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Based on our research into digital transformation, Trianz found that more than 70 percent of companies do not have the analytics capabilities to address the seismic shifts in their marketplace. What this means is that companies are drawing strategic conclusions only from available or convenient data.

This is an approach that will always fall short.

When digital change is taking place at a massive scale, “available data” perspectives will be outdated and never holistic. By the time the data is collected, the landscape has shifted, making obsolete any strategies developed from the data. Companies must get their strategies and executions right the first time.

That is a big part of why I wrote my book; to help leaders leverage the skills needed to learn from the past, analyze the present, and predict the future. These are your keys to crossing the faultline.

So how do you leverage the skills necessary to replace assumptions with data analysis? Let’s first explore the data ecosystems that every enterprise has at its disposal.

Data: The Open Secret in Your Ecosystem 

For a strategy to be truly effective in the digital age, it must be highly adaptive to change. To be inherently adaptive, it must rely on continuously collective and predictive insights.

To do this, you must extend your analytics capabilities beyond the traditional customer, supplier, and employee ecosystem, and analyze competitors, partners, regulators, and influencers. While the latter of these interactions may be non-transactional, they provide continuous data in the form of conversations about new patterns, research, technology, innovations, or opinions that influence others.

Only when you analyze all of this data regularly can you develop a holistic idea of your stakeholders and develop competitive or game-changing dynamics that create a data-driven culture.

Only then will fact-based decisions drive innovation.

The Four Stages of Digital Maturity

While removing assumptions and biases sounds logical, getting there is not so easy. Long-established habits, organizational dynamics, and turf battles can often override the practical logic that emerges from data-driven insights.  

To remove internal challenges, it is crucial to mature your organization’s data and analytics sophistication levels. These maturity levels can be summed up in four stages: 

Stage 1 – Reporting: The first stage is the ability to generate reports across your business; you must know what happened yesterday.  

Stage 2 – Business Intelligence: The second maturity stage is business intelligence and dashboards, or the ability to know what is happening today.

Stage 3 – Predictive Analytics: By investing in data science-based algorithms, you can develop an ability to predict what is likely to happen in your business.

Stage 4 – Prescriptive Analytics and AI: Once you gain command over your data and understand the human behaviors influencing your analytics production, your organization can generate prescriptive insights. In many cases, these can be further automated using AI technology, delivering the business quicker high-quality outcomes with less human intervention.

Since most companies today have only basic reporting capabilities, legacy players must leverage predictive analytics as soon as possible. When you gain the ability to understand, analyze, and interpret the data being generated within your company and its environment, you can replace assumptions with facts and catch up or leap ahead of the competition.

The cumulative effect of replacing assumptions with data-driven analysis is that organizational silos break down, and teams quickly learn that stakeholder expectations discovered through data are more important than opinions and biases.