Big decisions and the motivations behind them
There’s a high likelihood that routine business decisions will soon be made by machines. But what about those that require creative judgment and collaboration? How do data and analytics inform these decisions? And how is decision-making changing? We asked more than 2,100 C-suite leaders, business unit heads, and SVPs to predict a strategic or operational decision their company will need to make before 2020. Here’s what they said.
Before 2020 my company will decide to…
- develop a new product or service (31%)
- enter new markets with existing products or services (15%)
- make strategic investments in IT (14%)
- develop partnerships (11%)
- change business operations (10%)
- enter a new industry (7%)
Most are making these decisions to maintain or gain market leadership (40%). Fewer feel motivated by a need to survive (28%) or the ability to disrupt their own industry or another industry (25%). These same motivations will drive how data and analytics will be used.
Executives feel they understand and can manage risks related to their next big decision. But there are two notable exceptions: developing new products and services and entering new industries. These decisions feel riskier.
Two-thirds (61%) acknowledge their companies could rely on data analysis more and intuition less. They don’t consider their own organisations to be highly data-driven. This puts them at risk of being surpassed by their more data-driven competitors, given recent advances in technology and data and analytics techniques. Highly data-driven companies are significantly changing how they make decisions, improve operations, or use analytics to deliver products and services.
Data and analytics: What decision-makers want
To find out how executives think decision-making should improve, we asked about two dimensions: speed and sophistication. Speed describes how quickly and organization moves. Companies must get the right information to the right places at the right time and take action if they want to increase speed. Sophistication relates to insights—applying the right level of insight to the right problem to create the right value. For example: It’s more sophisticated to calculate the impact on revenue and profitability when entering a new market if you’re simulating the drivers and timing of market adoption as opposed to looking at the size of target segments alone. Focusing on improving both speed and sophistication helps maximise the return on investment for data and analytics.
We found that everyone wants decision-making to be faster, especially in banking, insurance, and healthcare. But decision-makers say there’s even more work to be done on sophistication. The gap between how sophisticated they are now and where they’d like to be by 2020 is greater.
These patterns tell us that companies may not yet be taking full advantage of the analytics they already have or they’re not sure they’re ready for something more advanced. In fact, half (56%) say they mostly use descriptive or diagnostic approaches, and a third (29%) say analytics are predictive. The most sophisticated companies use prescriptive approaches—which enable things like recommendation engines, automated trading, or dynamic pricing (13%).
Most decision-makers say the analysis they require relies primarily on human judgment rather than machine algorithms (59%), meaning they rely on judgment to frame the problem and help them ask the right questions. This view is more pronounced in Japan. Executives in China are more likely to say the opposite—that the analysis for their next decision will rely more on machines. Understanding this dynamic is important, particularly as the use of machine learning, natural language processing, conversational agents and other technologies evolve. The right mix of mind and machine can help reduce the impact of human bias and yield more accurate answers, even for complex problems. (See PwC’s strategy+ business article Beyond Bias for a helpful primer on human bias.)
Executives who once relied firmly on their intuition and experience are now face-to-face with machines that can learn from massive amounts of data and inform decisions like never before.
Decision-makers acknowledge it’s not data or analysis that holds them back from making decisions. Instead, they’re more likely to feel limited by a whole host of other factors: availability of resources, budgetary considerations, issues with implementation, leadership courage, operational capacity to act, policy constraints, and poor market responses.
What you can do
To improve decision-making capabilities at your company, you should continue to invest in strong leaders who understand data’s possibilities and who will challenge the business in four areas:
What’s your sphere of discovery? This is a creative question about how your company thinks about the possible uses for new and existing data and how you might use analytics to create new value. It requires judgment about what information would be most valuable to the business if you had it. It also requires the freedom to experiment and it takes persistent scanning for available data. For example, many of our survey respondents seek to change the value proposition for products and services, but our experience suggests few regularly scan crowd-sourced or social data to link emerging preferences to product designs.
How will you find the questions worth asking and the insights that answer them? Data scientists and business leaders must join forces. Finding what’s worth asking requires specific knowledge of the business and how value is created. Finding insights means matching the type of analysis to the task. For example, say you’ve prioritised a new product release and social data will help you understand how customers will respond to it. What questions should analysts pursue so that you have actionable information, when you need it, to design effective promotions?
Who will take what action? Insight is meaningless if decision-makers don’t have options for action. As you get more granular insights, you are likely to change your workflows, decision rights and organisational structure. If you plan to augment or automate a decision, this includes making choices upfront on what you expect machines to do and what you expect humans to do. If you build a dynamic pricing model, for example, will your staff have any rights to modify pricing for your customers?
How will you track the outcomes you’re shaping? The ability to create a continuous improvement feedback loop for data and analytics is the ultimate test of its value. Today, we’re seeing companies take three routes to measuring impact from data and analytic efforts: tracking operational and financial outcomes; setting up limited tests that measure actions taken and actions not taken, and creating new revenue streams for data and insights that are themselves valuable in other parts of a business ecosystem or value chain.
For this year’s look at big decisions, we wanted to get a better understanding of decision-makers and their perceptions about overall decision-making capabilities in their organisations. To do this, we collected diverse perspectives through many different stories. This type of data capture allowed us to look at the full range of perspectives to get a better view of the underlying patterns. Using a narrative-led approach helped us to see the kind of experiences that wouldn’t have been captured in standard survey instruments. As of May 15, 2016, we’ve collected micro stories and other signifying data from more than 2,100 people across more than 10 countries and 15 industries.