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Datatelligent FAQ

Data Analytics as a Service, or DAaaS, is a data analysis model that allows Datatelligent to partner with companies to provide not only the platform to execute data analysis, but also the people to run the analysis.  This way, companies can get the benefits of data analysis without having to create an internal team or buy software. 

Data Analytics as a Service is different from other data analytics companies because they provide both the platform (software) and people necessary to run the analytics.  Typically, companies have had to choose between purchasing software and hiring a team or contracting out for project work.  With DAaaS, you get the best of both worlds by having an always on, ready to engage team of data analysts.

Datatelligent provides data analytics strategy, data management, data integration, data visualization and reporting, advanced analytics, and user adoption. We help you transform your data into key insights and use them to drive growth for your organization. These services are all included in your subscription. 

When you partner with us, we become your data analytics team. Our specialists are dedicated to your success. First, we will work with you to align on your critical business strategies and be transparent with our timeline. After introductory assessments are done, we will plan our iterative approach powered by data, our team members, and our tools to build the best data analytics solution for you. Lastly, we help with implementation of new insights and work to ensure that our solution is satisfactory for your needs. 

With Datatelligent, getting started with your data analytics journey is easy.  All you need to do is contact us to arrange an introductory meeting.  There we will dive into our capabilities and uncover your needs to help customize a solution that works for you.  We offer 3-month trial periods to get you started right away and to start seeing the power of data. 


  • Business alignment and strategy
  • Data integration
  • Visualization and Reporting
  • Advanced analytics – Machine Learning and Predictive analytics
  • User adoption


  • Data Ingestion and Integration – we are currently using Snowflake, but can vary based on client needs
  • Visualization and Reporting – we are currently using Tableau, but again, can change based on client needs.



  • Customer Success Plan and Team
  • Strategic Planning and Roadmap
  • Capabilities Recommendations
  • Thought Leadership and Best Practices 

General Industry FAQ

At a basic level, Data Analytics is the transformation of raw data into information that leads to the finding of trends, patterns, and insights to answer questions. The specific process and techniques employed vary by the needs of individuals. 

Data Analytics builds solutions that allow companies to be more data driven in their decision making and help them transform their existing data assets into powerful tools for change. By devising strategies, using third party and an organization’s first party data, the insights derived reflect the most recent trends in the industry. 

Being more data-driven allows you to make better informed decisions. It can help you make personalized choices for your organization while also staying relevant to industry trends. Data analytics can be the step for you to take your organization to the next level and help you diagnose areas for improvement and optimization. 

Organizations often collect lots of data but lack the means to interpret the large amounts of data points. Data can be a powerful tool to help drive key insights for organizations. Data analytics helps turn those intimidating datasets into a reliable source for insights specific to your needs and your experience. While scholarly articles and other resources found online can be helpful, insights from your own data helps shed light on your organization’s specific problems, helping you devise specific and effective solutions. 

Big data refers to data that cannot be easily processed through traditional methods due to their size, quantity or complexity. The concept is defined by three main characteristics of big data in the 3 Vs: 

  1. Volume – describes the amount of data and their form
  2. Velocity – describes how fast the data is being collected and analyzed
  3. Variety – describes the type of data collected. 

Utilizing big data can help organizations make informed operational decisions based on industry data and internal data. 

Descriptive Data Analytics relies on converting large amounts of historical data into easier to understand bits to help organizations in their future decisions. 


  • ex. Convert large libraries of consumer data into simpler insights about consumer behavior 

Diagnostic Data Analytics utilizes historical data to uncover the root cause of a problem or event. 


  • ex. Use sales data in the most recent years to deduct why sales decreased for the past year. 

Predictive Data Analytics uses existing data to predict the likelihood of future outcomes. 

  • ex. Taking in survey data to predict the success of a product launch. 

Prescriptive Data Analytics suggests all favorable outcomes and courses of action to get to a specific outcome. 

  • ex. Maps software suggesting the quickest route. 

Machine learning is a data analysis method that utilizes artificial intelligence (AI) to automatically learn and improve without the constant direction of a programmer. In data analytics, machine learning is used to build models that can make predictions based on discovered patterns from given data. 

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