Data and technology have helped businesses evolve so rapidly that in just the past year, innovative technologies and capabilities such as data science, artificial intelligence, data analytics, and Big Data processing have allowed businesses to benefit from data in ways unimaginable before.
The possibilities of technology are incredibly exciting; however, technology is always a means, never an end destination. Lead with strategic direction and be clear on what you are targeting with data-driven innovations.
The main challenge for data monetization is building a solid foundation for running a new, scalable and profitable data business. Overcome it by focusing on creating horizontally connected teams and putting customers first. Install small teams at the edge of the organization with a clear vision and strategy that aim to drive rapid business outcomes. Ensure that they’re horizontally connected to include a mix of talented outsiders and forward-thinking corporate veterans, who help keep the bonds with the core business and break down silos between core and edge teams.
Execution Strategy of Enabling Business Outcomes
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Reliable and high-quality data
When enriched with other data sources and processed using with machine learning algorithms, the value of data increases immensely. Go for the infinite possibilities but temper with a dose of reality. Why? Not all data is created equal and being aware of bias in the data is crucial for understanding the extent of a model’s accuracy and avoiding shortcomings on algorithms and biased models.
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Transparency
Ensure model transparency that avoids black box and that algorithms remain accurate over time, meaning the reasoning behind a decision or recommendation can be explained at any time.
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Build in circular learning loops
Data-driven applications operate on diverse data sources, combining analytical and operational data to predict and prescribe what to do next by providing transparency and relevant insights in the form of industry benchmarks and peer-to-peer comparisons. Simulation capabilities and recommended actions based on algorithms guide the user in the decision-making process. Incorporate user-generated behavioral data and improve recommendations by correlating interactions back to the source of data to close the loop.
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Combine data and domain expertise
This is crucial because data-driven applications solve new problems in new ways by connecting the right data in a meaningful and contextualized way. The creation of value becomes much more problem-solving focused, providing solution for industries and for horizontal business functions like human resources, sales or finance. Use your domain expertise to find what problem to solve and let the data lead to new insights and signals. The best algorithms are the ones based on contextualized domain knowledge on processes in combination with data and behavior.