Normalize data

Corporate Registry Data Normalization Market Outlook

According to our latest research, the Global Corporate Registry Data Normalization market size was valued at $1.72 billion in 2024 and is projected to reach $5.36 billion by 2033, expanding at a CAGR of 13.2% during 2024–2033. One major factor driving the growth of this market globally is the escalating demand for accurate, real-time corporate data to support compliance, risk management, and operational efficiency across diverse sectors. As organizations increasingly digitize their operations, the need to standardize and normalize disparate registry data from multiple sources has become critical to ensure regulatory adherence, enable robust Know Your Customer (KYC) and Anti-Money Laundering (AML) processes, and foster seamless integration with internal and external systems. This trend is further amplified by the proliferation of cross-border business activities and the mounting complexity of global regulatory frameworks, making data normalization solutions indispensable for businesses seeking agility and resilience in a rapidly evolving digital landscape.

Regional Outlook

North America currently commands the largest share of the global Corporate Registry Data Normalization market, accounting for over 38% of the total market value in 2024. The region’s dominance is underpinned by its mature digital infrastructure, early adoption of advanced data management technologies, and stringent regulatory requirements that mandate comprehensive corporate transparency and compliance. Major economies such as the United States and Canada have witnessed significant investments in data normalization platforms, driven by the robust presence of multinational corporations, sophisticated financial institutions, and a dynamic legal environment. Additionally, the region benefits from a thriving ecosystem of technology vendors and solution providers, fostering continuous innovation and the rapid deployment of cutting-edge software and services. These factors collectively reinforce North America’s leadership position, making it a bellwether for global market trends and technological advancements in corporate registry data normalization.

In contrast, the Asia Pacific region is emerging as the fastest-growing market, projected to register a remarkable CAGR of 16.7% during the forecast period. This accelerated expansion is fueled by rapid digital transformation initiatives, burgeoning fintech and legaltech sectors, and a rising emphasis on corporate governance across countries such as China, India, Singapore, and Australia. Governments in the region are actively promoting regulatory modernization and digital identity frameworks, which in turn drive the adoption of data normalization solutions to streamline compliance and mitigate operational risks. Furthermore, the influx of foreign direct investment and the proliferation of cross-border business transactions are compelling enterprises to invest in robust data management tools that can harmonize corporate information from disparate jurisdictions. These dynamics are creating fertile ground for solution providers and service vendors to expand their footprint and address the unique needs of Asia Pacific’s diverse and rapidly evolving corporate landscape.

Meanwhile, emerging economies in Latin America, the Middle East, and Africa present a mixed outlook, characterized by growing awareness but slower adoption of corporate registry data normalization solutions. Challenges such as legacy IT infrastructure, fragmented regulatory environments, and limited access to advanced technology solutions continue to impede market penetration in these regions. However, a gradual shift is underway as governments and enterprises recognize the value of standardized corporate data for combating financial crime, fostering transparency, and attracting international investment. Localized demand is also being shaped by sector-specific needs, particularly in banking, government, and healthcare, where regulatory compliance and risk management are gaining prominence. Policy reforms and international collaborations are expected to play a pivotal role in accelerating adoption, though progress will likely be uneven across different countries and industry verticals.

Report Scope

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

Reference genes used in normalizing qRT-PCR data are critical for the accuracy of gene expression analysis. However, many traditional reference genes used in zebrafish early development are not appropriate because of their variable expression levels during embryogenesis. In the present study, we used our previous RNA-Seq dataset to identify novel reference genes suitable for gene expression analysis during zebrafish early developmental stages. We first selected 197 most stably expressed genes from an RNA-Seq dataset (29,291 genes in total), according to the ratio of their maximum to minimum RPKM values. Among the 197 genes, 4 genes with moderate expression levels and the least variation throughout 9 developmental stages were identified as candidate reference genes. Using four independent statistical algorithms (delta-CT, geNorm, BestKeeper and NormFinder), the stability of qRT-PCR expression of these candidates was then evaluated and compared to that of actb1 and actb2, two commonly used zebrafish reference genes. Stability rankings showed that two genes, namely mobk13 (mob4) and lsm12b, were more stable than actb1 and actb2 in most cases. To further test the suitability of mobk13 and lsm12b as novel reference genes, they were used to normalize three well-studied target genes. The results showed that mobk13 and lsm12b were more suitable than actb1 and actb2 with respect to zebrafish early development. We recommend mobk13 and lsm12b as new optimal reference genes for zebrafish qRT-PCR analysis during embryogenesis and early larval stages.

