Contemporary Analysis Process (Proposal Style)

CAN Philosophy for Implementing Data-Driven Decision Making

Through our experience designing and implementing data-driven decision making for our clients, CAN has consistently found these three things to be true:

1. Implementing data-driven decision making is a philosophical change for an organization, not simply a technological innovation. A single project alone will not bring that change.
2. The switch from making decisions reactively based on past data to using data proactively in strategic decision making must be driven by leaders who understand the organization’s culture and who ask the right questions. That leadership team needs to have the influence to persuade others to use data to make strategic decisions.
3. Finally, the models and visualizations used to make data-driven decisions need to be proven accurate and proven to save time before the organization will start to use them. If users believe their data is bad, then any models built from them will also be viewed as bad. This belief means that any one project will not convince users that data-driven decision making will make things better.

Because this process is a loop, many organizations have trouble finding a place to start. Also, because of the cost, the risk of being wrong keeps many organizations from investing the political (not necessarily monetary) capital necessary to lead the change. CAN has spent over ten years learning how to lead this change, has seen the best ways of making this change, and now helps organizations understand how and where to start.

The CAN Process

CAN has created a unique process for implementing data-driven decision making in organizations. This process gives our clients the right knowledge at the right time as well as a visual roadmap of where they are going and how to get there. Our process is a plan to help your organization move forward through the various levels of Data Hierarchy, which include:

1. Reporting:  Tracking and “What happened yesterday?”
2. Business Intelligence:  “What just happened?”
3. Descriptive Data:  “Why did that happen?”
4. Predictive Data:  “What is going to happen next?”
5. Prescriptive Data:  “What should we do to make it happen?”
6. Machine Learning:  “Automated recommendations”
7. Artificial Intelligence: “Automated decisions”

Following our process allows you to understand where you are, see the steps in front of you, plan for those steps, hire and then train the individuals you will need both now and in the future, and still have the technical and operational expertise you need all while de-risking the creation of data-driven decision making inside your company.

This is the only way we have seen success in implementing data-driven decision making at an enterprise level. We have completed this process multiple times across multiple industries. Those clients are now successful at making data-driven decisions.

Our goal is to empower you to build something greater. Here are the key steps in CAN’s process, which is illustrated in the graphic on the following page:

Innovate     Innovation is difficult to foster inside organizations organically because they consistently try to improve old processes. data-driven decision making is not an improvement of an old process but instead, a new one. CAN helps organizations by being the outside experts providing expertise to help the organization dream bigger than just improvement. We are here to build new processes and new solutions that are needed for success. Having a third party lead this process is faster and less expensive than doing it internally.

Train      In the Train step, we help you build and teach your team the necessary skills (both soft and technical) to perform and manage data-driven decision making. Building your team consists of identifying what skills and attributes are needed to be successful data scientists. Often your organization has employees that want to and could perform such roles, eliminating the need for hiring externally. Re-purposing and cross-training these employees is faster and easier than hiring new employees because they already understand how to work in your organization. If no one is available, we can help design your hiring and management system to attract and retain outside talent. Once the right team members are identified, we provide customized training through our accredited data science code school and through live development training.

Incubate    During project Incubation, we will work alongside your team to implement and maintain your projects and initiatives.  As your team ramps up its skills and ability, we will ramp down and transition more of the work to you. The timing for this transition will be based on your team’s needs and your organization’s goals.  Throughout this transition, we will be here to support you when you need specific skills, when you need additional capacity, and when you need oversight.

Mentor    Once your organization has a team that is trained and able, CAN steps into a mentor/peer role giving the reins back to the organization. No black boxes. We then help with overflow projects, temporary data scientist needs, and strategic knowledge. This gives your team the bravado to keep building without fear of failing.

Hypothetical Multi Project Timeline

Phase 1

1. Audit

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 now – access to tools, resources; what data is available and how it is being collected and stored; what expertise and capacity is available; and an understanding of your organization’s culture.

1. Strategic Plan

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, what frameworks or architecture you might need, and, build 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.

Below is an example of some of the key aspects of the Strategic Plan:

• Goals and issues for the organization
• Goals related to data governance, BI, data science, data security, etc.
• Definition of current situation
  • Current tools, data, etc.
  • Current resources and expertise
• History of data collection and use in the organization
• Implementation Plan
  • Define initial prototype
  • Define list of initial projects
    • Scope, benefits, costs
  • Define potential projects (beyond initial projects)
    • Description of initiatives, define scope as best as possible, outline and estimate of benefits and costs
• Resources Required
  • Internal – people, skills, leadership
  • External – external people, skills, software, data storage, etc.
• Management Plan
  • Define who needs to be involved – leadership, decision makers, stakeholders
  • Define what skills your team will need
  • Create a definition of success.
  • Funding Plan
  • Create a roadmap for Implementation, Maintenance, Training, Incubation, and Mentorship.

The Strategic Plan includes developing specific projects and initiatives to meet your goals. So the specifics of what is included in Phase 2 won’t be defined in detail until the completion of Phase 1. However, based on our experience, the process could be similar to what is listed below.

1. Prototype

In Phase 2, we will begin by designing and implementing a prototype project. The prototype is the important first project. It is not meant to solve a large problem, but instead, meant to do two things:

1. Show where the bottlenecks for implementation across the organization will be. Implementing a prototype is critical before the implementation of larger, more complicated projects.
2. 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.

1. Project 1 Design

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, sand boxed data. Additionally, this project (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.

1. Project 1 Implementation

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.

1. Project 2 Design

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.

1. Project 1 Maintenance

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.

1. Project 2 Implementation

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 your trained and ready team is working without us. We are still here as mentors, but only as a on-demand data science staff.

1. Project Support

Now that your organization has a capable team, CAN shifts to being your on-call data science team. We maintain a staff to help your team 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.