To find, understand, and trust the data within your enterprise, it is essential to have sound data intelligence practices. The organization may rely on these practices to enable the end users to utilize data effectively. We do this here at Collibra.
Depending on the size of the company, it is essential to periodically check the data capability (how effectively are people getting value out of their data) level of the organization. There are various frameworks that can help you assess your organization’s data intelligence capability/maturity statuses. Our Collibra Data Office used our own Collibra Data Intelligence Assessment to do just that.
Data Intelligence Assessment
We created our Data Intelligence Assessment tool with IDC. It rates an organization’s maturity based on four core pillars: Data Literate Culture, Data Analytics, Data Management, and Data Valuation. The model leverages anonymized data from a subset of Collibra customers as inputs to calculate a maturity level for each industry.
IDC surveyed senior data governance, data quality, data cataloging, and data privacy and security professionals to create The Data Intelligence Assessment tool. IDC then took the results of the survey and created four stages of maturity. These four stages are outlined in the model:
Questionnaire and results
Collibra Data Office
Since we commissioned this assessment, we thought it would be a good idea to test it out ourselves. The Collibra Data Office is in charge of data intelligence and governance across Collibra so we were the perfect test case for the Data Intelligence Assessment. The Collibra Data Office consists of three main verticles focusing on Data Engineering, Data Science, and Data Intelligence. Furthermore, the Data Office has been operating at full capacity for the past year and has made many inroads into managing the data within the organization by collaborating with and enabling various lines of business and departments.
Conversation during the session
We decided as a team to include all members of the Data Office to take part in working our way through the questionnaire. The questions focusing on various topics gave rise to many lively conversations. There were varying opinions about the state of maturity concerning roles and responsibilities, policy and data catalog. We spent time discussing the meaning and intent behind some of the questions. Furthermore, the team focused on clarifying the level of depth we are at in certain areas such as policy management. The conversation acted as a sounding board for ideas and gave a basis in which to level set within the team.
Results
Once the questionnaire was completed and answers submitted, the results were available for the whole team. Based on the answers, Collibra as an organization is currently at the Defined level 2. This result translates to 30%-40% of the organization having critical data cataloging capabilities, as well as fully functioning governance processes. In addition, 30% to 50% of the organization is tracking multiple data metrics. When it comes to data literacy, Collibra scored 3.65 on a scale of 1 to 10. The most significant benefit of having achieved the “Defined” maturity level is improved operational efficiency.
Moreover, the results document provided details highlighting various challenges that the organization face concerning data intelligence. These details depict various facets that make up the above score. Furthermore, the report highlights the potential improvement that would be gained by improving the data intelligence from the current state “Defined” to the final state “Optimized.”
Observations and lessons learned
Filling out the Data Intelligence Assessment together as a team was viewed as a healthy bonding experience for the group, connecting each of us in our specific roles to the greater Data Office strategy in alignment with the company, and we highly recommend this approach.
The questions that stemmed during the session gave rise to the realization that all teams within the Data Office didn’t have equal knowledge of the activities happening within the Collibra platform. Even though this is acceptable, as we have different areas of expertise, it needs to be improved. In addition, as a team, we were quite critical of our work, as well as the work done by the broader organization. Even though it may not be significant, this behavior might have influenced the final score. To compensate for this behavior it may have been prudent to include a few end users with substantial Collibra platform usage in the conversation so that we get their perspective.
Plan of action
The benchmark showed us that it is important to influence, inspire and instigate actions not just within the Data Office but throughout the entire organization. The expected result of these actions would be to improve the position on the Data Intelligence Assessment. The Data Intelligence Assessment really helped us map and reinforce ways to advance our maturity within our existing data strategy.
For example, the Data Literate Culture pillar showed us how important the Data Citizen Community is throughout our company, showcasing successful data projects led by teams around the business. This provides a learning opportunity for the data citizens and a moment to recognize the positive impact for the creators. Furthermore, while creating and delivering value to the business, users in the Community are providing valuable feedback that is recorded which would be included in the next iterations.
A great example of learning from the Data Intelligence Assessment is our use of the Trusted Business Reporting solution. This solution was created so that stewards and owners can certify reports, data sets and metrics to empower the business with trusted and reliable analytics. The value provided is to enable analysts to create and deliver insights with confidence, backed by consistent and certified data, to accelerate trusted business outcomes. All of this is directly reinforcing a Data Literate Culture pillar through strong Data Management techniques. Simla Sivanandan penned a blog on this project that provides further details. The product was packaged and made available to customers via the collibra marketplace.
In addition, Data Valuation and Data Analytics pillars prescribed in the Data Intelligence Assessment aligns well with our current strategy for focusing on Data quality utilizing Collibra Data Quality & Observability. As the Data Office we are moving forward with creating processes to detect, report, and resolve data issues and pipeline failures by utilizing tools such as Collibra Data Quality (CDQ).
Conclusion
Challenges and opportunities are rife as a result of the continuous and unprecedented advancement of the data environment within Collibra. This is a phenomenon that is common to many organizations regardless of the sector. To stay ahead of the curve it is essential to monitor and evaluate Data Intelligence maturity within the organization periodically. Collibra Data office utilized the Data Intelligence Assessment for this purpose.
During the assessment, lively questions and debates ensued within the team to understand and clarify the questions. This highlights an important conclusion that the resulting score would be dependent on the persona filling out the questionnaire. As such it would be more appropriate to work as a team; the panel may include both producers and consumers as well as governance professionals.
Following the evaluation, we could see that Collibra’s organization was performing exceptionally in certain aspects of data intelligence. However, it was clear there are many aspects that are less than the desired state. Drawing a clear line between these two aspects allowed us as a team to put in place projects and programs to improve as well as maintain the ground gained.
Finally, Collibra is the market leader in the Data Intelligence industry. However, our internal data intelligence process has a ways to go to achieve the desired state of “Optimized” highlighted by the Data Intelligence Assessment. By using our own software we are streamlining and improving our internal data operations. We call this “drink your own champagne”, which helps us become our own best customer while providing valuable feedback to product and engineering to maintain market leader position for the years to come.
Shiroshana has been with Collibra for more than 4 years as an Enterprise Architect, working in Customer Success and Collibra Data Office. Shiroshana has worked with hundreds of customers across the world helping them to be successful with their data programs with Collibra.
Alexandre started almost 3 years ago at Collibra and since then has helped shape the brand new Data Office department by focussing on data products.
