasets and models will not perform well. Whether you're building a fraud detection system or implementing large-scale enterprise AI, data acquisition plays a central role. It ensures that the right information is available when needed, driving both innovation and decision-making.

This section explains the role of data gathering in ML projects, how it affects model training quality, supports real-time decision-making, and enables scalability for AI systems.

Fueling Accurate Model Training 

The saying "garbage in, garbage out" means that poor input data leads to poor model performance. Input quality directly affects the results produced by machine learning models. Below are the main points that highlight why collecting clean, diverse data is necessary. Such data should reflect real-world conditions accurately.

1. Clean Data

The data used must be free from bias, consistent, and free of errors to train a reliable model. If a model is trained on outdated or incomplete data, it may learn misleading patterns and produce inaccurate predictions.

Data gathering should include data cleaning techniques such as removing duplicates, correcting errors, and formatting data properly. Paying attention to this builds a strong foundation for model performance.

1. Varied Data Sets

A good dataset includes many types of examples and edge cases. This variety allows the model to generalize better when handling new data.

For example, a facial recognition system trained on a dataset that includes different ethnicities, age groups, and lighting conditions will perform better across real-world scenarios.

Having variety improves model stability and helps it handle data diversity in production environments. The Executive Diploma in Machine Learning and AI with IIIT-B course offers a structured path for those aiming to shift into high-impact AI roles.

1. Volume of Data

Machine learning models need enough data to identify patterns, especially for complex problems. Effective data acquisition processes help gather enough volume to build reliable models.

Deep learning, in particular, benefits from large datasets. The more examples a model sees, the more accurate and robust it becomes.

Organizations should aim to collect enough relevant data to support their ML projects and improve prediction quality.

1. Timeliness of Data

Having timely data is necessary for keeping models up to date and working well. Models that predict stock prices or monitor supply chain performance, for example, depend on the most recent data to function correctly.

If data is outdated, the model may miss key changes or make incorrect decisions, which could lead to missed opportunities. Organizations need efficient data pipelines to ensure their models work with the most current information.

1. Data Annotation

Supervised machine learning models require labeled data to learn correctly. This step involves assigning meaningful tags to data so the model understands what it’s analyzing.

High-quality annotations are critical. If labels are incorrect or inconsistent, the model may not learn properly and could make wrong predictions in real-world situations. Careful attention to labeling increases model accuracy and reliability.

Enabling Real-Time Decision-Making 

In many fields, especially finance and healthcare, real-time decision-making is a constant need. These sectors rely on live data streams to function effectively. Data acquisition enables this by collecting and processing information instantly, helping organizations respond to changing conditions and emerging risks.

Below are key areas where real-time decision-making has a strong impact, all supported by the data acquisition process.

1. Fraud Detection

In banking, real-time transaction data is reviewed by machine learning algorithms to detect potentially fraudulent activity. By collecting and analyzing data instantly, systems can flag suspicious behavior as it happens. Fraud detection in machine learning helps catch fraud faster and more accurately.

For example, if a customer’s account is accessed from an unfamiliar location and a large withdrawal is made, the system can trigger an alert for fraud analysts to investigate. This protects both the institution and its customers from financial loss.

1. Predictive Maintenance

In manufacturing, IoT sensors track equipment conditions in real time. Data acquisition systems collect this information and feed it into predictive maintenance models that detect early signs of failure.

If a sensor picks up abnormal vibrations or overheating, maintenance teams can be notified immediately. This quick action helps avoid breakdowns and keeps operations running without delays or added costs.

1. Monitoring of health

Patients' vital signs such as oxygen levels or blood pressure can be tracked using wearable devices. These devices stream data in real time to healthcare professionals, allowing them to react quickly when changes occur.

For instance, if a patient’s oxygen level drops suddenly, the wearable sensor can send an alert. Medical staff can then take action right away, improving safety and care quality.

1. Customer Experience Optimization

Retailers use real-time data to better understand and respond to customer behavior. By tracking interactions and preferences live, companies can adjust marketing and sales strategies instantly.

For example, if a customer browses a specific product category repeatedly, the system can suggest related items or offer a discount on that product during their next visit. This personal touch increases engagement and loyalty.

1. Supply Chain Management

Real-time inventory and shipping updates allow businesses to respond faster to supply chain disruptions or sudden demand spikes. If an IoT sensor detects that a fast-selling product is running low in stock, it can automatically trigger a reorder. This prevents stockouts and helps businesses meet customer demand without losing sales.

Scalability for Enterprise AI Solutions 

Scalability means the ability of a system to grow and manage increased demand smoothly. For enterprise AI solutions, it involves handling large volumes of data from different sources without losing speed or accuracy.

To support growth, organizations need data collection systems that evolve along with their data needs. This includes adopting new data sources, managing higher volumes during busy periods, and adjusting to shifting business goals.

