Implementing a Phased Rollout of Self-Service Analytics

example of a timeline for a phased rollout of analytics

You lack the time or the technical personnel to add analytics to your application this quarter. Or, your team needs to time a full integration to next year’s release schedule. But you can still deploy self-service analytics this quarter. Here’s a typical scenario for a phased rollout that gives you analytics this quarter and beyond as your application matures.

Phase 1: Standalone BI Portal – A fully branded portal is a great way to deliver analytics before your next release. You can roll out a fully branded standalone analytics portal in five to 25 hours, without writing a line of code.

Alternatively, you can launch analytics from your existing portal or application. In either case, once you are ready to embed, you can build on all the work you’ve done in the portal, like roles and permissions, data modeling, and report and dashboard designs.

Phase 2: Embed Report and Dashboard Viewers – Once your users have become familiar with the BI portal, they will want to see analytics integrated directly into your application. A good place to start is to embed report and dashboard viewers. You can gradually phase in embedded designers, lists and some basic visualizations.

Phase 3: Complete Integration – It typically takes about 100 man hours of technical resources to fully embed analytics into your application. The end result: users get report and dashboard designers, visualizations like KPIs, charts and maps, and pixel-perfect forms – all of which they can create, customize and share. You can expose self-service functionality over time, as demand for analytic components and your user base expands.

Phase 4: Tenant Self-Administration – The next phase gives users more self-administration capabilities. They can drop in custom connectors to outside data sources, to bridge to other databases. They can create custom roles and permissions, schemas and attributes. They can set alerts and schedule reports. Our administrative UI lets them do all of this without coding.

Phase 5: In Context Analytics – Users can already view reports and dashboards. You can help them make better data-driven business decisions by embedding relevant KPIs, charts, graphs, maps and grids at decision points anywhere inside of your application’s workflows.

You can build heavy interactivity between your embedded components and your application’s native functionality. For example, by using URL-based drill downs, you make sure that when users see KPIs in the red, or thresholds that have been crossed, they can click through within your application to address those issues.

Phase 6: Advanced analytics – In context analytics tells users what is happening and what they should do based on that data. Beyond that, you can start to edge into the realm of more advanced statistics using R or Python libraries. You might use regression analysis, standard deviation, predictive models or fraud detection. These can help users understand the end result of continuing on a particular tangent by gauging its likelihood based on past outcomes.

Izenda is Portable across Maturity Stages

Regardless of your application’s stage of maturity, or your product timelines, Izenda can implement analytics, and any work you do in one stage can carry over into future phases.

download the ebook Overcoming the 7 Challenges to Embedding BI in Applications

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