Creating Value for Business: 2 Data Science Questions You Must Ask from the Start

Decisions in Data Science

Business goals are no doubt important, but in an analytic project it makes sense to balance the organization’s goals with those of the Data Science department. Most individuals will recognize balance as a principle of art, but the notion of creating a sense of equilibrium between the business and the Data Scientist is just as foundational in today’s insight economy. To not cultivate this balance is to invite ruin into the organization.

Question 1: What are the Data Science Goals?

As a Data Scientist working in an organization, it is important to understand how the intended outputs of the Data Science project enable the achievement of the business objectives. Imagine a situation where a business has a set of defined goals, but the analytics team had a different target in mind or vice versa. The result is extra cost, time delay, and missed business opportunities. Unfortunately, these sort of happenings are more common than you would imagine in everyday business – and with organizations big and small. As a Data Scientist serving a business, it is prudent to define your goals in tandem with the business objectives and obtain buy-in of your interpretation. This can be done by explicitly documenting what you expect the output to be like and confirming its usefulness to the business unit you are supporting.

Question 2: What is the Data Science success criteria?

Businesses should work with Data Scientists who know how to precisely define a correct outcome in technical terms. In truth, it could prove important to describe these outcomes in subjective terms; however, if this ends up being the case, the person in charge of making these subjective judgments needs to be identified. Neither the business nor the Data Science department will succeed with a moving target. Transparency and visibility are always good things in business. This allows individuals to manage towards a known expectation.

Organizations working with Data Scientists who simply have technical know-how are missing out on significant value within their analytic projects. Organizations should seek to find professionals who know how to translate business concepts into analytic outcomes. This skill should be considered primary over knowing the most advanced techniques and methods when analyzing data. Unfortunately, most organizations are still on a discovery mission with regard to what they need from Data Science. Organizations still remain beholden to the idea that if they hire a Ph.D. in some highly-analytical field then success is just around the corner for their organization. This is rarely the case. In fact, most Ph.D.’s need significant time to warm up to the corporate culture and learn the language of business before they can be fully effective.

It may seem obvious to the organization, but having your analytic superhero be able to quickly judge the type of Data Science problem that you are looking for them to contribute to is paramount to pulling it off.  Typically, being able to specify things like whether the target is a classification, description, prediction, or a clustering problem works well for all involved and starts to build context across disciplines in the organization. This becomes especially important when a Data Science department begins to grow and less experienced Data Scientists can learn to see more like senior Data Scientists; this can only happen with intentionality and purpose.

Organizations should come to expect that one way a good Data Scientist will often demonstrate his or her ability is by reframing or redefining the problem put before them by the company. The first few times this may seem off-putting, but organizations who learn to embrace this sort of transformation of the business problem will be able to compete for the future. Practically speaking this may look like shifting to “medical device retention” rather than “patient retention” when targeting patient retention delivers results too late to affect the outcome.

As a business concerned with the ROI from your Data Science investment, you will undoubtedly want to see activities of the Data Scientist which specify criteria for model assessment. These typically present themselves as model accuracy or performance and complexity. In many cases, it is indispensable to see that a Data Scientist has defined benchmarks for evaluation criteria. Even in the case of subjective assessment, criteria definition becomes important. At times it can be difficult to meet a company’s Data Science goal of model explainability – or data insights provided by the model – if the Data Scientist has not done a good job of uncovering this as a businesses need. So, the adage “to begin with the end in mind” should prompt the Data Scientist to ask an appropriate series of questions of the business to ensure value creation.

Summary

Remember that the Data Science project success criteria are without a doubt different than the business success criteria. Any Data Scientist with experience will say that it is always best to plan with deployment from the beginning of a project. If the organization experiences a Data Scientist not following this best practice, expect spotty results and a bit of frustration from business counterparts. As an organization, it is vital to push your Data Scientist to work hard and be assertive within the project – as well as to use their mind and imagination. This should give him or her the permission to shape the future your company desires.

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.

Summary

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.