Digital transformation: 8 best practices for building an analytics roadmap

Digital transformation: 8 best practices for building an analytics roadmap

Consider these best practices for making the most of analytics as part of your digital transformation efforts

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5. Perform a gap analysis and prioritize

Identify and prioritize the key capabilities that will enable the organization to accelerate its analytics capabilities in a way that will deliver benefits quickly.

“Once the business objectives are in place, enterprises need to make a set of decisions about four distinct building blocks,” Arora says.

Those building blocks are data (both structured and unstructured), tools (technology for data ingestion, analysis, and visualization), analytics and data science talent, and infrastructure (a foundation capable of handling the volume, variety, and velocity of the data and the complexity of the analytics).

6. Build for today, design for tomorrow

Integrating advanced analytics will be an ongoing process, and available technologies and capabilities are evolving quickly. “Design a flexible scalable architecture to support future state analytics workloads [which will include] machine learning and artificial intelligence,” advises Verma.

7. Create a framework for analytics implementations

Develop an end-to-end process for conceiving, developing, and implementing advanced analytics capabilities. “As part of this framework, businesses should consider including focused and measurable activities that can help improve decision-making and drive innovation in products, services, and internal operations through analytics platform modernization,” says Verma. Make sure to socialize and market these best practices.

8. Create a phased approach to analytics transformation

This will “ensure meaningful outputs and allow for agile changes to the business needs,” says Andrew Alpert, managing director with Pace Harmon. Companies that are new to analytics will learn exponentially from the initial deployments to better inform future needs. “Getting these elements right during the first phase is more important than implementing functionality,” Alpert says.

“From there, it will be a journey to establish an optimal data and information model for which information to capture and why, as well as to mature from reports to descriptive analytics and eventually into predictive analytics,” Alpert says. “The architecture can evolve over time as well.” 

In addition, within each analytics deployment, IT leaders can mitigate delivery risk by breaking up capabilities into phased releases. “This will provide adequate time and effort for platform stabilization upfront to optimize acceptance risk,” Alpert says.

[ How can automation free up more staff time for innovation? Get the free e-book: Managing IT with Automation. ] 

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