This archive contains affymetrix files necessary to normalize microarrays data and modified annotations files required in GIANT APT-Normalize tool for annotation of normalized data.

A data set used to normalize the detector response of the ARCS instrument see ARCS_226797.md in the data set for more details.

The CSV dataset contains sentence pairs for a text-to-text transformation task: given a sentence that contains 0..n abbreviations, rewrite (normalize) the sentence in full words (word forms).

Training dataset: 64,665 sentence pairs Validation dataset: 7,185 sentence pairs. Testing dataset: 7,984 sentence pairs.

All sentences are extracted from a public web corpus (https://korpuss.lv/id/Tīmeklis2020) and contain at least one medical term.

Security Data Normalization Platform Market Outlook

According to our latest research, the global Security Data Normalization Platform market size reached USD 1.48 billion in 2024, reflecting robust demand across industries for advanced security data management solutions. The market is registering a compound annual growth rate (CAGR) of 18.7% and is projected to achieve a value of USD 7.18 billion by 2033. The ongoing surge in sophisticated cyber threats and the increasing complexity of enterprise IT environments are among the primary growth factors driving the adoption of security data normalization platforms worldwide.

The growth of the Security Data Normalization Platform market is primarily fuelled by the exponential rise in cyberattacks and the proliferation of digital transformation initiatives across various sectors. As organizations accumulate vast amounts of security data from disparate sources, the need for platforms that can aggregate, normalize, and analyze this data has become critical. Enterprises are increasingly recognizing that traditional security information and event management (SIEM) systems fall short in handling the volume, velocity, and variety of data generated by modern IT infrastructures. Security data normalization platforms address this challenge by transforming heterogeneous data into a standardized format, enabling more effective threat detection, investigation, and response. This capability is particularly vital as organizations move toward zero trust architectures and require real-time insights to secure their digital assets.

Another significant growth driver for the Security Data Normalization Platform market is the evolving regulatory landscape. Governments and regulatory bodies worldwide are introducing stringent data protection and cybersecurity regulations, compelling organizations to enhance their security postures. Compliance requirements such as GDPR, HIPAA, and CCPA demand that organizations not only secure their data but also maintain comprehensive audit trails and reporting mechanisms. Security data normalization platforms facilitate compliance by providing unified, normalized logs and reports that simplify audit processes and ensure regulatory adherence. The market is also witnessing increased adoption in sectors such as BFSI, healthcare, and government, where data integrity and compliance are paramount.

Technological advancements are further accelerating the adoption of security data normalization platforms. The integration of artificial intelligence (AI) and machine learning (ML) capabilities into these platforms is enabling automated threat detection, anomaly identification, and predictive analytics. Cloud-based deployment models are gaining traction, offering scalability, flexibility, and cost-effectiveness to organizations of all sizes. As the threat landscape becomes more dynamic and sophisticated, organizations are prioritizing investments in advanced security data normalization solutions that can adapt to evolving risks and support proactive security strategies. The growing ecosystem of managed security service providers (MSSPs) is also contributing to market expansion by delivering normalization as a service to organizations with limited in-house expertise.

From a regional perspective, North America continues to dominate the Security Data Normalization Platform market, accounting for the largest share in 2024 due to the presence of major technology vendors, high cybersecurity awareness, and significant investments in digital infrastructure. Europe follows closely, driven by strict regulatory mandates and increasing cyber threats targeting critical sectors. The Asia Pacific region is emerging as a high-growth market, propelled by rapid digitization, expanding IT ecosystems, and rising cybercrime incidents. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as organizations in these regions accelerate their cybersecurity modernization efforts. The global outlook for the Security Data Normalization Platform market remains positive, with sustained demand expected across all major regions through 2033.

