CloverDX

Introducing CloverDX 6.0 - the latest version of CloverDX, out now with some great new features to bring the power of CloverDX to business users as well as IT teams. The major theme for this release is collaboration among IT and business users done right. We believe in data for everyone – and CloverDX 6 builds on top of the platform’s existing capabilities to broaden access to data to users across your organization. With this latest version, business users will be able to explore and use data in a self-serve way – without needing assistance from IT – but with IT still in control. New features include: - CloverDX Data Catalog: Where users – including non-technical users – can find and explore high-quality, contextual data sets that have been configured and published by IT. Users get live, self-serve data that they know is accurate and reliable, and IT teams maintain control but don’t need to respond to every business request for new data. - CloverDX Wrangler: A brand new interface for business users for self-service data preparation. Now non-technical users can transform and work with their data without the need for coding or having to ask IT teams for help.

The latest version of CloverDX is out this month, and we're hosting a live walkthrough of all the new features with CloverDX VP Product, Branislav Repcek. What's new and improved in CloverDX 6.5? Lots! Including: - A preview of a brand new feature - Data Manager - that enables non-technical data owners to manually review, edit and approve data as part of a larger automated process - Create aggregations more easily with a more powerful Aggregate component in Designer - Preview your data as it's sent to a target in Wrangler - Improved configuration management for easier deployment - UI improvements right across the platform for easier navigation Register now to join us on Tuesday July 16th at 10am EST (or register to get the recording sent to you afterwards): https://hubs.li/Q02FPjtJ0

This hits the nail on the head - don't make your new customers do all the work to get their data into your platform! If (as is likely) data migration is a part of your SaaS onboarding, you don't want to be building something from scratch for every new client - something that takes up way too much engineering time and becomes a bottleneck to getting customers live. Far better to build a framework you can reuse, with minimal effort each time, and that means you can say to customers 'just give us whatever data you have, we'll take care of it for you and just make it work'.

Most businesses now understand how vital data health is to their success, but how to get there? In one word: Automation. We’ve summed it up in 5 short and handy steps: 1. Standardize your data Manually standardizing millions of data points is daunting, if not impossible. Automation makes scaling to handle rapid data effortlessly, freeing time and maturing your data strategy. 2. Validate your data Manual validation tends to create bottlenecks. Automating this process helps relieve developers from repetitive tasks and frees up time for business growth. 3. Deduplicate data Getting rid of copies and siloed variants of the same data takes time when done manually. A streamlined automation process ensures you have one golden copy of a given data set or as few copies as possible. 4. Analyze data quality If you don’t know what needs cleaning or in what way, you won’t be able to ensure the highest possible level of quality. Smart tools keep an eye on your data health for you, identifying problems so they can be resolved without delay or issue. 5. Find out if you have a data quality problem Are you waving or drowning? Automation is a life raft in an ocean of bad data. Spot the signs like conflicting reports or disproportionate amounts of time spent on data preparation early and take action before it costs your business time and money. As data floods into every modern business, take the time to find the right data-cleansing tools and processes for your business. It’s what differentiates those organizations that swim with purpose and those who become lost at sea… #DataCleansing #DataQuality #DataIntegration #Automation #CloverDX ☘️

All businesses have errors in their data. It’s inevitable. It’s what you do with it that matters. Inaccuracies, omissions, duplicates, and more all compromise your data quality management. Creating an error management process and establishing auditing procedures are vital, but the first step is acknowledging that bad data exists and requires a strategy. The result? You won’t just remove potential pitfalls but also uncover tangible business improvements. 📊 Find out more: https://hubs.li/Q02fpMnb0 #DataQuality #DataManagement #BusinessInsights #CloverDX ☘️

Derive meaningful insights. Make informed decisions. Manage errors effectively. It all becomes the norm when you build your data pipelines with data quality in mind. Building robust data quality practices requires a smart approach that considers how your data is sourced and validated, along with other factors. Does your data pipeline stack up? Read more here: https://hubs.li/Q02fpN7M0 #DataQuality #DataPipelines #BusinessInsights #CloverDX ☘️

