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In this contributed article, Mayank Mehra, head of product management at Modak, shares the importance of incorporating effective dataobservability practices to equip data and analytics leaders with essential insights into the health of their data stacks.
In this special guest feature, Andy Petrella, CPO and founder of Kensu, points out that as application observability became a central element for DevOps teams, dataobservability is set to follow the same path and help data teams to lower maintenance costs, scale up value creation from data, and maintain trust in it.
In this video interview, Ashwin Rajeeva, co-founder and CTO of Acceldata, we talk about the company’s dataobservability platform – what "dataobservability" is all about and why it’s critically important in big dataanalytics and machine learning development environments.
Acceldata, a market leader in dataobservability, announced significant enhancements to its data reliability solution, including no-code/low-code options, intelligent alerting, targeted recommendations, and self-healing capabilities to solve the most complex data reliability challenges while improving operational efficiency and reducing costs.
To learn more about dataobservability, don’t miss the DataObservability tracks at our upcoming COLLIDE Data Conference in Atlanta on October 4–5, 2023 and our Data Innovators Virtual Conference on April 12–13, 2023! Are you struggling to make sense of the data in your organization?
Companies are spending a lot of money on data and analytics capabilities, creating more and more data products for people inside and outside the company. These products rely on a tangle of data pipelines, each a choreography of software executions transporting data from one place to another.
Read on for the highlights from this panel – including actionable tips to ensure success in your 2025 data, analytics, and AI initiatives. Yoğurtçu also states that you need to “ensure that trusted data is served in a timely fashion.” Take a proactive approach. Leverage AI to enhance governance.
Revefi also sells an AI feature for insights, helping data teams move quickly to identify and correct “critical data issues.” ” The company is led by CEO Sanjay Agrawal , who was previously co-founder and head of the Seattle office for AI analytics company ThoughtSpot.
Read on for the highlights from this panel – including actionable tips to ensure success in your 2025 data, analytics, and AI initiatives. Yoğurtçu also states that you need to “ensure that trusted data is served in a timely fashion.” Take a proactive approach. Leverage AI to enhance governance.
Author’s note: this article about dataobservability and its role in building trusted data has been adapted from an article originally published in Enterprise Management 360. Is your data ready to use? That’s what makes this a critical element of a robust data integrity strategy. What is DataObservability?
DataObservability and Data Quality are two key aspects of data management. The focus of this blog is going to be on DataObservability tools and their key framework. The growing landscape of technology has motivated organizations to adopt newer ways to harness the power of data.
That’s why today’s application analytics platforms rely on artificial intelligence (AI) and machine learning (ML) technology to sift through big data, provide valuable business insights and deliver superior dataobservability. What are application analytics?
In this blog, we are going to unfold the two key aspects of data management that is DataObservability and Data Quality. Data is the lifeblood of the digital age. Today, every organization tries to explore the significant aspects of data and its applications. What is DataObservability and its Significance?
It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may prompt you to rethink your dataobservability strategy. In either case, the change can affect analytics.
It includes streaming data from smart devices and IoT sensors, mobile trace data, and more. Data is the fuel that feeds digital transformation. But with all that data, there are new challenges that may require consider your dataobservability strategy. Is your data governance structure up to the task?
Right now, over 12% of Fortune 1000 businesses have invested more than $500 million into big data and analytics, according to a NewVantage Partners survey. The post How Enterprises Can Leverage DataObservability for Digital Transformation appeared first on DATAVERSITY. But are they using it effectively?
If data processes are not at peak performance and efficiency, businesses are just collecting massive stores of data for no reason. Data without insight is useless, and the energy spent collecting it, is wasted. The post Solving Three Data Problems with DataObservability appeared first on DATAVERSITY.
Generative AI and Data Storytelling (Virtual event | 27th September – 2023) A virtual event on generative AI and data storytelling. The event is hosted by Data Science Dojo and will be held on September 27, 2023. The speaker is Andrew Madson, a dataanalytics leader and educator.
Rapid advancements in digital technologies are transforming cloud-based computing and cloud analytics. Big dataanalytics, IoT, AI, and machine learning are revolutionizing the way businesses create value and competitive advantage. In a connected mainframe/cloud environment, data is often diverse and fragmented.
IMPACT is a great opportunity to learn from experts in the field, network with other professionals, and stay up-to-date on the latest trends and developments in data and AI. The summit will be held on November 8th, 2023.
Instead of developing a custom solution solely for the immediate concern, IBM sought a widely applicable data validation solution capable of handling not only this scenario but also potential overlooked issues. That is when I discovered one of our recently acquired products, IBM® Databand® for dataobservability.
