In line with a latest survey, knowledge engineers are reporting practically twice as many knowledge incidents this yr as final. On common, it takes 15 hours per incident to succeed in a decision. And practically 75% of the time, enterprise stakeholders are the primary to establish knowledge points. As knowledge leaders know, when enterprise customers encounter knowledge that’s lacking, faulty, or in any other case inaccurate, decision-making is compromised and belief in knowledge erodes.
That’s why we’re excited to announce that uniting numerous knowledge group personas to collectively guarantee knowledge high quality simply acquired simpler, due to a brand new integration between Monte Carlo’s knowledge observability platform and Atlan’s lively metadata platform.
Collectively, these market main instruments make it doable for firms that depend on knowledge for a aggressive benefit to know and enhance knowledge high quality, whereas making certain knowledge customers have all of the context they want, inside their present workflows, to make knowledgeable selections with knowledge.
Monte Carlo provides groups end-to-end knowledge observability by way of automated detection, alerting, and incident decision for knowledge high quality points. Atlan is a house for numerous knowledge groups, serving as a single supply of fact that prompts metadata throughout the fashionable knowledge stack to allow new modalities of collaboration. And each platforms already present deep integrations throughout the fashionable knowledge stack, together with Slack, Snowflake, dbt, Databricks, Sigma, and Fivetran.
Now, these two platforms work in tandem to supply knowledge groups with enhanced visibility into key knowledge operations and granular insights for knowledge asset discovery and exploration. Information customers can simply entry up-to-date details about the standard of knowledge property earlier than they use them, streamlining collaboration throughout the group whereas constructing belief in knowledge.
How companies can leverage Monte Carlo + Atlan
With Monte Carlo and Atlan, groups can achieve an up-to-date understanding of their knowledge well being, construct belief in knowledge, and assist revolutionary new methods to method distributed knowledge infrastructure.
Extending visibility into knowledge well being
Groups utilizing Monte Carlo and Atlan can now shortly perceive the well being of particular knowledge property throughout their Information Property. Information standing updates in Atlan about knowledge well being are knowledgeable by Monte Carlo, and knowledge groups can now view screens and exams created for every manufacturing desk.
“Earlier than we modernized our knowledge stack, our knowledge monitoring was very reactive,” stated Michael Weiss, Senior Director of Product Administration (NAM, Information Entry and Analytics) at NASDAQ. “The pipeline would possibly succeed, however we wouldn’t know if the info was proper, mistaken, or detached. With Monte Carlo and Atlan, we are able to catch knowledge incidents early on, and supply everybody with clear visibility into the present standing of knowledge accuracy. That is proving useful and has been important for the chief group to have faith we are able to ship on our promise of dependable, reliable knowledge.”
By extending visibility into knowledge well being, knowledge groups can work proactively to resolve points quicker and guarantee any impacted enterprise customers are conscious of potential downstream impacts.
Growing knowledge belief and collaboration throughout the enterprise
With this new integration, knowledge customers can view particulars of the newest knowledge incidents and anomalies detected by Monte Carlo. This helps all knowledge group personas, no matter technical skillset, to maintain shut tabs on the reliability of any given asset, based mostly on a typical metadata management aircraft.
“With 1,600 staff serving over 1,000 purchasers with actionable, data-driven insights, we churn by way of huge volumes of knowledge day by day,” stated Kenza Zanzouri, Information Governance Strategist at Contentsquare. “Our inside groups are all the time centered on creating worth with new dashboards, fashions, or knowledge explorations, so making certain that knowledge is dependable is crucial. With Monte Carlo and Atlan, we’ve been capable of shift from guide checks and testing to automated knowledge high quality protection—and make it readily obvious to enterprise customers when knowledge property could also be impacted by high quality points. This helps us scale in the long run and enhance communication between departments.”
By dramatically enhancing visibility throughout knowledge operations and streamlining communication, thereby enabling wider belief in knowledge, knowledge customers and engineers can work with knowledge in additional environment friendly, collaborative, and revolutionary methods.
Enabling domain-oriented knowledge administration
Ahead-thinking knowledge organizations are more and more transferring to undertake distributed knowledge architectures just like the knowledge mesh. Monte Carlo and Atlan assist present these knowledge groups with peace of thoughts about knowledge reliability, which generally is a problem when property are owned by area groups and obtainable by way of self-serve entry.
“At BairesDev, we offer main companies all over the world with expertise groups on demand, and applied a knowledge mesh method to attain knowledge high quality, availability, and efficiency throughout our group,” stated Matheus Espanhol, Information Engineering Supervisor at BairesDev. “Automation is an absolute necessity to attain sturdy knowledge governance throughout decentralized domains. With Monte Carlo and Atlan working in tandem, we are able to automate knowledge high quality requirements whereas sustaining visibility into how every group follows international insurance policies and units native insurance policies throughout the area.”
With end-to-end visibility into knowledge well being and a centralized supply of fact, it’s now doable for knowledge groups to facilitate self-serve analytics with out compromising on governance and high quality requirements.
How the combination works
With this new integration, Atlan and Monte Carlo work collectively seamlessly to centralize data and communication about knowledge high quality.
Information incidents detected by Monte Carlo will likely be surfaced in Atlan, with all of the context wanted to know precisely what and the place knowledge has been impacted. When incidents are resolved, they’ll be cleared—which means an absence of incidents signifies all is properly with a given asset. What’s extra, any Monte Carlo customized screens will likely be recorded as native property in Atlan, offering the road of sight for knowledge customers to the underlying knowledge high quality framework applied with Monte Carlo.
By uniting a wider spectrum of knowledge personas, Monte Carlo and Atlan allow the activation of knowledge shopper enterprise context, such that this useful IP might be productionized to additional advance knowledge belief and a deeper tradition of knowledge high quality.
The mixing takes 5 minutes or fewer to configure, with no API use wanted, and all knowledge is offered by way of the Atlan Chrome extension. This implies groups can entry wealthy metadata context immediately within the workflows the place they use the underlying property, equivalent to Information Clouds, Lakehouses, Warehouses, and standard Enterprise Intelligence tooling
Get began as we speak
To be taught extra about constructing belief in knowledge with Monte Carlo and Atlan, discover our documentation.
Create an account-service API key
Configure the combination in 5 minutes (with interactive walkthrough)
Metadata sourced from Monte Carlo
Prepared to start out enhancing your knowledge high quality with end-to-end knowledge observability and cataloging? Attain out to request a Monte Carlo demo to see knowledge observability in motion or begin a free trial with Atlan to see knowledge collaboration work.
