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Three steps for conquering the last mile of analytics

Becoming insights-driven is now the ultimate prize of digital transformation, and many organizations are making significant progress toward this goal. However, putting insights into action – the “last mile” of analytics – is still a challenge for many organizations. 

With continued investments in data, analytics and AI, as well as the broader availability of machine-learning tools and applications, organizations have an abundance of analytical assets. Yet the creation of analytical assets should not be the only measure of success for organizations. In reality, deploying, operationalizing, or putting analytical assets into production should be the driver for how organizations are able to get value from their AI and data science efforts.

In a traditional data and analytics continuum, data is transformed into insights to support decision-making. If organizations want to break out from experimentation mode, avoid analytics assets becoming shelfware, and empower front lines to make analytics-powered decisions, they must start with decisions. Then they need to decide how to find, integrate and deliver the insights; and identify data to enable that.

These days, many organizations would argue they’re doing just that – they’ve hired analytics talent and appointed chief data officers (CDOs) or chief analytics officers (CAOs) to collaborate with business leaders to become more data- and analytics-driven. But many organizations are not seeing the desired impact and value from their data and analytics initiatives and are not able to quickly put their pilot projects into production.

According to IDC, only 35% of organizations indicate that analytical models are fully deployed in production. Difficulty in deploying and operationalizing analytics into systems or applications – and being consumed by downstream processes or people – is a key barrier to achieving business value.

Some might argue that the main focus within analytics projects has been on developing analytical recipes (e.g., data engineering, building models, merits of individual algorithms, etc.), while not much attention, priority or investment is done for operationalization of these assets. This is easier said than fixed. Data does not provide differentiation; decisions at scale do. Applying insights consistently to turn data into decisions will let organizations build a true software-led system of insights to grow and break away from competitors.

Check out the full article to learn how can organizations put analytics into action in a systematic, scalable manner and conquer the last mile.

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