Component Analysis

The Security Data Normalization Platform market is segmented by component into software and services. Software solutions form the core of this market, providing the essential functionalities for data aggregation, normalization, enrichment, and integration with downs

As economic conditions in the United States continue to improve, the FOMC may consider normalizing monetary policy. Whether the FOMC reduces the balance sheet before raising the federal funds rate (or vice versa) may affect the shape of the yield curve, with consequences for financial institutions. Drawing lessons from the previous normalization in 2015–19, we conclude that normalizing the balance sheet before raising the funds rate might forestall yield curve inversion and, in turn, support economic stability.

The values in this raster are unit-less scores ranging from 0 to 1 that represent normalized dollars per acre damage claims from antelope on Wyoming lands. This raster is one of 9 inputs used to calculate the "Normalized Importance Index."

1. Microbiome sequencing data often need to be normalized due to differences in read depths, and recommendations for microbiome analyses generally warn against using proportions or rarefying to normalize data and instead advocate alternatives, such as upper quartile, CSS, edgeR-TMM, or DESeq-VS. Those recommendations are, however, based on studies that focused on differential abundance testing and variance standardization, rather than community-level comparisons (i.e., beta diversity), Also, standardizing the within-sample variance across samples may suppress differences in species evenness, potentially distorting community-level patterns. Furthermore, the recommended methods use log transformations, which we expect to exaggerate the importance of differences among rare OTUs, while suppressing the importance of differences among common OTUs. 2. We tested these theoretical predictions via simulations and a real-world data set. 3. Proportions and rarefying produced more accurate comparisons among communities and were the only methods that fully normalized read depths across samples. Additionally, upper quartile, CSS, edgeR-TMM, and DESeq-VS often masked differences among communities when common OTUs differed, and they produced false positives when rare OTUs differed. 4. Based on our simulations, normalizing via proportions may be superior to other commonly used methods for comparing ecological communities.

Flight Data Normalization Platform Market Outlook

According to our latest research, the global flight data normalization platform market size reached USD 1.12 billion in 2024, exhibiting robust industry momentum. The market is projected to grow at a CAGR of 10.3% from 2025 to 2033, reaching an estimated value of USD 2.74 billion by 2033. This growth is primarily driven by the increasing adoption of advanced analytics in aviation, the rising need for operational efficiency, and the growing emphasis on regulatory compliance and safety enhancements across the aviation sector.

A key growth factor for the flight data normalization platform market is the rapid digital transformation within the aviation industry. Airlines, airports, and maintenance organizations are increasingly relying on digital platforms to aggregate, process, and normalize vast volumes of flight data generated by modern aircraft systems. The transition from legacy systems to integrated digital solutions is enabling real-time data analysis, predictive maintenance, and enhanced situational awareness. This shift is not only improving operational efficiency but also reducing downtime and maintenance costs, making it an essential strategy for airlines and operators aiming to remain competitive in a highly regulated environment.

Another significant driver fueling the expansion of the flight data normalization platform market is the stringent regulatory landscape governing aviation safety and compliance. Aviation authorities worldwide, such as the Federal Aviation Administration (FAA) and the European Union Aviation Safety Agency (EASA), are mandating the adoption of advanced flight data monitoring and normalization solutions to ensure adherence to safety protocols and to facilitate incident investigation. These regulatory requirements are compelling aviation stakeholders to invest in platforms that can seamlessly normalize and analyze data from diverse sources, thereby supporting proactive risk management and compliance reporting.