What is Data Acquisition? It refers to several components that support smooth data collection and processing. When these components are in place, businesses are better positioned to use machine learning technologies and drive innovation. The following elements highlight how data acquisition supports scalability in enterprise AI systems.

1. Cloud-Native Systems

Cloud-native tools such as AWS Kinesis and Azure Stream Analytics enable real-time data ingestion and processing. They allow companies to handle large-scale data streams from sources like IoT devices, social media platforms, and transaction logs. These systems continue to function well even under heavy loads.

1. Flexibility and Adaptability

Cloud-based data acquisition systems allow businesses to scale without needing major infrastructure investments. As needs evolve, companies can update their data acquisition methods to reflect new goals or sources, making the setup more adaptable and easier to maintain.

1. Integration with Machine Learning Platforms

Cloud-native systems often work directly with machine learning platforms. For instance, AWS Kinesis integrates with AWS SageMaker, enabling the development of models based on real-time data. This seamless integration supports faster experimentation and deployment of AI applications.

1. Cost-Effectiveness

Cloud-native infrastructure reduces overall costs by eliminating the need for physical hardware. Companies only pay for the resources they use. The infrastructure also adjusts automatically to meet workload changes, which makes it suitable for scaling operations on demand.

1. Greater Interdepartmental Coordination: 

Cloud technology improves collaboration across departments. When data is collected and stored in a central location, teams such as sales, operations, and marketing can all access the same datasets. This shared access improves coordination and supports more aligned decision-making.

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3. Challenges in 2025 Data Acquisition Systems 

Knowing what is data acquisition helps us better understand the challenges that modern systems face in 2025. These include stricter regulations, overwhelming volumes of IoT and 5G-generated data, and growing demands for accurate data labeling and annotation. These challenges must be managed to maintain smooth data collection, processing, and analysis for effective use of AI and ML.

Data Privacy & Regulatory Compliance 

Meeting regulatory standards has become a major concern due to rising awareness of data protection. Laws like the GDPR, CCPA, and new AI-specific regulations have introduced strict rules on how data is collected, processed, and stored. Companies must follow these rules while still maintaining efficient operations. 

Below are key areas of concern:

1. GDPR Principles

Under the General Data Protection Regulation (GDPR), companies must limit personal data collection to what is strictly necessary for specific purposes. This approach, known as data minimization, reduces privacy risks.

For example, AI systems used in healthcare must anonymize patient data to remain compliant while still offering reliable predictions.

GDPR also requires companies to explain clearly how data is used and to apply strong protections to prevent unauthorized access.

1. CCPA Obligations

The California Consumer Privacy Act (CCPA) gives individuals the right to know what data is collected, request deletion, and opt out of data sales. Businesses must use transparent practices to meet these requirements.

This includes clear privacy notices, easy-to-use opt-out mechanisms, and prompt handling of consumer requests. Ignoring these can lead to penalties and harm to a company’s reputation.

1. AI-Specific Law

Governments are introducing laws specific to AI that demand transparency, fairness, and accountability. For instance, GDPR requires automated decision systems to be interpretable.

This presents a challenge for models that function as “black boxes.” In response, companies are investing in explainable AI (XAI) techniques to improve model clarity without reducing performance.

1. Cross-Border Data Transfers

AI systems often operate across multiple countries, making data transfers between borders a regular task. This raises compliance challenges due to differences in regional data protection rules.

To manage this, companies use data localization, encryption, and legal tools such as Standard Contractual Clauses (SCCs) to meet requirements and guard against unauthorized access.

To meet these challenges, companies should adopt privacy-by-design practices, perform regular audits, and use systems that manage user consent actively. These actions help ensure compliance and build trust with users.

Handling High-Velocity Data Streams 

As IoT devices and 5G networks expand, high-speed data streams are growing rapidly. These streams come from smart devices, industrial equipment, sensors, and wearables, producing huge amounts of real-time data. While this offers the chance for real-time insights, organizations face several challenges when trying to store, manage, and process this data effectively. Some of the main issues include:

1. Data Ingestion Bottlenecks

Traditional batch processing systems struggle with continuous, high-volume data. Real-time ingestion tools like Apache Kafka allow organizations to process millions of events per second. For those looking to get started or deepen their understanding, Apache Kafka tutorials offer step-by-step guidance on setting up and optimizing Kafka workflows.

Industries such as finance, healthcare, and e-commerce rely on Kafka’s distributed architecture for low-latency processing, where real-time decision-making is often necessary.

1. Scalable Storage Solutions

Traditional batch processing systems struggle with continuous, high-volume data. Real-time ingestion tools like Apache Kafka allow organizations to process millions of events per second.

Industries such as finance, healthcare, and e-commerce rely on Kafka’s distributed architecture for low-latency processing, where real-time decision-making is often necessary.

