Red Lobster - A Messy Divestiture Decision Process?

It seems that Red Lobster is being sold, despite some investors arguing that the decision made is a poor one. The article states that Golden Gate Capital is buying the restaurant chain from Darden Restaurants, Inc. for $2.1 Billion in cash, "defying pressure from an activist investor who opposed plans to shed the struggling chain." There were actually two "activist investors" who opposed the sale, in fact.

One viewpoint is that the sale "could destroy as much as $800 Million of shareholder value." Another viewpoint is that Red Lobster and Olive Garden should have been joined into a separate company, while Longhorn and Capital Grille get combined into another company, both still under Darden.

This just screams "DECISION MODEL NEEDED" at me - how about you?

"Darden, which expects the deal to close in the first quarter of 2015, said it explored several options to separate Red Lobster, but concluded that the Golden Gate deal maximized the value of the business and its real estate assets."
Other information in the article leads us to infer that the restaurant chain is "struggling" in part due to competition from "fast-casual" restaurants, two of which are mentioned.

So, now I've set the stage for you. Do you see yet why a decision model would have been such a great tool to use in this process?

A few thoughts, then I'll take a shot at a high level of what a decision model would look like.

  1. A decision model makes decisions transparent. Darden management could have easily pulled the two activist investors into the boardroom, shown them the decision model, run the simulations through time, and gotten the investors on board with the decision. 
  2. They could have put the investors' ideas into the model and had evidence of whether or not those ideas were workable, viable, and would achieve the outcomes.
  3. The outcomes would have been agreed up front. It is possible that the desired outcome was "get the best deal for Red Lobster and get rid of the chain," in which case neither of the investor ideas would have helped achieve the outcome. That said, we think it more likely that the outcome would be more along the lines of "increase value to shareholders" or "increase revenue from Red Lobster." 
  4. A decision model could have put more possible decisions into the mix. For example, did Darden consider morphing any of its casual-dining restaurants into fast-casual? Is there a "blue ocean" to be created still in the crowded dining industry? 
Only with a decision model would they know how decisions like these would ripple through the future. Most importantly, the decision model would put all of the options on the table, simulate the outcomes through time, and allow everyone to be on board with the final decision.

Financial spreadsheets won't do this. Predictive analytics won't do this on their own. These feed into the decision model as inputs, along with external factors like the drift factor from casual dining to fast-casual.

Yes, this particular model will be complex. I've created a skeleton of the model below, with very simple dependency arrows. In an actual decision model, many of these items will have sub-items, and may be informed by a predictive analytic, a database, a spreadsheet, publicly available data, or even human experience.

Levers combine with externals to provide intermediates, which then inform the outcomes. By creating assumptions around the externals, e.g. food cost will not change by more than 3% over two years (an example only), we can create a visual simulation of the future achievement of outcomes.

Going back to the article, and the allegation that this move will "destroy as much as $800 Million of shareholder value," the decision model could look at present shareholder value, the value of cash-in-hand for the sale, the potential sale proceeds, market projections, and assumptions of how portfolio changes impact shareholder value. The model would compare the different decisions and how they each impact shareholder value, so that a determination could be made as to the best possible decision - which now is made in full view of the future state, given these factors. The activist investors would either be proven right, or they would be quieted by seeing their ideas evaluated against all options in an evidence-based, visual manner. 

As a problem-solver, I find this conversation energizing and exciting. Not only would this be incredibly valuable for any organization going through a decision process around mergers, acquisitions, and divestitures, it would also be a rather fun model to build. 

As it happens, I also love lobster. You?

Capacity to Decide - One of Humanity's 15 Greatest Challenges

In a meeting earlier today, I found myself referring once more to The Millennium Project's "15 Greatest Challenges Facing Humanity." I find the graphic very compelling, as it does a wonderful job of illustrating not only the challenges, but how they relate to each other: the inter-dependencies, as we call them.

For example, an impact on clean water will also have an impact on energy, peace and conflict, sustainable development and climate change, health issues, etc.  When we look at this in the context of world issues, it's easy to get our heads around how one thing can impact another, as in it's easy to understand how clean water can impact health issues, isn't it?  

Of course, of critical importance to us here at Quantellia is #9 on the list - "Capacity to decide." How do we decide where to invest time, energy, and money? How do we understand where our investments will have the biggest impact? How do we know how our decisions will impact other areas? How do we understand how decisions we make today will impact the future?

Here's the thing.

These same questions apply in business and in government. Where a foundation might decide to invest time and energy in clean water in a region of the world, a business might choose to invest in marketing, or social media, or manufacturing. Understanding how those decisions ripple through an organization, what unintended consequences could result, and, most importantly, the impact on the future achievement of the organization's goals - these are real concerns.

