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
Data, undoubtedly, is one of the most significant components making up a machine learning (ML) workflow, and due to this, data management is one of the most important factors in sustaining ML pipelines.
Reading Larry Burns’ “DataModel Storytelling” (TechnicsPub.com, 2021) was a really good experience for a guy like me (i.e., someone who thinks that datamodels are narratives). The post Tales of DataModelers appeared first on DATAVERSITY. The post Tales of DataModelers appeared first on DATAVERSITY.
This time, well be going over DataModels for Banking, Finance, and Insurance by Claire L. This book arms the reader with a set of best practices and datamodels to help implement solutions in the banking, finance, and insurance industries. Welcome to the first Book of the Month for 2025.This
So, I had to cut down my January 2021 list of things of importance in DataModeling in this new, fine year (I hope)! The post 2021: Three Game-Changing DataModeling Perspectives appeared first on DATAVERSITY. Common wisdom has it that we humans can only focus on three things at a time.
A unified datamodel allows businesses to make better-informed decisions. By providing organizations with a more comprehensive view of the data sources they’re using, which makes it easier to understand their customers’ experiences. appeared first on DATAVERSITY.
In this article I want to explore how to integrate data requirements with product features and user stories; the result is some very useful traceability to where a particular data entity or attribute is being used across a product.
Through big datamodeling, data-driven organizations can better understand and manage the complexities of big data, improve business intelligence (BI), and enable organizations to benefit from actionable insight.
They deliver a single access point for all data regardless of location — whether it’s at rest or in motion. Experts agree that data fabrics are the future of data analytics and […]. The post Maximizing Your Data Fabric’s ROI via Entity DataModeling appeared first on DATAVERSITY.
This section explores four main challenges: data quality, interpretability, generalizability, and ethical considerations, and discusses strategies for addressing each issue. Download end-to-end articles with codes 1. Models built on pre-crisis data may become inaccurate, as historical relationships between features and outcomes change.
Datamodels play an integral role in the development of effective data architecture for modern businesses. They are key to the conceptualization, planning, and building of an integrated data repository that drives advanced analytics and BI.
In the contemporary business environment, the integration of datamodeling and business structure is not only advantageous but crucial. This dynamic pair of documents serves as the foundation for strategic decision-making, providing organizations with a distinct pathway toward success.
Therefore, it was just a matter of time before this chess-inspired outlook permeated my professional life as a data practitioner. In both chess and datamodeling, the […] The post Data’s Chess Game: Strategic Moves in DataModeling for Competitive Advantage appeared first on DATAVERSITY.
And we have short delivery cycles, sprints, and a lot of peers to share datamodels with. The post Quick, Easy, and Flexible DataModel Diagrams appeared first on DATAVERSITY. Many of us have a lot to do. In search of something lightweight, which is quick and easy, and may be produced (or consumed) by other programs?
As more and more companies start to use data-related applications to manage their huge assets of data, the concepts of datamodeling and analytics are becoming increasingly important. Companies use data analysis to clean, transform, and model their sets of data, whereas they […].
But decisions made without proper data foundations, such as well-constructed and updated datamodels, can lead to potentially disastrous results. For example, the Imperial College London epidemiology datamodel was used by the U.K. Government in 2020 […].
aka DataModeling What?) Sometimes the obvious is not that … obvious. Many people know that I am on the graph-y side of the house. But explaining a simple matter like […]. The post What’s in a Name? appeared first on DATAVERSITY.
This requires a strategic approach, in which CxOs should define business objectives, prioritize data quality, leverage technology, build a data-driven culture, collaborate with […] The post Facing a Big Data Blank Canvas: How CxOs Can Avoid Getting Lost in DataModeling Concepts appeared first on DATAVERSITY.
Power Pivot is an Excel add-in that is used to perform powerful data analysis and create sophisticated datamodels. It can handle large volumes of data from several sources and all of this within a single Excel file. In this article, you will learn the following topics. What Is a Power Pivot? Read More.
Likewise, Python is a popular name in the data preprocessing world because of its ability to process the functionalities in different ways. In this article, we will discuss how Python runs data preprocessing with its exhaustive machine learning libraries and influences business decision-making.
Many BAs struggle to produce ‘normalized’, function-independent datamodels (or don’t produce them at all). Very few business stakeholders can appreciate such models as “… a picture worth a thousand words.”
