Query every table in your own words

Get answers to plain-language questions about your tables, since Beryl Lens writes and executes the query for you. Lens can also design a table from a description, fill columns with live data, flag unusual changes, and add prediction scores.

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Ask a question and get the rows that answer it

Beryl Lens queries your records in Tables together with the live data in People, Channels, OneCloud, Teams, and Wallet. When you ask for the partners whose deals are worth more than $100K and who have no messages in Channels from the last two months, Lens writes one query across your Tables records, Channels activity, and People profiles, then returns the partners who match. Each row in the answer links to the record that it comes from, so your team can check any figure without writing a query.

Ask your data a question and read the answer as rows

Beryl Lens answers questions about your tables in Tables and fills their columns from the live records in your other Delalify products. It also flags unusual changes and adds prediction scores, as the steps below show.

What Lens does with the data in your tables

Natural language to structured queries

When the answer to a question is in your data, someone will have to write a SQL query, a spreadsheet formula, or a pivot table to get it out. If only one person on your team can write those, everyone else will have to wait for that person.

Lens turns a question that you type in your own words into a query, executes it, and returns the rows that match. You can ask for the students at risk of missing the enrollment deadline or for the invoices older than 45 days with a particular tag.

Cross-service column auto-fill illustration

Cross-service column auto-fill

A table in Tables can include columns for contact status, last message date, shared file count, and team owner, with each value coming from a different product. If your team keeps those columns current by hand or through custom integrations, each new column will add to that upkeep.

Lens fills table columns with live data from other Delalify products, such as the last message date from Channels, the shared file count from OneCloud, relationship health from Insight, the team owner from Teams, and the payment status from Wallet. Lens updates each column whenever the source data changes, so your team maintains no separate integration for those columns.

Table designs generated from a description

To set up a new table, someone will have to pick each column's type, write its label, add validation, and connect it to related tables. On a table with 40 columns, that setup can leave gaps that your team finds only once it starts entering data.

When you describe the data that you want to track, Lens generates the table design, with column types, relationships between tables, validation rules, and suggested views. Lens fills the table with sample rows so that you can review the design before you create it. When you import your existing data, Lens matches each incoming column to a column in the new design.

Anomaly detection in datasets

Errors can build up in a table one record at a time, such as a duplicate record one day, a wrong currency the next, and a contact in the wrong category the week after. By the time someone on your team notices them, there can be enough errors to throw off the reports that your team builds from that table.

Lens checks every table for outliers, breaks in a pattern, and unusual changes, such as a spike in one category, a drop in a routine metric, or a field with more empty values than usual. Lens flags each one with the rows involved, so your team can correct the error on the day that it appears.

Predictive columns with probability scores

Your team may want to know which deals are likely to close, which students are at risk of dropping out, or which invoices are likely to be paid late. The data for those predictions is in your tables; however, a prediction model built from that data will need a data scientist to design it.

When you add a predictive column to a table, Lens calculates a score for each row from your own historical data, such as the likelihood of a deal closing from win rates and engagement, a student's dropout risk from attendance and grades, or an invoice's collection risk from payment history. Lens shows a confidence level beside each score, together with the factors that contribute most to it.

Data cleaning and enrichment

An imported customer list can include inconsistent country names, three spellings of the same company, addresses with no postal code, and companies with no industry classification. If your team cleans that list by hand, they will have to repeat the work with every import.

Lens standardizes country names, merges duplicates with your existing records, looks up missing postal codes from each address, and assigns industry classifications from public company data. Lens records every change in an audit log, so you can reverse any step that you disagree with.

Why teams query Tables through Lens

Answers from your data without a query language

Anyone on your team can ask Lens a question in their own words, because Lens writes and executes the query and returns the rows that match.

Live columns across every product

Lens keeps the columns in Tables in step with People, Channels, OneCloud, Teams, and Wallet, so each value matches the source product when you open the table.

Correct errors as they appear

Lens flags outliers and breaks in a pattern as soon as it finds them, so your team can fix an error while it affects a single row.

Predictions grounded in your history

Lens calculates close likelihood, risk scores, and priority rankings from your organization's own records. Each prediction lists the factors that it depends on, so your team can judge how far to trust it.

How organizations use Beryl Lens

A sales operations team adds a close likelihood score to the weekly forecast

A sales operations team adds a predictive column to its pipeline table. Lens calculates a close likelihood score for every active deal from the historical win rate at each stage, the engagement in Channels, the activity on the proposal file in OneCloud, and the role of the deal's champion in People. Beside each score, Lens lists the three factors with the largest effect on that score.

Result

The sales operations team builds the weekly forecast from the score on each deal, together with the factors that Lens lists. Reps can then spend their time on the deals that Lens scores as most likely to close.

A sales operations team adds a close likelihood score to the weekly forecast

An admissions team finds the admitted students at risk of not enrolling

A university admissions team keeps a table of every incoming student in Tables. Lens flags the students whose onboarding activity falls below the usual level, ranks them by the risk that they will not enroll, and lists the signals for each one, such as an unanswered housing form, no advisor meeting on the calendar, or a missing document upload in OneCloud. The outreach team then contacts the flagged students before the enrollment deadline.

Result

The outreach team calls the highest-risk students first and opens each conversation with the signal that Lens lists, such as the housing form that is still open. Because Lens updates the ranking as students complete each step, the outreach list contains only the students who still have steps left.

An admissions team finds the admitted students at risk of not enrolling

A finance team ranks unpaid invoices by collection risk

A finance team keeps every open invoice in a table in Tables. Lens fills columns with each customer's payment history from Wallet, their health score from Beryl Insight, and the date of the last contact from Channels. Lens also flags invoices whose customers stop replying, while it scores each invoice by collection risk in a predictive column.

Result

The finance team contacts the customers with the highest-risk invoices early in the collection cycle, because Lens ranks every open invoice by risk. Each collector can check the customer's payment history and last contact in the same row before making the call.

A manufacturer spots a supplier whose defect rate rises

A manufacturer's procurement team keeps every supplier in a table with columns for defect rate, on-time delivery, unit cost, and inspection results. Lens adds a predictive column for the risk that the next purchase order arrives late and flags each supplier whose defect rate rises above its own baseline. From two years of receiving history, Lens also suggests which parts the team can buy from a second supplier.

Result

Procurement contacts a supplier as soon as Lens flags a rise in its defect rate, before the defective parts reach the production line. The buyers bring the supplier's own figures from the table to each supplier review, so the supplier can see the same data and respond to it.

A manufacturer spots a supplier whose defect rate rises

Explore more of Beryl

Context

Beryl Context answers your question from People, Channels, OneCloud, and Teams and links each answer to its source.

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Insight

Beryl Insight scores the health of every relationship and alerts the account owner when a score drops.

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Orchestrate

Beryl Orchestrate performs each step of a workflow and pauses at sensitive steps until someone approves.

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View all Beryl features

Put Lens to work on your Tables data

Beryl Lens queries your tables together with the live data in your other Delalify products, so each answer, column, and prediction comes from current figures.

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