What are your organization's "15 Greatest Challenges?" Be sure to include "Capacity to decide" as one of them, unless you already have a proven, documented, and actually-used decision-making process. Our research indicates that only 14% of organizations have that process in place and are using it; perhaps you are one of them.

If you're not, we should talk.  

Do We Need Data Scientists, or Do We Need Decision Scientists?

If you're thinking something along these lines: "We have all this data, and we need data scientists to tell us what we can do with it," you're at the wrong end of the trail - and you're walking backwards. Blindfolded.

Do we need data scientists, or do we need "Decision Scientists"?

If you're thinking that you need data scientists to tell you what you can do with your data, you don't need data scientists. Step back from the hype; step away from the storage salesperson trying to convince you to "store everything and we'll figure out what to use it for later." Step back from the "Big Data Initiative" you are in the midst of planning or executing.

Here's why:
If you don't know what to do with your data, spending time and money analyzing it is not going to get you where you want to go - because you don't know where you want to go.
Enter the Decision Scientist, and a decision-making methodology that makes everything else fall into place in a nice, orderly, relevant way:

Step One: Start with the objective in focus. What is the end game? What is the outcome, or what are the outcomes you wish to achieve? What decisions can you make that will enable those outcomes, and how will those decisions impact the future?

Step Two: Now determine what information is necessary to analyze the decisions and their impact on the outcomes. Is there information to be gleaned from Big Data? Great. Likely you also have information embedded in various systems: in a data warehouse, in spreadsheets, in predictive analytics, and even information that isn't documented anywhere, living in the heads of the people around the table. This information can all be leveraged, and the work already being done in data management is utilized - even if the data isn't perfect or even if it is missing.

Step Three: What external factors will also impact our outcomes? What assumptions do we need to make about those externals? Are we assuming, for example, that hourly rates for resources will not deviate more than 2%? Are we making decisions based on an assumption that our competitors' prices will remain constant? All of these factors must be weighed in, as there could be a profound impact on your business based on factors that you can't control.

Step Four: It's time to create a decision model. Now, this is really hard to do with a spreadsheet, but it's possible. World Modeler™ by Quantellia is one of the few (if not the only) software platforms specifically designed for this purpose that is visual, easy to use, and in "normal" language. In creating the decision model, the dependencies between decisions, externals, and outcomes are calculated, and the impact of decisions made today on outcomes are clearly shown - in a 3D visualization.

Step Five: Experiment with the decision model to see how changes to decisions will ripple through time to impact outcomes. Are the outcomes realistic? Can you act on the decisions you've determined are relevant? Watch the model change, and you'll start realizing how decisions today will impact the future.

Step Six: Act on the decisions, monitor the externals, and manage the decisions based on actual information as it accumulates - knowing that you are doing so with a clear direction toward the outcomes that are critical for your organization to achieve.

Maybe you do need data scientists. Starting with the outcomes defined, and developing a decision process will allow you to position those expensive resources in the places where they will have the most impact on the organization's goals.

Program Management - Optimizing OpEx Throughout

In a recent meeting with the executive sponsor of a complex corporate program, I was asked to explain the picture below. This is the most-requested clarification point, and I know that there are many transformation projects are going on right now that could benefit from this view. 

Here is the explanation:
Programs have goals, and the projects get mapped out to achieve the end target.  However, especially in transformation projects, the ROI is not just an end goal.  The project execution can be optimized to return ROI whenever possible throughout the program.  In short, visibility and linkage to the business's goals and rules allow things like the order of projects and project execution to be truly optimized.
Does this sound about right?  In my conversations with a number of program managers, the picture above represents a view of program management that typical tools don't cover.  Do you agree?

The picture is included in a case study that Quantellia has published on a Global Network Transformation Project, in which tens of millions of dollars were saved through leveraging Decision Intelligence in addition to program and project management tools. Would you like to read it? You can get the Case Study here.

Data Warning Signs - Stop the Madness!

Recently, we participated in yet another meeting which began with the distribution of spreadsheets, and, as we usually do, we asked the question, "What are you trying to achieve?"

Cue the crickets.

You see, everyone in the room was absolutely certain that these spreadsheets would yield untold riches of information, allowing critical decisions to be made, departments to become aligned, and peace to reign within the kingdom.  However, not one person could tell us "decisions about what?"

Now cue the violins.

