This site uses cookies to improve your experience. To help us insure we adhere to various privacy regulations, please select your country/region of residence. If you do not select a country, we will assume you are from the United States. Select your Cookie Settings or view our Privacy Policy and Terms of Use.
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Used for the proper function of the website
Used for monitoring website traffic and interactions
Cookie Settings
Cookies and similar technologies are used on this website for proper function of the website, for tracking performance analytics and for marketing purposes. We and some of our third-party providers may use cookie data for various purposes. Please review the cookie settings below and choose your preference.
Strictly Necessary: Used for the proper function of the website
Performance/Analytics: Used for monitoring website traffic and interactions
Migrating data to the public cloud offers a wide range of benefits for enterprises; data teams can more easily access their data, write, and test data science models, evaluate new data platforms and test applications, run POCs, and deploy in production.
Organizations learned a valuable lesson in 2023: It isn’t sufficient to rely on securing data once it has landed in a clouddata warehouse or analytical store. As a result, data owners are highly motivated to explore technologies in 2024 that can protect data from the moment it begins its journey in the source systems.
The transition from hybrid to multi-cloud environments is more than just a buzzword: It’s a fundamental shift in how organizations manage and utilize their data. As these complex architectures evolve, the importance of robust multi-clouddatagovernance cannot be overstated.
It’s common for enterprises to run into challenges such as lack of data visibility, problems with data security, and low Data Quality. But despite the dangers of poor data ethics and management, many enterprises are failing to take the steps they need to ensure quality DataGovernance. Let’s break […].
With that, I’ve long believed that for most large cloud platform providers offering managed services, such as document editing and storage, email services and calendar […]. The post DataGovernance at the Edge of the Cloud appeared first on DATAVERSITY.
Banks – and their data volumes – are at the epicenter of the world’s digital transformation. The pace of change mirrors the velocity, volume, and variety of data within the industry. It is where new products, new markets, and new touchpoints mean new – often cloud-based – ways to do business in financial services.
As I’ve been working to challenge the status quo on DataGovernance – I get a lot of questions about how it will “really” work. In 2019, I wrote the book “Disrupting DataGovernance” because I firmly believe that […]. The post Dear Laura: What Role Should Leadership Play in DataGovernance?
Introduction Struggling with expanding a business database due to storage, management, and data accessibility issues? To steer growth, employ effective data management strategies and tools. This article explores data management’s key tool features and lists the top tools for 2023.
For many enterprises, a hybrid clouddata lake is no longer a trend, but becoming reality. With a cloud deployment, enterprises can leverage a “pay as you go” model; reducing the burden of incurring capital costs. With an on-premise deployment, enterprises have full control over data security, data access, and datagovernance.
According to Gartner, data fabric is an architecture and set of data services that provides consistent functionality across a variety of environments, from on-premises to the cloud. Data fabric simplifies and integrates on-premises and cloudData Management by accelerating digital transformation.
In today’s information-driven society, there is perhaps nothing more ubiquitous and nothing that is multiplying at a more rapid pace than data. According to Forbes, more than 90% of the data that is available worldwide today was created within the last two years alone.
The Data Race to the Cloud. This recent cloud migration applies to all who use data. We have seen the COVID-19 pandemic accelerate the timetable of clouddata migration , as companies evolve from the traditional data warehouse to a datacloud, which can host a cloud computing environment.
Big data has led to some huge changes in the way we live. John Deighton recently posted about this in an article on The Economic Times. John Deighton is a leading expert on big data technology. His research focuses on the importance of data in the online world.
The three of us talked migration strategy and the best way to move to the Snowflake DataCloud. As Vice President of DataGovernance at TMIC, Anthony has robust experience leading cloud migration as part of a larger data strategy. Creating an environment better suited for datagovernance.
Many organizations adopt a long-term approach, leveraging the relative strengths of both mainframe and cloud systems. This integrated strategy keeps a wide range of IT options open, blending the reliability of mainframes with the innovation of cloud computing. Best Practice 5.
The first post in the series defines data culture , its benefits, and the three pillars of data culture. The second and fourth posts take a deeper look at data search & discovery and datagovernance, respectively. What is Data Literacy? How to Build Data Literacy. Subscribe to Alation's Blog.
Cloud computing offers scalability, flexibility, and a range of services that can significantly enhance operational efficiency. Without careful planning and management, clouddata costs can quickly escalate, impacting the overall […] However, these benefits come with a price.
Without effective and comprehensive validation, a data warehouse becomes a data swamp. With the accelerating adoption of Snowflake as the clouddata warehouse of choice, the need for autonomously validating data has become critical.
A data lake becomes a data swamp in the absence of comprehensive data quality validation and does not offer a clear link to value creation. Organizations are rapidly adopting the clouddata lake as the data lake of choice, and the need for validating data in real time has become critical.
Data as the currency of connected products One of the past blogs in this series—“ Data at the Edge ”—talked about handling all the data that is generated at the edge. Connected products send and receive lot of data to the cloud. Let us know what you think.
