Automated Decisions

Once you have mastered Machine Learning and trust the recommendations the models are making, you can now take the final step in the Data Hierarchy - automating the decisions. Automated Decisions mean you can finally automate those pesky recommendations into full blown decisions - tasks like auto-sending emails to customers found to have a low customer score. It frees your team to tracking those customers who respond (or don't respond) with the final human push. AI doesn't eliminate people, it frees them to do the most valuable part of the business process - human interaction

Automated Recommendations

Once you know the levers to pull, and are planning strategically, you begin to see you need more information to make better decisions. You will want data from other datasets inside your organization (a different division) or from other organizations outside your organization (purchased data) to try and enhance your current datasets in an effort to find new patterns. You will also begin to automate models running on your data to serve to you recommendations for you to make decisions on. These recommendations give your team the ability to act at speeds and in ways you never could before. You can know your team is managing in the most effective way it can, the most amount of customers it can.

What should we do to make it happen?

Once you can predict outcomes you can start looking at how changes to the variables change the outcomes: E.g. If we hire more people now, off season, how does that speed up production in season? If we wait to buy inventory and the price goes down, how does that affect our profitability? You will know the levers to pull and begin to strategize more effectively.

What's going to happen next?

With a robust data set you can use the information you know for a fact to predict outcomes. You can now act proactively--Change a belt before it breaks or order a tire before it blows! This level of intelligence means you are now planning for things that have a high accuracy of happening when you have the time or dollars--not when the action dictates it. This will lead to less cost, faster action, and better results.

Why did that happen?

With a robust data set you can use the information you know for a fact to predict outcomes. You can now act proactively--Change a belt before it breaks or order a tire before it blows! This level of intelligence means you are now planning for things that have a high accuracy of happening when you have the time or dollars--not when the action dictates it. This will lead to less cost, faster action, and better results.

What Just Happened

Business Intelligence takes the data you’re tracking and puts it into a visible format. Visuals allow you to identify trends and see patterns you could never discern otherwise. It also allows you to make decisions off this data rapidly. This rapid decision making and formerly unknown pattern recognition makes Business Intelligence a minimum for success. 

What Happened Yesterday

The first step to working with data is simple record keeping. Tracking your data points allows you to build more complex data driven decisions down the line. It is important to make sure you’re tracking the valuable data points that drive value later.

This is a very important aspect of the entire process. Understanding the history of your organization, your culture, your goals, and the people are critical to establishing a good foundation to build a successful program.

During this phase, we will interview the key leadership and stakeholders. We will also want to know where you are now – access to tools, resources; what data is available; how it is being collected and stored; what expertise and capacity is available; and an understanding of your organization’s culture.

After an audit of your organization, we will work with your leadership and project managers to develop a strategic plan for designing and implementing initiatives that will help you accomplish your stated goals and put you on a path to long-term success.

Included in this plan will be a roadmap on how to get to your destination. We will outline the things you are missing and how to find them. This includes how to get the skills you need, what software you might need, what people you should be looking for, and what frameworks or architecture you might need. You will also get a plan on how, but more importantly when, these things will need to be initiated.

Your specific plan will be customized to fit your unique goals, issues, history and culture.

The prototype is the important first project. It is not meant to solve a large problem, but instead, meant to do two things:

• Show where the bottlenecks for implementation across the organization will be. Implementing a prototype is critical before the implementation of larger, more complicated projects.
• Prove to managers, administrators, and users that data-driven decision making can save time and/or money and accomplish your goals.

This is also where we will begin to identify those traits mentioned in the TRAIN step that will become important in choosing your first data scientists. We will help you choose those employees (internal or external) and begin their training.

This is where we design and build out the first main project for you. This project will be much larger than the prototype and will be created using non-real-time, sandboxed data. Additionally, this project will (as will all projects) be “clear box”–meaning we will show you how everything is done. No black boxes.

This is also where we start incorporating your team into the process. Your team will be right beside us as we build and implement together.

Once Project 1 has been designed and tested, it needs to be implemented into the enterprise level of the organization. This includes automating the model as well as using real time data.

This usually includes connecting with the data team that may not have been involved in the design of the project.

ingly.

Shortly after or potentially in parallel with Project 1 Implementation, we (CAN and your team) will start designing more projects. This design is led by your team. We are still there for support, but your team takes the lead, builds the project and implements the prototype.

This happens during project incubation — meaning our team is on call, sometimes in the same room, available to you when you need. In this way, we provide technical support only when needed, gentle guidance through a daily check-in, and lots of moral support.

Project maintenance is a wholly different process than implementation. This phase of a project is placing a person in charge of making sure the model doesn’t break when data changes or as the need of the model changes over time.

This skill set for maintenance is not usually the same as for design and implementation. Therefore, we recommended using a different person or team to maintain the models. We will help you understand those skill sets and manage accordingly.

At this point, your organization has a team that is capable of implementing the model. Different from Project 1 Implementation, Project 2 Implementation is done without us onsite.

This is the first time you’re trained–and-ready team is working without us. We are still here as mentors, but only as a on-demand data science staff.

Now that your organization has a capable team, CAN shifts to being your on-call data science team. Our team can step in when:

• You need a project designed and implemented because your team is busy designing and maintaining your other models.
• You need temporary, skilled data scientists to augment your team (usually when multiple models need to be updated or maintained at once).
• You have a short-term need for someone with a distinct knowledge base or skill set and do not want or need to hire a senior-level data scientist for the project.
• You need training. Data science is a rapidly-evolving industry. New technologies that did not exist even three years ago quickly become industry standards.

We are bleeding-edge consultants with knowledge and expertise in emerging concepts, ideas, trends and software. We are always available to train as the need arises.