By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    data science anayst
    Growing Demand for Data Science & Data Analyst Roles
    6 Min Read
    predictive analytics in dropshipping
    Predictive Analytics Helps New Dropshipping Businesses Thrive
    12 Min Read
    data-driven approach in healthcare
    The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
    6 Min Read
    analytics for tax compliance
    Analytics Changes the Calculus of Business Tax Compliance
    8 Min Read
    big data analytics in gaming
    The Role of Big Data Analytics in Gaming
    10 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: 5 Principles of Analytical Hub Architecture (Part 2)
Share
Notification Show More
Latest News
ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science
ai software development
Key Strategies to Develop AI Software Cost-Effectively
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > IT > Hardware > 5 Principles of Analytical Hub Architecture (Part 2)
AnalyticsHardwareIT

5 Principles of Analytical Hub Architecture (Part 2)

RickSherman
Last updated: 2013/03/21 at 1:44 PM
RickSherman
2 Min Read
SHARE

Continuing the discussion on analytical hub design, here’s the second part of my post on the architecture principles. If you missed the first two principles (1. Data from everywhere needs to be accessible and integrated and 2. Building solutions must be fast, iterative and repeatable) see this earlier blog post.

3. The advanced analytics elite needs to “run the show”

Continuing the discussion on analytical hub design, here’s the second part of my post on the architecture principles. If you missed the first two principles (1. Data from everywhere needs to be accessible and integrated and 2. Building solutions must be fast, iterative and repeatable) see this earlier blog post.

3. The advanced analytics elite needs to “run the show”

More Read

analytical hub architecture

5 Principles of Analytical Hub Architecture (Part 1)

Design Patterns
Monitoring a System

IT has traditionally managed the data and application environments. In this custodial role, IT has controlled access and has gone through a rigorous process to ensure that data is managed and integrated as an enterprise asset. The enterprise, and IT, needs to entrust data scientists with the responsibility to understand and appropriately use data of varying quality in creating their analytical solutions. Data is often imperfect, but data scientists are the business’s trusted advisors who have the knowledge required to be the decision-makers.

4. Solutions’ models must be integrated back into business processes

When predictive models are built, they often need to be integrated into business processes to enable more informed decision-making. After the data scientists build the models, there is a hand-off to IT to perform the necessary integration and support their ongoing operation.

5. Sufficient infrastructure must be available for conducting advanced analytics

This infrastructure must be scalable and expandable as the data volumes, integration needs and analytical complexities naturally increase.  Insufficient infrastructure has historically limited the depth, breadth and timeliness of advanced analytics as data scientists often used makeshift environments.

Read more about this in my free white paper on Analytic Data Hub design entitled Analytics Best Practices: The Analytical Hub.

 

TAGGED: hub, system architecture
RickSherman March 21, 2013
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai in automotive industry
AI Is Changing the Automotive Industry Forever
Artificial Intelligence
SMEs Use AI-Driven Financial Software for Greater Efficiency
Artificial Intelligence
data security in big data age
6 Reasons to Boost Data Security Plan in the Age of Big Data
Big Data
data science anayst
Growing Demand for Data Science & Data Analyst Roles
Data Science

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

analytical hub architecture
AnalyticsBest PracticesBig DataData QualityITModelingPredictive Analytics

5 Principles of Analytical Hub Architecture (Part 1)

3 Min Read

Design Patterns

9 Min Read

Monitoring a System

5 Min Read

SmartData Collective is one of the largest & trusted community covering technical content about Big Data, BI, Cloud, Analytics, Artificial Intelligence, IoT & more.

ai in ecommerce
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
ai is improving the safety of cars
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence

Quick Link

  • About
  • Contact
  • Privacy
Follow US

© 2008-23 SmartData Collective. All Rights Reserved.

Removed from reading list

Undo
Go to mobile version
Welcome Back!

Sign in to your account

Lost your password?