Data Decision Gap Risks Holding Back Economic Recovery, Global Research Finds

Nine in ten global organizations struggle with data despite increased focus during the Covid-19 pandemic. This is the data decision gap that Contextual Decision Intelligence has set out to close.

Quantexa has published the first ever Data in Context report based on interviews with 750 IT and data decision makers in the Financial Services, Insurance and Public sectors, across three continents. 

The research shows that 95% of organizations suffer from the data decision gap, which is the inability to bring together the internal and external data needed to make accurate and trusted decisions. This is due to inaccurate and incomplete datasets, which ultimately impact the bottom line. The top three effects of this are:  

• Regulatory scrutiny and compliance issues, 47% 

• Missed customer experience opportunities and retention problems, 44%  
• Resource drainage due to increased manual data workload, 42% 

Inevitably, strategic and operational decisions suffer when they are based on a poor and incomplete picture. Quantexa’s study finds 50% of strategic decisions are missing crucial intelligence because organizations can’t take full advantage of data. Only slightly better for operational decisions, just 52% manage to rely on data. 

The pandemic put data in the spotlight. Digitization has meant organizations face an increasing tsunami of data, and many found they couldn’t take strategic advantage of the opportunity that connected data brings. Today’s organizations have all the data assets they need to make better decisions, but the data decision gap means they can’t extract meaning or value out of their data, as they can’t connect it to generate the single, accurate view needed.

Vishal Marria, CEO and founder, Quantexa

The Three Biggest Problems: Data Foundation, Contextual Analysis & Automation

Organizations typically struggle firstly with establishing an enterprise-wide data fabric as the foundation to effective decision making. Secondly, that data needs to be connected to create the relationship view that is crucial to managing everything from enterprise risk through to customer experience. Without this, organizations can’t spot emerging patterns from real-life entities. For example, uncovering the real beneficiaries behind offshore companies, detecting fraudulent credit applications, and discovering buying patterns that convert shoppers into long-term loyal customers. 

The data decision gap is growing fast. The installed base of enterprise storage is growing at an annualized growth rate of 31%, totaling 5,451 exabytes (EB) in 2025 according to IDC.

If the data foundation is disjointed and incomplete, analytic models are inaccurate and lack explainability.  

Even when an organization solves the issues around analyzing data in context accurately, the question becomes how to do it at scale. The answer is automating decisions. The research discovered that just 14% have already implemented successful automation initiatives in operational decisions, while 9% have yet to start the journey.  

The majority, 77%, are somewhere in the automation journey, and facing key challenges. Nearly two in five, 39%, struggle with inaccuracies in decision-making. A further 38% of the total do not fully trust those decisions due to the lack of explainability. 

Overall, the inability to see data in context, analyze it for trusted decision making, and automate it at scale, add up to the data decision gap. 

Contextual Decision Intelligence (CDI) 

Vishal Marria continued: “Contextual Decision Intelligence (CDI) turns traditional data approaches on their head, connecting each datapoint to all others in the organization and external data sources. With this connected basis, decision makers can see a single view of customers, from which they can extract real-world intelligence and take action. Then, with the addition of Artificial Intelligence (AI), organizations can scale automated decisions across the business, freeing humans.”