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: Will Dwinnell: 6 Reasons You Hired the Wrong Data Miner
Share
Notification Show More
Latest News
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
ai in omnichannel marketing
AI is Driving Huge Changes in Omnichannel Marketing
Artificial Intelligence
Aa
SmartData Collective
Aa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Analytics > Will Dwinnell: 6 Reasons You Hired the Wrong Data Miner
Analytics

Will Dwinnell: 6 Reasons You Hired the Wrong Data Miner

DeanAbbott
Last updated: 2012/12/19 at 10:04 PM
DeanAbbott
4 Min Read
SHARE

By Will Dwinnell

By Will Dwinnell

As is in any discipline, talent within data mining community varies greatly.  Generally, business people and others who hire and manage technical specialists like data miners are not themselves technical experts.  This makes it difficult to evaluate the performance of data miners, so this posting is a short list of possible deficiencies in a data miner’s performance.  Hopefully, this will spare some heartache in the coming year.  Merry Christmas!

1. The data miner has little or no programming skill.

More Read

data science anayst

Growing Demand for Data Science & Data Analyst Roles

Predictive Analytics Helps New Dropshipping Businesses Thrive
The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas
Analytics Changes the Calculus of Business Tax Compliance
The Role of Big Data Analytics in Gaming

Most work environments require someone to extract and prepare the data.  The more of this process which the data miner can accomplish, the less her dependence on others.  Even in ideal situations with prepared analytical data tables, the data miner who can program can wring more from the data than her counterpart who cannot (think: data transformations, re-coding, etc.).  Likewise, when her predictive model is to be deployed in a production system, it helps if the data miner can provide code as near to finished as possible.

2. The data miner is unable to communicate effectively with non-data miners.

Life is not all statistics: Data mining results must be communicated to colleagues with little or no background in math.  If other people do not understand the analysis, they will not appreciate its significance and are unlikely to act on it.  The data miner who can express himself clearly to a variety of audiences (internal customers, management, regulators, the press, etc.) is of greater value to the organization than his counterpart who cannot.  The data miner should should receive questions eagerly.

3. The data miner never does anything new.

If the data miner always approaches new problems with the same solution, something is wrong.  She should be, at least occasionally, suggesting new techniques or ways of looking at problems.  This does not require that new ideas be fancy: Much useful work can be done with basic summary statistics.  It is the way they are applied that matters.

4. The data miner cannot explain what they’ve done.

Data mining is a subtle craft: there are many pitfalls and important aspects of statistics and probability are counter-intuitive.  Nonetheless, the data miner who cannot provide at least a glimpse into the specifics of what they’ve done and why, is not doing all he might for the organization.  Managers want to understand why so many observations are needed for analysis (after all, they pay for those observations), and the data miner should be able to provide some justification for his decisions.

5. The data miner does not establish the practical benefit of his work.

A data miner who cannot connect the numbers to reality is working in a vacuum and is not helping her manager (team, company, etc.) to assess or utilize her work product.  Likewise, there’s a good chance that she is pursuing technical targets rather than practical ones.  Improving p-values, accuracy, AUC, etc. may or may not improve profit (retention, market share, etc.).

6. The data miner never challenges you.

The data miner has a unique view of the organization and its environment.  The data miner works on a landscape of data which few of his coworkers ever see, and he is less likely to be blinded by industry prejudices.  It is improbable that he will agree with his colleagues 100% of the time.  If the data miner never challenges assumptions (business practices, conclusions, etc.), then something is wrong.

DeanAbbott December 19, 2012
Share this Article
Facebook Twitter Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

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

Stay Connected

1.2k Followers Like
33.7k Followers Follow
222 Followers Pin

You Might also Like

data science anayst
Data Science

Growing Demand for Data Science & Data Analyst Roles

6 Min Read
predictive analytics in dropshipping
Predictive Analytics

Predictive Analytics Helps New Dropshipping Businesses Thrive

12 Min Read
data-driven approach in healthcare
Analytics

The Importance of Data-Driven Approaches to Improving Healthcare in Rural Areas

6 Min Read
analytics for tax compliance
Analytics

Analytics Changes the Calculus of Business Tax Compliance

8 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?