The Growing Need for Interdisciplinary Data Science

Data Science students during class

It’s hard to name an industry or a company that doesn’t rely on data for important business decisions. Data scientists collect and analyze big data to develop better internal processes, help develop better products, and predict market trends and consumer behavior, to name just a few of the applications for this growing field.

Data science is, by its nature, an interdisciplinary field. Data scientists are called on to extract meaningful conclusions from big data that can be applied to help solve business problems. Data scientists take something that is abstract – large sets of data – and turn it into something concrete: actionable information that informs decision-making at all levels of the organization. In addition, if your job includes data analysis or systems engineering, you may be called on to visualize trends and statistics. Data scientists often work in and with a wide range of departments and teams.

The Role of Data Scientists in Taming Big Data

Big data is a term that describes datasets that are measured in exabytes rather than megabytes. In the internet age, huge amounts of data are available. This information has great value for companies – if they can harness it. That’s where data scientists come in.

A data analyst use sophisticated tools to collect and analyze big data, distilling enormous datasets into useful summaries. The greater your level of education, the more opportunities will be open to you. Data scientists who possess advanced degrees to work with AI and machine learning will be on the cutting edge of big data job development in the next decade.

Characteristics of a Great Data Scientist

Great data scientists have broad skill sets that enable them to respond quickly to the changing needs of their team or organization. To fill the data scientist role, you should be proficient in a range of techniques, from data mining to data visualization to managing big data. You’ll need technical skills that can include proficiency in SQL, SAS, Spark, and Python. You’ll also need to be an expert user of more common programs such as Tableau and Excel. These programs allow you to visualize data and present it to your team in formats that are easy for colleagues without training in data science to understand and utilize.

Data mining is the process of scouring big data for patterns or outliers. The data scientist role might be to use statistics and machine learning to assist with data mining. Data mining can also involve looking for outliers or anomalies: records with unusual values that are, perhaps, much higher or lower than the median within the dataset. Data scientists sometimes need to identify and weed out anomalies that could skew the results of the data analytics.

Data mining is distinct from data scraping. Also called web scraping, data scraping is the process of collecting data from across the web and aggregating it. Data scientists may be called on to create programs to scrape data from competitors’ websites for price or product comparison, for example. While a human could manually view competitors’ websites and record the information, the process would be slow and onerous, particularly if there are many competitor sites to view. Data scraping automates the process so it doesn’t eat up valuable personnel time and can be repeated on a regular basis. Part of the data scientist role in data scraping is to verify that the scraped data is clean and uncorrupted. One of the most common tools that data scientists use to create data scraping programs is the programming language Python.

Data visualization helps teams understand the information data scientists provide. A spreadsheet filled with numbers isn’t meaningful until you interpret it. Data visualization allows you to present data in a visual format that is easy for others to digest. When you show trends on a graph, your team can grasp the message contained in the numbers. A pie chart makes information about market share clear and easy to understand. Data visualization is often part of the data scientist role. If you aren’t artistically inclined, however, don’t worry. There are many great tools that make the graphic part of data visualization easy for data scientists. Tableau is the best known, but data scientists also use software such as FusionCharts, Sisense, and others. You may choose to use more than one app for data visualization so you can best visualize the type of information your team needs.

A great data analyst is also a great communicator. While data scientists may enter the field because they love to dig into big data and reveal its secrets, good communication with your team is essential. At the end of the day, data scientists serve the interests and goals of their teams, including for product development, market research, and data analysis. To succeed in the data scientist role, you need to be able to understand your organization’s goals and clearly present data analysis that furthers those objectives. While great data scientists can often find ways to exceed expectations and deliver exceptional value by finding insights that team members didn’t think to request, it’s important to focus on the targets your team has set. It’s easy to get lost in the weeds and present too much detail or too many different analyses. The more you can distill complex data points into simple and actionable statistics, trends, and summaries, the more you will excel in the data scientist role.

Models of Data Science Team Integration

There are several different models for integrating data scientists into teams and organizations. The model that works well for one company may not be practical at another. It’s important to consider all options as you integrate data scientists into teams or departments.

Here are several approaches to consider when you assemble a team to harness the power of big data and put it to use within your organization.

Center-of-excellence data science teams

The center-of-excellence model is also called the research model or the centralized model. In this structure, data scientists are organized into their own department. The central data science department model puts a team of data scientists with MS- or PhD-level education together to develop predictive models, data mining, machine learning models, and data analytics.

