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: 3 Effortless Tactics to Be a Data Science Success in Business
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 > Analytics > Modeling > 3 Effortless Tactics to Be a Data Science Success in Business
Modeling

3 Effortless Tactics to Be a Data Science Success in Business

Damian Mingle
Last updated: 2015/12/04 at 12:39 PM
Damian Mingle
7 Min Read
SHARE

 

Contents
Producing a Data Science Project Plan Project Plan in Action Summary 

“Move out of the way – I am ready to model.”

 

“Move out of the way – I am ready to model.”

More Read

ai in business

Selecting the Right AI Business Model for Your Startup

Top 10 Powerful Data Modeling Tools For 2021
Learn Why Doctors Look To Data To Increase Patient Engagement
Spectral Clustering Can Be A Game Changer—Here’s How
How to Overcome Data Visualisation Problems

That is the typical sentiment of a Data Science team when given a business problem. However, in the context of a dynamic business, things are not that simple; instead, business needs require that the Data Science team be detailed in the communication of their process. The last thing a Data Science team wants to do is produce a project plan they feel is a pedestrian artifact aimed to pacify their business counterparts. They tend to prefer a more fluid and creative style as opposed to one that is stiff and inflexible. Data Scientists may be tempted to promote the idea that they cannot let anything get in the way of creativity and brilliance or it will be to the detriment of the business. However, in many cases, Data Scientists may be allowing their human fear of transparency and accountability to dictate how they approach what the business needs – maximum visibility. Don’t fall into the trap of believing that these templated documents merely exist to check the proverbial box in order to placate the MBAs and Project Managers in the room. Data Science teams designed for success will most certainly deliver a Data Science project plan and use it throughout their analytics project. 

Producing a Data Science Project Plan 

 

You might ask what the intended purpose behind such a fancy business document really is at its core. The Data Science project plan is incredibly straightforward: its sole purpose is to be the battle plan for achieving the Data Science goals which in turn achieve the business goals. Successful Data Science teams will know that there is immense value in not only being able to achieve the Data Science goals, but in being able to relate them back to the business on a constant basis. It’s the burden of the Data Scientist to be sure that clear communication exists between the two groups. The challenge for a Data Scientist is translating Data Science into business terms. This is the kind of thing that is built through experience and through learning what the business expects in a traditional project plan. If a business had a choice between a model with higher predictive accuracy by a Data Scientist without a project plan and a model with lower predictive accuracy by a Data Scientist with a project plan, they most certainly would choose to work with a Data Scientist who could communicate in terms of business, translate Data Science ideas, and understand the power of leveraging other individuals in the organization to contribute to the overall outcome. 

Project Plan in Action 

 

The nuts and bolts of a Data Science project plan will be different for each team and each organization, but there are core elements you will see in almost all effective Data Science project plans – sort of a Tao of Data Science Project Plans. 

Three Effortless Tactics: 

  1. List the stages in the project 

 The business should not have to make assumptions about the stages you may take them through as a Data Scientist. Display your expectation to everyone and let them know how much time each stage may take. Also, do the obvious things like listing the resources required as well as the types of inputs and outputs your team expects. Lastly, list dependencies. After all, you will want your counterparts to be aware that you cannot move forward until “x” event happens; for example, the Data Scientist may be waiting to receive a data feed from IT. This is precisely the kind of thing to call out in the Data Science project plan. 

2. Define the large iterations in the project 

 Most business users will not be intimately involved in how a Data Science team works or why it may change when you encounter a classification problem versus a regression problem. So in an effort to be clear and meaningful, share stages that are more iterative as well as their corresponding durations – such as modeling or the evaluation stages. The best Data Scientists know how to  appropriately manage expectations from the business through communication with the broader organization.  

3. Point out scheduling and risks  

Virtually all working individuals know that it’s unrealistic to think everything happens only in ideal scenarios. Data Scientists should take the necessary time to consider scheduling resources and the inherent risk they could encounter in the project. Give the business the comfort that only a trusted advisor can provide them. Think through what could happen and what you would recommend to them if they encounter turbulence – because turbulence is inevitable. Taking this extra step is the hallmark of a Data Science professional. 

Summary 

Do not view the Data Science project plan as training wheels for a junior Data Scientist who is new to working with business, but rather what a skilled Data Scientist will review each time his or her team begins a new task within the Data Science project. Crafting a Data Science project plan to pacify the business – and never utilizing it for team guidance – is a grave mistake that one day could end in ruin for the Data Science team, the business, or both. An effective Data Scientist will work from the perspective that a goal without a plan is simply a wish and nothing more. Or, said differently, an effective Data Science team works a plan at all times. 

Damian Mingle December 4, 2015
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

ai in business
Business Intelligence

Selecting the Right AI Business Model for Your Startup

11 Min Read
data modeling tools to analyze
Modeling

Top 10 Powerful Data Modeling Tools For 2021

8 Min Read
patient engagement
Big DataExclusiveModelingPredictive Analytics

Learn Why Doctors Look To Data To Increase Patient Engagement

9 Min Read
Big Data and the SME
AnalyticsModeling

Spectral Clustering Can Be A Game Changer—Here’s How

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 chatbots
AI Chatbots Can Help Retailers Convert Live Broadcast Viewers into Sales!
Chatbots
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?