But with growing concerns around user privacy, how can companies achieve this level of personalization without compromising our personal data? In todays fast-paced digital landscape, we all love a little bit of personalization.
The right ETL platform ensures data flows seamlessly across systems, providing accurate and consistent information for decision-making. Effective integration is crucial to maintaining operational efficiency and data accuracy, as modern businesses handle vast amounts of data. What is ETL in Data Integration?
In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform.
Making the experts responsible for service streamlines the data-request pipeline, delivering higher quality data into the hands of those who need it more rapidly. Some argue that datagovernance and quality practices may vary between domains. Are they just for this season, or an article you can rely on for life?
While the mounting cost of raw materials may not be the culprit, enterprises are simultaneously watching the cost of data starting to rise as well. The post The Rise of Enterprise Data Inflation appeared first on DATAVERSITY. Inflation is on everyone’s minds, with consumer prices soaring by 7.9% What do I […].
Click to learn more about author Balaji Ganesan. Sources indicate 40% more Americans will travel in 2021 than those in 2020, meaning travel companies will collect an enormous amount of personally identifiable information (PII) from passengers engaging in “revenge” travel.
Organizations have become highly data-centric in the past years, increasing complications and costs as the volume of data rose. However, data integrity issues alone cost organizations $12.9 million annually, on average, according to Gartner.
We have seen an unprecedented increase in modern data warehouse solutions among enterprises in recent years. Experts believe that this trend will continue: The global data warehousing market is projected to reach $51.18 The reason is pretty obvious – businesses want to leverage the power of data […]. billion by 2028.
Data cleaning (or data cleansing) is the process of checking your data for correctness, validity, and consistency and fixing it when necessary. No matter what type of data you are handling, its quality is crucial. What are the specifics of data […].
After decades of languishing as the exhaust from the business processes, in the last decade, “data” has earned its rightful place as an asset of the business. However, for the most part in a data economy, it is a loss-leading asset, meaning while it is invaluable to […]. Click to learn more about author Gary Bhattacharjee.
However, most enterprises are hampered by data strategies that leave teams flat-footed when […]. The post Why the Next Generation of Data Management Begins with Data Fabrics appeared first on DATAVERSITY. Click to learn more about author Kendall Clark. The mandate for IT to deliver business value has never been stronger.
In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently. The platform enables quick, flexible, and convenient options for storing, processing, and analyzing data. Data Security and Governance Maintaining data security is crucial for any company.
The data value chain goes all the way from data capture and collection to reporting and sharing of information and actionable insights. As data doesn’t differentiate between industries, different sectors go through the same stages to gain value from it. Click to learn more about author Helena Schwenk.
This article was co-written by Lynda Chao & Tess Newkold With the growing interest in AI-powered analytics, ThoughtSpot stands out as a leader among legacy BI solutions known for its self-service search-driven analytics capabilities. Suppose your business requires more robust capabilities across your technology stack.
In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].
As we near the end of 2023, it is imperative for Data Management leaders to look in their rear-view mirrors to assess and, if needed, refine their Data Management strategies.
It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. Click to learn more about author Joan Fabregat-Serra.
Organizations manage data in the cloud through strategic planning and the implementation of best practices tailored to their specific needs. This involves selecting the right cloud service providers and technology stacks that align with their data management goals.
The worldwide shift toward cloud computing significantly changes how businesses approach data management and operation. Regardless of whether private, public, or hybrid cloud models are employed, the advantages of cloud computing are numerous, including heightened efficiency, reduced expenses, and increased flexibility.
The possibility for businesses to achieve efficiency, flexibility, and scalability is greatly enhanced by the fact that cloud computing technology is now available to all types of enterprises and marketplaces.
Organizations are sitting on a mountain of data and untapped business intelligence, all stored across various internal and external systems. Those that utilize their data and analytics the best and the fastest will deliver more revenue, better customer experience, and stronger employee productivity than their competitors.
Organizations are sitting on a bevy of data and intelligence, all stored across various internal and external systems. Those that utilize their data and analytics the best and the fastest will deliver more revenue, better customer experience, and stronger employee productivity than their competitors.
The ways in which we store and manage data have grown exponentially over recent years – and continue to evolve into new paradigms. For much of IT history, though, enterprise data architecture has existed as monolithic, centralized “data lakes.” The post Data Mesh or Data Mess?
Unlocking value from data is a journey. It involves investing in data infrastructure, analysts, scientists, and processes for managing data consumption. Even when data operations teams progress along this journey, growing pains crop up as more users want more data. You don’t have to grin […].
We organize all of the trending information in your field so you don't have to. Join 17,000+ users and stay up to date on the latest articles your peers are reading.
You know about us, now we want to get to know you!
Let's personalize your content
Let's get even more personalized
We recognize your account from another site in our network, please click 'Send Email' below to continue with verifying your account and setting a password.
Let's personalize your content