Okay, I’ll admit, I am pretty biased when it comes to how people within organizations work together to ensure successful data projects. I have been involved in too many projects that failed to take into account the importance of collaboration across departments and functions. They were stuck on data and only the data.

This is a guest post for Integrate.io written by Bill Inmon, an American computer scientist recognized as the "father of the data warehouse." Inmon wrote the first book and magazine column about data warehousing, held the first conference about this topic, and was the first person to teach data warehousing classes. Data science is immature. This statement is not pejorative; it is simply a statement of historical fact. As such, it is not arguable.

You have been putting in the work, and your company has been growing manifold, Your client base is growing more than ever, and the projects are pouring in. So what comes next? it is now time to focus on the data that you are generating. When programming an application, DevOps engineers keep track of many things, such as bugs, fixes, and the overall application performance. This ensures that the application operates with minimum downtime and that any future errors can be predicted.

Too many organizations start their data projects at the wrong end of the pipeline. Although challenges with data quality, integrity, access, and visibility are all important issues to address, a project should never start with the data. The reality is that all investments in data are meaningless if no business value can be gained. And this requires starting at the other end of the spectrum - evaluating the business problem to identify how data can help drive change within the organization.

There is no question about the usefulness of big data these days. However, if you want the best data, you need it to be as accurate as possible. That means that your data has to be up-to-date, correct, and clean. Using one of these top data cleansing tools can help you be sure of this.

The AlloyDB connector from Integrate.io empowers organizations to rapidly consolidate all of their data into AlloyDB—a high-powered, Google Cloud database that is 100% compatible with open-source PostgreSQL. By serving as an easy-to-set-up, high-speed data pipeline to AlloyDB, Integrate.io helps businesses modernize their legacy proprietary databases by migrating them to open-source, PostgreSQL-compatible systems.

What pray tell is a tautology? A tautology is something that, under all conditions, is true. It is kind of like gravity. You can throw a ball in the air and, for a few seconds, it seems to be suspended. But soon gravity takes hold, and the ball falls back to earth.

A Data Lake is used to refer to a massive amount of data stored in a structured, unstructured, semi-structured, or raw form. The purpose is just to consolidate data into one destination and make it usable for data science and analytics algorithms. This data is used for observational, computational, and scientific purposes. The database has made it easier for AI models to gather data from various resources and implement a flawless system that can make informed decisions.

In the competitive market of ETL solutions, platforms like Hevo, Fivetran and Integrate.io are amongst the top contenders. While they all are ETL/ELT platforms, each of them has their own unique set of features to offer. The best ETL tool for your business is the one that is best aligned to your requirements. So how do you decide which tool meets your business needs?

Big Data started as a replacement for data warehouses. The Big Data vendors are loath to mention this fact today. But if you were around in the early days of Big Data, one of the central topics discussed was — if you have Big Data do you need a data warehouse? From a marketing standpoint, Big Data was sold as a replacement for a data warehouse. With Big Data, you were free from all that messy stuff that data warehouse architects were doing.

Data mesh is being talked about a lot to describe the way data is managed across the organization. But what does it really mean for your organization’s data management strategy and how can its framework support your business needs and drive data pipeline success? On a high level, data mesh is about connecting and enabling data management across distributed systems.

Data plays a vital role in the growth of an organization. Companies spend large amounts of money on building data and big data infrastructures such as data vaults, data marts, data lakes, and data warehouses. These infrastructures are populated via multiple data sources using robust ETL pipelines that function throughout the day. A data infrastructure must operate 24/7 to provide real-time analysis and data-driven business insights.

Does your Ecommerce business team understand the customer journey? By tracking the history of individual customer behavior and customer interactions across different channels, your organization can better understand what motivates your audience — and cater to them with the right marketing campaigns.

This is a guest post with exclusive content by Bill Inmon, Mary Levins, and Georgia Burleson. Bill “is an American computer scientist recognized by many as the father of the data warehouse. Inmon wrote the first book, held the first conference, wrote the first column in a magazine, and was the first to offer classes in data warehousing.” -Wikipedia.

Conventional databases are no longer the appropriate solution in a world where data volume is growing every second. Many modern businesses are adopting big data technologies like data lakes to counter data volume and velocity. Data lake infrastructures such as Apache Hadoop are designed to handle data in large capacities. These infrastructures offer benefits such as data replication for enhanced protection and multi-node computing for faster data processing.

Here’s what you need to know about how to use your customer data platform (CDP) as your data warehouse: Whether you’re a mom-and-pop store or an ecommerce giant, understanding the customer journey is crucial to your organization’s success. When you collect data across a wide range of customer touchpoints, you can use this wealth of information for many different use cases: performing audience segmentation, improving your marketing campaigns, boosting customer engagement, and more.