Data engineers act as gatekeepers that ensure that internal data standards and policies stay consistent. DataObservability and Monitoring Dataobservability is the ability to monitor and troubleshoot data pipelines.
More sophisticated data initiatives will increase data quality challenges Data quality has always been a top concern for businesses, but now the use cases for it are evolving. 2023 will continue to see a major shift in organizations increasing their investment in business-first data governance programs.
With Tangent Works companies are able to solve challenges such as losses resulting from poor forecasting and missed ROI from time-series data. Blueprint Blueprint utilizes its expertise in data management, including analytics, migration, governance, and centralization to enable its clients to get the most from their data.
As organizations steer their business strategies to become data-driven decision-making organizations, data and analytics are more crucial than ever before. The concept was first introduced back in 2016 but has gained more attention in the past few years as the amount of data has grown.
While one approach is to move entire datasets from their source environment into a data quality tool and back again, it’s not the most efficient or ideal – particularly now, with countless businesses moving to the cloud for data and analytics initiatives. And great news , all of the content is now available on demand!
Leaders feel the pressure to infuse their processes with artificial intelligence (AI) and are looking for ways to harness the insights in their data platforms to fuel this movement. Indeed, IDC has predicted that by the end of 2024, 65% of CIOs will face pressure to adopt digital tech , such as generative AI and deep analytics.
In any strategic undertaking, trusted data is key to making better decisions that unlock new opportunities for your organization. One of the first steps in the process is to access and replicate the data you need to the cloud for robust analytics and reporting. But the process doesn’t end there.
With data catalogs, you won’t have to waste time looking for information you think you have. Once your information is organized, a dataobservability tool can take your data quality efforts to the next level by managing data drift or schema drift before they break your data pipelines or affect any downstream analytics applications.
That means finding and resolving data quality issues before they turn into actual problems in advanced analytics, C-level dashboards, or AI/ML models. DataObservability and the Holistic Approach to Data Integrity One exciting new application of AI for data management is dataobservability.
For instance, you may have a database of customer names and addresses that is accurate and valid, but if you do not also have supporting data that gives you context about those customers and their relationship to your company, that database is not as useful as it could be. That is where data integrity comes into play.
Advanced analytics and AI/ML continue to be hot data trends in 2023. According to a recent IDC study, “executives openly articulate the need for their organizations to be more data-driven, to be ‘data companies,’ and to increase their enterprise intelligence.”
The mesh approach allows for more streamlined data-driven decisions and processes within specific departments and puts responsibility in the hands of the people who actually use the information. AI in DataObservability Automation has steadily become more common in data management software, but it’ll reach new heights in 2023.
Video of the Week: Beyond Monitoring: The Rise of DataObservability Watch as Monte Carlo’s Shane Murray introduces “DataObservability” as the game-changing solution to the costly reality of broken data in advanced data teams.
Data-driven decision-making has never been more in demand. A recent survey found that 77% of data and analytics professionals place data-driven decision-making as the leading goal for their data programs. And yet less than half (46%) rate their ability to trust data for decision-making as “high” or “very high.”
To be clear, data quality is one of several types of data governance as defined by Gartner and the Data Governance Institute. Quality policies for data and analytics set expectations about the “fitness for purpose” of artifacts across various dimensions. These integrations let us provide a whole product.
40% of data and analytics professionals report that their organizations have decreased staff/resources as a result of economic downturn, and 37% report a decrease in budget, according to the 2023 Data Integrity Insights and Trends Report , published in partnership between Precisely and Drexel University’s LeBow College of Business.
Key Takeaways: • Implement effective data quality management (DQM) to support the data accuracy, trustworthiness, and reliability you need for stronger analytics and decision-making. Embrace automation to streamline data quality processes like profiling and standardization. It reveals several critical insights: 1.
Key Takeaways Data Mesh is a modern data management architectural strategy that decentralizes development of trusted data products to support real-time business decisions and analytics. It’s time to rethink how you manage data to democratize it and make it more accessible. What is Data Mesh?
This has created many different data quality tools and offerings in the market today and we’re thrilled to see the innovation. People will need high-quality data to trust information and make decisions. Alation has been leading the evolution of the data catalog to a platform for data intelligence.
The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Let’s explore more of the report’s findings around data integrity maturity, challenges, and priorities.
The 2023 Data Integrity Trends and Insights Report , published in partnership between Precisely and Drexel University’s LeBow College of Business, delivers groundbreaking insights into the importance of trusted data. Data-driven decision-making is the top goal for 77% of data programs. One major finding?
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