3 Effortless Tactics to Be a Data Science Success in Business

Damian Mingle - Business Decision

“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.

[Originally posted on LinkedIn]

5 Unbelievable Ways You Can Be a Better Data Scientist in Business


Most Data Scientists like to get their hands dirty with data just as quickly as possible, but it is important to practice some delayed gratification and first dig into the details of the Data Science project before you start modeling. A Data Scientist who has the business in mind will attempt to determine what factors might get in the way of the business experiencing success with the project. At different phases there are differing needs for information, but once you have moved past gathering the initial stage of understanding the business, a successful Data Scientist’s objective becomes diving into the details quick and deep.

1: Conduct a Resource Inventory


As a Data Scientist, it is important to know the in’s and out’s of the available resources of a Data Science project. This is not just about how much computer power you have to run your analysis. A professional Data Scientist needs to consider many things like the business experts, data experts, technical support, and other data scientists. In addition, there are important variables such as fixed extracts, access to live data, warehoused data, and operational data. However, no one should forget the computing resources such as hardware and software. Any Data Scientist who takes on a project without seriously considering these areas is walking into a minefield, never knowing when something might explode.

2: Understand the Requirements, Assumptions, and Constraints

Most Data Scientists know they have to be better than average at predicting outcomes for whatever the business has selected as a target, but highly successful Data Scientists know that there is more to it than simply gaining a few more points in predictive accuracy. Take for example a Data Scientist who considers all the assumptions that are known about the project both from a bushiness perspective and an analytical perspective. These assumptions can take many forms – however, the ones that rear their ugly heads most often are about the data. Sometimes assumptions are not verifiable as they relate to the business – these can be the riskiest. If at all possible these risky assumptions should be prioritized at the top of the list because they could affect the validity of the results you aim to discover.

Data Scientists need to watch for traps. Consider making explicit any and all availability of resources, even technology constraints. Think outside the box when it comes to limitations. For example, is the size of the data practical for modeling? This may seem obvious, but many Data Scientists overlook this important consideration.

3: Determine Risk and Contingencies

Have you ever started a data analysis project that ended up falling apart only because there were external delays to the project? It is a wise move to consider contingency plans up front. Many Data Scientists take a short-cut here and do not take seriously the insurance that this sort of preparation can provide when needed. It can be extremely helpful to have a backup plan or two in place in the event unknown risks try to derail your projects success. Experience would say that something is always trying to cause you to fail, so plan for alternatives from the beginning.

4: Document Meaning

The question “What do you mean?” is a particularly important question to answer when working with inter-disciplinary teams in a business environment. It should be obvious that we all do not speak the same language when it comes to our domains. Taking the time up front to develop a working glossary of relevant business terminology can keep you and others on track. Another good practice is to have Data Science terminology defined and illustrated with examples, but only work with the terms that directly relate to the business problem at hand. This does not need to be a 700-page document; rather, keep things cogent and useful to all parties involved. Keep in mind others want you to be the Data Scientist; only at the highest level do others want to know the underbelly of statistics and coding.

5: Calculate Cost and Benefits

It is good practice to demonstrate value in your Data Science projects. Remember that as a professional who supports the business it is important to ask and answer the question, “Is the Data Science project of value?” A simple comparison of the associated costs of the project against the potential benefits if successful will go a long way for both you and the business. Knowing this at the beginning of the project is clearly more beneficial to you and the organization than at the close. In my judgment, to not ask and answer this question is a career limiting move that your most successful Data Scientist will seek to get right straight out of the gate. Have the common sense to take on this activity yourself and not wait on your business counterparts or leaders to ask you to do it.


As Data Science matures in a business context, a Data Scientist needs to be more aware of assessing the situation, taking an inventory, learning about the risk and developing contingencies, and understanding the cost benefits of having a successful Data Science project. Not every Data Scientist will take these steps, but then again not every Data Scientist is highly successful. Like water in the desert is a solid Data Science methodology to a business. Do not leave your organization thirsty when it needs you most.