Across all industries, companies are looking to Data Science for ways to grow revenue, improve margins, and increase market share. In doing so, many are at a tipping point for where and how to realize these value improvement objectives.

Those that see limited growth opportunities to grow through their traditional application and services portfolios may already be well underway in this data science transformation phase. For those that don’t see the need to find real value in their data and information assets (Data Monetization), it may be a competitively unavoidable risk that jeopardizes a business’s viability and solvency.

Either way, increasing the valuation of a company or business line through the conversion of its data and information assets into actionable outcome-oriented business insights is the single most important capability that will drive business transformation over the next decade.

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Data and information have become the single most important assets needed to fuel today’s transformational growth. Most organizations have seen the growth in revenue and margin plateau for organic products and services (those based on people, process, and technologies). The next generation of corporate value will come through the spelunking (exploration, evaluation, and visualization) enterprise, information technology, and social data sources.

“Data is the energy source of business transformation and Data Science is the engine for its delivery.”

This valuation process, however, is not without it challenges. While all data is important, not all data is of value. Data science provides a systematic process to identify and test critical hypotheses associated with increased valuation through data.

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Once validated, these hypotheses must be shown to actually create or foster value (Proof of Value – POVs). These POVs extract optical models from sampled data sets. Only these proven objective-oriented models, that have supported growth hypotheses, are extended into the enterprise (e.g., big data, data warehousing, business intelligence, etc.).

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The POV phase of value generation translates growth objective-based goals into model systems, from which value can be optimally obtained.

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This objective-based approach to data science different, but complements, traditional business intelligence programs. Data science driven actives are crucial for strategic transformations where one does not know what they don’t know. In essence, data science provide the revelations needed identify the value venues necessary for true business transformations.

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For those solutions that have clearly demonstrable value, the system models are scale into the enterprise. Unfortunately, this is where most IT-driven process start and often unsuccessfully finish. Enterprise data warehouses are created and big data farms are implemented, all before any sense of data value is identified and extracted (blue). Through these implementations, tradition descriptive statistics and BI reports are generated that tell us mostly things that we know we don’t know, an expensive investment in knowledge confirmation. The objective-based data monetization approach, however, incorporated only those information technology capabilities into the enterprise that are needed to support the scalability of the optimized solutions.

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While there are many Objective-Based Data Monetization case studies, a common use can be found in the insurance and reinsurance field. In this case, a leading global insurance and re-insurance company is facing significant competitive pricing and margin (combined ratio) pressure. While having extensive applications covering numerous markets, the business line data was not being effectively used to identify optimal price points across their portfolio of products.

Using Objective-Based Data Monetization, key pricing objectives are identified, along with critical causal-levers that impact the pricing value chain. Portfolio data and information assets are inventoried and assessed for their causality and correlative characteristics. Exploratory visualization maps are created that lead to the design and development of predictive models. These models are aggregated into complex solution spaces that then represents a comprehensive, cohesive pricing ecosystem. Using simulated annealing, optimal pricing structures are identified, which are implemented across their enterprise applications.

Data science is an proven means through which value can be created from existing assets in today’s organization. By focusing on an hypothesis-driven methodology that business objective outcome based, value identification and extraction can be maximized in order to prioritized the investment needed to realize them in the enterprise.

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