Use Case
Drive towards deeper and faster insights that can fuel data-driven business decisions
Create analytics transformation plan that directly addresses your current needs and provides a platform to build upon into the future
Have full understanding of current analytics needs and confidence in the path of your digital transformation
Provide self-service data analytics access to knowledge workers
Leverage advanced analytics to gain a full understanding of your customers
Data science and analytics are the key to unlocking the answers your business needs to compete. They enable you to connect the dots among complex business factors so you can understand and predict customer behavior and uncover new insights about your markets.
But no company becomes data driven and insight rich overnight. Analytic transformation is a journey, not a destination, and it requires collaboration, determination, and dedication from all departments. Without processes in place, you’re opening a Pandora’s box. Line-of-business leaders will demand insights faster and faster. Data analysts will receive an endless stream of follow-up questions, creating backlogs. IT, the gatekeepers of data, will become a bottleneck for analysts wanting data and access. And data scientists will become bogged down with mundane tasks that require their skills but prevent them from working on higher-value projects.
Before embarking on the journey, smart companies examine themselves to understand, define, and agree on their analytics maturity.
1) Starting out – Companies in this stage still rely on spreadsheets and labor-intensive tools to generate insights. Typical goals include standardizing month-end figures and analyzing product launch numbers. Their focus is on accessing and combining all data sources for a 360-degree view, moving to a single platform for reporting requests, and automating processes to deliver insights faster.
2) Moving towards operationalizing advanced and predictive analytics – Their data is connected; users are taking advantage of self-service tools and basic analytics is operationalized. They understand the impact of analytics on their bottom line. Their focus now is on moving to predictive and advanced analytics by upskilling their knowledge workers and building collaboration across departments.
3) Expanding advanced analytics throughout the organization – With analytics embraced, companies in this stage turn to the state of their data sources. They focus on dismantling the data silos that have evolved with years of data accumulation. They continue investing in their users’ skills with the goal of eliminating time spent on manual tasks related to analytics and insight generation.
4) Building a culture of analytics – At this point, analytics is embedded in their DNA and at every layer. With their analytics transformation accomplished, they establish and follow best practices, and they regularly review their infrastructure and the quality of the data on which they base their insights.