Managing the data roadmap when attempting to make analytics available to everyone in the business certainly poses challenges. The webinar provided attendees insights on how businesses can get the right data in the right form to the right people – setting every user on the path to uncovering powerful insights. Vesset and Distler covered streamlining enterprise data and illustrated approaches that enable users to freely explore data in any direction – in a governed environment, without compromising the underlying information.
Below are some webinar Q&A highlights:
Q: How do you measure the value of your data?
A: To measure the value of your data, you would first need to look at how your data is being used or could be used by the business. This would then enable you to track and measure the business outcomes derived, thus giving a sense of value against the data in question.
Q: With the increase in sharing and combining data from disparate sources, what are ways to manage data architecturally?
A: Qlik Data Catalyst is a viable solution allowing you to build a secure, enterprise-scale catalog of all the data your organization has available for analytics, regardless of where it is. Additionally, its automated data preparation and metadata tools can help streamline and transform raw data into analytics-ready information for users to leverage.
Q: Why is time spent on Data Governance considered a waste of time?
A: According to IDC, nearly 30% of internal time on data governance is wasted because of poor data governance and inefficiencies that exist when data governance policies and procedures are not in place. Here are two instances where that can happen:
- Data ownership becomes unclear, thus consuming internal time and cycles spent on identifying who owns the data, who has the right to access it, and who has the right to change it.
- When people are not able to find certain data, they tend to recreate the asset and build another set of reports and/or another set of metrics.
Q: Is there a guideline as to when someone should use descriptive reports and when someone should use predictive and prescriptive reports?
A:It really depends on the use case. As an example, descriptive analysis of sales data would be fine for calculating sales commissions, but predictive analysis would be required if your intent was to plan stock levels based on the same sales data.
To access the live recording of our Mastering Your Data Journey webinar series, click here.