Given the unprecedented rise in the adoption of business applications, Unified Business Analytics (UBA) is emerging as a key data strategy for helping organizations unearth end-to-end business insights. And to enhance the functioning of this unified ecosystem, data pipelines are playing the crucial role of feeding enriched data into the system.
Technology 1.0 used to be all about exploring technology, where organizations would run with one or two applications for specific business needs. Then Technology 2.0 witnessed the rapid adoption of business applications. This created a need to glean and analyze data across these apps for cross-functional insights, which gave birth to Unified Business Analytics. Now Technology 3.0 is here, and it’s all about preparing and enhancing the data that gets pumped into this unified ecosystem for deeper, more meaningful analytics.
It’s crucial for businesses in the transition between Technology 2.0 and 3.0 to deploy a UBA engine to break data and insight silos that get created due to unconnected data sources. It’s also been observed that 80% of analysis time is spent on data preparation, because poor quality data often returns insights that can’t be trusted.
Our 5-layer plug and play model guides organizations through a series of steps to build a UBA engine. It starts by defining your data strategy for key focus areas, then identifying and aligning your data sources with your data strategy. Upon completing the first two steps, it’s now easy to build a data pipeline to prepare your data for analysis. And with all this data inside your BI application, you can now blend and visually analyze your data for cross-functional insights. Finally, it just becomes a matter of driving adoption among teams to sustain this unified engine.
In this blog, let’s imagine an IT consulting service business with a data strategy to build a unified sales, marketing, and support ecosystem. They want to understand the cross-functional impact of these departments, and how they can work together to achieve their business goals. They’ve identified Infusionsoft CRM, Twitter Ads, and Zendesk as their data sources, and Zoho Analytics as their BI platform. The next step is to build a data pipeline.
A robust data pipeline is the mainstay for any UBA engine. It can help you fetch data from multiple sources, build customized data models, transform complex data models for precise analytics, and move that data into a data warehouse. This can substantially reduce heavy data transformations that happen at the analytics layer.
For this example, we’ve aggregated data from Twitter Ads and Infusionsoft CRM, custom built a data model, and enriched that data by adding a layer of transformation before parking it inside Amazon Redshift to build our data pipeline.
Our webinar will teach you how to build a robust data pipeline to address specific business problems.
Once you’ve set up your data pipeline, you can seamlessly move the data from Amazon Redshift or from any hosted cloud service into Zoho Analytics, by simply connecting the two. And now we have our sales data (from Infusionsoft CRM) and marketing data (from Twitter Ads), in Zoho Analytics. But to fully build a unified sales, marketing, and support engine for this business, we need to import their support data from Zendesk.
Using our built-in advanced intelligence, you can do everything: blend data from all three sources, perform cross functional analysis, and build your Unified Business Analytics engine.
The above dashboard is your system command center. It gives you end-to-end business insights on your sales, marketing, and support departments. It also unearths granular insights on the impact of sales on marketing, sales on support, marketing on sales, and more.
This mission control also enables you to track KPIs across functions. In this case, KPIs such as revenue, expenses, ROIs, CSAT (customer satisfaction), and many more can be closely tracked and monitored in the form of widgets.
To demonstrate the cross-functional analytical capabilities of this engine, let’s consider the example of a revenue versus marketing spend report. The revenue data is from Infusionsoft CRM and the marketing spend data is from Twitter Ads. By blending these datasets, you can analyze the impact of your marketing spends on revenue, when otherwise these data and insights would have remained in silos.
Businesses can also forecast their metrics using advanced analytics. In this example, an IT consulting service business can foresee their expected marketing spends and how that will impact their revenue in the future. This can enable them to plan their business efforts more proactively.
You can also add your CSAT scores to this analysis to better understand how the 3 metrics (revenue, marketing spend, and CSAT score) are interlinked.
Sustain the engine
For this engine to continuously push out cross-functional business insights for organizations to stay ahead of the competition, it’s crucial for leaders to nurture a culture of collaboration across teams. That’s why we designed and built this engine with advanced collaborative functionalities that will help drive adoption within your organization.
For example, this dashboard can be instantly shared with individuals or teams, and fine-grained access controls ensure high levels of data governance within organizations. It can be easily embedded in the context of an application’s workflow, websites, webpages, and more, to feed teams with contextual insights at every stage of decision-making. And contextual commenting enables teams to collaborate quickly and in real time.
To build a Unified Business Analytics engine, businesses must watch out for these two key enablers—a BI platform equipped with advanced intelligence to perform cross-functional analysis, and a robust data pipeline to feed this engine with relevant data.
Sign up now to explore how you can build a Unified Business Analytics engine for your business.
Then check out our webinar “Power up your Unified Business Analytics with Robust Data Pipelines” to learn more.