1. Low-Latency Processing

Certain AI use cases such as autonomous car and predictive maintenance require data to be processed virtually in real-time. Delayed processing results in failure up to disaster, for instance, accidents by autonomous vehicles or missing defects while manufacturing. 

Stream processing engines like Apache Flink and Apache Storm support increase computations at scale in real-time, supporting timely decision-making.

1. Data Quality Problems

IoT data may be inaccurate, incomplete, or noisy due to device failure or communication problems. Inaccurate data adversely affects the precision of machine learning models and analytics. To counteract this, companies must have data cleansing streams that eliminate errors from data, standardize datasets, and include anomaly detection methodologies to ensure data integrity.

To eliminate such limitations, businesses will have to adopt future-proof engineering techniques, which include real-time ingestion processes, scalable storage, and reduced-latency computation capacities. An Executive Post Graduate Certificate Programme in Data Science & AI can help to deal such limitations efficiently. With all these measures in place, businesses are able to handle growing volumes of high-speed data.

Labeling & Annotation Complexities 

Labeling and annotation are crucial when training supervised machine learning models. The two activities are considerably resource- and capital-intensive, however. The requirement for labeled data becomes even more significant with the increased use of unstructured forms like images, video, and text data in AI technologies. Companies must make pioneering efforts to speed up annotation processes efficiently.

1. Manual Labeling Expense

Traditional manual labeling methods take an extremely long time and require large numbers of human annotators. Labeling medical images for the diagnosis of diseases or labeling traffic signals for autonomous vehicles usually requires millions of manual annotations. It is susceptible to human error and inconsistency, which can negatively impact the performance of AI models.

1. AI-Fueled Annotation Tools

AI-fueled tools like Scale AI and Label Studio are transforming the data labeling tools in the market. AI-fueled annotation tools utilize machine learning models that run repetitive tasks automatically, such as object detection and image segmentation. For instance, an AI-fueled annotation tool can auto-box cars in images of traffic to reduce human effort and increase efficiency.

1. Pre-Labeling Approaches

Pre-labeling begins with early annotation using AI algorithms that can later be post-edited by human evaluators. Pre-labeling using the half-and-half approach creates a blend of human and automation to sacrifice speed for accuracy or vice versa.

With machine-generated labels supported by human vetting, organizations can significantly accelerate the process of annotation without compromising high-quality labeled data.

1. Management of Unstructured Data

Annotation of challenging data types like videos, handwritten notes, and medical images is especially difficult. Annotation of videos is frame-by-frame, which is labor-intensive. Keyframe selection and automated transcription are modern techniques that accelerate the process.

In healthcare, AI-powered annotation technologies assist radiologists in detecting potential abnormalities in medical imaging scans to facilitate quicker and more accurate diagnoses.

1. Synthetic Data Generation

In an attempt to keep human marking to a bare minimum, synthetic data generation is now quickly becoming a highly demanded solution. Synthetic data generation refers to the creation of simulated datasets that represent realistic situations for AI models to learn from diverse data without being overburdened with extensive human markings.

Synthetic data is being used more and more in autonomous vehicle operations, robotics, and security applications to make AI model operations more efficient.

With the addition of automation platforms, AI-driven data aggregation, AI-driven annotation tools, and synthetic data generation processes, organizations can automate labeling. These platforms improve efficiency, reduce expenditure, and deliver high-quality training data for AI models.

Also Read: 16+ Best Data Annotation Tools for 2025: Features, Benefits, and More

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4. Building a Data Acquisition Pipeline in 2025 

A solid grasp of what is data acquisition is the first step toward building a robust pipeline. As companies become increasingly dependent on data for decision-making and innovation, there is a need to develop a strong pipeline for data sourcing. An efficiently running pipeline enables easy data collection, processing, and storage. Let’s explore a step-by-step solution to build a data acquisition pipeline.

Step 1: Source Identification & Integration 

The initial process of creating a data pipeline is finding and integrating data collection sources. It is a vital process, as it determines the quality and diversity of the data that is received. The following are some of the common sources for sensor data collection:

1. APIs

Application Programming Interfaces (APIs) enable companies to retrieve data in a structured manner from websites like social media, banking, and customer relationship management (CRM) platforms. APIs provide real-time data, i.e., companies can have updated data for decision-making.

For example, an online shopping company can use APIs to monitor customer behavior across different channels. Documented APIs ensure smooth integration, but companies have to handle rate limits and authentication needs efficiently.

1. Web Scraping

Unstructured data scraping from the web by automated scripts is termed web scraping. Web scraping can be used for publicly available data harvesting, like product prices, customer reviews, and market trends. Web scraping could be legally prohibited via website terms of use.

Firms must follow ethical scraping behaviors and robots.txt rules to ensure compliance. Scraped data tends to require heavy cleaning due to structural variations across sites.

