5 Questions to Ask About Prescriptive Analytics

Prescriptive Analytics is used for performance optimization. This optimization is accomplished by using a variety of statistical and analytical techniques to identify the decisions that need to be taken in order to achieve the desired outcomes. The data sources used for the determination of outcomes can range from structured data (e.g., numbers, price points etc.), semi-structured data (e.g., email, XML etc.) and unstructured data (e.g., images, videos, texts etc.).

If done correctly, Prescriptive Analytics is the Holy Grail of analytics. However, if done incorrectly, it can result in misinformed decisions that can be outright dangerous. Individuals and organizations have to understand that even if the data is correlated that does not mean that there is some sort of causation. A general example of this is when in a news report, the host(s) says that survey has shown that x is correlated with y but then they go on how y was caused due to x. This is simply what I call “jumping the data gun” and organizations that are not aware of this can fall into this trap.

Another thing to be aware of is that after the Prescriptive Analytics gives you certain courses of action and you apply those actions, keep track of how well your Prescriptive Analytics is performing as well. In other words, you have to measure the performance of your performance optimization ways. The reason to do this is because over time you can see if the models presented by your Prescriptive Analytics engine is worth following, re-doing or dumping.

To get you started, here are a few questions to ask:

Currently

In the Future

Who uses prescriptive analytics within, across and outside your organization? Who should be using prescriptive analytics within, across and outside your organization?
What outcomes do prescriptive analytics tells you? What outcomes prescriptive analytics should tell you?
Where is the data coming from for prescriptive analytics? Where should the data be coming from for prescriptive analytics?
When prescriptive analytics is used? When prescriptive analytics should be used?
Why prescriptive analytics matters? Why prescriptive analytics should matter?

When you are asking the above questions, keep in mind that Prescriptive Analytics uses data to create a model (aka a data version of the world) that is used by individuals and organizations to make real-world decisions. But if the model itself is flawed then you are bound to get answers that although might look visually appealing are completely wrong. It is not all doom and gloom though. In fact, Prescriptive Analytics is used in determining price points, expediting drug development and even finding the best locations for your physical stores. Companies like Starbucks have been using Prescriptive Analytics in the last few years to determine the best locations for their next coffee stores. Interestingly, some have claimed that wherever Starbucks goes, the real-estate prices also increase. While there is some correlation between a Starbucks coffee store opening with increased real-estate prices but this does not mean that because of Starbucks coffee stores the real-estate prices increase.

Analytics Trophies

 

References:

  1. 5 Questions to Ask About Business Transformation
  2. 5 Questions to Ask About Your Information
  3. Starbucks Tries New Location Analytics Brew

5 Questions to Ask About Predictive Analytics

Predictive Analytics is a branch of data mining that uses a variety of statistical and analytical techniques to develop models that help predict future events and/or behaviors. It helps find patterns in recruitment, hiring, sales, customer attrition, optimization, business models, crime prevention and supply chain management to name a few. As we move to self-learning organizations, it is imperative that we understand the value of Business Analytics in general and Predictive Analytics in particular.

It turns out that Predictive Analytics is about Business Transformation.  But in order for this Business Transformation to take place, you have to take into account the organizational contexts in the following ways:

  1. Strategic Perspectives: Not all organizations are the same and thus what works in one organization might not work in yours. Based on the knowledge of your organization’s maturity, you have to decide if Predictive Analytics is going to be a top down, bottom up, cross functional or a hybrid approach. Additionally, take into account what should be measured and for how long but be flexible in understanding that insights might be gained from data that might initially seem unrelated.
  2. Tactical Perspectives: One of the key factors in Business Transformation is change management. You need to understand how change would affect your organization in terms of people, processes and technologies. You have to take into account the practical implications of this change and what kind of training is needed within your organization.
  3. Operational Perspectives: It is all about how execution of Predictive Analytics is done within your organization. To fully integrate Predictive Analytics into your organization, you have to learn from best practices, learn the pros and cons of your technology infrastructure and determine if the necessary tools are intuitive enough for people to make use of them.

Now that you understand the different organizational perspectives, it is time to ask the following:

 

Today

Tomorrow

Who uses Predictive Analytics to make decisions? Who should use Predictive Analytics to make decisions?
What happens to decisions when Predictive Analytics is used? What would happen to decisions if Predictive Analytics will be used?
Where does the data for Predictive Analytics come from? Where should the data for Predictive Analytics come from?
When is Predictive Analytics relevant? When should Predictive Analytics be relevant?
Why Predictive Analytics is being used? Why Predictive Analytics should be used?

When you ask the above questions, keep in mind that reliability of the information and how it is used within the organization is paramount. A pretty picture does not guarantee that the insights you get are correct but you can reduce decision-making errors by having people who understand what the data actually means and what it does not.

Measurement

Measurement

 

5 Questions to Ask About Your Information

Information collection, understanding and sharing has been a worthwhile pursuit since the dawn of humanity. At the beginning, now and in the foreseeable future this pursuit will continue, even if the “tools” change. We will continue to use information to make short-term and long-term decisions for our groups and ourselves. But depending upon the sources of the information, we might make good decisions or we might not. It is only until the results of the decisions are evident that we will know if where we ended is where we wanted to be. Sometimes we will make quick decisions and sometimes we will take our own time to make a decision. But in all of these circumstances, we will always hope that the information sources that we used to make our decisions are credible.

In order to understand information, we need to understand the various “flavors” of information that we receive. Lets explore them below:

  1. Redundant Information: Think about how many times you have received the same information from two different secondary sources. In your mind, you might be thinking that since two different secondary sources are providing the same information then it must be true. But what if the primary source of the information is the same? What if nothing new has been added to the information that you received? This is the concept of Redundant Information where the primary source of the information is the same and nothing new has been added to it.
  2. Corroborated Information: Think about how many times you have received the same information from two different secondary sources and are sure that the primary sources of the information are different. In your mind, you might be thinking that since the two primary sources are different then it must be true. This is the concept of Corroborated Information where the primary sources of the information are not dependent on each other.
  3. Contradicting Information: Think about how many times you have received the same information from two different secondary sources and found out that they were saying opposite things. This is the concept of Contradicting Information where the information that we receive does not agree with each other.
  4. Perspective-Dependent Information: Think about how many times you have received the same information from two different secondary sources and determine that there are various versions of the truth. One version might be at a high level while another version might be at a lower level. This is the concept of Perspective-Dependent Information where information that you receive has been looked at from top-down, bottom-up and horizontal perspectives.
  5. Biased Information: Lets face it, everyone has biases at some level based on their history, culture, societal norms, politics, religion, age, experiences, interactions with others and various other factors. These biases can creep into the information that we receive from others but also influence us when we make our own decisions. This is the concept of Biased Information where even in front of mounting evidence that challenges your views, you are still holding on to your conscious and unconscious thought processes to make decisions.

Now that you understand the various flavors of the information that you receive, it is time to ask the following:

 

Currently

In the Future

  Who receives information? Who should receive information?
  What happens to information? What would happen to information?
  Where does information come from? Where would information come from?
  When is information being shared? When would information be shared?
  Why information is collected? Why should information be collected?

When you ask the above questions, keep in mind that the information flavors and contexts are closely related. Even if you understand the information flavors being used but do not understand the context around them then your decisions will be skewed. On the other hand, be mindful of only looking at information that confirms your views (aka cherry picking) since you will miss something that might have helped you better understand the world around you.

Information Flavors

Information Flavors