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An anticipated drop in the cost of Internet of Things (IoT) nodes (for example, microcontroller units and sensors) is fueling the rise in available data. Advances in machine learning, data science, and computing power can turn these vast amounts of data into value-creating insights. Our new report, Achieving business impact with data, looks into these issues deeply; this article highlights some of the report’s key points.
Fundamentals of the insights value chain
The ability to capitalize on what data has to offer hinges on a series of fundamentals along what we call the insights value chain, which includes a range of technical capabilities as well as solid business processes (See Exhibit 1). Broadly speaking, capturing the most value from the wealth of potential data begins with excellence in identifying, capturing, and storing that data; moves through the technical capability to analyze and visualize that data; and ends with an organization that is able to complement analytics with the domain knowledge of human talent and rely on a cross-functional, agile structure to implement relevant insights.
Capturing value from data requires excellence in each element of each link of the “insights value chain.”
Capturing value from data depends on the integrity of the entire insights value chain, and the chain is only as good as its weakest component. Organizations looking to be successful in data insight must ensure excellence in all components and steps of the insights value chain.
Insights-driven use cases
Insights-based value creation models in the evolving spaces of the connected world can be grouped into one of three overarching categories:
Top-line use cases typically help companies improve customer-facing activities (See Exhibit 2). These use cases can enhance activities in the areas of pricing, churn prevention, cross- and upselling, and promotion optimization to drive growth.
Bottom-line use cases employ data-driven insights to optimize internal processes. Predictive maintenance, supply chain optimization, and fraud prevention are among the processes that can be improved with the benefit of data. These use cases are becoming increasingly relevant due to the growing number of IoT applications and the collection of massive amounts of data that can be used to improve business processes.
New business models is the category of data-enabled use cases that moves beyond processes and brings value by expanding a company’s portfolio of offers. This can include the straightforward selling of the data itself, selling insights gleaned from data, and offering analytics as a service.
The best analytics are worth nothing with bad data. The importance of understanding and working on all components of the insights value chain is mission critical.
Insight-driven use cases create value by either supporting growth (top line) or reducing costs (bottom line).
Translating data into business value
With a solid operating model in place, organizations can begin the process of turning data into value. A systematic approach maps a series of actions to the insights value chain described above. The first two action areas—data collection and data refinement—comprise the tech-heavy upstream activities. This is followed by the people- and process-driven downstream activities of defining and adopting actions, as well as building the tools and governance that support sustained engagement around these insights-based activities (See Exhibit 3).
A systematic approach translates data insights into business value.
Generating and collecting relevant data
Defining certain requirements based on particular use cases will help ensure that only relevant data is captured. First, identify business use cases you believe in, and then think about the models and data you need to operationalize them, not vice versa. Some use cases will require significant time series of data. Others depend on the timeliness and “freshness” of data. Another important aspect of the generation and collection of data is data layering. Carefully organizing data into several logical layers and then employing a logic by which to stack these layers can help generate more meaningful data.
Once the organization has successfully captured all relevant raw data, it must begin the process of making sense of it all. The first step here is to enrich the data with the knowledge of domain experts, never losing sight of the fact that human expertise is as important to making data useful as is the power of analytics and algorithms.
The second step—again drawing on the input of data scientists—is the number crunching. A combination of descriptive, predictive, and prescriptive analytics will help identify the patterns that form the basis of actionable insights.
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