1. IoT Sensors

IoT produces vast volumes of real-time data from IoT-enabled devices. Medical implants, industrial machinery, and home sensors take constant temperature, pressure, and motion measurements. It is worthwhile to use such data in applications like predictive maintenance, where early signs of anomalies help avoid equipment failure.

As IoT data is high-velocity in nature, organizations must adopt real-time processing patterns or risk introducing bottlenecks.

1. Databases

Relational databases such as MySQL and PostgreSQL hold business information in a structured format in the form of customer orders and stock reports. They are rich in query and integrity features. Organizations utilize NoSQL databases such as MongoDB or Cassandra if they wish to hold unstructured or semi-structured data.

With both forms of databases, either individually or in hybrid form in a combined state, there is greater flexibility while maintaining various collections of data. Following a step-by-step PostgreSQL tutorial is one of the best ways to understand advanced query optimization techniques.

1. File Uploads

Manual data uploads can be utilized by organizations for third-party data or legacy data. These files, typically CSV or Excel, can contain useful data to be analyzed. Manual uploads are vulnerable to stale data or glitches, though.
Organizations must avoid such issues as much as possible by using validation checks and automating ingestion wherever possible.

By consolidating these diverse sources, organizations can compile an enormous amount of useful information. This forms a strong base for decision-making.

Step 2: Preprocessing & Noise Reduction 

While gathering data, preprocessing pipelines’ data and noise removal are necessary. It sanitizes the data, makes it consistent, and prepares it for analysis. The following methods help in data preprocessing and noise reduction:

1. Outlier Detection

Outliers are uncommon values that are far from other data. They distort statistical models and create incorrect predictions. Outliers analysis is done using methods such as Z-score analysis or interquartile range (IQR) filtering.

For example, in an online transaction, an extremely anomalous purchase value might indicate fraudulent behavior and needs to be investigated. Companies are required to deal with outliers carefully to find the right balance between data validity and real business insight.

1. Normalization

Various data sources may have different scales and units of measurement and, hence, may not be comparable. Normalization in data mining scales values to a standard range to enhance consistency. Min-max scaling and Z-score normalization are techniques that scale values to the range 0 to 1 and scale data based on mean and standard deviation, respectively.

Normalization plays a major role in machine learning since inconsistent scales can negatively impact model performance.

1. Imputation of Missing Values

The majority of real-world datasets contain missing values and must be handled in a way that preserves accuracy. In the case of numerical data, mean or median imputation might be applied, whereas for complex cases, K-nearest neighbors (KNN) imputation estimates missing values based on similar records.

For example, in the field of medicine, missing blood pressure values can be replaced with patient history. The choice of imputation must be made carefully to avoid introducing bias.

1. Data Transformation

Raw data typically needs to be reshaped into forms suitable for analysis. Categorical variables, such as product type, can be represented with numeric values by employing one-hot encoding. Time-series data can be summarized into meaningful intervals, e.g., hourly or daily means, to facilitate analysis. Good transformations enhance data regularity and tractability.

1. Noise Reduction

Sensor measurements, audio signals, and text data often carry noise that can lead to misleading analysis. Moving average filtering smooths time-series oscillations. Kalman filters are commonly applied in IoT systems to make sensor measurements more reliable.

Stopword removal from text data eliminates words that are not meaningful for analysis and improves natural language processing efficiency. Noise reduction increases overall data reliability.

Investment in preprocessing pipelines’ data and denoising enhances the quality of datasets. Cleaner data produces more reliable results.

Step 3: Architecture Design and Implement Data Ingestion Mechanisms 

Once the data sources are defined and data is preprocessed, the design of a pipeline architecture that can scale and be flexible follows. It has to be able to consume different types of data and handle different sizes and speeds.

The choice of tech stack is the most critical, Apache Kafka, Spark, or batch processing frameworks make the system robust as requirements increase.

According to architecture definition, place controls to ingest data into the system. This is done through APIs, SQL queries, or streaming technologies for ingesting data from external as well as internal data sources.

Batch or real-time ingestion, depending on business requirements, is supportable. Duplicate detection and format validation quality rules should be enforced at this stage.

Step 4: Secure Storage & Accessibility 

The last step involves secure storage along with easy accessibility of the data for analysis. The following are the key points to ensure secure storage and accessibility:

1. Hybrid Storage Solutions

Blending on-premises and cloud storage is versatile. On-premises databases are secure and provide control, while elastic cloud storage solutions like AWS S3 are scalable. The hybrid model provides organizations with flexibility to balance cost, performance, and compliance. If you're dealing with large volumes of data, exploring the best cloud storage services is crucial for optimizing performance and cost.

For instance, on-premises storage of sensitive financial data can be maintained while less sensitive data is stored in the cloud. The selection of the proper blend of storage solutions provides both security and efficiency.