Since the field covers such a vast array of services, data scientists can find a ton of great opportunities in their field. Data scientists use algorithms for creating datamodels. These datamodels predict outcomes of new data. Data science is one of the highest-paid jobs of the 21st century.
Agile datamodeling involves a collaborative, iterative, and incremental approach to datamodeling. In this article, we discuss how MySQL Document Store could be used for agile datamodeling.
Mark van Rijmenam, the founder of DataFlaq is one of the world’s leading experts on the intersection between big data and interpersonal communication. He discussed this topic in detail in one of his articles. There are a number of ways that big data is changing the nature of these relationships.
You can’t talk about data analytics without talking about datamodeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right datamodel is an important part of your data strategy.
Three different types of datamodels exist, each of which plays a distinct role in datamodeling. They help an organization’s efforts in organizing, understanding, and making productive use of enterprise data resources.
How can GraphQL help with datamodelling in the Enterprise? This online guide aims to answer pertinent questions for software architects and tech leaders, such as: Why would you use GraphQL? Why should you pay attention to GraphQL now? By Daniel Bryant.
I have always been a data fanatic. It started when I was a programmer analyst and learned to love such arcane data structures as ISAM (a relic), VSAM (simple, but efficient), and later DB2 (powerful and flexible). My recent database exposure has been MySQL for websites, but only as a BA.
One of the most important questions about using AI responsibly has very little to do with data, models, or anything technical. How can […] The post Ask a Data Ethicist: How Can We Set Realistic Expectations About AI? It has to do with the power of a captivating story about magical thinking.
In this article, we’ll take a closer look at why companies should seek new approaches to data analytics. Based on this assumption, specialists relied on false predictive datamodels that could only reflect a simplified picture of the possible future.
Therefore, machine learning is of great importance for almost any field, but above all, it will work well where there is Data Science. Data Mining Techniques and Data Visualization. Data Mining is an important research process. Data Science vs Data Mining: Concluding Thoughts.
Data Governance describes the practices and processes organizations use to manage the access, use, quality and security of an organizations data assets. The data-driven business era has seen a rapid rise in the value of organization’s data resources.
Specifically, records, sealed classes, and pattern matching work together to enable easier data-oriented programming in Java. Project Amber has brought a number of new features to Java in recent years. While each of these features are self-contained, they are also designed to work together. By Brian Goetz.
In this article, I describe a method of modellingdata so that it meets business requirements. Central to this method is that modelling not only the required data, but also the subset of the real world that concerns the enterprise.
Many software developers distrust data architecture practices such as datamodeling. They associate these practices with rigid and bureaucratic processes causing significant upfront planning and delays.
ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses.
In this article, authors discuss the data lineage as a critical component of data pipeline root cause and impact analysis workflow and how automating lineage creation and abstracting metadata to field-level helps with the root cause analysis efforts. By Mei Tao, Xuanzi Han, Helena Muñoz.
In part one of this article, we discussed how data testing can specifically test a data object (e.g., table, column, metadata) at one particular point in the data pipeline.
In this article, you’ll discover: upcoming trends in business intelligence what benefits will BI provide for businesses in 2020 and on? Features: interactive tables, graphs, dashboards data publishing access to a broad data range custom analytic applications data storytelling web and mobile. SAP Lumira.
Every aspect of analytics is powered by a datamodel. A datamodel presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. Datamodeling organizes and transforms data.
Power Pivot is an Excel add-in that is used to perform powerful data analysis and create sophisticated datamodels. It can handle large volumes of data from several sources and all of this within a single Excel file. In this article, you will learn the following topics. What is a Power Pivot? Read More.
Larry Burns’ latest book, DataModel Storytelling, is all about maximizing the value of datamodeling and keeping datamodels (and datamodelers) relevant. Larry Burns is an employee for a large US manufacturer.
These days, there is much conversation about the necessity of the datamodel. The datamodel has been around for several decades now and can be classified as an artifact of an earlier day and age. But is the datamodel really out of date? And exactly why do we need a datamodel, anyway? […]
And therefore, to figure all this out, data analysts typically use a process known as datamodeling. It forms the crucial foundation for turning raw data into actionable insights. Datamodeling designs optimal data structures and relationships for storage, access, integrity, and analytics.
Next, I will explore business intelligence in roles such as data analyst and BI analyst. You can discover the importance of strong business acumen, datamodeling, and ETL skills. I also cover business process management jobs, including Process Modeler, Process Analyst, and Process Architect.
We organize all of the trending information in your field so you don't have to. Join 57,000+ 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