We use cookies, including third-party cookies from Google to serve personalized ads through AdSense, to operate this site and understand how it is used. By continuing to browse, you accept this use. See our Privacy Policy and Terms of Use for details, including how to opt out of personalized advertising.
Accept
SmartData CollectiveSmartData Collective
  • Analytics
    AnalyticsShow More
    chatgpt image jul 21, 2026, 04 34 30 pm
    4 Core Benefits of Predictive Maintenance after Vibration Analysis
    10 Min Read
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results -- AI-generated illustration
    How Does Data Mining Boost Customer Satisfaction in Logistics? Harnessing Analytics for Results
    11 Min Read
    chatgpt image jul 13, 2026, 04 23 45 pm
    How Data Analytics Helps Companies Improve User Engagement
    19 Min Read
    chatgpt image jul 13, 2026, 03 59 46 pm
    How Data Analytics Improves Multi-Location Search Strategies
    10 Min Read
    cybersecurity efforts
    How Behavioral Analytics and AI Are Redefining Cybersecurity for Boca Raton Businesses
    14 Min Read
  • Big Data
  • BI
  • Exclusive
  • IT
  • Marketing
  • Software
Search
© 2008-25 SmartData Collective. All Rights Reserved.
Reading: 5 Steps to Setting your Big Data Goals
Share
Notification
Font ResizerAa
SmartData CollectiveSmartData Collective
Font ResizerAa
Search
  • About
  • Help
  • Privacy
Follow US
© 2008-23 SmartData Collective. All Rights Reserved.
SmartData Collective > Big Data > Data Mining > 5 Steps to Setting your Big Data Goals
AnalyticsData MiningHadoopPredictive Analytics

5 Steps to Setting your Big Data Goals

Joshua Polsky
Joshua Polsky
5 Min Read
5 Steps to Setting your Big Data Goals
Illustration generated with Qwen Image.
SHARE

We all have goals, or at least should.

We all have goals, or at least should. I’m reminded of a t-shirt that says, “If you don’t have goals, you’ll never score.” Meaning, if you don’t realize and set goals, you won’t get the desired outcome.In the context of Big Data, over half of projects started are never completed, and can even have very bad results.  

Having done both Big Data as well as BI projects before, we’ve learned not only what to do, but what not to do. If we could help even one person out there with this advice, it would make us all warm and fuzzy.

1) Your initiative needs a sponsor. Likely a C level employee within the organization that knows what value needs to come from the project. This person will take ownership of the project, and will be held accountable for failures and setbacks. He or she will check progress and milestones as well as address potential and actual blockers. It’s important that there is only one person overseeing the project, so that there aren’t instructions coming from various sources, resulting in people being pulled in different directions. On a positive note, the team members involved will know that they have one person to turn to, and that person is there with only one thing in mind, a clear vision of the outcome. Please note that this doesn’t mean there shouldn’t be other managers assisting, but everyone involved should be on the same page.   

More Read

How Wix Is Using Predictive Analytics To Deliver Top-Tier Websites
How Wix Is Using Predictive Analytics To Deliver Top-Tier Websites
Handling The Big Data Faucet
Unintended Effects of Adblockers
Google and corporate espionage
What You Need to Know About Latency Before the Holiday Season

2) Define your business questions. Business questions are crucial to discovering what business problems exist, so they can be understood and solved for the betterment of the company. What do you want to know? Maybe you want to know which of your campaigns worked the best based on user acquisition rate by geography and/or time of day. Perhaps you want to know how to reduce product shrinkage or optimize your warehouse layout.  If you’re an ecommerce, it would be good to know the revenue at a product level as well as average customer support calls/chat/tickets for a specific time frame.  

3) Start small. Don’t bite off more than you can chew. Focus on the most important questions first. This is not easy because you probably feel, and rightfully so, that all questions are important. They are, but which is most pertinent to the project is what needs to be targeted at this juncture. Questions will evolve and new ones will be added. Stay focused, and handle them at a later stage.

4) Invest in understanding the data. Where is it? Which data is coming from where? The best way to handle this is the process of data profiling. Also, expect schema changes and plan for your system to be able to handle those changes. If you can identify the problem areas at the beginning, it will be less difficult and take less time to handle them up front as opposed to once the system is built. Lastly with your data, expect data corruption and just bad data in general. Again, plan for this up front, it will save headaches in the long run.  

5) Get an expert or two. You’ll need a technical expert that knows the ins and outs of the platform and how it is to be built. If your technical expert isn’t well versed in the business side of the company, get someone that does.  He or she should know every aspect of the business model, the finances, the products and/or services, and how it is all tied together.  

This process will not be easy, but it will make going forward easier than if you did not undertake it at all. 

original post

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Managing Application Access When Corporate VPNs Reach Capacity Limits -- AI-generated illustration
Managing Application Access When Corporate VPNs Reach Capacity Limits
Exclusive IT Security
How Digital Knowledge Repositories Facilitate Self-Directed Research and Information Discovery -- AI-generated illustration
How Digital Knowledge Repositories Facilitate Self-Directed Research and Information Discovery
Exclusive News
7 MDR Providers Combining Offensive Security Testing With 24/7 Monitoring -- AI-generated illustration
7 MDR Providers Combining Offensive Security Testing With 24/7 Monitoring
Exclusive IT Security
The Information Governance Practices That High-Demand Social Work Roles Require -- AI-generated illustration
The Information Governance Practices That High-Demand Social Work Roles Require
Data Management Exclusive Policy and Governance Security

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

Obstacles to Personal Genetic Testing in the U.S. and Abroad
Best PracticesBig DataCulture/LeadershipData ManagementModelingPolicy and Governance

Obstacles to Personal Genetic Testing in the U.S. and Abroad

5 Min Read
Business Intelligence: Decisions, Decisions
Business IntelligenceData Mining

Business Intelligence: Decisions, Decisions

4 Min Read
Image
Big DataCloud ComputingPredictive AnalyticsSecurity

Where the Fog Meets the Edge

4 Min Read
SumTotal Systems Sums Up Human Capital Management
AnalyticsCollaborative DataInside Companies

SumTotal Systems Sums Up Human Capital Management

7 Min Read

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

From Bolts to Bots: How AI Is Fortifying the Automotive Industry
From Bolts to Bots: How AI Is Fortifying the Automotive Industry
Artificial Intelligence
5 Great Tips for Using Data Analytics for Website UX
5 Great Tips for Using Data Analytics for Website UX
Big Data

Quick Link

  • About
  • Contact
  • Privacy
Follow US
© 2008-26 SmartData Collective. All Rights Reserved.
Welcome Back!

Sign in to your account

Username or Email Address
Password

Lost your password?