To avoid the playing of violins in your organization, we've developed this quick checklist of warning signs that will tell you if you are headed for violins or trumpets.
  1. Are we looking at data before we know what it’s for?  Stop. How do you know if the data you are reviewing is even relevant to where you need to go?
  2. Have we invested in a data migration, enterprise data warehouse, data quality, or other database project before we have a clear understanding of the business purpose or desired business outcome?  Stop. You likely can make really great decisions from the data you have, in whatever condition it may be.  
  3. Is our team insisting on completely clean/joined-up data before they will talk about what it’s for? Stop. This is a big expense that may be unnecessary.  
  4. Do we know what our decision levers are? Stop. If you don't know what changes you can make that will impact your outcomes, all the data in the world is not going to help.
  5. Is anybody saying "We have to collect the data first, then we’ll see what it can be used for?” Stop. Do you want your storage vendor's opinion to dictate your data management strategy?
  6. Are all stakeholders aligned?  Stop. Stakeholder alignment around specific outcomes is critical to success.
  7. Do we have a clear sense of what part of the decision is best automated, and what part needs a human in the loop? Caution. Many decisions require human expertise, which is why most decisions are made after the data is put aside. Our brains can't handle all that, so we end up making decisions based only on the systems that our in our heads, and ... guess what? ... that doesn't work so well.
  8. Have we considered human expertise in our decision-making process?  Caution. You're right, that's another way of saying what we asked and answered in Question 7. 
  9. Have we ensured that business stakeholders agree on the outcome? Caution. The outcomes need to be specific, and agreement must extend to timeframes as well as measurements.
  10. Do we know how our decisions impact our outcomes? Caution. Without understanding how our decisions map to the future outcomes we desire, we are shooting in the dark. The least desirable situation is that one decision enables one outcome while checkmating another at the same time.
  11. Are our “outcomes” really just summary statistics, like “My outcome is to know the number of subscribers in Winnetonka?” Caution. This is not an outcome. This is what we call a "proxy outcome" which obfuscates the real outcome. Generally, real outcomes will tie to the corporate KPIs at some level.
If you take nothing else out of this article, please take this, my favorite quote, courtesy of Lewis Carroll:  If you don't know where you're going, any road will get you there.  

In other words, start with the outcomes. Any other starting point is a clear path to throwing money down a rabbit hole.

Don't Read This If You Have Perfect Data

Perfection. Ahhh.  The penultimate achievement, the holy grail, the pinnacle.

Arguably, we have witnessed perfection at various times during our shared history - the Olympic Games being the best example perhaps, where, back in the seventies, we saw a couple of gymnasts achieve perfect scores for their routines.  We've also heard the adage "strive for excellence, as perfection is not within reach of mere mortals."

In the business world, could we agree that perfection is not possible?  Would we further agree that even excellence is sometimes elusive?  We do the best we can, certainly; we strive to chart the best course, hire the best people, be the best organization, have the best customers.... you get my drift.  Yet at no time, in the history of the universe, has there ever been a perfect planning process, hiring process, organizational culture, or customer acquisition process.  If these things existed, there would only be one book about each of them, right?

So why does there seem to be a worldwide fixation on perfect data?  Hundreds, perhaps thousands, of companies exist that sell products and services around making data perfect, and millions upon millions of dollars are spent every year in the quest to make data perfect so that it can be useful.

Data is useful, don't get me wrong. (In fact, I love the stuff.)  It tells you where you've been, and what occurred, and how your organization performed within a given time period.  For the purpose of charting the past, data is critical, and it can likely be a perfect representation of history.

But.... given that most organizations prefer to drive looking through the windshield, rather than the rear view mirror, we are now heavily engaged in making data perfect so that we can also chart the future. More millions....

But ... what if data didn't have to be perfect? What if data didn't even have to be excellent?   What if you could get where you want to go, right now?

See, here's the thing.  Data projects take a long time, and we have to make decisions even while those projects are going on. We have to make the best decisions possible for our organizations. We don't stop deciding even when we have imperfect data, and, in many cases, we don't even consult the data because it isn't perfect yet - or worse, because the data we need doesn't even exist.

So we walk into meetings, with our best spreadsheets, pivot tables, charts, and graphs, and we grapple with decisions. We look at all of the information we have available, we load it all up into our brains, and then we put the papers down and try to make decisions based on what's in our heads.

Sometimes we get it right, and, more often, we don't. Some studies suggest that we miss the mark over 40% of the time. That's a big swing, with a latitude traditionally only given to meteorologists and professional sports players.

What if we could drop that percentage? What if we could create an environment where we can look around corners, and over the horizon, to understand the potential and future impacts of our decisions? What if we could create a way to pivot quickly, with a solid understanding of the reasoning behind the pivot?

What if we could create a framework for decision-making that doesn't depend on perfect, or even excellent, data? What if that framework were designed to work the way that we humans make decisions?

Wouldn't that be great?

It is great.

Photo credit: with thanks to Eddi van W.

Why Stop with Predictive Analytics? Go Beyond...

What if the future is not like the past?

Predictive analytics is a very valuable technology for many industries. Predicting which patients are likely to readmit, based on historical information about readmission - done.  Predicting how products will sell based on placement on the store shelves - done.  Predicting what you might want to buy, based on what others have purchased, or on what you've purchased before - done (though with a few flaws; as we all know, gift buying can really mess up those recommendations).


