Most business leaders today believe in the value of using data and analytics (D&A) throughout their organizations, but say they lack confidence in their ability to measure the effectiveness and impact of D&A and mistrust the analytics used to help drive decision making, according to a global survey from KPMG International.
Most businesses, the survey shows, use D&A tools to analyze existing customers (50 percent), find new customers (48 percent) and to develop new products and services (47 percent). Yet, executives do not trust that they are managing their D&A processes effectively to generate desired outcomes and lack the necessary measures to assess the efficacy of 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,” said Christian Rast, Global Head of D&A, and a partner with KPMG in Germany. “Organizations that continue to invest in D&A without determining its effectiveness could likely make decisions based on inaccurate models, which would perpetuate a cycle of mistrust in the insights.
Rast continued: “Failing to master analytics will not only make it increasingly hard for organizations to compete, but will expose their brands to new and growing risks. Seventy percent of executives believe that by using D&A they expose their organizations to reputational risk.”
Low levels of trust may filter from the top down – confidence lacking in key areas
Only just under half of respondents are very confident about the insights they’re deriving from D&A in the areas of risk and security (43 percent), for customer insight (38 percent) and only one-third are very confident about their insights around business operations (34 percent).
“There is no doubt that subjective, gut-feel decision-making is being augmented by data-driven insights to allow organizations to better serve customers, drive efficiencies and manage risk,” said Bill Nowacki, Managing Director, Decision Science, KPMG in the US. “The survey, however, indicates executives’ level of confidence in their insights is not where it should be, given these organizations’ plans for increasing investment in and returns on D&A.”
These low levels of trust may originate at the top and filter down through the organization, the survey data suggests. Nearly half of respondents report that their C-level executives do not fully support their organization’s D&A strategy. This low level of confidence points to a lack of trust in the insights generated by D&A, which may be due to D&A’s inherent complexity.
“Transparency about the use and impact of an organization’s D&A is key to overcoming the long-held bias that conventional decision-making is more reliable,” said Brad Fisher, US D&A leader, and a partner with KPMG in the US. “We need to take D&A out of the ‘black box’ to encourage greater understanding about its use and purpose to help organizations trust the new insights it can bring.”
The four anchors: managing trust across the analytics lifecycle
A closer look at the analytics lifecycle reveals gaps in trust. Trust is highest at the beginning of the lifecycle – data sourcing – and drops significantly thereafter.
According to the findings, 38 percent of respondents have the most trust in data sourcing, which is determining which data is relevant for analysis. Twenty-one percent have the most trust in the second stage, analysis and/or modeling, and 19 percent have the most trust in the third phase, data preparation and blending.
Trust slides dramatically at the fourth and fifth stages of the lifecycle. Only 11 percent have the most trust in using/deploying analytics and 10 percent said the same about measuring the effectiveness of their analytics efforts.
“This drop in trust indicates broader challenges associated with teasing out insights generated from analytics,” said Mr. Fisher. “Merely being a data-driven enterprise doesn’t cut it. To drive trusted insights that deliver value, organizations need to do the work upfront – mapping out the desired outcomes and devising the necessary plans, processes and metrics to ensure effective execution.”
To assess where the trust gaps may be within an organization’s analytics model, respondents rated how well their processes aligned and performed against the capabilities outlined under four anchors of D&A: quality, effectiveness, integrity and resilience.
1. Quality – ensuring inputs and development processes for D&A meet quality standards appropriate for the context in which the analytics will be used
Anchor 1 key finding: While data sourcing was cited as the stage 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 D&A.
2. Effectiveness – outputs of models work as intended and deliver value
Anchor 2 key finding: Less than a fifth (16 percent) of respondents excel in ensuring the accuracy of models they produce.
3. Integrity – acceptable use of D&A, including compliance with regulations and laws such as data privacy and ethical issues around D&A use
Anchor 3 key finding: With the exception of D&A 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. Only 13 percent perform well in all areas of privacy and ethical use of D&A.
4. Resilience – optimization of D&A applications, processes and methodologies for the long term. This includes frameworks for governance, authorizations and security.
Anchor 4 key finding: Only 18 percent say they have appropriate frameworks in place across all areas of D&A governance.
Recommendations
“KPMG offers recommendations that can assist organizations with closing the trust gaps across the analytics lifecycle,” said Rast. “These comprise seven key areas: 1) assessing the trust gaps; 2) creating purpose by clarifying goals; 3) raising awareness to increase internal engagement; 4) developing an internal D&A culture; 5) opening up the ‘black box’ to encourage greater transparency; 6) having a 360-degree view by building ecosystems; and 7) stimulating innovation and analytics R&D to incubate new ideas and maintain a competitive stance.
“It’s imperative that D&A leaders make trust a high priority,” Rast continued. “To be a competitive, D&A-driven organization, business leaders must navigate the complex processes, systems, compliance requirements, and governance to confidently and consistently move from insights to measurable action.”
