Clarity Insights Blog

TensorFlow and Neural Networks: a Primer

on Apr 10, 2018 11:30:00 AM By | Shantanu Raghav | 0 Comments | data science
Prinicpal Data Science Consultant Shantanu Raghav, along with Director of Data Science Gabriel Mohanna and Data Science Consultant Adam Zebrowski, break down neural networks, TensorFlow and common use cases.
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The Foundation of Business Insights

on Mar 8, 2018 9:00:00 AM By | Don Gooldy | 0 Comments | data architecture
Take a look at the second piece in Don Gooldy's series on data architecture.
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Is Your Data Architecture Really Your Architecture, or is it Your Plumbing?

on Feb 22, 2018 10:00:00 AM By | Don Gooldy | 0 Comments | data architecture
Senior Principal and Data Architect Don Gooldy explores data architecture's vital relationship to business function.
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Where's the timely demo value and quality in data and analytics?

on Feb 20, 2018 6:00:00 AM By | John Lucas | 0 Comments | Analytics agile strategy
Clarity's own John Lucas asks the question in his latest article, leveraging his experience as a stage lighting director to shed new light on the topic.
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Integrating Data on a Shared Hadoop Platform for the Financial Sector

on Jan 12, 2018 2:42:39 PM By | Michael Shaw | 0 Comments | Data Lake Financial Services Hadoop
Financial services firms are notorious record-keepers. They have to be—the financial/banking industry is among the most regulated in the nation. Despite the fastidious data-gathering, banks are like most organizations in that their information architecture evolved organically over time and consists of a patchwork of systems and solutions. This approach may have worked for a while, but the piecemeal setup now prevents timely insights, thwarts accurate reporting and hinders financial crime prevention efforts.
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Health Innovation Framework - Ideation. Incubation. Industrialization

on Nov 9, 2017 2:37:12 PM By | Teresa Letlow | 0 Comments | Healthcare Advanced Analytics agile
Clarity Insight’s Health Innovation Framework has been formalized across years of client experiences and is applied both externally with Clarity clients and on internal Clarity initiatives within our Health Innovation Cloud. By embracing an agile methodology, clients are able to identify and prioritize ideas, test ideas, fail or achieve success in weeks to days, and take successes to market quickly. This reduces the risk associated with projects, providing the flexibility to start small and prove ROI before implementing across the organization.
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Is Bigger Always Better with Influencer Marketing?

on Oct 23, 2017 11:17:40 AM By | Mark Lewis | 0 Comments | marketing analytics marketing effectiveness
Influencer marketing has been positioned as the answer for marketers overwhelmed with the sheer vastness of the digital landscape and who need help cutting through the noise to reach their target audience. At some point in the sales cycle, prospects need validation from their peers that a particular service or product is worth purchasing, and an influencer marketing strategy provides that approval.
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Identifying Your Entire Customer and Prospect Universe

on Oct 19, 2017 6:20:00 AM By | Callie Wheeler | 0 Comments | Customer View micro-segmentation customer experience
What if you had a customer and prospect universe at your fingertips? A view of every customer and would-be customer means the ability to create segmentation frameworks, understand walletshare and pursue the best prospects. Data—whether collected over years or purchased—abounds. Market leaders are leveraging their data to create customer and prospect universes, where every customer and potential customer are identified, recorded and stored for analysis. This single view of the customer means targeting, segmentation and niche strategies are all possible.
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Top 3 Modern Data Architecture Mistakes

on Oct 16, 2017 6:30:00 AM By | Mark Lewis | 0 Comments | Big Data data architecture Data Warehouse Modernization
Transforming legacy data warehouse architecture to support real-time data processing is a massive undertaking, and it's not unusual for modernization efforts to hit some bumps along the way. To make creating a modern data architecture easier, learn from others' mistakes. Here are three of the most common mistakes companies make with their data warehouse modernization efforts:
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