How does one measure the effectiveness of a new Augmented Data Discovery solution? Once the business has chosen data democratization and implemented a self-serve analytics solution, it must measure ROI & TCO and establish metrics that will compare business results achieved before and after the implementation.
Without measurable results, it will be difficult to garner and sustain support for business transformation and for creating an environment where Citizen Data Scientists can thrive. Senior managers, corporate stakeholders and naysayers may resort to gut feelings and business users may perceive that there is little to be gained from embracing self-serve advanced analytics tools if the management team does not value these new tools.
When an organization establishes metrics, it must consider its goals and objectives and analyze the results of actions taken and decisions made with the support of data analytics versus those made ‘the old way’. For example, if one were to set new pricing, decide on a new business location or create a new promotion based on Assisted Predictive Modeling, the business would measure the success and results versus the results achieved when decisions were made without data analytics.
Many organizations depend on the vision of one individual or a small team of executives who have market or industry experience. This approach can be difficult to defeat without clearly defined metrics and results to support the case for data-driven decisions. At its inception, these tools may be used to ‘test’ a theory or compare a direction or decision to another option. But, that is OK. By offering recommendations and options, the team will begin to consider other possibilities and, as the data foundation is created, the argument for ‘going with your gut’ will fade.
As the enterprise shifts its focus, and team members adopt and depend on Augmented Analytics for presentations, data sharing and reporting, the culture will shift and the business will gain a new appreciation for clear, precise data and the results it can produce in the planning process, in day-to-day decisions and in solving problems and identifying market potential.
Original Post: How Do I Prove the Value of Self-Serve Augmented Data Discovery?
