Capgemini

MAY 28, 2020

From stringent regulatory requirements to product specifications, non-compliance can lead to significant issues, from dissatisfied customers to fines and class-action lawsuits. Manufacturers typically work with components from multiple sources, so assembling all the data from suppliers and the shop floor is critical for tracing back defects.

Artificial Intelligence 52

Artificial Intelligence Artificial Inteligence Automotive Data Engineering 52

Altexsoft

FEBRUARY 8, 2021

These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development. Optimization opportunities offered by analytics.

Business Intelligence 87

Business Intelligence Analytics Analysis Data 87

Altexsoft

SEPTEMBER 9, 2022

In data science , metadata is one of the central aspects: It describes data (including unstructured data streams) fed into a big data analytical platform, capturing, for example, formats, file sizes, source of information, permission details, etc. Types of metadata. There are multiple ways to categorize metadata.

Tools 59

Tools Technical Review Software Review Systems Review 59

Altexsoft

JUNE 11, 2021

Process mining is a set of techniques for the analysis of operational processes based on event logs extracted from company’s databases, information systems, or business management software such as enterprise resource planning (ERP), customer relationship management (CRM), electronic health records (EHR), etc. What is process mining?

Applications 69

Applications Weak Development Team Systems Review Software Review 69

O'Reilly Media - Ideas

FEBRUARY 11, 2019

Highlights and use cases from companies that are building the technologies needed to sustain their use of analytics and machine learning. In a forthcoming survey, “Evolving Data Infrastructure,” we found strong interest in machine learning (ML) among respondents across geographic regions. Temporal data and time-series analytics.

Big Data 87

Big Data Technology Tools Cloud 87

Capgemini

NOVEMBER 14, 2022

And they also need to trust that the data is current, relevant, and available. Moving a business forward requires fast access to good data for more applications. But that turns IT and analytics teams into the bottleneck. For self-service BI to succeed, however, the entire data value chain may need to be fixed.

Agile 52

Agile Technical Review Systems Review Artificial Intelligence 52

Hu's Place - HitachiVantara

JANUARY 3, 2019

Public cloud, agile methodologies and devops, RESTful APIs, containers, analytics and machine learning are being adopted. ” Deployments of large data hubs have only resulted in more data silos that are not easily understood, related, or shared. Happy New Year and welcome to 2019, a year full of possibilities.

Trends 86

Trends Artificial Inteligence Data Center Machine Learning 86

Altexsoft

NOVEMBER 9, 2021

If you want to streamline your procurement and gain more visibility into this process, you have to get hold of available data, analyze it, and extract value to make informed decisions. What is procurement analytics and the opportunities it offers? Main components of procurement analytics. Procurement and its challenges.

Analytics 52

Analytics Software Review Systems Review Technical Review 52

Existek

JANUARY 11, 2022

It might be challenging as it requires a detailed analysis of the business-specific needs and a deeper understanding of the overall market situation. . The cloud computing market covers many areas like business processes, infrastructure, platform, security, management, analytics supported by cloud providers. Data and analytics.

Azure 52

Azure AWS Cloud How To 52

Cloudera

DECEMBER 10, 2020

CDW’s engines such as Impala and Hive low-latency analytical processing (LLAP) directly operate on extremely compact open file formats, such as Parquet and ORC. These file formats not only help avoid data duplication into proprietary storage formats but also provide highly efficient storage formats.

Data 98

Data Technical Review Storage Systems Review 98

O'Reilly Media - Ideas

NOVEMBER 13, 2018

The Future of Privacy Forum and Immuta recently released a report with some great suggestions on how one might approach machine learning projects with risk management in mind: When you’re working on a machine learning project, you need to employ a mix of data engineers, data scientists, and domain experts.

Machine Learning 92

Machine Learning Artificial Inteligence Software Review Conference 92

TIBCO - Connected Intelligence

OCTOBER 15, 2020

Towards these trends, our clients have significantly invested in data-related technologies and talent over the past several years, including: Data governance to cater to enterprise-level financial reporting and regulatory compliance as an example. Self-service data discovery (e.g.,

Virtualization 50

Virtualization Data Energy Innovation 50

Altexsoft

FEBRUARY 10, 2021

Similar to how DevOps once reshaped the software development landscape, another evolving methodology, DataOps, is currently changing Big Data analytics — and for the better. DataOps is a relatively new methodology that knits together data engineering, data analytics, and DevOps to deliver high-quality data products as fast as possible.

