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How to spot opportunities for innovation

What is data-led innovation, and how can data analytics help us find innovation opportunities? Watch this video and explore the answers.

Now let’s discuss one of the other ways that data analytics can bring a competitive advantage — namely, by showing us opportunities for data-led innovation.

For many organizations, innovation is crucial to their long-term sustainability. In times of rapid change, the ability to remain relevant and profitable depends on your ability to innovate.

While the chance to innovate is exciting and can be greatly accelerated if we’ve become data-led, we first need to lay the groundwork, by implementing the data analytics infrastructure.

Perhaps more importantly, we need to have adopted a data analytics mindset. To be data-led, we need to trust in what our data is telling us while also recognizing that data analytics is a tool, and it’s up to us to choose whether to act — or not — upon what we’re being told.

When the organization has adopted a data analytics mindset and practice, the opportunities for data-led innovation become more apparent. But where do these innovations come from?

What is data-led innovation?

In data analytics, we constantly strive to find new trends, new correlations, and new insights that allow us to optimize existing processes and bring change. Yet, how many of those can we say are true innovation, rather than optimization? And to this point, does it matter?

While we must always ensure we’re striving for optimization, the difference between optimization and innovation matters. Optimization enhances things within existing attitudes and boundaries; it’s about improving things in the present and for the short-term future. True innovation seeks to do something completely different: solve a problem in an entirely new way. Innovation has lasting implications for competitiveness and bringing dramatic changes to the structure and culture of your organization.

How can we spot opportunities for data-led innovation?

Data analytics can’t provide all the answers, and it won’t provide a direct opportunity for innovation, but it can provide the patterns, insights and projected outcomes to steer us in the right direction. Due to the vast volume of data, data analytics has the ability to uncover patterns that people might not pick up on. In the hands of people who work within the associated business functions, these patterns can be revealing. Either directly or indirectly, our human skills are needed to find opportunities for innovation from data analytics.

But this requires a shift in mindset, too. In the past we may have been encouraged to find the common denominator, or reduce the scope of the investigation as we simply did not have the capacity or resources to analyze huge amounts of data. As John Behrens explains in the video, if we are to benefit from the power of the data analytics infrastructure and systems, we need to adjust how and where we look for our insights.

This is one of the ways that we can combine the functionality of data analytics with a shift in mindset (in John’s example, how we think about and deal with variance or variability).

But having the insights and the data analytics reports isn’t enough. An organization’s leadership needs to embrace data analytics and use it to innovate. If you’re striving for data-led innovation, you need to be purposeful about it and prepare the entire organization by adopting a data analytics mindset and helping your teams do the same.

There’s a better chance for innovative ideas if everyone in the organization has a fundamental working knowledge of data analytics and the role they play within the data analytics ecosystem. Because the biggest source of innovation is people, it requires:

  • A learning culture
  • Collaboration and cooperation
  • A tolerance of measured risk
  • An openness to new ideas and approaches

Leadership and management need to create a culture and practice in which data analytics and innovation will thrive. Innovation is not a single event but a constant exercise.

For example, someone within the finance and reporting function that reviews data analytics outcomes may spot a trend of greater efficiencies on a particular assembly line when a certain work crew is on duty.

The work crew members themselves are more likely to know why their team is able to achieve greater efficiencies. An investigation like this can reveal new opportunities for practices that lead to greater efficiencies and perhaps new innovations or approaches.

Employees with detailed knowledge about processes and workflows within the organization are well positioned to seek opportunities for innovation if they’re empowered to do so. There is a dual responsibility for employees to constantly look out for opportunities to improve, optimize and innovate and for leaders to ensure they’re open to receiving new ideas from anywhere.

Although innovation derives from the organization’s internal teams, these ideas and inspirations don’t have to come solely from internal data-sets. By using relevant external data-sets, we gain access to new knowledge, changes in the market or industry, and unexpected events, all of which can bring innovation opportunities.

Our ability to combine, analyze, and process these internal and external data-sets using data analytics provides us with the ability to search for innovative ideas.

The human story is one of innovation. It’s the key to our survival and our ongoing success. Likewise, innovation is crucial to the long-term future of any organization, especially in the world of modern business, where change is a constant.

With a clear data-strategy, solid data analytics infrastructure, and the right mindset, we can use data analytics to fuel innovation.

Having understood the possibilities for innovation, it’s natural that we might want to jump straight into a data analytics project.

But we need to make sure that it’s the right project for our organization’s needs. How do we assess this? That’s what we’ll be discussing in the next activity of this course.

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Introduction to Business Intelligence and Data Analytics

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