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: Why Oh Why Is It So Difficult? Implementing Sales and Operations Planning
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 Quality > Why Oh Why Is It So Difficult? Implementing Sales and Operations Planning
AnalyticsBusiness IntelligenceData QualityDecision Management

Why Oh Why Is It So Difficult? Implementing Sales and Operations Planning

Ray Major
Ray Major
7 Min Read
Why Oh Why Is It So Difficult? Implementing Sales and Operations Planning
Illustration generated with FLUX.2 [klein 4B] via Cloudflare Workers AI.
SHARE

Sometimes the hardest part about implementing Sales and Operations Planning (or any project) is just getting started.

A few weeks ago I was thinking about conversations I’ve been having with clients in the early stages of implementing an S&OP strategy.  They were relying on a phased approach, they knew that they needed an integrated set of business processes to go with their newly purchased technology.  They understood that the focus needed to be on information, not just the volumes of data they had at hand.  They knew that in order to implement a successful S&OP they needed clean, current, and accurate data.  As with many organization, time and effort was being wasted gathering data that had minimal importance to the overall project.  But in this case, senior leadership was able to articulate the business problem they were trying to solve, and were able to help define, with some difficulty mind you, the minimum data necessary for the project.

It all sounds wonderful on paper, and we were destine for success!   But, like other businesses their attempts to implement S&OP were frustrated by internal tensions between departments.  What followed was this seemingly innocent statement on my part:  “Not everyone will be a convert immediately, so we watch for resistance and address it as part of our strategy.  Push, but not too hard, or we will get resistance.”

And that’s when the fireworks started. Or to be precise, my somewhat nonchalant mention of possible resistance sparked some great comments and questions.

More Read

Why Data Should Be a Business Asset: The 1-10-100 Rule
Why Data Should Be a Business Asset: The 1-10-100 Rule
A Recap of Dreamforce 2010
Cloud Metadata Is Making Waves with Business Intelligence
Six IT Essentials for Life Science Systems Integration
Big Data Means Big Need for BI-Educated College Grads

Classic best practice suggests that S&OP must “belong” to the Chief Executive Officer. If that’s not possible then a strong united coalition of department heads may be able to lead the process if they set clear ground rules and boundaries for working together.  In this case, we had senior management buy in and support, but what we really needed was their “ownership” of the project.

Some amount of resistance is inevitable, and it usually boils down to cultural/people issues, not in any way exclusive to SO&P implementations, but let’s go ahead and tackle them in the context of a SO&P project. Here they are:

The Internal Obstructionist — This is the presence of a few highly-regarded and influential employees who either passively or actively undermine the changes in behavior that the new initiative requires. You know you have this problem if the water-cooler conversation sounds something like this: “That new BI program won’t work for us…”, “We’ve always done it this way…”, and “That new initiative will make us have to change.” (Basically anything eluding to “change is bad.”)

The Dirty-Data Diversion — This is the belief that there’s no use starting a BI implementation until the company’s data is polished, scrubbed, cleaned, pressed and folded to perfection. It’s the same argument some use to avoid going to the gym: I got to get in shape first!

I’ll address the Internal Obstructionist in a future post. Today, let’s grapple with #2.

The evil genius of the dirty-data roadblock is its apparent logic and deceptive concern for company well-being. In a BI context, artful proponents of this argument actually appear to have the best interests of the company in mind. We don’t want the C-suite making decisions based on bad data; that’s bad for all of us. Let’s get it cleaned up first.

Of course what’s really at work here is what Seth Godin calls the assertiveness of the lizard brain. AKA the resistance, AKA fear. No one wants the bright light pointed at his bad data or poor processes.

And since no data-scrubbing project has ever succeeded, there’s little risk in promising a thorough clean-up as a prelude to a SO&P kick-off. That’s the diversion. Confucius might say it like this: “If you think you need to finish before you can begin, you will never begin.”

Here’s how to overcome this type of resistance in three steps:

1.     Inject some honesty

Everyone knows there are data quality issues. Yes, even management knows. In fact, they’ve known for a long time. That’s not news. The point is to work together to improve the speed and decision-making ability of the enterprise, not place blame. This gives everyone who needs it some “cover,” and puts the lizard back in its cage.

 2.     As soon as possible start reviewing some basic KPIs in the new SO&P system

Embrace the fact that the data isn’t where it needs to be. Make reliable data part of the project, not a prerequisite. Doing this will help everyone envision the desired future and will set the stage for some quick wins. And quick wins will unleash the dynamic duo of momentum and optimism. The system should be viewed as a catalyst for operational improvement, not a tyrant that demands operational perfection to function.

 3.     Meet often, especially in the beginning

In fact, consider daily meetings. More importantly, set overall priorities in a way that gives the team the time and the freedom they need to make progress between meetings. This helps reinforce everyone’s commitment to a successful outcome. In every meeting talk about how awesome life will be when the system is trusted and providing accurate information.

Nike got it right with its slogan “Just do it.” Starting has its own virtues.

Share This Article
Facebook Pinterest LinkedIn
Share

Follow us on Facebook

Latest News

Flat editorial illustration: The article examines AI agents that escalate from legitimate data retrieval to attempted intrusions
OpenAI’s Government Website Incidents Raise a Hard Question for AI Agents: When Should They Stop?
Artificial Intelligence News Security
Flat editorial illustration: The article's core relationship is the alignment between customer behavioral data (visit frequency,
Data-Driven Loyalty: How Restaurants Use Behavioral Analytics to Optimize Revenue
Exclusive
Flat editorial illustration: The article's core relationship is that reliable eCommerce attribution depends on a unified, well-st
How eCommerce Data Teams Can Build Attribution That Holds Up
Big Data Exclusive
Flat editorial illustration: The article's core relationship is the contrast between fragmented inherited data infrastructure (wh
Data Stack Consolidation as a Data Quality and Governance Strategy for Mid-Market Teams
Big Data Exclusive

Stay Connected

1.2KFollowersLike
33.7KFollowersFollow
222FollowersPin

You Might also Like

What Big Data Doesn't Appear to Tell Us, But Actually Does
AnalyticsBig DataBusiness IntelligenceData ManagementInside CompaniesJobsWorkforce Analytics

What Big Data Doesn’t Appear to Tell Us, But Actually Does

4 Min Read
What Mobile BI Used To Look Like, And Where It’s Going (Back to the Future!)
Business Intelligence

What Mobile BI Used To Look Like, And Where It’s Going (Back to the Future!)

8 Min Read
Data Analytics Can Bolster HR in Niche Industries
Analytics

Data Analytics Can Bolster HR in Niche Industries

7 Min Read
Identifying Influencers on Twitter
Predictive Analytics

Identifying Influencers on Twitter

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.

Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence for eCommerce: A Closer Look
Artificial Intelligence
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
The Art of Conversation: Enhancing Chatbots with Advanced AI Prompts
Chatbots

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?