By using this site, you agree to the Privacy Policy and Terms of Use.
Accept
SmartData Collective
  • Analytics
    AnalyticsShow More
    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
    analyst,women,looking,at,kpi,data,on,computer,screen
    Promising Benefits of Predictive Analytics in Asset Management
    11 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-23 SmartData Collective. All Rights Reserved.
Reading: The Pros and Cons of Collaborative Data Modeling
Share
Notification Show More
Latest News
ai digital marketing tools
Top Five AI-Driven Digital Marketing Tools in 2023
Artificial Intelligence
ai-generated content
Is AI-Generated Content a Net Positive for Businesses?
Artificial Intelligence
predictive analytics in dropshipping
Predictive Analytics Helps New Dropshipping Businesses Thrive
Predictive Analytics
cloud data security in 2023
Top Tools for Your Cloud Data Security Stack in 2023
Cloud Computing
become a data scientist
Boosting Your Chances for Landing a Job as a Data Scientist
Jobs
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Data Management > Best Practices > The Pros and Cons of Collaborative Data Modeling
Best PracticesCollaborative Data

The Pros and Cons of Collaborative Data Modeling

Brett Stupakevich
Last updated: 2017/07/18 at 10:15 PM
Brett Stupakevich
4 Min Read
SHARE

In the field of analytics – as in life – there are often multiple ways to come up with a solution to a problem. Since the types of business problems companies attempt to solve in today’s fast-paced and increasingly complex business environment are often multi-layered and difficult to crack, brainstorming can frequently deliver the best set of options for tackling even the most vexing issues.

Just as shrewd business leaders have come to rely on the collective intelligence and experience of their top lieutenants for effective decision making, so too are enterprise analytics teams increasingly relying upon collaborative approaches to problem solving.

In its Gartner Predicts 2012 research reports, the research firm says organizations will increasingly include the vast amounts of data from social networking sites in their decision-making processes. However, Gartner also says that over half of the investments made by companies in analytics tools will be wasted, because of cultural immaturity, a lack of required skills and inappropriate training levels.

In a Spotfire blog post from earlier this year, we also talked about the benefits of drawing upon the collective wisdom of a group by crowdsourcing analytics . One such forum is Kaggle, an online platform for predictive modeling competitions. Platforms such as Kaggle are making it possible for data scientists to come together on a wide variety of data modeling exercises.

More Read

database compliance guide

Four Strategies For Effective Database Compliance

How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency
What Are the Most Serious Privacy Concerns Regarding Big Data?
7 Consequences of a Data Intrusion: Insights From Asiaciti Trust & MGM International
How To Improve Incident Response Time for Data Breaches

As described on its web site, Kaggle offers companies a cost-effective way to harness the “cognitive surplus” of the world’s best data scientists. For instance, Kaggle recently fielded a competition with a prize pool of $10,000 for teams of data scientists to accurately predict market responses to large trades.

Nonetheless, collaborative data modeling can also be fraught with challenges, as noted in an article on the topic by Ventana Research Vice President and Research Director David Menninger (@dmenningervr). Some approaches to collaboration have centered on the use of social media tools. But as Menninger argues, while social media can be a vehicle for supporting conversations between people, data modeling is a considerably more complex exercise that requires workflow techniques and approval processes. These are important factors for decision makers to take into account.

Still, some online communities that have cropped up have shown promise for new approaches to collaborative data modeling. For example, Cross Validated is a free, community-driven Q&A forum for statisticians, data analysts, data miners, and data visualization experts.

Some straightforward programmer-type questions such as “Does anyone know a way to segment words into syllables using R?” are fairly easy to answer in a Q&A forum such as Cross Validated. But other problems are likely to generate a variety of opinions where there isn’t necessarily a single valid answer. For instance, “What should k be in a k-fold cross validation?” Under these circumstances, disagreements between community members are likely to break out as to whether cross-validation works.

Participants and visitors can view the hottest threads based on votes or views, such as the best method to visualize large interaction between two factors. Another popular thread asks participants to name the most famous statisticians and what it is that made them famous.

More of these types of communities will continue to populate, creating additional opportunities for companies of all sizes to leverage the collective wisdom of the crowd. And while many of these sites aren’t perfect, they offer data scientists a terrific chance to connect with each other across all corners of the globe to brainstorm on approaches to tackling vexing problems.

Data Modeling

Brett Stupakevich December 20, 2011
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

ai digital marketing tools
Top Five AI-Driven Digital Marketing Tools in 2023
Artificial Intelligence
ai-generated content
Is AI-Generated Content a Net Positive for Businesses?
Artificial Intelligence
predictive analytics in dropshipping
Predictive Analytics Helps New Dropshipping Businesses Thrive
Predictive Analytics
cloud data security in 2023
Top Tools for Your Cloud Data Security Stack in 2023
Cloud Computing

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

Sign Up for Our Newsletter

Subscribe to our newsletter to get our newest articles instantly!

[mc4wp_form id=”1616″]

You Might also Like

database compliance guide
Data Management

Four Strategies For Effective Database Compliance

8 Min Read
data access for engineers
Big Data

How The Explosive Growth Of Data Access Affects Your Engineer’s Team Efficiency

11 Min Read
big data privacy concerns
Privacy

What Are the Most Serious Privacy Concerns Regarding Big Data?

8 Min Read
painful lessons from major data breaches
Security

7 Consequences of a Data Intrusion: Insights From Asiaciti Trust & MGM International

6 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 and chatbots
Chatbots and SEO: How Can Chatbots Improve Your SEO Ranking?
Artificial Intelligence Chatbots Exclusive
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?