Analytics 75

Analytics DevOps Development Software Review 75

Cloudera

NOVEMBER 12, 2020

Typically, when we talk about data warehousing at an enterprise level on the cloud, one of the biggest concerns is that moving workloads from on-premises to the cloud is not seamless and opens up new risks for data safety and security. The last thing you need is another 1-3 month analysis project. Key areas of concern are: .

Data Center 103

Data Center Cloud Data Policies 103

DataRobot

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

Artificial Inteligence 52

Artificial Inteligence Artificial Intelligence Machine Learning IoT 52

Cloudera

FEBRUARY 15, 2024

While there are clear reasons SVB collapsed, which can be reviewed here , my purpose in this post isn’t to rehash the past but to present some of the regulatory and compliance challenges financial (and to some degree insurance) institutions face and how data plays a role in mitigating and managing risk.

Insurance 88

Insurance Compliance Technical Review Banking 88

O'Reilly Media - Ideas

MARCH 31, 2020

It may even be necessary to do image or video analysis to make content-based recommendations, detect fraud, or reject content that violates your rules (for example, live shooter videos). It turns out that type of data infrastructure is also the foundation needed for building AI products. If you can’t walk, you’re unlikely to run.

Product Management 139

Product Management Artificial Inteligence Machine Learning Software Review 139

Cloudera

OCTOBER 19, 2023

Add to this, too, the difficulty of integrating potentially dissimilar compliance frameworks: for example, separate telcos might be operating under different regulatory guidelines, appropriate to specific jurisdictions or business practices, requiring the merged entity to formalize a single, unified framework for compliance.

Data 78

Data Compliance Architecture Data Engineering 78

Altexsoft

OCTOBER 29, 2020

But before you dive in, we recommend you reviewing our more beginner-friendly articles on data transformation: Complete Guide to Business Intelligence and Analytics: Strategy, Steps, Processes, and Tools. What is Data Engineering: Explaining the Data Pipeline, Data Warehouse, and Data Engineer Role.

Testing 63

Testing Tools Software Review Technical Review 63

CIO

AUGUST 18, 2022

Previously, Walgreens was attempting to perform that task with its data lake but faced two significant obstacles: cost and time. Those challenges are well-known to many organizations as they have sought to obtain analytical knowledge from their vast amounts of data. You can intuitively query the data from the data lake.

Data 350

Data Artificial Inteligence Technical Advisors Technical Review 350

Altexsoft

MARCH 16, 2021

It’s referred to as big data, since, for example, just a single complete human genome sequence produces about 200 Gb of raw data. Now, we know where big data in healthcare originates. Check our article on data engineering to get a detailed understanding of the data pipeline and its components.

Big Data 116

Big Data Healthcare Applications Data 116

Altexsoft

MARCH 18, 2021

ETL and ELT are the most widely applied approaches to deliver data from one or many sources to a centralized system for easy access and analysis. It is the process of collecting raw data from disparate sources, transmitting it to a staging database for conversion, and loading prepared data into a unified destination system.

Systems Review 109

Systems Review Technical Review Software Review Compliance 109

Mobilunity

NOVEMBER 24, 2021

In most digital spheres, especially in fintech, where all business processes are tied to data processing, a good big data engineer is worth their weight in gold. In this article, we’ll discuss the role of an ETL engineer in data processing and why businesses need such experts nowadays. Who Is an ETL Engineer?

Development 52

Development Storage Recruiting Architecture 52

Cloudera

OCTOBER 25, 2022

Enabling data and analytics in the cloud allows you to have infinite scale and unlimited possibilities to gain faster insights and make better decisions with data. Cloud data lakehouses provide significant scaling, agility, and cost advantages compared to cloud data lakes and cloud data warehouses.

Cloud 52

Cloud Data Firewall AWS 52

Altexsoft

JANUARY 14, 2021

A data warehouse is often abbreviated as DW or DWH. You may also find it under the name of an enterprise data warehouse (EDW). It is usually created and used primarily for data reporting and analysis purposes. As such, it is possible to retrieve old archived data if needed. Data warehouse architecture.

