Bigeye

With Bigeye, working with data has never been easier. Our customers now monitor the health of their dashboards in real-time, making it easy for everyone who uses data to do their job- from an engineer, to an analyst, to a VP. 

🚀 New Blog Post Alert! 🚀 When implementing data observability tools, should you opt for standalone tools or embedded solutions? Our latest blog post explores both options to help you make an informed choice. Dive into the pros and cons and future trends in data observability. https://lnkd.in/eYFerZve

We're headed to Australia! Swing by Booth 410 at the Gartner Data and Analytics Summit to say g’day and grab some swag! 🇦🇺✨ Want a personalized demo? Book a spot here ⬇ https://hubs.la/Q02GzXq40

Hey #data folks and network...check out this Product Management opportunity to join great group of folks at Bigeye! #productmanagement #dataobservability

Bigeye is hiring a Product Manager! Know someone who'd be perfect for this role? Tag them in the comments or share so they can apply 📝 https://lnkd.in/e3D4K6B8

Many data teams opt for "self-service," where analysts can directly access data and tools. However, just having access isn't enough. Without proper data quality monitoring and governance, self-service can waste time and create confusion. Providing analysts with effective data quality tools is easier said than done. So, how can organizations make self-service data quality achievable? Read more on making self-serve successful: 👉 https://lnkd.in/edABMtmT

Getting Executive Buy-In For Data Initiatives 5 Questions To Ask ⬇️ In fast-growing organizations, data quality and reliability often take a backseat until a crisis hits, like an unexpected outage or a flawed machine learning model. Unless it’s a real threat to the business, only small investments in data reliability are likely to get buy-in. But once an outage does occur, you can be prepared with an action plan, and get buy-in while everyone still feels the heat. Ask: 1. How much time do data engineers spend on data reliability issues? 2. Do executives trust the data they use? How many decisions lack data backing? 3. What's the potential cost of an ML model outage? Could it be $1,000/hr or $1M/hr? 4. What are the compliance or PR risks of inaccurate customer data? 5. How might a lack of data reliability opens the company up to public embarrassment? You can try the Wall Street Journal test for this: Let's say the WSJ discovered that all your customers were sent invoices for 3x their actual usage. Would the business be impacted by this news coverage?

See you at IT Symposium in October! 🎃

Hear from our COO, Eleanor Treharne-Jones, on how Bigeye is advancing data observability for global enterprises. 🌍 We’re honored to be mentioned as a Representative Vendor in the 2024 Gartner® Market Guide for Data Observability Tools. The Gartner report also notes that, “By 2026, 50% of enterprises implementing distributed data architectures will have adopted data observability tools to improve visibility over the state of the data landscape, up from less than 20% in 2024.“ Read Eleanor's full thoughts in the press release here: ⬇ https://lnkd.in/eFteS6dW

We’re excited to share that Bigeye has been named a Representative Vendor in the 2024 Gartner® Market Guide for Data Observability Tools! 🎉 We believe this recognition shows our key role in helping companies keep their data reliable. The Gartner Market Guide is an excellent resource for data and analytics leaders, giving a clear overview of the data observability market and explains how different tools can improve data quality and business operations. Using this report, data leaders can learn about data observability before deciding on the best solutions for their needs. We’re proud to be featured and are committed to providing top-notch data observability tools for large enterprises. 🌟 Download your complimentary copy here: https://hubs.la/Q02F247s0