Business Executives Lack Confidence in Generating Trusted Insights from Data and Analytics: KPMG Survey
KPMG survey examines how businesses use data and analytics to manage risk, cost and growth – and where confidence is lacking
Gaps in capabilities around quality, effectiveness, integrity and resilience drive a cycle of mistrust
Nearly half of respondents report that their C-level executives do not fully support their organization’s data and analytics strategy
With the rapid growth of digital information over the last decade, more and more businesses are understanding the hidden value they hold in their large volumes of company data. If analyzed correctly, such data can help them to understand their customers’ needs, improve the efficiency of their business processes and identify red flags for accounting irregularities, fraud and regulatory non-compliance. With such huge potential benefits, why is it then, that so many business leaders are reluctant to place trust in the analysis of their own data?
Most executives today believe in the value of using 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 new survey from KPMG International.
For the report, Building Trust in Analytics, KPMG commissioned Forrester Consulting to survey 2,165 respondents from 10 countries to identify in which areas businesses are using D&A, and to what extent they lack trust in their D&A models and processes to drive decision making and desired outcomes. The report shares insights and recommendations on suggested processes, practices and governance for building trust in D&A using KPMG’s four anchors of trust, a framework for assessing quality, effectiveness, integrity and resilience.
Seventy percent of businesses, the survey shows, consider D&A tools to be an integral part of their decision making in the areas of: monitoring business performance, detecting fraud, driving strategy/change, understanding how their products are used, and complying with regulatory requirements. 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.
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 U.S. “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 data and analytics 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 data and analytics 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 U.S. “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.”
“Mistrust in data often starts with the selection of the right data for analysis,” said Douglas Webb, partner with KPMG in Thailand. “Many companies are unaware of the vast amount of different types of data that they already hold in their own systems.”
“Moreover, data is often stored across different platforms. The amount of time needed to cleanse the data and to link disparate data sets to allow meaningful analysis is often underestimated. But once the data sourcing issues are addressed, D&A offers the potential for cost-effective and insightful solutions.”
Mr. Webb continued “For example, identifying incidents of fraud in the Procure-to-Pay process is often like trying to find the proverbial ‘needle in a haystack’, which is further complicated when a business uses multiple systems as well as offline manual processes. D&A offers a cost-effective approach to examine 100% of your transactions with greater effectiveness than the traditional method of testing a small sample of transactions.”
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 findings: 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 data and analytics.
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.
“KPMG offers recommendations that can assist organizations with closing the trust gaps across the analytics lifecycle,” said Mr. 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,” Mr. 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.”
Learn more about KPMG’s 4 Anchors of Trust and recommendations for managing trusted analytics here.
KPMG’s Data and Analytics Global team commissioned Forrester Consulting to examine the power of trust in data and analytics by exploring organizations’ capabilities across four anchors of data and analytics: 1) quality, 2) effectiveness, 3) integrity and 4) resilience.
Forrester surveyed 2,165 data and analytics decision-makers (director and above - business and IT decision-makers with involvement in setting strategy for, or management of, business intelligence, data analytics, data warehousing, data management/big data management initiatives) in China, Germany, India, U.K., U.S., Canada, South Africa, France, Brazil and Australia. Industries polled cover banking/financial services, insurance, telecom, healthcare/life sciences and retail.
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