Additionally, the growing complexity of aircraft systems and the proliferation of connected devices in aviation have led to an exponential increase in the volume and variety of flight data. The need to harmonize disparate data formats and sources into a unified, actionable format is driving demand for sophisticated flight data normalization platforms. These platforms enable stakeholders to extract actionable insights from raw flight data, optimize flight operations, and support advanced analytics use cases such as fuel efficiency optimization, fleet management, and predictive maintenance. As the aviation industry continues to embrace data-driven decision-making, the demand for robust normalization solutions is expected to intensify.

Regionally, North America continues to dominate the flight data normalization platform market owing to the presence of major airlines, advanced aviation infrastructure, and early adoption of digital technologies. Europe is also witnessing significant growth, driven by stringent safety regulations and increasing investments in aviation digitization. Meanwhile, the Asia Pacific region is emerging as a lucrative market, fueled by rapid growth in air travel, expanding airline fleets, and government initiatives to modernize aviation infrastructure. Latin America and the Middle East & Africa are gradually embracing these platforms, supported by ongoing efforts to enhance aviation safety and operational efficiency.

Component Analysis

The component segment of the flight data normalization platform market is broadly categorized into software, hardware, and services. The software segment accounts for the largest share, driven by the increasing adoption of advanced analytics, machine learning, and artificial intelligence technologies for data processing and normalization. Software solutions are essential for aggregating raw flight data from multiple sources, standardizing formats, and providing actionable insights for decision-makers. With the rise of clou

The tar file contains two directories: data and models. Within "data," there are 4 subdirectories: "training" (the clean training data -- without perturbations), "training_all_perturbed_for_uq" (the lightly perturbed training data), "validation_all_perturbed_for_uq" (the moderately perturbed validation data), and "testing_all_perturbed_for_uq" (the heavily perturbed validation data). The data in these directories are unnormalized. The subdirectories "training" and "training_all_perturbed_for_uq" each contain a normalization file. These normalization files contain parameters used to normalize the data (from physical units to z-scores) for Experiment 1 and Experiment 2, respectively. To do the normalization, you can use the script normalize_examples.py in the code library (ml4rt) with the argument input_normalization_file_name set to one of these two file paths. The other arguments should be as follows:

--uniformize=1

--predictor_norm_type_string="z_score"

--vector_target_norm_type_string=""

--scalar_target_norm_type_string=""

Within the directory "models," there are 6 subdirectories: for the BNN-only models trained with clean and lightly perturbed data, for the CRPS-only models trained with clean and lightly perturbed data, and for the BNN/CRPS models trained with clean and lightly perturbed data. To read the models into Python, you can use the method neural_net.read_model in the ml4rt library.

Equipment Runtime Normalization Analytics Market Outlook

According to our latest research, the global Equipment Runtime Normalization Analytics market size reached USD 2.31 billion in 2024, demonstrating robust momentum across diverse industrial sectors. The market is expected to grow at a CAGR of 12.8% from 2025 to 2033, reaching a forecasted value of USD 6.88 billion by 2033. This remarkable growth is primarily driven by the increasing adoption of industrial automation, the proliferation of IoT-enabled equipment, and the rising need for predictive maintenance and operational efficiency across manufacturing, energy, and other critical industries.

A key growth factor for the Equipment Runtime Normalization Analytics market is the accelerating pace of digital transformation within asset-intensive industries. As organizations strive to maximize the productivity and lifespan of their machinery, there is a growing emphasis on leveraging advanced analytics to normalize equipment runtime data across heterogeneous fleets and varying operational contexts. The integration of AI and machine learning algorithms enables enterprises to standardize runtime metrics, providing a unified view of equipment performance regardless of manufacturer, model, or deployment environment. This normalization is crucial for benchmarking, identifying inefficiencies, and implementing data-driven maintenance strategies that reduce unplanned downtime and optimize resource allocation.

Another significant driver is the rise of Industry 4.0 and the increasing connectivity of industrial assets through IoT sensors and cloud-based platforms. These technological advancements have generated an unprecedented volume of equipment performance data, necessitating sophisticated analytics solutions capable of normalizing and interpreting runtime information at scale. Equipment Runtime Normalization Analytics platforms facilitate seamless data aggregation from disparate sources, allowing organizations to derive actionable insights that enhance operational agility and competitiveness. Additionally, the shift towards outcome-based service models in sectors such as manufacturing, energy, and transportation is fueling demand for analytics that can accurately measure and compare equipment utilization, efficiency, and reliability across diverse operational scenarios.