1. Role-Based Access Controls (RBAC)

Companies may need to apply access control efforts to protect sensitive information. Role-Based Access Control (RBAC) implements data authorizations based on roles and responsibilities. An employee from the marketing department can be granted access to view transaction history, while a marketing analyst can view customer demographics. Implementing RBAC reduces security threats and ensures compliance with data privacy regulations.

1. Data Encryption

Data at rest, as well as data in transit, must be secured. Encryption ensures that even if data is intercepted, it cannot be read without decryption keys. Organizations must implement robust encryption techniques like AES-256 for data at rest and TLS for data in transit. Rotation of keys on a regular basis also increases security.

1. Data Recovery & Backup

Backups prevent data loss resulting from system failures or cyberattacks. Automatic backup mechanisms duplicate key datasets at regular intervals, and restoration is straightforward. Companies that perform financial transactions, for instance, need redundant backups to prevent service downtime. Cloud backup facilities offer extra redundancy and disaster recovery features.

1. Easy-to-Use Interfaces

BI dashboards present complexity in an easy-to-use format so non-technical people can access data with ease. Interactive dashboards enable employees to view trends, create reports, and make data-driven decisions without having to run SQL queries.

The use of BI tools such as Tableau or Power BI guarantees that insights are accessible to all team members, allowing them to be more productive overall.

By prioritizing access and storage security, organizations can create a strong data pipeline. It supports ongoing analysis and decision-making in 2025 and beyond.

Step 5: Monitor and Maintain Data Flow 

Once the pipeline for data acquisition is live, ongoing monitoring is needed to ensure data moves consistently from the source to storage at the expected speed. Any disruption in data flow can affect downstream processes such as analytics and reporting.

Monitoring tools play a major role in keeping systems stable. Technologies like Prometheus, Grafana, and native cloud monitoring services track pipeline performance and detect issues in real time. These include data lags, ingestion errors, or failed processes. Teams receive instant alerts when problems occur, allowing for quick responses and minimal downtime.

Log monitoring is another essential aspect of this step. Logs contain detailed records of system events and failures. Tools commonly used for this purpose include:

• ELK Stack (Elasticsearch, Logstash, Kibana)
• Splunk

These platforms help collect, filter, and analyze logs to trace the root causes of crashes or slow performance.

Maintaining the pipeline also involves making regular updates to system configurations. As business needs change, new data sources may be introduced or existing ones may be altered. For example, a company may switch its CRM platform or begin using additional IoT devices. The pipeline must adjust to these changes without reducing efficiency.

Configuration management tools like Ansible and Terraform allow teams to automate updates. This reduces human error and helps maintain consistency across different environments.

Step 6: Optimize for Scalability 

With data volumes continuing to grow in 2025, pipelines must be designed to scale without sacrificing performance. A scalable system supports expansion while maintaining reliability, speed, and accuracy.

The first priority is to build in fault tolerance so the pipeline can function even when certain components fail. This is achieved by integrating systems that can reroute or recover automatically. Technologies that support this include:

• Apache Kafka
• Google Pub/Sub
• AWS Kinesis

These platforms buffer and process large volumes of data, ensuring stable operation even during traffic spikes.

Horizontal scaling is another strategy for growth. Instead of depending on a single high-capacity server, workloads are spread across multiple smaller servers. This prevents bottlenecks and allows each part of the pipeline to scale independently. Services like Amazon EC2 Auto Scaling and Google Kubernetes Engine (GKE) make this process easier to implement.

Scalable pipelines also benefit from incremental data processing. Rather than reprocessing full datasets, the system only works with new or updated records. Change Data Capture (CDC) tools make this possible. For example, a PostgreSQL database using CDC can detect and capture only the rows that changed since the last update, saving processing time and reducing storage demands.

Finally, performance tuning should not be overlooked. This includes using:

• Proper indexing strategies
• Query caching
• Efficient storage formats such as Parquet or Avro

Together, these practices support high-speed processing without overwhelming system resources. Scalability is not just about handling more data. It’s about doing so efficiently, securely, and affordably, keeping the system aligned with business growth and delivering consistent value.

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5. Top Tools for Data Acquisition in 2025 

As data collection becomes more diverse, organizations need to utilize new instruments to improve processes for efficiency and quality. Automated data labeling, IoT-to-cloud pipeline management, and synthetic dataset generation are the most vital instruments in 2025. They maximize efficiency and facilitate conformity to standards.

Automated Data Labeling Platforms 

Machine learning-based labeling platforms are helping companies speed up data preparation. Automatic platforms use machine learning and complex algorithms to label large datasets in real time. They eliminate time and expense related to manual tagging, and they improve consistency and accuracy. The following are some of the popular automated data labeling platforms:

1.SuperAnnotate

It is a rich-featured, lightweight annotation platform. SuperAnnotate supports manual and automated labeling so that teams can efficiently label datasets. SuperAnnotate integrates easily into machine learning pipelines, and data scientists can annotate data effortlessly to feed into AI models.

