How data analysis fuels successful companies
This is part seven in a series presenting, in small, easily consumable bites, findings and insights from the IBM Institute for Business Value’s latest study and paper “Analytics: The speed advantage - Why data-driven organizations are winning the race in today’s marketplace,” by Glenn Finch, Steven Davidson, Christian Kirschniak, Marcio Weikersheimer, Cathy Reese and Rebecca Shockley.
In part six of this series, we finished our exploration of the first of three key stages within the analytics lifecycle (Acquire) and suggested recommendations and practical actions for that stage. In part seven we will look at the study’s second key stage within the analytics lifecycle, Analyze, which focuses on analyzing the data and identifying the insights most likely to create a positive business impact.
The ability to acquire and integrate data quickly is foundational to creating a speed advantage. Organizations must be able to source and manage data in ways that create flexibility and agility in how and when the data is used. We identified three capabilities that most differentiate Front Runners in terms of their ability to ingest data quickly.
- Blend traditional data infrastructure components with newer big data components
- Use real-time data processing and analysis to act in the moment
- Implement information governance to accelerate trust, integration and standardization within their data environments
Analyze diverse datasets
Front Runners differentiate themselves by analyzing robust, often external datasets to create business-driven insights that impact organizational performance. We identified nine out of 18 data sources that Front Runners are twice as likely to analyze (see Figure 7).
This data provides a richer context for customer interactions, marketing and product development by providing sentiment, product and service feedback, as well as crowd-sourced innovation at both the individual and aggregate levels. Integrating this information into product and service development can boost the customer experience.
This data creates a more robust version of internal datasets, enabling a deeper level of insights to support marketing and sales tactics, operational efficiencies and financial forecasts. It may also reduce risks from external forces, such as competitor moves and weather.
This data enables in-depth analysis on operations within an organization, which can lead to cost reductions, cost avoidance and increases in productivity and efficiency. This data also creates the agility needed to meet the needs of today’s ever more demanding customers.
Use advanced analysis approaches
Speed-driven organizations accelerate data analysis not only by using more advanced analytics, but by using them more extensively and broadly across the organization. This pervasive use of advanced analysis methods differentiates Front Runners from Joggers.
These advanced analysis techniques can be broken into four types: descriptive, diagnostic, predictive and prescriptive. Each of these types of data has a particular use within an organization’s analysis, depending on the business challenge to be solved.
A majority of respondents from each cluster use the four key types of analysis methods at some level within their organizations, but none use them as extensively as Front Runners. Two-thirds of Front Runners use descriptive analytics extensively, compared with less than half of Joggers and even fewer in the other two clusters. Similarly, a majority of Front Runners use diagnostic analytics extensively versus only one-third of other clusters. Front Runners also outpace others in the use of forward-looking predictive analytics with more than one-third using them extensively throughout a variety of business processes, while more than one-third of Joggers use prescriptive analytics to drive and automate processes.
In each annual IBM Institute for Business Value analytics survey since 2010, the top challenge for organizations, no matter how we asked the question, has been “the ability to understand how to use data and analytics to impact” business performance, business outcomes or competitive advantage. This is true again in 2014, with 56 percent of all respondents ranking this inability as their biggest challenge.
This challenge continues due to the difficulty in finding individuals who can combine business and analytics knowledge to create insight. Front Runners acutely feel this gap in talent, with more than two-thirds identifying it as a one of the top three skills gaps, and slightly more than one-third also selecting skills for business analysis and data analysis separately.
But the talent shortage of those who can combine data and business analysis is felt most by every other cluster. The data suggests, however, that as organizations move to broaden their skills across the enterprise, the need for the combined skills becomes more apparent. This makes sense, as the combination of business and analytics skills is critical within speed-driven organizations, enabling a quicker translation of insights into actions based on a deeper knowledge of the business drivers, and the related data to understand them, that are most likely to impact performance.
In part eight, we will examine recommendations and practical actions for the Analyze stage. These recommendations will focus on the data and insights most likely to create a positive business impact.