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Tag: datamanagement

DataEd Webinar: Essential Metadata Strategies

To view the slides from this presentation, click HERE>>

This webinar is sponsored by:

About the Webinar

The first step towards understanding data assets’ impact on your organization is understanding what those assets mean for each other. Metadata — literally, data about data — is a practice area required by good systems development, and yet is also perhaps the most mislabeled and misunderstood Data Management practice. Understanding metadata and its associated…… Read more...

What Is a Data Silo?

Data silos have often had a negative connotation. They describe isolated data islands that appear or are discovered upon finding disjointed Data Management components. Systems that cannot programmatically work with other systems because of older or incompatible codeFixed data that is controlled by one department or team but is cut off from others in the organization.… Read more...

Implementing Data Strategy Across the Data Lifecycle

Organizations wishing to implement a Data Strategy—a set of decisions that form a pattern, charting a high-level course of action—face significant challenges in unifying their message across the data lifecycle. Only 30% of companies have a clear organizational Data Strategy, leaving their different departments to figure out how to manage company data assets. Moreover, two out of three Data Management practices originate at a departmental level instead of from the top, leaving…… Read more...

Factors and Considerations Involved in Choosing a Data Management Solution

When a business enters the domain of data management, it is easy to get lost in a flurry of promises, brochures, demos and the promise of the future. In the first article in our two-part series, entitled, ‘Data Warehouse, Data Lake, Data Mart, Data Hub: A Definition of Terms’, we defined the terms and differences in the market so that businesses can better understand the possibilities of Data Warehouses, Data Marts, Data Lakes and Data Hubs.
In this article, we will present the factors…

Continue reading: http://www.datasciencecentral.com/xn/detail/6448529:BlogPost:1071225

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Why Dynamic Algorithms Still Haven’t Replaced Human Rules

The general perception among data-centric organizations is that data management technology is progressing linearly. Cloud warehouses, for example, are generally deemed superior to on-premise relational ones. Kubernetes’ portability is viewed as more utilitarian than monolithic ERP systems are, and dynamic algorithms that improve over time are considered the successor to static, human made rules—especially for analytics.

The rationale for the purported triumph of machine learning’s aptitude over that of human devised rules is relatively simple and, for the most part, convincing. “Most importantly, on a fundamental level, rules are by definition backwards looking,” posited Forter COO Colin Sims. “You write a rule based on something you know that happened, and then you’re assuming that more is going to happen based on the past.”… Read more...

Is Unstructured Data the Future of Data Management?

Click to learn more about author Nahla Davies.

In an increasingly tech-reliant world, data informs and powers much of our day-to-day lives. Data can be used to enhance AI capabilities, create personalized experiences, or be applied in medical research to help save lives. However, the biggest question remains: What is the best method to store, organize, and use the vast amounts of data at our disposal?

Enter unstructured data management. Organizations are increasingly looking to unstructured data for analytic, regulatory, and decision-making processes. From business intelligence to marketing campaigns, it’s not uncommon for unstructured data analysis to drive human decision-making.… Read more...

Working with Metadata Management Frameworks

Metadata Management will grow into 2021 and beyond. According to a DATAVERSITY® Trends in Data Management Report, 84 percent of business respondents had a Metadata Management initiative in place or had plans for one.

MarketWatch, a consulting firm, expects massive growth by 2026. How much success a company will have with Metadata Management will depend on implementing a useful Metadata Management framework.

Getting a handle on metadata makes sense for companies in complying with data regulations, improving data quality, exploring machine learning, and using data better. But Metadata Management goes beyond the technical, to the people and policies that support it.… Read more...

Announcing the winners of the 2021 Next-generation Data Infrastructure request for proposals

In April 2021, Facebook launched the Next-generation Data Infrastructure request for proposals (RFP). Today, we’re announcing the winners of this award.
VIEW RFPThe Facebook Core Data and Data Infra teams were interested in proposals that sought out innovative solutions to the challenges that still remain in the data management community. Areas of interest included, but were not limited to, the following topics:

Large-scale query processing
Physical layout and IO optimizations
Data management and processing at a global scale
Converged architectures for data wrangling, machine learning, and analytics
Advances in testing and verification for storage and processing systems

Read our Q&A with database researchers Stavros Harizopoulos and Shrikanth Shankar to learn more about database research at Facebook, the goal of this RFP, and the inspiration behind the RFP.… Read more...

Features are the New Data

In my prior blog “Reframing Data Management: Data Management 2.0”, I talked about the importance of transforming data management into a business strategy that supports the sharing, re-using and continuous refinement of the data and analytics assets to derive and drive new sources of customer, product, and operational value. If data is “the world’s most valuable resource”, then we must transform data management into an offensive, “data monetization” business strategy that proactively guides organizations in the application of their data to the business to drive quantifiable financial impact (Figure 1).

