Businesses rely more than ever on data to make informed decisions. Identifying trends and patterns in data can provide insights into customer behavior, revenue opportunities, compliance risks, and changing market conditions.
But there's a catch—while using data to move your business forward sounds simple, putting it into practice is another story. Analyzing such massive amounts of data requires an infrastructure capable of handling the scale. And the velocity of data only adds to the complexity—new data is constantly being generated in real-time, making it challenging to analyze quickly.
That's why we're so excited to announce that our new product, Own Discover, is now generally available to organizations using Salesforce. Discover is a secure, scalable data service that leverages the backups that you store with Own for business continuity and presents them in a time-series format. Discover eliminates the manual effort around extraction and data integrations to harness your data's full value.
So, what are some of the ways you can use Discover? While many companies and products say they can help you use AI to improve efficiency, scale, and more, we have actual use cases from real organizations that are doing it. While Discover has been limitedly available over the past several months, our pilot customers have been using the product in exciting ways. As customer zero for Discover, Own has also been employing the product. Here are a few of the ways we and our pilot customers have been using Discover and how you might be able to leverage it, too.
Tap into an alternate source of production data
Our solutions can save valuable time when optimizing data access and reporting. Take one of our pilot customers who purchased Tableau to report on Salesforce. Querying data from Salesforce took them about 15 minutes, so Salesforce recommended they connect their data to a data warehouse to save time. The challenge was that getting a new data warehouse to pipe their data from Salesforce would incur data storage costs and additional maintenance the team did not account for.
With Own, the customer can conduct operational reporting much faster than directly from Salesforce without incurring costs for a data warehouse. Discover reduces the burden on the customer's org and improves their production org's overall responsiveness.
Report on past data
Another one of our customers wanted to better plan their labor allocation. Salesforce is their source of truth for project management—they had ~37,000 active projects and ~100 tasks per project. They wanted to know the answer to questions like, "How much of payroll in each period goes towards projects that are still in progress?"
The customer had no way of tracking changes within the project (e.g., task status at the start and end of a pay period) as Salesforce does not retain a history of these changes. This was such a pain point for them that they considered building their solution to export Salesforce data and store it externally for reporting.
Now, the customer can use backups from Own Recover and Discover, which puts the data into a time-series format, to query that data in other BI tools. The customer can now inform how they allocate labor by comparing task statuses at the start and end of a pay period.
Trend or forecast with on-demand historical data
Gaining visibility into historical trends can enhance forecasting accuracy and strategic decision-making. At Own, our sales reps use 6Sense account scores to gauge an account's intent to purchase based on online actions. Sales reps view a single score at a time but lack visibility into score changes over time (e.g., Is this a higher or lower account score compared to previous periods?). While higher account scores correlate with a higher likelihood of closed-won opportunities, the correlation between extreme score changes and closed-won opportunities is unclear.
Using a cloud-based data platform and traditional data warehouse would have taken four hours of work and 1000s of lines of additional code. With Discover, an onerous series of queries to our existing data lake were reduced to just 15 lines of SQL to access the exact data we needed. The insight was instantaneous -- we found we could accurately predict the propensity to buy more than six months in advance of a purchase decision.
Automatically populate AI/ML models with time-series data
Integrating time-series data into AI/ML models significantly improves the model’s output. For example, AEs, managers, and executives want to quickly understand the key changes in deals since the last time it was viewed. However, asking an AE to regularly summarize opportunities is tedious, time-consuming, and takes away from selling activities. Additionally, with manager reviews, forecast calls, executive briefings, and more, the requests to summarize changes never seem to end.
At Own, we rely on Discover to populate time-series data into an AI engine that enables anyone to automatically summarize key changes to an opportunity based on the past day, week, or month. This has saved significant time across our sales team and enables each user to adjust to their own needs without requiring an AE to stop selling.
Activate your data with Own
Discover eliminates any manual extraction and data integrations into other systems to harness your data’s full value. With Discover, you can activate your data to:
- Generate business analytics faster
- Uncover unique business insights with historical backups
- Optimize decision-making from accurate forecasts and trends
- Deliver on AI/ML initiatives immediately
If you’re ready to start Discover-ing more, check out the data sheet or visit our website.