SuperAnnotate includes collaboration features, where teams can edit, refine, and finish annotations in real time. This reduces errors and provides best-in-class quality in the final dataset.

 2. Amazon SageMaker Ground Truth

Amazon SageMaker Ground Truth uses machine-based labeling and human validation to facilitate automated data annotation. Amazon SageMaker Ground Truth natively supports image, text, and video label workflows that deliver high-quality machine learning model datasets.

It facilitates automated labeling portions to minimize human labeling so that costs can be kept low and the process can be accelerated.

It can further be paired with other AWS services so that organizations can securely process and store labeled data in the cloud. Ground Truth also includes quality control features, including active learning, to improve annotation quality over the long term.

3. Labelbox

Labelbox is an enterprise platform used to automate data labeling processes. It handles various types of data like images, video, text, and sensor data. The platform leverages human intelligence augmented by AI-driven labeling to label large datasets at speed.

Labelbox has built-in analytics features through which companies can track labeling performance, identify bottlenecks, and automate.

Labelbox also has built-in quality control features to verify that labeled data is of the desired quality before being fed into AI models.

4. Clarifai

It is an AI-driven annotation platform used for computer vision. It leverages pre-trained machine learning models to automate data annotation, halving the time used for labeling large datasets.

It also accommodates various forms of data, including images and video, and is therefore ideally suited to be used across a broad spectrum of industries.

Clarifai also features a clean API, which allows it to integrate well with other machine learning and AI systems. Companies can use Clarifai to achieve the highest possible efficiency in data annotation without sacrificing high accuracy.

5. Dataloop

Dataloop provides a single platform for data management and data annotation for ML teams. Companies can manage the entire data lifecycle, from data collection and annotation to validation and deployment, through Dataloop.

Dataloop incorporates AI-driven labeling, keeping human annotation to an absolute minimum while maintaining high speed and precision.

It features real-time collaboration software, where teams are able to annotate large data assignments with optimal efficiency. Organizations can make their labeled datasets AI-ready and accurate with automated quality control.

Also Read: Top Data Analytics Tools Every Data Scientist Should Know About

Edge-to-Cloud Data Aggregators 

Edge-to-cloud data aggregators manage the movement of data from devices to the cloud. These tools enable organizations to gather, process, and analyze real-time data, integrating edge devices with cloud systems seamlessly.

1. Particle.io

Particle provides an end-to-end IoT platform that makes it easy to connect devices, merge data, and integrate with the cloud. It enables organizations to capture real-time information from IoT devices and securely send data to the cloud. The device management capability allows companies to monitor IoT devices remotely and update them when and where required.

Particle's robust APIs make it simple to integrate with existing applications, allowing companies to build scalable IoT solutions without extensive development time.

2.Losant 

Losant offers enterprise-class IoT software focused on real-time data visualization and processing. The software supports workflow automation, enabling organizations to process incoming IoT data dynamically and respond based on conditions.

It includes device management features, allowing organizations to monitor the performance and health of devices.

With integrated analytics and visualization tools, Losant helps companies make informed choices from streams of real-time IoT data.

3.AWS IoT Core

AWS IoT Core offers secure, scalable, two-way cloud-to-IoT device communication. It gives companies the ability to process, ingest, and analyze large volumes of data streaming in from connected devices.

Two-way communication allows seamless interaction between edge devices and cloud applications.

AWS IoT Core is integrated with other AWS services like Lambda and SageMaker and supports analytics and machine learning. Its advanced security features, such as device encryption and authentication, make it suitable for handling personal data.

4.Microsoft Azure IoT Hub

Microsoft Azure IoT Hub is a comprehensive platform designed to improve the connectivity, tracking, and management of IoT devices. It includes secure data transmission, ensuring cloud applications can capture data from edge devices reliably.

Azure IoT Hub includes device access control and authentication for added protection in large-scale IoT deployments. It also integrates with Azure machine learning and AI services to help companies analyze and interpret IoT data.

5.Google Cloud IoT

Google Cloud IoT offers a strong set of features for collecting and analyzing IoT data from connected devices. The platform supports real-time data streaming, allowing organizations to respond to incoming data instantly.

It is backed by AI and machine learning tools, enabling predictive analytics and automation.

Its scalability makes it suitable for large companies with extensive IoT deployments and high-volume data needs

Synthetic Data Generation Tools 

Synthetic data generation tools are gaining popularity as businesses look for ways to produce compliant datasets without compromising privacy. These tools generate realistic fake data that mimics actual data while ensuring privacy protection. This helps companies train machine learning models even when real-world data is limited or restricted.