Backup 115

Backup Azure Software Review Systems Review 115

Altexsoft

JULY 19, 2022

She formulated the thesis in 2018 and published her first article “How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh” in 2019. So, to avoid any confusion, please be aware that data mesh is NOT. Some organizational approaches to analytics ownership are explained in our article about data science teams.

Architecture 52

Architecture Data Analytics Data Engineering 52

Altexsoft

MARCH 2, 2023

Data management is a technical implementation of data governance and involves the practical aspects of working with data, such as data storage, retrieval, and analysis. Compliance. Data governance provides a platform for meeting government regulations like GDPR or HIPAA. Improved data management.

Government 52

Government Tools Data Weak Development Team 52

O'Reilly Media - Ideas

MARCH 18, 2020

More than half of respondent organizations identify as “mature” adopters of AI technologies: that is, they’re using AI for analysis or in production. One-sixth of respondents identify as data scientists, but executives—i.e., What is more, almost three-quarters of survey respondents say they work with data in their jobs.

Enterprise 134

Enterprise Survey Technical Review Weak Development Team 134

BPM

JANUARY 18, 2019

That augmentation must be in a form attractive to humans while enabling security, compliance, authenticity and auditability. Process mining will be incorporated into larger suites of professional business process analysis tools. This will aid the faster creation of baseline models, enabling analysis and process improvement.

Artificial Inteligence 111

Artificial Inteligence Artificial Intelligence Machine Learning Weak Development Team 111

Cloudera

DECEMBER 7, 2017

Cloud promises many advantages as an environment for machine learning and analytics. Cloud offers elasticity of those resources to efficiently support transient analytics workloads and data pipelines. Yet there is often a dark side to using public cloud service providers’ analytics offerings in the cloud, too.

Data 40

Data Machine Learning Artificial Inteligence Analytics 40

Altexsoft

SEPTEMBER 23, 2019

Why would you need a detailed business analysis? There’s a large variety of analytical activities when we’re talking about software development. In this article, we’ll explain what business analysis in software development is and the role of a business analyst (BA) within this process. Analytical thinker. Data Analyst.

Weak Development Team 62

Weak Development Team Software Development Research Project Management 62

Altexsoft

NOVEMBER 5, 2021

So, we’ll only touch on its most vital aspects, instruments, and areas of interest — namely, data quality, patient identity, database administration, and compliance with privacy regulations. Data analysis, transformation, and decision support revolve around deriving knowledge and insights critical for enhancing patient care.

Technical Review 59

Technical Review Technology Software Review Healthcare 59

TIBCO - Connected Intelligence

DECEMBER 20, 2021

TIBCO DQ will become the new data quality product family, through an evolution of our current data quality offerings, significantly enhancing current capabilities available throughout the TIBCO data fabric with built-in AI and ML to automate quality, detection, monitoring, and anomaly resolution.

Innovation 26

Innovation Analytics Virtualization Cloud 26

Altexsoft

NOVEMBER 23, 2021

But this data is all over the place: It lives in the cloud, on social media platforms, in operational systems, and on websites, to name a few. Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. Single point of failure.

Virtualization 69

Virtualization Tools Data Architecture 69

Altexsoft

NOVEMBER 10, 2021

A data warehouse (DW) is a centralized repository for data accumulated from an array of corporate sources like CRMs, relational databases , flat files, etc. Typically used for data analysis and reporting, data warehouses rely on ETL mechanisms to extract , transform , and load data into a destination.

Architecture 52

Architecture Data Storage Machine Learning 52

O'Reilly Media - Data

MARCH 22, 2018

In fact, the ability to account for the fairness and transparency of these predictive models has been mandated for legal compliance. At DataScience.com , where I’m a lead data scientist, we feel passionately about the ability of practitioners to use models to ensure safety, non-discrimination, and transparency. Introducing Skater.

Off-The-Shelf 115

Off-The-Shelf Machine Learning Artificial Inteligence Weak Development Team 115

Cloudera

JUNE 4, 2018

Using the Solr search engine to index the data hub content initially helped the company better understand the data ingested. With Cloudera Enterprise Data Hub as the foundation for data acquisition, centralization, and indexing, more intelligent applications can be built on top of it to support: insight discovery.

Pharmaceuticals 47

Pharmaceuticals Data Big Data Systems Review 47