Existing organizational processes are unable to accommodate advancements in analytics and automation, often because protocols for decision making require multiple levels of approval. If you see your organization struggling with these impediments to scaling data-analytics efforts, the first step is to make sure you are doing enough to adopt some of the new tools that are emerging to help deal with such challenges. These tools deliver fast results, build the confidence of the front line, and automate the delivery of analytic insights to it in usable formats. But the tools alone are insufficient. Organizational adaptation is also needed to overcome fear and catalyze change. Management teams need to shift priorities from small-scale exercises to focusing on critical business areas and driving the use of analytics across the organization. And at times, jobs need to be redesigned to embrace advancements in digitization and automation. An organization that quickly adopts new tools and adapts 8 Getting big impact from Big Data Business opportunities McKinseyonMarketingandSales.com 1 See the full McKinsey Global Institute report, Big data: The next frontier for innovation, competition, and productivity, May 2011, on mckinsey.com. 2 To learn how marketing functions in Google’s datadriven culture, please see our forthcoming interview with Lorraine Twohill, the company’s head of marketing, on mckinsey.com. 3 See Stefan Biesdorf, David Court, and Paul Willmott, “Big data: What’s your plan?,” McKinsey Quarterly, March 2013; and Brad Brown, David Court, and Paul Willmott, “Mobilizing your C-suite for big-data analytics,” McKinsey Quarterly, November 2013, both available on mckinsey .com @McK_MktgSales 9 itself to capture their potential is more likely to achieve large-scale benefits from its dataanalytics efforts. Why data-analytics efforts bog down before they get big As recently as two or three years ago, the key challenges for data-analytics leaders were getting their senior teams to understand its potential, finding enough talent to build models, and creating the right data fabric to tie together the often disparate databases inside and outside the enterprise. But as these professionals have pushed for scale, new challenges have emerged. Exhibit How to accelerate your data-analytics transformation Executives can often point to examples such as this one where early efforts to understand interesting patterns were not actionable or able to influence business results in a meaningful way.
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