85% of big data projects fail to get off the ground. Only those businesses that follow the right data architecture principles and build them into the very heart of their strategy and culture are the ones who escape that statistic. How can you do that? Here are 4 key data architecture best practices that all businesses should integrate for success: 1. Validate all data at the point of entry Bad data quality impacts your bottom line. Manually catching every error is nigh-on impossible, but a data integration platform can validate your data automatically at the point of entry and prevent bad data from spreading throughout your system. 2. Strive for Consistency Embrace a common vocabulary in your data architecture to enhance collaboration. Foster a "single version of truth," enabling accurate data models with consistent entity relationships. Consistency is critical for robust data architecture. 3. Document Everything Regular ‘data discoveries’ will allow your organization to check how much data it’s collecting, which datasets are aligned, and which applications need updating. This becomes seamless when made a part of your data integration process. 4. Avoid Duplicating Functionality Copying data between applications eats into developer time. Instead, invest in an effective data integration architecture that automatically keeps your data in a common repository and format. Now, everyone can operate from a single version of the truth without needing to update and verify every piece of information. Remember: your data architecture is only as good as its underlying principles. Without the right intent, standards, and universal language, getting your strategy off the ground isn't easy. #DataArchitecture #DataStrategy #DigitalTransformation #DataIntegration #CloverDX ☘️

Spaghetti or straightforward? Scalable or stuck in the past? Innovative or just plain in your way? Depending on your approach, in-house data manipulation can go very right or wrong. We’ve collected some of the most common pitfalls businesses encounter when handling their data, from security risks and invisible costs to inefficiency and siloed projects. Have a look and see how your business measures up: https://hubs.li/Q02fpF9-0 #DataQuality #DataManipulation #DataManagement #CloverDX ☘️

Data integration offers businesses so much value and innovation, but there are challenges along the way. Failing to address these issues can undermine any organization and ultimately hamper their growth. Here are 6 of the biggest pitfalls: 1. Your data isn’t where you need it to be You want your data in one centralized place, but you struggle with the execution. Sound familiar? Relying on developers to curate and combine takes valuable time. An intelligent data integration platform cuts out the middleman and speeds up your innovation goals. 2. Your data *is* there, but it’s late Some processes require real-time or near-real-time data collection. Doing that manually is more or less impossible. Automation can act as your secret weapon, ingesting and processing data without exhausting your resources. 3. Your data isn’t formatted correctly Data that’s incoherent or in the wrong format isn’t actionable. Data transformation tools eliminate this problem by analyzing the original base language, determining the correctly formatted language, and automatically making the change. 4. You have poor quality data Every business acquires it eventually. By proactively validating your data as soon as it’s ingested, you lower the amount that enters your systems and spot errors before they become larger issues. 5. There are duplicates throughout your pipeline More than 92% of businesses are aware of duplicate data in their systems, quite often resulting from a ‘silo mentality.’ Promoting a data-sharing culture and deploying tools that help standardize validated data help mitigate these risks. 6. There is no clear shared understanding of your data Effective communication between teams and establishing a common vocabulary of data definitions and permissions is vital. A clear ownership of your data and roadmap helps your business avoid misinformation and misalignment. Have these challenges affected your business? #DataIntegration #BusinessTransformation #DataManagement #CloverDX ☘️

95% of businesses have seen impacts related to poor data quality. Is yours one of them? Low-quality data can undermine decision-making, lower efficiency, and potentially result in severe financial loss. There are two types of businesses: those who know they have bad data and those who don’t. Those companies that take ownership of their data quality are the ones that ultimately reap the rewards. See our 3 expert tips to clean up your data here: https://hubs.li/Q02j6v5T0 #DataQuality #BusinessRisks #DataManagement #CloverDX ☘️