Predictive analytics about income streams for healthcare providers went out the window with the Affordable Care Act.  Predictive analytics about stock performance went down the chute with the financial collapse of 2008.  Predictive analytics about the travel industry went sadly, horribly awry in September of 2001.  The mobile telephone market's future was upended with the introduction of the iPhone.

Predictive analytics is not so helpful in understanding these “black swan” events, because it is based on a "revert to mean" approach that, arguably, was even at the core of the most recent US presidential election.  (Read more here).

Where predictive analytics is focused solely on historical data —collected, captured, cleaned, and perfected—Predictive Intelligence focuses first on the desired outcome.  Instead of collecting information to determine what kinds of decisions can be made, Predictive Intelligence provides a decision roadmap so that organizations can achieve the outcomes they require.  It provides a “focusing lens” on what could otherwise be an overwhelming amount of data, and identifies which information is the most critical for achieving those outcomes. This information often includes human expertise, when data is in short supply.  In other words, Predictive Intelligence lives at the juncture of data (including big data) analytics and human expertise, combining the robust and evidence-based approach from the former with the agility, intuitiveness, and speed of the latter.

Predictive Intelligence is a robust systems model that leverages the results of predictive analytics, complexity theory, systems analysis, data management, pattern recognition, big data, and more, but goes further - incorporating human expertise, assumptions, external factors, dependencies, and inter-dependencies.   It combines these elements in a piecewise way into a framework that provides the best of both worlds. Where predictive analytics typically answers one question, Predictive Intelligence manages and models multiple outcomes at the same time.  It’s a bridge between the data-driven, fully automated decision-making systems of the past, and what is needed to navigate an uncertain future.

Imagine this conversation:

VP of Sales:  "The predictive analysis we've run tells us that our sales will increase by 5% next year, so we just need to keep doing what we've been doing."

CEO:  "I heard that our biggest competitor is getting ready to launch a loyalty program.  Should we have a loyalty program, too?"

VP of Sales:  "I don't know - the model didn't take any changes to the competition into account.  We'll need to re-run the model."

VP of IT: "I don't think we have historical data collected on our competition.  We'll need to figure out how to get that.  That could take a few weeks, and we might get it wrong."

CFO: "What impact will a loyalty program have on our sales revenues?  I'm concerned about the cost of this program and whether we'll see a net increase."

COO:  "We've asked the manufacturing organization to cut their costs by 5%.  How does that support an increase in sales by 5%?  If we implement a loyalty program, will we have to manufacture more?"

VP of Sales:  "I only analyzed sales.  You all should be running your own predictive analyses to determine how to run your departments."

VP of HR:  "What if your top salesman leaves us?  Will we still increase sales by 5% next year?"

VP of Sales: "The analysis doesn't account for big changes in the future, and that would be a big change.  I don't know how to answer that question.  It takes a while to get a new rep up to speed, for sure."

Well, you can imagine the rest of the conversation, and perhaps you've even translated it to something that happens in your organization.  The point of the story, of course, is that, while predictive analytics has a well-deserved reputation in specific use cases, it doesn’t apply to every situation. 

Predictive Intelligence, on the other hand, significantly broadens the use-case landscape.  By starting with the outcomes, considering external factors like competitive actions, interest rates, and predictive analysis of customer churn, and weaving in human expertise, logic, and situational awareness, Predictive Intelligence takes an organization to the next level - true Decision Intelligence.  Had Predictive Intelligence been part of the conversation above, the answers would have been readily apparent, and decisions about the next year could have been made right then and there. And because Predictive Intelligence must have “low cognitive friction” for decision makers to find it valuable, it is intuitive  - interactive, 3D, and actually a bit fun – and, most importantly, Predictive Intelligence matches the natural way that decision makers think about complex situations, based on years of research into collaborative decision making at hundreds of organizations.

Organizations with Decision Intelligence achieve success at a different level than their competitors who are still doing things the old-fashioned way.  A couple of examples: 

·         A massive IT program went from missing projections by $40M to saving over $10M more than was initially projected, with an ROI of 300:1

·         A developing country has learned how to extract every penny of value from donor dollars to reduce the likelihood of future conflict

As seen in the sales conversation above, every decision ripples through an organization and has consequences (some intended and some unintended) on the organization as a whole.  What is critical in today’s complex world is the ability to see over the horizon and around corners to understand the impact on all of the desired outcomes, and to understand how managing and navigating multiple decisions simultaneously can achieve the desired outcomes.  Predictive Intelligence is the critical tip of the Decision Intelligence pyramid, finally answering the question “If I make this decision today, what will be the impact on the future?” 

To find out more about Predictive Intelligence and Decision Intelligence, see our video library at, visit, or email Lorien and Margaret at