The growing focus on sustainability and regulatory compliance is also propelling the adoption of Equipment Runtime Normalization Analytics. As governments and industry bodies impose stricter standards on energy consumption, emissions, and equipment maintenance, enterprises are increasingly turning to analytics tools that can provide standardized, auditable reports on equipment runtime and performance. These solutions not only help organizations meet compliance requirements but also support sustainability initiatives by identifying opportunities to reduce energy consumption, minimize waste, and extend equipment lifecycles. The convergence of these market forces is expected to sustain strong demand for Equipment Runtime Normalization Analytics solutions in the years ahead.

Regionally, North America currently leads the market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The dominance of North America can be attributed to the early adoption of industrial IoT, advanced analytics, and a mature manufacturing base. Europe’s strong emphasis on sustainability and regulatory compliance further drives adoption, while Asia Pacific is emerging as a high-growth region due to rapid industrialization, government initiatives to modernize manufacturing, and increasing investments in smart factory technologies. Latin America and the Middle East & Africa are also witnessing steady growth, supported by expanding industrial infrastructure and the increasing penetration of digital technologies.

Component Analysis

The Component segment of the Equipment Runtime Normalization Analytics market is categorized into Software, Hardware, and Services. Software solutions form the backbone of this market, comprising advanced analytics platforms, AI-driven data processing engines, and visualization tools that enable users to normalize and interpret equipment runtime data. These software offerings are designed to aggregate data from multiple sources, apply normalization algorithms, and generate actionable insights for operational decision-making. The demand for robust

Neutron scattering Data from a Vanadium cylinder. Acquired on the ARCS spectrometer in white beam mode to normalize the detector efficiencies. During Cycle 2022B

Dataset

This dataset was created by Hemanth S

Contents

The technological advances in mass spectrometry allow us to collect more comprehensive data with higher quality and increasing speed. With the rapidly increasing amount of data generated, the need for streamlining analyses becomes more apparent. Proteomics data is known to be often affected by systemic bias from unknown sources, and failing to adequately normalize the data can lead to erroneous conclusions. To allow researchers to easily evaluate and compare different normalization methods via a user-friendly interface, we have developed “proteiNorm”. The current implementation of proteiNorm accommodates preliminary filters on peptide and sample levels followed by an evaluation of several popular normalization methods and visualization of the missing value. The user then selects an adequate normalization method and one of the several imputation methods used for the subsequent comparison of different differential expression methods and estimation of statistical power. The application of proteiNorm and interpretation of its results are demonstrated on two tandem mass tag multiplex (TMT6plex and TMT10plex) and one label-free spike-in mass spectrometry example data set. The three data sets reveal how the normalization methods perform differently on different experimental designs and the need for evaluation of normalization methods for each mass spectrometry experiment. With proteiNorm, we provide a user-friendly tool to identify an adequate normalization method and to select an appropriate method for differential expression analysis.

Genome-wide analysis of gene expression or protein binding patterns using different array or sequencing based technologies is now routinely performed to compare different populations, such as treatment and reference groups. It is often necessary to normalize the data obtained to remove technical variation introduced in the course of conducting experimental work, but standard normalization techniques are not capable of eliminating technical bias in cases where the distribution of the truly altered variables is skewed, i.e. when a large fraction of the variables are either positively or negatively affected by the treatment. However, several experiments are likely to generate such skewed distributions, including ChIP-chip experiments for the study of chromatin, gene expression experiments for the study of apoptosis, and SNP-studies of copy number variation in normal and tumour tissues. A preliminary study using spike-in array data established that the capacity of an experiment to identify altered variables and generate unbiased estimates of the fold change decreases as the fraction of altered variables and the skewness increases. We propose the following work-flow for analyzing high-dimensional experiments with regions of altered variables: (1) Pre-process raw data using one of the standard normalization techniques. (2) Investigate if the distribution of the altered variables is skewed. (3) If the distribution is not believed to be skewed, no additional normalization is needed. Otherwise, re-normalize the data using a novel HMM-assisted normalization procedure. (4) Perform downstream analysis. Here, ChIP-chip data and simulated data were used to evaluate the performance of the work-flow. It was found that skewed distributions can be detected by using the novel DSE-test (Detection of Skewed Experiments). Furthermore, applying the HMM-assisted normalization to experiments where the distribution of the truly altered variables is skewed results in considerably higher sensitivity and lower bias than can be attained using standard and invariant normalization methods.

