Good hygiene and an efficient, sensitive data setup can make organizations’ data invaluable – accessible and available to everyone who should be using it.
If data is the new gold, then controlling organizational data is of immeasurable value, especially during turbulent economic times. Capital is significantly harder to acquire, and entrepreneurs who were receiving unsolicited termsheets a few months ago are now exploring how to stretch the trail. Data can be an organization’s superpower. When used effectively, data can enable go-to-market teams to do more with less, including:
- Personalize onboarding and product experiences to increase conversion rates
- Determine where users are upset and provide proactive support
- Applying sales pressure at the right time can lead to expansion revenue that would have naturally occurred several months later.
Read also : How the rise of Data Lakehouse is a new era of data value
In contrast, user data is typically siled within product and technical teams, isolated from marketing and sales, and rarely tied to monetization goals.
Teams can ensure that data is accessible and available to all authorized users through effective data hygiene and efficient and appropriate data configuration.
Organizations face a significant challenge in turning actual product usage into economic value. The measurement and retention of user data diminishes the value of these essential activities. For this reason, holding a cross-functional team meeting is beneficial while establishing the data structures to review facts and metrics.
Defining measures against facts
In the product, facts are actions that are performed. Engineers and product managers are adept at uncovering and collecting facts about data warehouses. On the other hand, measurements are calculations derived from data. Metrics can tell the story of the value of the facts they’re based on or highlight the importance of a specific step in the user’s journey. Metrics can be more complex, such as a running count of the number of times a user has visited a pricing page or an activation threshold. Organizations should delegate engineering and fact tracking to product engineers and product designers, then build a team around the metrics. The best teams treat metrics as if they were a product, conducting user interviews across support, marketing, and sales to determine how teams are engaging with customers and leveraging market see and use this data and develop a strategy to create meaningful metrics.
Implementation of data collection and dissemination
Once the security team has determined what they want to monitor, the next critical question is to assess how to store it. It seems like a new data solution is introduced to the market every day, and less technical audiences and startup owners can find themselves overwhelmed with options for storing, consuming, and analyzing their data.
If needed, metrics and facts can then be integrated into employee-facing tools using a reverse ETL tool to democratize them.
Many alternatives are available for data warehousing, ETL, and reverse ETL to transport data. It is essential to integrate not only the engineering team, but also the product teams and the company roundtable to produce the measurements. This way, no one will miss actionable data in their tools.
Act on data
After recording the data and identifying and generating the ideal team metrics, the final and most challenging step is to make this information accessible where your team works on a daily basis. Convincing sales, support, and success teams to log into a dashboard and act on the data daily is a challenge. It’s crucial to integrate their data into the technologies they already use.
This is when data democratization becomes more art than science. Businesses should use reverse ETL to import these metrics into a CRM, customer success platform, or marketing automation tools. They can design dynamic marketing for accounts that are beginning to discover the value of the product or provide the sales team with highly active users for direct outreach.
The industry is obsessed with companies doing amazing things with their data, but the underlying structures and frameworks that allowed them to get there are rarely discussed. All of these playbooks are data-enabled, but they can only be implemented if organizations have good data hygiene and structures and distribute information to the right people.