1.Mostly AI

Mostly AI specializes in generating GDPR-compliant synthetic data. It uses advanced algorithms to create data that appears natural and maintains the same statistical properties as original datasets, without containing any personally identifiable information (PII).

This allows businesses to work with realistic data while protecting privacy.

2. Synthesis AI

Synthesis AI focuses on creating artificial images to train computer vision models. By generating varied image sets for different scenarios, it helps companies avoid the cost and difficulty of collecting real-world labeled images.

This is especially helpful in areas like autonomous driving, where collecting images of rare events is nearly impossible.

3. DataGen

DataGen offers a way to generate tabular data for direct use in machine learning models. By imitating real-world distributions and relationships, DataGen allows organizations to train models more affordably without relying on historical data. It is useful when actual data is limited or skewed and cannot effectively train accurate models.

4.Gretel.ai

Gretel.ai is a platform built to create synthetic text datasets with a strong focus on privacy compliance. As an NLP-based platform, it suits businesses that need to build language models without exposing sensitive information. It also features similarity analysis tools to compare real and synthetic datasets, helping ensure high fidelity.

5.Hazy

Hazy is designed to produce synthetic datasets that are as functional as original ones without breaching privacy policies. By using methods like differential privacy, Hazy helps organizations generate data while protecting individual privacy. This makes it a good fit for industries like banking and healthcare that manage sensitive information.

Read More: The Role of GenerativeAI in Data Augmentation and Synthetic Data Generation

How upGrad Can Help

upGrad provides training programs in data acquisition and AI-based data processing. These courses are practical, featuring hands-on projects, expert-led sessions, and real-world examples. They help learners apply techniques directly to machine learning, real-time analytics, and advanced AI workflows.

Here are some of the courses offered:

6. Future Trends in ML Data Acquisition 

In 2025, machine learning (ML) data collection is shifting rapidly due to new technologies, broader AI adoption, and tighter regulations. Companies are changing how they gather, process, and use data to support privacy efforts, enable real-time tracking, and build flexible systems.

To stay competitive, businesses are exploring new methods like decentralized data gathering and automated data curation. These approaches help solve key challenges such as privacy concerns, inefficiencies from manual steps, and the need for scalable insights.

These updates lead to faster, safer data acquisition and help ensure machine learning models are trained using high-quality data that meets international standards.

Federated Learning & Decentralized Data 

Federated learning is changing how data is collected. It focuses on privacy and supports collaborative model training across devices. This decentralized process allows model development without needing a central data storage system. Below are some benefits of federated learning:

1. Privacy-Preserving Data Collection

No data is shared among individual devices like laptops, smartphones, or sensors on the internet of things in federated learning. It is not sent to a central server. This reduces the risk of data breaches and complies with privacy laws like GDPR and CCPA.

For example, a health app can train forecasting models using patient data without transferring sensitive information. Keeping data local helps organizations protect user privacy and build customer trust, which is increasingly valuable in today's privacy-aware world.

2.Collaborative Model Training

Each device trains a local model using its own dataset. Only model updates such as weights and gradients, to a central server. This allows organizations to benefit from shared learning without compromising individual datasets.

For instance, multiple hospitals can collaborate to improve diagnostic models without exchanging patient records. This collaborative model development improves accuracy and drives innovation while maintaining data confidentiality.

3.Reduced Bandwidth Needs

Since only the model updates are being delivered and not the original data, making this method less bandwidth-heavy. This is useful in areas with limited or costly internet access, such as rural or developing regions.

Since less data travels over the network, operational costs are reduced. It also speeds up training, making AI solutions more accessible in areas with weaker infrastructure.

4.Real-Time Flexibility

Federated learning allows models to be trained incrementally as new data becomes available on devices. This makes it easier for organizations to adapt quickly to new patterns or behaviors.

For example, a health-tracking mobile app can continuously improve its recommendations based on recent user activity and health trends.

This adaptability ensures that models remain current and effective in environments where conditions or behavior often change.

While federated learning has many advantages, it also comes with challenges. Ensuring model accuracy across different devices and managing communication costs are major concerns.

Organizations need to invest in strategies to handle device diversity. Devices vary in processing power and data quality, making it harder to ensure consistent model training. Addressing these gaps is necessary for broader adoption.

AI-Driven Automated Data Curation 

To anticipate where technology is headed, we must look back and ask what is data acquisition today. Also, how it’s evolving to meet tomorrow’s machine learning data systems’ needs. Future data collection in AI will rely more on AI-powered curation tools. These tools prepare, organize, and manage data automatically before analysis begins. They change how organizations work with large and complex datasets. Here are the key advantages:

1.Auto-Tagging Datasets

Natural Language Processing (NLP) enables real-time annotation of unstructured data such as text, images, or video by scanning for key content elements. This makes data easier to search and retrieve.

For instance, an online shopping site can use visual tags like color, shape, or brand to label product images automatically. This improves search accuracy and enhances the user experience.

