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analytics centers of excellence

The Agile Solution for Successful Analytics Centers of Excellence

Most Analytics Centers of Excellence do not deliver results. To address these challenges, Blue Hill Research conducted deep primary research with leading consultancies, systems integrators, and multi-national companies to understand how to construct successful analytics CoEs.

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Why Most Big Data Projects Fail

Organizations need to make big data analytics an integral part of their everyday workflows, which means continuously running analytics and automating processes. For this to work seamlessly, organizations need to tear down the walls between IT and business, remove hurdles, to strike a balance between demands for business speed and agility with IT governance and control. Tools such as Lavastorm help organizations capture big data-based insights while enabling a more agile and analytics-driven business culture.

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How Pharmaceutical Companies Can Automate & Improve Compliance Reporting

The Sunshine Act has added another layer of regulatory complexity for healthcare and pharmaceutical companies. Traditional tools cannot effectively manage this process, and require large investments of time and resources. Automating the data collection, analysis and reporting process with advanced end-user tools like self-service data preparation and advanced analytics is a better, faster, cheaper way to meet strict compliance requirements.

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Four Situations Where You Need Agile Data Management

Enterprises awash in data often struggle to answer basic business questions because they can’t supply data to the people who need it, when they need it. Situations where you’re dealing with, exploring external data, changing data sources, adjusting business logic and changing data fields are all desperate for agile data management. Organizing, prepping, transforming, and provisioning useful data shouldn’t be difficult. In fact, it isn’t: if you’re using the right tool. And that’s what agile data management is all about.

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Five Ways to Improve Data Prep for Tableau

The demand for Tableau and its ability to transform walls of spreadsheets into interactive and visual dashboards is not slowing down. However, the strength of Tableau relies on the quality and breadth of the data inputted. Helping users prepare, transform and deliver the most accurate and up-to-date information is critical to leveraging your dashboards and the data behind them. Learn 5 ways to improve the data quality you feed to Tableau.

 

data prep tools

Breaking Through the Analytic Limitations of Access and SQL

Although they are often a cornerstone of a company’s analytic toolkit, traditional databases, such as Access, and query tools, such as SQL, are designed for storage and simple queries, not for creating the complex analytics that are required by today’s fast-moving businesses. New technologies can help organizations get over the analytic limitations of Access and SQL- especially when dealing with the demands to processing more data, changing analytics more frequently, and making analytics available to more decision makers.

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Breaking Through The Analytic Limitations of Access and Excel

Although they are often a cornerstone of a company’s analytic toolkit, tools like Access and Excel are designed for data storage and basic analytics, not for creating the complex analytics that are required by today’s fast-moving businesses. New technologies can help organizations move past the analytic limitations of Access and Excel, especially when dealing with the demands of processing more data, and making analytics available to more decision makers and stakeholders – all at greater speed than ever before.

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Empowering the Citizen Data Scientist with Self-Service Advanced Analytics

The growth of social media, the Internet of Things, and digital business is dramatically increasing the volume, velocity, and variety of data flowing into business organizations. Most businesses recognize the value of detailed information about customer and operational activities, but establishing and managing a process to convert the mass of data into actionable decision-making can be daunting.