Activate Data Natively In Tools
Introduction
Data democratisation is an essential goal for organisations seeking to empower teams to make data-driven decisions across the board. Companies invest substantial resources in extracting, cleaning, and modelling data, but these efforts are wasted if insights are not easily accessible and actionable for the entire organisation. Too often, data initiatives end in reports or dashboards that require interpretation by technical users, limiting their impact on day-to-day business decisions. For data democratisation to succeed, organisations must address key challenges around accessibility, literacy, trust, and bias.
Barriers
1. Data Literacy
Low data literacy remains a pervasive challenge. Studies show that a significant number of people struggle with basic numeracy, which impacts their ability to interpret data effectively. A 2018 OECD survey, which studied 245,000 people across 38 countries, ranked England 21st and the United States 28th for basic numeracy skills. Shockingly, six out of ten Americans in this survey struggled to “recognise and work with mathematical relationships, patterns, and proportions” or to perform basic analyses of data presented in texts, tables, and graphs. To democratise data, companies must focus on upskilling their teams, fostering a foundational level of data literacy that empowers non-technical users to leverage data insights effectively.
2. Data Trust
For data democratisation to succeed, employees must trust in the quality of the data they use. Many executives recognise the value of data: a recent Experian study revealed that 98% of C-suite leaders consider high-quality data “extremely important” for achieving business objectives. However, 40% of respondents in the study admitted they did not trust the insights generated from their data, and 28% described their customer and prospect data as inaccurate. When there is a lack of trust in data, teams are more likely to disregard insights, undermining the organisation’s overall data strategy. Data democratisation initiatives need to establish robust standards and transparency around data quality, ensuring that employees can confidently rely on data to guide their decisions.
3. Bias in Decision-Making
Even when data is available, decision-makers often allow personal bias or experience to guide their choices, rather than objective data. This can distort perceptions and lead to selective focus on data that confirms preconceptions, while evidence that contradicts these views is ignored. To counteract this tendency, data democratisation must ensure that insights are delivered in a way that encourages unbiased, fact-based decision-making across the organisation. By automating insights and embedding them directly within familiar tools, data teams can help limit subjective biases and reinforce data-driven decisions.
4. Minimising Human Error with Automation
Manual data processes introduce a substantial risk of human error. For example, tasks like downloading attendee lists and uploading them to marketing tools like HubSpot or double-checking data in visualisation tools before using it can be time-consuming and prone to error. Manual data activation does not scale efficiently, and as data points grow, the risk of mistakes and delays also increases. Automating data flows between systems is crucial for scaling data access and consistency while reducing errors. Where you have made significant time and effort to automate your data modelling, it does not really make sense to then serve this up for a person to activate manually.
5. Leveraging Familiar Tools
Many companies opt to use their existing tools, such as Customer Relationship Management (CRM) and marketing tools like HubSpot or Salesforce, for data activation. Leveraging existing tools is convenient, as employees are already familiar with their functionalities. It allows for a quick start in data activation without the need to invest in new systems and so long as companies consider data quality and oversight of these tools, then they can act as that single source of truth. But therein lies the problem. Relying on these tools can magnify data quality issues. Even something as simple as a misclassification of a sales lead could mean substantial time and marketing spend wastage. While these tools often incorporate features like propensity models and lead scoring, it is quite common for them to employ simple, one-size-fits-all models, providing generic insights that may not be specific to a company’s unique needs.
The Power of Reverse ETL in Data Democratisation
One effective approach to activating data across an organisation is Reverse ETL. Unlike traditional ETL processes, which bring data into a data warehouse for reporting, Reverse ETL sends this modelled data from the warehouse to operational tools. For example, by integrating customer churn data into a CRM, teams can proactively address retention without having to interpret reports manually. This approach enhances several aspects of data democratisation:
Centralised Source of Truth: Reverse ETL uses data from a unified warehouse or lakehouse, reducing data silos and ensuring all teams have access to the same metrics and insights. This contrasts with Customer Data Platforms (CDPs), which are often used exclusively by marketing and can create isolated data sources.
Actionable Insights: By embedding notifications, flags, or lists within the applications teams already use, modelled data becomes a “guiding light” that simplifies decision-making, making it easier for teams to act on insights without needing deep technical knowledge. This also mitigates data literacy and bias challenges, as actions are suggested directly by the data models.
Automation and Error Reduction: Reverse ETL automates data flow between systems, minimising manual handling, reducing human error, and freeing up resources. This consistency across platforms ensures that insights are more accurate and reliable.
Empowering Non-Technical Users: Reverse ETL democratises data access, allowing non-technical users to work with data directly within tools they are comfortable with. This eliminates the need for SQL expertise or deep technical skills, making it easier for all departments to participate in data-driven decision-making.
When to Consider Reverse ETL
Once your data is centralised in a data warehouse or data lake, the next step is often to make this data actionable. You should consider using Reverse ETL right at the start of your idea generation phase. Why? Because it is always good to think about activation first and foremost. How will your business stakeholders interact with and use your data? What are the tools they are already using today? How can you make mass decision-making easier?
There are of course a few other considerations:
Data Readiness: Data should be clean, well-structured, and properly formatted for efficient transfer to target systems.
Integration Complexity: Evaluate the integration needs and ensure that the Reverse ETL tool supports connections with the organisation’s primary platforms.
Scalability: Select a Reverse ETL solution that can scale with data needs and organisational growth.
Compliance and Security: Data transfers must comply with regulatory standards (e.g., GDPR, CCPA), and security measures should protect sensitive information throughout the process.
Conclusion
Activating data within tools that teams already use helps democratise data across an organisation, enabling timely, data-driven decisions without requiring manual interpretation or error-prone processes. This approach bridges the gap between technical and non-technical users, making data a shared asset that drives company-wide insights. If you’re ready to explore data democratisation through solutions like Reverse ETL, the 173tech team can help assess your organisation’s readiness and guide your implementation.