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3 Effortless Tactics to Be a #DataScience Success in Business

16 Min
3 Effortless Tactics to Be a #DataScience Success in Business

Link to Data Science Central: 3 Effortless Tactics to Be a Data Science Success in Business

“Move out of the way – I am ready to model.” That is the typical sentiment of a Data Science team when given a business problem. However, in the context of a dynamic business, things are not that simple; instead, business needs require that the Data Science team be detailed in the communication of their process. The last thing a Data Science team wants to do is produce a project plan they feel is a pedestrian artifact aimed to pacify their business counterparts. They tend to prefer a more fluid and creative style as opposed to one that is stiff and inflexible. Data Scientists may be tempted to promote the idea that they cannot let anything get in the way of creativity and brilliance or it will be to the detriment of the business. However, in many cases, Data Scientists may be allowing their human fear of transparency and accountability to dictate how they approach what the business needs – maximum visibility. Don’t fall into the trap of believing that these templated documents merely exist to check the proverbial box in order to placate the MBAs and Project Managers in the room. Data Science teams designed for success will most certainly deliver a Data Science project plan and use it throughout their analytics project.

Producing a Data Science Project Plan 

You might ask what the intended purpose behind such a fancy business document really is at its core. The Data Science project plan is incredibly straightforward: its sole purpose is to be the battle plan for achieving the Data Science goals which in turn achieve the business goals. Successful Data Science teams will know that there is immense value in not only being able to achieve the Data Science goals, but in being able to relate them back to the business on a constant basis. It’s the burden of the Data Scientist to be sure that clear communication exists between the two groups. The challenge for a Data Scientist is translating Data Science into business terms. This is the kind of thing that is built through experience and through learning what the business expects in a traditional project plan. If a business had a choice between a model with higher predictive accuracy by a Data Scientist without a project plan and a model with lower predictive accuracy by a Data Scientist with a project plan, they most certainly would choose to work with a Data Scientist who could communicate in terms of business, translate Data Science ideas, and understand the power of leveraging other individuals in the organization to contribute to the overall outcome.

Project Plan in Action

The nuts and bolts of a Data Science project plan will be different for each team and each organization, but there are core elements you will see in almost all effective Data Science project plans – sort of a Tao of Data Science Project Plans.

Three Effortless Tactics:

1. List the stages in the project 

The business should not have to make assumptions about the stages you may take them through as a Data Scientist. Display your expectation to everyone and let them know how much time each stage may take. Also, do the obvious things like listing the resources required as well as the types of inputs and outputs your team expects. Lastly, list dependencies. After all, you will want your counterparts to be aware that you cannot move forward until “x” event happens; for example, the Data Scientist may be waiting to receive a data feed from IT. This is precisely the kind of thing to call out in the Data Science project plan.

2. Define the large iterations in the project 

Most business users will not be intimately involved in how a Data Science team works or why it may change when you encounter a classification problem versus a regression problem. So in an effort to be clear and meaningful, share stages that are more iterative as well as their corresponding durations – such as modeling or the evaluation stages. The best Data Scientists know how to  appropriately manage expectations from the business through communication with the broader organization.

3. Point out scheduling and risks

Virtually all working individuals know that it’s unrealistic to think everything happens only in ideal scenarios. Data Scientists should take the necessary time to consider scheduling resources and the inherent risk they could encounter in the project. Give the business the comfort that only a trusted advisor can provide them. Think through what could happen and what you would recommend to them if they encounter turbulence – because turbulence is inevitable. Taking this extra step is the hallmark of a Data Science professional.


Do not view the Data Science project plan as training wheels for a junior Data Scientist who is new to working with business, but rather what a skilled Data Scientist will review each time his or her team begins a new task within the Data Science project. Crafting a Data Science project plan to pacify the business – and never utilizing it for team guidance – is a grave mistake that one day could end in ruin for the Data Science team, the business, or both. An effective Data Scientist will work from the perspective that a goal without a plan is simply a wish and nothing more. Or, said differently, an effective Data Science team works a plan at all times.

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