A data scientist is an expert who helps an organization learn more about its customers, mitigate risks, forecast trends, and expand and monetize the data they have.
Data science is an umbrella term for various duties that involve making sense of large data sets in an actionable way. Most positions require a solid background in mathematics, statistics and even machine learning. A recent piece in Inc. stated that soon we will be wondering which activity sectors don't need a data scientist. This is a huge marketplace, estimated to surpass $200 billion by 2020.
Enhance your organization
Data is ubiquitous, and making sense of it is like having a map, a compass and a flashlight in an unknown city at night. There are several ways in which a data scientist can turn numbers into growth for your company; here we'll list the most popular five.
1. Learn more about your clients.
Most organizations have relevant logs about transactions, customer profiles or leads' interactions with the company. Also, the current rise of social media has created zettabytes of user-generated data in the form of pictures, reviews and comments. It's the data scientist's job to convert this info into answers to these common marketing questions:
- What makes our accidental clients become regulars?
- What determines how a lead will become a customer?
- What made clients switch to the competition's offer?
A shocking but relevant finding could even show that your current business proposition is not satisfying your most profitable client group.
2. Evaluate risks.
The financial industry has foreseen the applications of data science in measuring and mitigating risks. Mathematical models based on probabilities are appropriate tools for assessing the risks related to bankruptcy, investing in a new market or hiring more team members. Starting with survival analysis models, a data scientist can compute client churn rates and employee turnover rates.
3. Expand your business strategically.
Data is like a rough diamond waiting to be polished. This means that by applying the right analysis tools, it can show significant trends, new expansion directions or the total failure of a recent product. Big data, especially coming from unstructured sources, including social media, video and user-generated content, offers scientists the challenge to uncover unconventional growth directions.
4. Monetize data.
If you have important data lying around that isn't being used, you should ask yourself who would pay for it. Then, use your data scientist's knowledge to convert raw information into usable, clean and organized folders. This is exactly how Google and Facebook make money, but other companies can join in for specific niches. Selling your categorized information to the right people is one of the most innovating business models.
The beauty of machine learning and deep learning is that once it has absorbed patterns and behaviors, it can accurately estimate outputs for given inputs. This helps the data scientist create relevant forecasts even in conditions of high uncertainty, since the model already includes white noise, cyclical and random variations.
Having an estimation based on mathematical models and data can help eliminate personal bias from making long-term decisions. This helps stakeholders have less divergent opinions and abandon personal ambitions.
Profile of a great data scientist
This is a versatile job requiring a mix of knowledge, curiosity and design thinking. Since it is an exploratory role, it is not enough just to look for candidates who have a solid background in science and coding; you must also seek out individuals who are passionate about puzzle-like challenges and not easily discouraged.
Don't go for the mad scientist who locks himself in the lab. You need a communicative individual fluent in different dialects of code (R, Matlab, Python, SPSS, Tableau, etc.) but who also understands the economic determinants of your business. The successful candidate needs to make the connection between statistically relevant and useful on the market. Although not mandatory, a previous understanding of the industry can help an applicant identify more links between variables and processes.
The best data scientists can work as part of a cross-functional team and are motivated by finding new aspects of old problems. They take pride in seeing things differently and are not tributary to standard solutions.
To take advantage of all the benefits previously listed, should you hire a full-time data scientist as an extension of your IT department, create a stand-alone data analysis office, get the best freelancer money can buy or outsource everything to a data science consulting firm? The simple answer is that dreaded response: It depends.
If your business intends to create a secondary income stream from data analysis activities and even take on a consultancy role in the industry, providing other similar businesses with insights, then an in-house data scientist or an entire team makes perfect business sense. A freelancer could be an option, yet you have little control over their work. This could be a transitory step before deciding if you need such an expert in your team full time.
On the other hand, if you are just looking to use big data and artificial intelligence as tools for enhancing your current business and need some quick answers, the cost-effective solution is to outsource the problem to data science consultants who have both the experience and tools to provide you with a pay-as-you-go solution.
This is a relatively new job, but it is here to stay. Probably the biggest advantage of hiring a data scientist now is creating a competitive advantage. Practically, you get marketing insights, risk assessment, a business growth hacker and a forecaster all rolled into the same specialist.
Even the most experienced employees in this role don't have more than eight to 10 years of experience. Getting a proficient data scientist or data analyst on board as soon as possible, even as a collaborator, will give them enough time to learn more about your business, get the data in the right and clean form, and start creating a useful and centralized data lake solution. You might not need one now, but the predictions show that this will be a human resource in high demand.