2.High-Impact Sample Prioritization

AI systems can scan datasets for samples that will most improve model performance. By focusing on high-value data early, companies can train more effective models while reducing wasted effort.

For example, fraud detection models can prioritize past transactions that are highly correlated with fraud. This enables models to learn from more relevant examples.

3.Data Quality Improvement

Data curation applications automatically detect anomalies or irregularities in datasets. They fix data before it is used for analysis. Preparing data in advance results in quality datasets, which are essential for accurate ML outcomes.

For example, a software program can identify duplicate records or labeling errors in a dataset. It may flag them for review or correct them using predefined rules.

4.Streamlined Workflows

These tools automate various steps in data preparation such as cleaning, normalizing, and structuring. This saves time for data analysts and data scientists, allowing them to focus on strategic tasks instead of manual ones.

This shift boosts team productivity and encourages innovation. More time can be spent designing and refining complex analysis models.

5.Integration with Existing Systems

Curation software is also designed to work with current data management systems. This enhances data collection without needing to replace existing infrastructure.

For instance, adding an automated curation module to a database system makes upgrades easier and supports better collaboration between departments.

By using trends such as federated learning and AI-powered curation, organizations avoid future data collection challenges. These technologies support privacy compliance and help extract more value from datasets. They also improve efficiency and promote the safer, more thoughtful use of machine learning.

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7. How upGrad Helps You Master Data Acquisition 

As data acquisition continues to evolve, professionals need hands-on experience, relevant certifications, and the right skillset to stay ahead. upGrad offers programs designed to fill skill gaps in data acquisition, machine learning systems, and AI data pipelines. These programs support career transitions into roles in data engineering, AI, and cloud technologies.

Industry-Aligned Certification Programs 

upGrad’s courses focus on real-world data collection, real-time ingestion, and automated data pipelines. These help professionals move from roles such as Data Analyst or Cloud Engineer to jobs like Data Acquisition Engineer, AI Engineer, or ML Data Architect. Here is how upGrad courses stay aligned with what the industry actually needs:

• Students work on projects on automation that replicate real data pipeline situations.
• Training is intense on trending technologies like Apache Kafka, Spark, and leading cloud platforms.
• Courses are designed on practical application, allowing students to tackle real-life issues.
• Subject matter experts are engaged in the process of course development to ensure it is relevant and employer-ready.
• All of the courses align with specific career goals, thus it is easier to move to AI and data roles with certainty.

Mentorship and Networking Opportunities 

upGrad offers support from industry professionals, AI specialists, and data engineers. This guidance helps learners manage data quality, comply with privacy laws, and work with ML datasets.

Key benefits include:

• 1:1 mentorship sessions with professionals in AI and data acquisition
• Live sessions for doubt clearing and solving real-world data problems
• Access to an alumni network for referrals, career advice, and job strategies

This support helps learners stay competitive in a changing data-focused job market.

Career Transition Support 

Landing a role in data acquisition, AI-powered data aggregation, or ML engineering requires more than just technical knowledge. upGrad’s career support program helps learners prepare for the job market with practical tools and guidance.

Key career support services include:

• Resume Building & Profile Optimization: Craft a professional resume that highlights your skills in data acquisition components, preprocessing workflows, and real-time ingestion.
• Mock Interviews & Technical Assessments: Practice real-world challenges involving machine learning systems to gain confidence.
• Placement Assistance: Access job openings from top hiring companies in AI and data engineering.
• 1:1 Career Counseling: Receive expert advice on choosing the right path within AI and data acquisition.

With focused learning, hands-on mentorship, and job support, upGrad prepares professionals to shift into high-paying roles across AI and machine learning.

Conclusion 

Understanding what is data acquisition is necessary for building reliable AI systems and smart automation workflows. By 2025, data systems must handle real-time ingestion, edge computing, and meet compliance requirements. These systems also need to provide high-quality training datasets for machine learning models.

As AI tools continue to evolve, professionals must keep up with new methods in data collection. This includes skills like automated pipelines, IoT integration, and AI-supported aggregation. These tools help improve both accuracy and creative output in models. Knowing how to manage data privacy, preprocessing, and cloud-based collection also supports stronger AI system development.

Building expertise in data acquisition opens new career paths. It also helps businesses stay ahead as decision-making and operations move toward AI and automation. Still unsure about what path is best for you? Discuss your options with our experts or schedule a free 1:1 session today. Your design future may be closer than you imagine.

Want to become job-ready in AI and ML? Check out upGrad’s Job-ready Program in Artificial Intelligence & Machine Learning and learn ML model training, data pipeline automation, and synthetic data generation.

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References:
https://www.precedenceresearch.com/data-acquisition-system-market
https://www2.deloitte.com/us/en/insights/topics/analytics/insight-driven-organization.html