Figure 1: Activating Data Management

In this blog I want to drill into the importance of Machine Learning (ML) “Features”.


Understanding Static Data Management

Photo by Markus Winkler on Unsplash

Before worrying about where user data will be stored and handled, you should probably worry about how you will handle your static data. Static data refers to data that doesn’t change frequently. This is often the data that defines the configuration and structure of the system. It might not seem that important, and you probably think that this is just overhead. However, if you don’t set up an automatic and efficient way to handle this data, you will have a difficult time when the system starts getting bigger, and you need to migrate to another system.


Reframing Data Management:  Data Management 2.0

A cartoon making its way around social media asks the provocative question “Who wants clean data?” (Everyone raises their hands) and then asks, “Who wants to CLEAN the data?” (Nobody raises their hands).  I took the cartoon one step further (apology for my artistic skills) and asked, “Who wants to PAY for clean data?” and shows everyone running for the exits (Figure 1).

Figure 1: Today’s Data Management Reality

Why does everyone run for the exits when asked to pay for data quality, data governance, and data management?  Because we do a poor job of connecting high-quality, complete, enriched, granular, low-latency data to the sources of business and operational value creation.


Dual grants totaling $2.25 million help students take a byte out of data science | Penn State University

UNIVERSITY PARK, Pa. — All of the data produced or used in 2020 was estimated to be about 59 zettabytes, each of which equals a billion terabytes. If each terabyte represents a mile, 59 zettabytes would allow for almost 10 full round trips from Earth to Pluto.

Understanding and managing data requires strong critical thinking and problem-solving skills, skills that are essential for engineering students, according to Rebecca Napolitano, assistant professor of architectural engineering at Penn State. However, contextualized data science courses that teach students to apply such skills to their fields — including the importance of data management for other sectors — are not typically a requirement for students in engineering and other disciplines.


Top Data Science Interviews at Google You Must Be Aware Of


August 30, 2021

Analytics Insight features the top Data Science interviews at Google for aspiring data scientists to crack an offer

Data Science is thriving in the global data-driven market owing to a massive growth of real-time data from data explosion, especially in the post-pandemic scenario. Tech giants like Google, Amazon and many more tech companies need professional data scientists for effective data management. Digitalization has created a huge demand for data scientists in the last few years. Aspiring data scientists have a desire to work at Google in the nearby future. Thus, they need to be aware of some Data Science interviews to efficiently crack the requirements of tech companies and receive a lucrative salary package per year with sufficient work experience for a better future.


Shrinking the Education Gap in Data Science

(SFIO CRACHO/Shutterstock)

There’s a growing mismatch between the growth of data and the growth of data skills and knowledge. The former is increasing at a healthy rate, while the latter is struggling to keep up. One outfit that’s hoping to close the data education gap is Data Society. The company, which provides tailored education and training sessions to companies and organizations (as opposed to individual users), seeks to give users a basic foundation of core skills in the areas of data management, data analytics, and data science.

The Washington, D.C. company employs 50 full-time educators, who present a data science curriculum that was created by a team of professional content creators and data scientists.


How to ensure success with augmented data management?


Augmentation is the growing trend that organizations adopt to automate most data management workflows to free up vital time for their data scientists. Machine learning and artificial intelligence are used to automate manual data management tasks in augmented data management (ADM). Gartner predicts that ADM will free up to 20% of data science teams’ time by 2023.

As per the report, organizations that dynamically automate, connect, and optimize their data management processes through active metadata, artificial intelligence, Machine Learning, and Data Fabric will spend 30% less time on Data Integration processes by the same time. It helps organizations to make correct decisions quickly and maximize their business processes’ benefits.


The Secret Life of Metadata

It’s important to acknowledge that metadata is complex, varied and there is more than one type of metadata. Each of them could serve a very different purpose in understanding and managing information resources. Below is a summary of three main types of metadata according to the Data Management Body of Knowledge.

Image by Author

Looks lengthy and complicated, right? Don’t worry. I only have two key takeaways for you to keep in mind.

Firstly, some metadata could be more relevant for business users who are searching for a dataset while other details dictate how businesses protect sensitive data, comply with privacy laws and mitigate security risks.


Blockchain emerging as Next-Generation Data and Model Governance Framework

Introduction and Motivation

The blockchain technology has led to a strong foundation for different applications related to asset management, medical/health, finance, and insurance. Data analytics provided by the blockchain network helps efficient data management, analysis, privacy, quality assurance, access, and integration in heterogeneous environments.

The role of blockchain in data privacy is evidently becoming more strong when the current breakthroughs in quantum computing render present encryption technologies ineffective and make them susceptible to brute-force attacks. As the volume of data that blockchain networks store is also rapidly increasing over time, let’s explore how blockchain technology can play a dominant role in Data Governance.