Organizations lack trust in insights from data and analytics
While many respondents are using analytics, execs lack faith that results can be trusted.
A majority of business leaders believe in the value of using data and analytics to drive decisions in their organization, but say they lack confidence in their ability to measure the effectiveness and impact of data and analytics. Further, they mistrust the analytics used to drive those decisions.
Those are among the findings of a new Forrester Research study commissioned by KPMG, “Building Trust in Analytics.” The study polled 2,165 professionals from 10 countries to identify in which areas businesses are using data and analytics, and to what extent they trust the models and processes they used to drive decision making and desired outcomes.
The survey revealed that a large number of organizations (50 percent) use data and analytics to analyze existing customers; to find new customers (48 percent); and develop new products and services (47 percent). But the same executives say they do not trust that they are managing their data and analytics processes effectively to generate desired outcomes, and they lack the measures necessary to evaluate those models.
“As analytics increasingly drive the decisions that affect us as individuals, as businesses and as societies, there must be a heightened focus on ensuring the highest level of trust in the data, the analytics, and the controls that generate desired outcomes,” noted Christian Rast, global head of data and analytics, and a partner with KPMG in Germany. “Organizations that continue to invest in data and analytics without determining its effectiveness could likely make decisions based on inaccurate models, which would perpetuate a cycle of mistrust in the insights.”
Among the highlights of the report:
To help assess where the greatest trust gaps are within an organization’s analytics model, respondents were asked to rate how well their processes align against what KPMG identified as the four anchors of data and analytics: quality, effectiveness, integrity, and resilience. The results were as follows:
Quality: Ensuring inputs and development processes for data and analytics meet quality standards appropriate for the context in which the analytics will be used.
“While data sourcing was cited as the state of the analytics lifecycle that survey respondents say they trust most, only 10 percent said their organizations excelled across all areas in developing and managing data and analytics,” the report revealed.
Effectiveness: Outputs of models work as intended and deliver value.
“Less than a fifth (16 percent) of respondents excel in ensuring the accuracy of models they produce,” the report says.
Integrity: Acceptable use of data and analytics, including compliance with regulations and laws such as data privacy and ethical issues around data and analytics use.
“With the exception of data and analytics regulatory compliance, where respondents say they perform strongest, they fall well below in achieving excellence in the areas of ethics and privacy with respect to managing trusted analytics,” the report says. “Only 13 percent perform well in all areas off privacy and ethical use of data and analytics.”
Resilience: Optimization of data and analytics applications, processes and methodologies for the long term. This includes frameworks for governance, authorization and security.
“Only 18 percent say they have appropriate frameworks in place across all areas of data and analytics governance,” the report concluded.
Those are among the findings of a new Forrester Research study commissioned by KPMG, “Building Trust in Analytics.” The study polled 2,165 professionals from 10 countries to identify in which areas businesses are using data and analytics, and to what extent they trust the models and processes they used to drive decision making and desired outcomes.
The survey revealed that a large number of organizations (50 percent) use data and analytics to analyze existing customers; to find new customers (48 percent); and develop new products and services (47 percent). But the same executives say they do not trust that they are managing their data and analytics processes effectively to generate desired outcomes, and they lack the measures necessary to evaluate those models.
“As analytics increasingly drive the decisions that affect us as individuals, as businesses and as societies, there must be a heightened focus on ensuring the highest level of trust in the data, the analytics, and the controls that generate desired outcomes,” noted Christian Rast, global head of data and analytics, and a partner with KPMG in Germany. “Organizations that continue to invest in data and analytics without determining its effectiveness could likely make decisions based on inaccurate models, which would perpetuate a cycle of mistrust in the insights.”Among the highlights of the report:
- The study found that gaps in capabilities around quality, effectiveness, integrity and resilience are driving a cycle of mistrust.
- Nearly half of respondents say their C-level executives do not fully support their organization’s data and analytics strategy.
- Some 70 percent of business leaders believe that using data and analytics can expose the organization to reputational risk.
To help assess where the greatest trust gaps are within an organization’s analytics model, respondents were asked to rate how well their processes align against what KPMG identified as the four anchors of data and analytics: quality, effectiveness, integrity, and resilience. The results were as follows:
Quality: Ensuring inputs and development processes for data and analytics meet quality standards appropriate for the context in which the analytics will be used.
“While data sourcing was cited as the state of the analytics lifecycle that survey respondents say they trust most, only 10 percent said their organizations excelled across all areas in developing and managing data and analytics,” the report revealed.
Effectiveness: Outputs of models work as intended and deliver value.
“Less than a fifth (16 percent) of respondents excel in ensuring the accuracy of models they produce,” the report says.
Integrity: Acceptable use of data and analytics, including compliance with regulations and laws such as data privacy and ethical issues around data and analytics use.
“With the exception of data and analytics regulatory compliance, where respondents say they perform strongest, they fall well below in achieving excellence in the areas of ethics and privacy with respect to managing trusted analytics,” the report says. “Only 13 percent perform well in all areas off privacy and ethical use of data and analytics.”
Resilience: Optimization of data and analytics applications, processes and methodologies for the long term. This includes frameworks for governance, authorization and security.
“Only 18 percent say they have appropriate frameworks in place across all areas of data and analytics governance,” the report concluded.
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