Today, the issue is no longer about owning the most data but rather about how to gain the most insights from it. In short, how to turn data into insights, and insights into real business advantage. Data is pervasive. telling us everything. But do companies really know where to look? The reality is that turning mountains of data into valuable. practical and actionable analytics is not nearly as straightforward as people believe.

From Ad Hoc Data To Sophisticated Business Insights

Data Sciences’ role in the future of business strategy is unquestionably huge. The fact is that winning in today’s markets requires world-class insight and the capacity to change direction at short notice. And the only way to achieve that is through enterprise-wide Data Science enablement. In the past, decision making was largely driven by intuition and experience: whoever had the best ‘gut feel’ would win. Today, the winners are defined by who has the more insightful grasp of their data. The problem is that while everyone may instinctively know that Data Science is going to be big, most still struggle to do it effectively.

Organizations are hitting roadblocks in determining which data to collect, in asking and answering the right questions and in getting value from their analytics. Decisions are further complicated by a rapidly evolving ecosystem which is disrupting organizations ability to adapt. New technologies are emerging and new sources of data are being uncovered at a breakneck pace. Data sources such as social media feeds, customer blogs and mobile data are all increasing complexity and creating new opporrunities for organizations. Those that have taken an early lead in harnessing and exploiting social media data are already demonstrating the value that outside data can add. For example, KPMG recently worked with a car manufacturer whose internal data identified four key ‘charac1eristics believed to be vital to customers. But once social media data was overlaid using a Mass Opinion Business Intelligence (MOBI) platform, it quickly became clear that only one of their selected Characteristics actually mattered 10 their customers. And, in the process. two new characteristics were identified and validated.

Business leaders will also need to focus on ensuring that they can count on the veracity of their data. In fact, we believe that. as more and more unstructured and external data becomes integrated into the enterprise data science. Confidence in the accuracy of their data , and the insights gleaned from the data – will help ensure smarter business decisions.

In China, the rise of Data Science is becoming one of the major strategic tools many Chinese entities are looking at to get closer to the customer. Many entities are beginning to leverage their vast storage of data to give them insights into the buying patterns of their customers and provide more targeted services. However, the fact that just a quarter of respondents said that Data Science was ‘critically’ important 10 their organizations highlights our concern that businesses don’t fully comprehend the enormous potential value of Data Science.

Organizations also reported being more focused on using Data Science to analyze their data with greater speed (i.e. doing the same things but faster) than as an opportunity to expand or sharpen their insights (i.e. doing different things). Almost 80 percent said that analytical speed was a key benefit of using Data Science, versus 65 percent that said they expected to be able to identify insights that otherwise would have been missed, and 59 percent that said they hoped to achieve more granular insights.

Where are organizations currently applying all of their analytical capacity and capability? According to our survey, organizations are fairly split between operational improvements and innovation. About a third of respondents said they are focusing their Data Science on operational improvements, a third said innovation and another third said some combination of both.

Respondents from the EMEA region were about a third more likely to say they are focusing on operational improvements (such as process or performance efficiencies) than innovation, while respondents from the Americas were more likely to focus on innovation than operational improvements. Respondents were also most likely to say that the primary focus of their internal Data Science activity was going towards cost cutting. In fact, almost three quarters (72 percent) of respondents said their Data Science capabilities were being applied towards operating costs, while only about a third (35 percent) said they were looking to improve opportunity development.

Efficient Workforce Intelligence

Where are organizations currently applying all of their analytical capacity and capability? According to our survey, organizations are fairly split between operational improvements and innovation. About a third of respondents said they are focusing their Data Science on operational improvements, a third said innovation and another third said some combination of both. Respondents from the EMEA region were about a third more likely to say they are focusing on operational improvements (such as process or performance efficiencies) than innovation, while respondents from the Americas were more likely to focus on innovation than operational improvements.

Respondents were also most likely to say that the primary focus of their internal Data Science activity was going towards cost cutting. In fact, almost three quarters (72 percent) of respondents said their Data Science capabilities were being applied towards operating costs, while only about a third (35 percent) said they were looking to improve opportunity development. How best to put Data Science to work within a busines is a huge consideration for most organizations. Data Science can offer both top and bottom-line growth, but this requires organizations to have a clear line-of-sight from the data through to th value drivers of organizational performance.

Many organizations are still focused on how they can achieve the piece that is driven by business case value, rather than applying their data science towards more strategic, ‘blue sky’ opportunities. The fact that respondents in the US (where Data Science is seen to be more mature) are more focused on innovation shows that – as growth returns – organizations will increasingly verge towards using Data Science to drive new business opportunities rather than cost cutting.

Organizational culture is one area that will require particular attention if businesses hope to move towards a data-driven operating model .Executives need to find ways to encourage their workforce to be more analytical in their decision making. From there, organizations should quickly start to see data science driving their overall people performance, their operational performance, their customer performance and their selling performance. As such, one of the most underestimated challenges facing many organizations is how to move from ‘gut feel’ decision making to a data-driven culture. This requires a pervasive shift in mind-set that can often be accelerated by adopting a collaborative federated model that cuts across silos. The real market leaders here use analytics in their day-to-day operations in areas such as HR, where organizations are applying analytics to help evaluate applicants’ resumes against outputs from the interview process. The best organizations are creating combined teams of functional and analytical professionals as a way to truly embed analytics-thinking into key business functions.

As is often the case, the key question to ask is what kind of insights or intelligence are you trying to gather from your Data Science. Zeroing in on the business problems and identifying key hypotheses may not be the easiest thing to achieve up front, but it is far more efficient and effective in the longer term.