Competing Smarter with Advanced Analytics

In spite of numerous hurdles in implementation and use, business executives frequently rate advanced analytics schemes as popular and point to several concurrent paybacks

The Economist Intelligence Unit (EIU) carried out a survey of more than 300 executives who are familiar with their company’s data analytics practices. The survey found that companies are moving beyond first-generation big data applications based on internal assets and are reporting considerable success with innovative market-facing initiatives that use a wide range of transactional and external data. Competitor-focused initiatives are given the highest priority, with customer – and operations-focused measures comprising a significant number of initiatives.

Business and Data Challenges

Executives most frequently point to data and analytics silos within their organizations as the biggest business-related hurdles. The rate of this is about 43 percent. Other top challenges include gaining sufficient executive support, analyzing data across silos to develop a holistic view and lack of personnel with sufficient data expertise (all 41%). All of these challenges appear to stem from the fact that new and innovative data analytics initiatives are most commonly driven by lines of businesses where data analytics expertise usually does not reside. Several factors are behind this trend. Business owners are often the first to perceive needs and the first to recognize the benefits of innovation. Moreover, a range of new tools gives them access to advanced analytics independent of their enterprise IT functions. “Sales units can use both big data and data-mining tools to categories customers and develop new products to maximize profits,” says Atlas Lu, Vice President of China Airlines Information Management division. “Managers can use business intelligence tools to quickly analyze current operations data and facilitate new strategic planning, while IT personnel maintain clear lines of communication and supplement missing data,” added Lu.  And finally, the expected cost of initial forays into big data is generally low enough that line-of-business owners do not need to demonstrate ROI for an experimental initiative. In fact, demonstrating ROI is the least important challenge, cited by only 10% of executives.

Satisfaction Levels

Survey respondents report high levels of satisfaction with their big data analytics initiatives. Overall, 80% say they are satisfied, including 23% very satisfied and 57% somewhat satisfied. These results are supported by a broad range of specific benefits that executives report. Reduced cost is the most frequently cited benefit. This is surprising, as reduced costs were not among the top objectives of respondents’ advanced analytics initiatives. To some extent, this may reflect unexpected cost savings from parallel actions such as moving to cloud-based analytics platforms. Another consideration is that reduced costs are easy to recognize while other benefits can take the time to appear. But China Airlines’ Atlas Lu cautions that seeking cost reductions can be a distraction. “Our goal is to find hidden information with potential for results that surpass all imagination,” says Lu. “Through data analytics, we can identify our customers’ consumption habits, stimulate purchasing behavior and increase corporate earnings on a basis of increased customer loyalty-reaching our long-term goal of corporate sustainability. Cost reductions are not our main concern” he further added.

Keys to Success

The high degree of satisfaction with the past and current analytics initiatives has engendered optimism about the future. More than 90% of respondents say that they are likely to pursue further market-facing advanced analytics initiatives. The executives surveyed have clearly learned from their experiences and are now ready to innovate further. They report that selecting the right data-driven initiative—and assembling the right team to execute it—are the most important success factors. This is another indication that considerable experimentation is still going on in the field of analytics. Collaborating, garnering senior executive support and choosing the right technology are also important success factors cited by at least one-third of respondents. Priorities for market-facing advanced analytics over the next 12-18 months are just as varied as they have been in the recent past. Various competitor-focused initiatives are anticipated by between 36% and 41% of respondents, followed closely by customer/operations-focused projects ranging from 30% to 36%.

Conclusion

First-generation big data applications focused on internal initiatives such as supply-chain optimization or customer segmentation—because that was where the data were and could be used. As companies gain expertise and as software grows more sophisticated, industry leaders are now expanding their data priorities to include market-facing initiatives. These are external analyses, sometimes leveraging external data sources that are used to undercut competitors’ pricing, build new business opportunities and increase revenues. However, these more complex initiatives create commensurate challenges. Data and analytics silos, multiple data sets and the integration of externally curated data are the primary problems.

The initial benefit is cost-reduction, as data enables more efficient approaches and as the move to cloud lowers direct costs. But users cite further benefits, including increased revenue, new business opportunities and the ability to cross-sell existing products to customers. In sum, data is no longer just about analytics; it is about creating a whole new enterprise. The keys to success are finding the right initiative, mobilizing qualified personnel and selecting the right software and technologies. High levels of satisfaction are found in these early users, with four out of five satisfied with their current initiatives and nine out of ten planning market-facing data initiatives in the near future.