Equipment Runtime Normalization Analytics Market Outlook

As per our latest research, the global Equipment Runtime Normalization Analytics market size was valued at USD 2.43 billion in 2024, exhibiting a robust year-on-year growth trajectory. The market is expected to reach USD 7.12 billion by 2033, growing at a remarkable CAGR of 12.7% during the forecast period from 2025 to 2033. This significant expansion is primarily driven by the escalating adoption of data-driven maintenance strategies across industries, the surge in digital transformation initiatives, and the increasing necessity for optimizing equipment utilization and operational efficiency.

One of the primary growth factors fueling the Equipment Runtime Normalization Analytics market is the rapid proliferation of industrial automation and the Industrial Internet of Things (IIoT). As organizations strive to minimize downtime and maximize asset performance, the need to collect, normalize, and analyze runtime data from diverse equipment becomes critical. The integration of advanced analytics platforms allows businesses to gain actionable insights, predict equipment failures, and optimize maintenance schedules. This not only reduces operational costs but also extends the lifecycle of critical assets. The convergence of big data analytics with traditional equipment monitoring is enabling organizations to transition from reactive to predictive maintenance strategies, thereby driving market growth.

Another significant growth driver is the increasing emphasis on regulatory compliance and sustainability. Industries such as energy, manufacturing, and healthcare are under mounting pressure to comply with stringent operational standards and environmental regulations. Equipment Runtime Normalization Analytics solutions offer robust capabilities to monitor and report on equipment performance, energy consumption, and emissions. By normalizing runtime data, these solutions provide a standardized view of equipment health and efficiency, facilitating better decision-making and compliance reporting. The ability to benchmark performance across multiple sites and equipment types further enhances an organization’s ability to meet regulatory requirements while pursuing sustainability goals.

The evolution of cloud computing and edge analytics technologies also plays a pivotal role in the expansion of the Equipment Runtime Normalization Analytics market. Cloud-based platforms offer scalable and flexible deployment options, enabling organizations to centralize data management and analytics across geographically dispersed operations. Edge analytics complements this by providing real-time data processing capabilities at the source, reducing latency and enabling immediate response to equipment anomalies. This hybrid approach is particularly beneficial in sectors with remote or critical infrastructure, such as oil & gas, utilities, and transportation. The synergy between cloud and edge solutions is expected to further accelerate market adoption, as organizations seek to harness the full potential of real-time analytics for operational excellence.

From a regional perspective, North America currently leads the Equipment Runtime Normalization Analytics market, owing to its advanced industrial base, high adoption of digital technologies, and strong presence of key market players. However, Asia Pacific is anticipated to witness the fastest growth over the forecast period, driven by rapid industrialization, increasing investments in smart manufacturing, and supportive government initiatives for digital transformation. Europe remains a significant market due to its focus on energy efficiency and sustainability, while Latin America and the Middle East & Africa are gradually catching up as industrial modernization accelerates in these regions.

Component Analysis

The Equipment Runtime Normalization Analytics market is segmented by component into software, hardware, and services. The software segment holds the largest share, accounti

Raw data used to normalize detector performance on the ARCS instrument For the run cycle starting in June 2023. This is a V cylinder and the T0 chopper is set to 150 Hz and phased for 300 meV. All Fermi Choppers are out of the Beam.