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Investing in Big Data

The average adult makes 70 conscious decisions a day, or more than 25,000 a year. Many of those decisions are inconsequential to organizations, but a few of them can create substantial opportunities or problems. While organizations cannot prevent bad decisions from being made, firms can minimize the risk by investing in data and analytics capabilities.
Data and analytics isn’t a new concept. It has been formed over the last century with the aid of two key macroeconomic trends.

The first was the migration of the workforce from labor-intensive to knowledge-intensive roles and industries. The second was the introduction of decision-support systems into organizations in the 1960s. As an increased number of knowledge workers began to interact with more powerful technologies and accompanying data stores, analytics began to take a more critical role within organizational decision-making and execution.
However, firms initially had some difficulties incorporating data and analytics into their operations. They gathered a limited number of variables and stored them in multiple data stores with different formats and structures. Additionally, filtering the data to validate what is relevant and impactful, or the signal, from the noise became difficult as the amount of data increased exponentially. Based on a study conducted by IDC, an IT consultancy, the amount of data available globally grew 27-fold to approximately 2.8 trillion gigabytes from 2005 to 2012. The study also noted that roughly 25% of this data is useful, but only 3% of it has been tagged for leverage and only 0.5% of it is currently analyzed.
Most leading organizations see a need to enhance internal capabilities to collect, store, access, and analyze these exponentially large, complex datasets, increasingly known as Big Data. However, leaders need to allocate greater investments to Big Data capabilities in order to fully realize the value potential. These investments need to be made across the five segments of the data and analytics value chain.
1. Collection & Readiness: Large, complex datasets need to be collected and managed effectively. Organizations generate data within independent silos. In order to maximize data leverage, organizations should maintain data standards to ensure data accuracy, consistency, and transferability.
2. Processing: Data must be processed in real time. Gaining a few days on competitors can be the key to survival. Therefore, organizations should evaluate their architecture, algorithms, and even programming language to substantially increase processing speed.
3. Visualization: Processed data needs to be presented in a manner that can be readily understood. Humans struggle with processing large amounts of numerical and textual data. Organizations should use visualization tools to enhance human pattern recognition, insight, and actions.
4. Interpretation: Visualized data has to be interpreted correctly and communicated to knowledge consumers. Organizations should screen for biases that can distort insights, while guarding themselves against “gut-feeling” decision-makers as well as data extremists because both ends can lead a firm to act sub-optimally.
5. Refinement: Knowledge consumers must provide feedback and guidance to knowledge producers. Organizations should facilitate a feedback loop across diverse stakeholder groups, which can support continual analysis, learning, and issue identification in order to attain informational scale and scope.
Organizations have significant hurdles to overcome in order to capture the value potential of Big Data. These hurdles span the continuum of investment capacity, skill availability, legacy infrastructure, and operating models. However, organizations that are able to effectively leverage data and insights to drive differentiated value propositions and outcomes will dominate their industries. Ultimately, these organizations will be industry leaders rather than just industry participants.

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