Opinion by Thought Leaders
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Growth and Scaling Downfalls – Part IV

 

In the previous post “Growth and Scaling Downfalls – Part III” we discussed strategy aspects of a scaling project. The next topic on the scaling preparation “to do” list is measuring success and failure.

Scaling and growth both depend a great deal on experimentation: be it at tactical level deciding who will do what to strategic level defining success or failure. That being said that kind of decision making naturally requires a great deal of analysis; qualitative or quantitative.

Quantitative

Data driven quantitative analysis is or should be the basis of virtually all business decisions. Though an established field, the quantity of data that has been previously inaccessible or impractical for usage has changed the field. The same quantity of the data sets that are now available have also created several other side effects for small and mid-size organizations; ranging from increased cost for proper analysis to “analysis paralysis”. Hence, the usage has to be defined in terms of practicality: both the collection and analysis of data have to be defined within the context of cost and impact.

Qualitative

In a previous discussion about decision making we discussed the usage of qualitative decision making. Those parameters previously discussed i.e. strong pattern recognition as part of the qualitative decision making are particularly applicable when it comes to growth and scaling. In practical terms it translates to a combination of using practical experiences both industry related as well as general business experiences to decide on both tactical and strategic level: the industry know-how combined with generic business experience will provide the sort of “umbrella” coverage that will leave little room for “guessing”.

On the front line

Interestingly enough there are some unique aspects to data usage when it comes to scale and growth: though the basic methodology of collection and analysis is the same, the decision making direction should entail a more dynamic version of “bottom to top” or “top to bottom”: Micro decisions vs. Macro decisions: 

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Best Decision Making - Experience Versus Data-based?

 

The daily life of any executive entails an endless amount of decisions: those decisions are made based on factors such as experience, data, organizational needs and goals. Those decisions are likely to be additionally impacted by the ever increasing demand for speed. Hence creating a tempting environment to excessively rely on decision making based on experience. This begs the question: does relying on experience as sole point of reference for decision making viable? And if it is, how do we maximize the odds of better outcome for those decisions?

Variety of experience

It is a fair to stay that we all perceive reality differently: people can be in the same situation or conversation yet have an entirely different take away. The same applies to “experience”; one single instance of “experience” can be sufficient to deter or encourage a particular action based on the perceived “lesson learned”; it is even entirely possible to classify the same instance of “experience” as good or bad solely based on the perception of the experience and/or its outcome. This leads us to the question: if the said experience is the basis of one or more decisions, how can potential errors or bias be minimized?

Single or multiple experiences

It goes without saying that a single instance of an experience is rather a debatable proposition when it comes to decision making. It should be rather obvious that a single instance of “data point” be it qualitative or quantitative can’t possibly be considered as reliable basis for fundamental decisions. That being said when can experience be reasonably viable? Is it a functional of quantity? Quality? The answer is not that one dimensional. 

A single instance of virtually anything can signal flawed results and conclusions because there are many variables that can change the actual and or perceived outcome. Some of those factors include stakeholder’s behavior and actions, circumstantial organizational resource limitations and or allocation as well as interpretation biased by multiple level of internal and external actors. Hence, logic dictates that one, two or any quantity of an experience is susceptible to flawed conclusion analysis.

Patterns

So, if even multiple instances of a given experience can’t be relied upon, what is the solution? One possible solution is reliance of patterns; this method would strip away a lot of the shortcoming of utilizing the experience or experiences as a data point by looking at common denominator’s as opposed to evaluating the experience in its entirety. Additionally, it would allow for larger set of qualitative data points because it eliminates the necessity of using only personal experience as opposed to being able to include external and/or third party input even unrelated to specific projects and/or industries.

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Is Your Digital Transformation Strategy for Real?

 

Digital transformation is a good thing and it's been talked about the last several years as CIOs, CEOs, and other execs look to make a move. However, lately it's just become a "buzzword," generating a lot of hype. How can you ensure you have a solid digital transformation strategy?   Many businesses, large and small, are attempting to transform their processes using digital tools. Many cases of transformation have been successful, creating solutions that enhance problem solving while driving efficiency and bottom line gains, says Forbes. Digital transformations are even tougher than traditional change efforts to make work. However, the most effective transformations usually rely on certain factors for success. Here are some things you should be doing.

1. Secure Strong Leaders

Digital transformations demand change at all levels of your organization, particularly from key decision makers and tech-savvy leaders. Research has shown that companies that engage a Chief Digital Officer (CDO) to be a supportive force behind their transformations are nearly two times more likely to have a successful digital transformation than those that do not. When people in leadership roles are heavily involved and invested in the planning and execution, the transformation is far more likely to succeed.

2. Take Inventory

Once your key decision-makers have committed to making a digital transformation strategy work, it's time to take stock of your company's tech stack, including competencies and gaps, as part of your email marketing, CRM and internal collaboration systems so you can better streamline your processes. Often times, digital transformations stem from a desperate need to re-platform. Maybe your current system is obsolete or maybe your existing system just isn't working for your employees and users any longer. Whatever the case, changing your technologies can reawaken your whole business.


3. Craft a Digital Roadmap

Creating a vision to strive for is important because digital transformation isn't just about implementing new technologies and stopping there. Rather, it's a systemic grassroots effort that needs to be fueled by a well-thought-out vision. Know how you will leverage your digital tools as part of a detailed plan for execution across your whole company.


4. Reiterate Your Goals

Take another look at your goals, going over your digital transformation agenda over and over, making sure you have included:

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