See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
See why hundreds of industry leaders trust Secoda to unlock their data's full potential.
Get the guideOrganizations are collecting data faster than they can effectively use it. With the growing need for data to power business decisions, data teams are central to business success. But Data teams are burdened by the increasing volume of data requests to feed business needs. On top of this, poor data discovery is costing companies time and money. According to Seagate, nearly 70% of data available to businesses goes unused.
Making data accessible and easy to use for individuals outside of the data team is essential for improving organizational efficiency. The path forward is through Data Enablement.
Data enablement is the system an organization uses to turn their data into action. Effective data enablement consists of people, process, and technology to ensure business stakeholders are able to easily access and use the data products designed to power their decisions.
The purpose of this report is to help leaders in the data community to advocate for necessary resources, prioritize projects, and benchmark current data enablement practices.
Here are some key points from the report about data enablement challenges organizations face.
55% of respondents reported the usability of data tools for nontechnical stakeholders and teams as the largest barrier to increasing data enablement at their company.
Data sprawl and a growing tech stack is one of the most common challenges for data leaders to overcome when implementing data enablement initiatives.
68% of data leaders reported that their teams spent the largest proportion of their time answering questions from business teams.
Over 50% of respondents reported that they were prioritizing observability and catalog tooling.
Data teams have started adopting an internal data consulting model where stakeholders "pay" for data asset creation or support requests.
70% of respondents are from teams of <10 while being required to support 50+ data consumers
Establish a dedicated team focused on developing data literacy courses, providing training to non-technical users, and facilitating access to data. Their role is to empower users by
building their data skills and helping them understand the connection between their analyses and the working product.
Foster engagement through weekly office hours, syncs with key stakeholders, and monthly insights presentations. Focus on effective communication by never sending deliverables to stakeholders without walking them through it, recording explanatory videos, or providing written guides for interpreting the data.
Follow an "anyone can contribute" model by providing clear documentation, automated checks, and well-defined onboarding processes. This includes SQL linting, validation checks to ensure changes won't impact others, and comprehensive documentation to support users in understanding and contributing to the data transformation setup.
Consider adopting an internal data consulting model where stakeholders "pay" for data asset creation or support requests. Additionally, explore charging teams a baseline fee for accessing internal data products, encouraging responsible usage and covering maintenance costs.
Invest in self-service analytics tools and teach users how to explore data independently. This empowers them to access the data they need, reducing dependency on the data team and
fostering a culture of self-sufficiency.
Utilize data visualization tools like Tableau, Looker, or Secoda to create interactive dashboards and reports. Make these tools available to stakeholders for self-serve exploration and analysis.
Leverage automated data ingestion and publishing processes aligned with Data Mesh principles. Implement additional data curation and productization for critical business
areas like Finance and Marketing. Maintain a data catalog with comprehensive documentation,
pipeline status, SLA monitoring, and data quality checks.
Implement cross-functional governance of enterprise data systems, establishing a centralized
data management function and an enterprise lexicon. This ensures consistency, reliability, and accuracy in data usage across the organization.
Conduct regular data clinics and training sessions to address ad-hoc questions and provide assistance on various topics. Establish dedicated support channels like Slack or forums where users can seek help and share knowledge.
Each organization may adopt a unique combination of these strategies based on their specific needs and resources.
Implementing data enablement initiatives within an organization can be a transformative journey, but it's not without its challenges. These are the top challenges faced by data leaders when implementing data enablement initiatives.
One of the most significant challenges is the lack of data literacy among stakeholders. Many individuals may not be familiar with data analysis concepts or tools, which can hinder their ability to leverage data effectively. To address this challenge, organizations should invest in comprehensive data literacy programs, providing training and resources to enhance stakeholders' understanding of data and its potential.
Even when data is accessible, ensuring its utilization can be challenging. Stakeholders may not prioritize data-driven decision-making or may not feel accountable for using data in their day-to-day activities. To overcome this challenge, organizations need to foster a data-driven culture by promoting the value and benefits of data-driven decision-making. Leadership support, clear communication, and tying data usage to performance metrics can incentivize stakeholders to embrace data in their work.
Timeliness is crucial in data enablement. Organizations need to deliver insights quickly to facilitate informed decision-making. Slow data pipelines, complex data models, or inefficient processes can hinder speed to insights. Addressing this challenge requires a focus on optimizing data pipelines, streamlining data processes, and investing in tools and technologies that enable faster data processing and analysis.
There is often a perception that structured data practices are unnecessary for a few files or simple database updates. However, relying on tacit knowledge without explicit documentation and processes can impede scalability and hinder maintenance efforts. Establishing clear data governance practices, documentation standards, and onboarding processes can ensure that knowledge is accessible and transferable, even as the organization grows.
Documentation plays a pivotal role in capturing and sharing knowledge. By implementing standardized documentation practices, organizations create a central repository of information that can be easily accessed and understood by all stakeholders. This includes data dictionaries, data lineage documentation, data transformation rules, and other relevant documentation that clarifies the meaning and context of the data.
As new team members join the organization, providing them with a structured onboarding process is crucial for knowledge transfer. This includes training programs, mentoring initiatives, and access to documentation repositories. By ensuring that new employees have access to comprehensive resources and training, organizations can accelerate their integration into the data-driven culture and make the most of their skills and expertise.
Stakeholders already have primary responsibilities that may not revolve around data analysis. Investing time in training and learning new tools can be a challenge. To address this, organizations should provide comprehensive and targeted training programs that align with stakeholders' workflows and job responsibilities. It's important to demonstrate the value and impact of data analysis in their specific roles to foster engagement and adoption.
Limited resources, both in terms of personnel and finances, can hinder data enablement initiatives. Organizations need to prioritize resource allocation for data enablement, even in challenging economic environments. This may involve seeking cost-effective solutions, leveraging open-source tools, or reallocating resources to prioritize data initiatives.
Data enablement efforts can be hindered by incorrect data pulls or misinterpretation of data. Implementing data dictionaries, ensuring data quality checks, and establishing clear data definitions and guidelines can mitigate these challenges. Collaboration between data teams and stakeholders is crucial to bridge the gap and ensure a shared understanding of data.
It’s essential to address foundational issues that can hinder onboarding, debugging, and enablement processes. Building a solid data stack is crucial as teams and use cases expand. This requires tackling three key angles.
Firstly, addressing upstream data issues such as inconsistent or duplicate events that can compromise the reliability of the modeling layer. Tracking critical metrics or models and collaborating with engineering teams to ensure their maintenance is vital.
Secondly, removing unused data models and columns by leveraging column-level lineage, identifying models with no dependencies, and utilizing ownership models like dbt 1.5 to distinguish internal and public configurations.
Lastly, implementing a dashboard deprecation process to regularly remove unused dashboards and reports, while setting expectations with data consumers and avoiding the creation of unnecessary dashboards.
Organizations are accumulating data at a rate that has outpaced their ability to use it. With a focus on data enablement, business stakeholders and other data consumers can be brought closer to their data in a way that allows them to make decisions faster. Consumers need to be enabled before they can fully realize the benefits of data products. This calls for an improved focus on not only what is being built, but if the stage is set for it to be well received.
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