In a centralized data science team, the group reports to one top data scientist. This model can work well for smaller companies who haven’t reached the stage where they can afford to embed data scientists and engineers in product development teams. It can also make it easier to recruit skilled data scientists who are educated in the latest developments in artificial intelligence and predictive analytics.

Pardis Noorzad, the head of data science at Carbon Health, has laid out the pros and cons of several different models for data science team integration. She notes that the center-of-excellence model can foster collaboration and creativity among the members of the data science team, putting data scientists at the heart of innovation in the company. However, Noorzad points out that it has several drawbacks. When you isolate data scientists in a separate team, they may not be able to fully understand and respond to the problems the business needs to solve. It can be hard to communicate across teams, particularly if the data science group develops its own internal language and fails to prioritize communication with less technical but still vital members of the organization’s product development pipeline.

Treating data scientists as internal consultants

Another model for data scientists is to treat them as consultants within the organization. When someone needs a predictive model or data analytics, they make a discreet request to the data science team. The project is assigned to one of the data scientists. When they complete the request, that data scientist is available again for another project from a different department or team.

This model ensures that data scientists retain broad capabilities in many areas of predictive analysis. However, when you treat data scientists as consultants, you force them to juggle their own schedules and priorities, which can lead to missed deadlines and a lack of clarity about who is responsible for which aspects of the project. Also, because data scientists are essentially acting as external service providers for other teams, they miss out on the sense of satisfaction of seeing a product go from development to launch and beyond. According to Noorzad, this can reduce job satisfaction among the organization’s data scientists.

Integrating data scientists into product teams

Also called the embedded model, this data science team integration strategy puts product development teams in charge of hiring and retaining their own data scientists. As members of the team, these data scientists have a chance to work on products from beginning to end. This can give them opportunities to refine their predictive models and find new sources of big data relevant to the team’s projects. Each business unit doesn’t have to compete with other groups inside the organization for the time and attention of data scientists. Teams are able to get the in-depth data analysis they need. This model fosters good communication among members of the team with different roles, which can give data scientists the opportunity to refine their skills at presenting their work.

This decentralized model of data science team integration does have downsides. It can be harder to manage and mentor data scientists within the organization. Data scientists who become deeply involved in the processes of their team may not keep up on the latest developments and innovations in big data and data analytics, especially if the team’s projects don’t demand this. Noorzad points out that organizations who use this model of data science integration run the risk of missing out on new technologies they could use to increase their success.

Hybrid model for data science team integration

The hybrid or product model for data science team integration takes aspects of both the centralized and decentralized models and merges them. The hybrid structure requires more layers of administration within the organization, as data scientists are both dedicated participants on product teams and also members of a central data science unit. This allows all data scientists in the organization to stay apprised of developments in their field and to follow standard procedures companywide. At the same time, individual data scientists have clearly-defined roles within their respective product teams. Each unit benefits from the perspective that data scientists can bring and the predictive modeling and analysis they’re able to perform.

There is an additional administrative expense associated with this hybrid model, but Noorzad concludes that this is the most efficient model for data science team integration. Of course, the most effective way to build a data science team will depend on the size and the needs of your organization. You may need to change your data science team integration model over time, as your company grows and your business needs change.

Data scientists as part of the IT department

These aren’t the only options for integrating data scientists into your organization. Some companies place their data scientists in the IT department. This is similar to the centralized model and can also operate like the consultant model, where data scientists get requests for data analysis and predictive analytics, much like the IT department’s repair tickets.

This can be a cost-effective solution for budget-conscious businesses. However, some expertise and innovation can be lost when you lump the relatively new and rapidly-evolving field of data science with the relatively older and often more hide-bound area of IT management.

An integrated variation of this model can task the data scientists on the team with creating predictive models and analysis and leveraging big data, while the IT members of the unit build dashboards that allow other teams to access the information.

One pitfall of lumping data science team members into the IT department may be the temptation to utilize existing IT staff as data analysts when that is not what they were trained for. There is a cost to organizations to bring in specialized data scientists; however, if businesses want to benefit from the power of big data, they need to invest in trained staff who can provide meaningful predictive analytics.

The pitfalls of democratizing data

One term that has currency in many organizations is “democratizing data.” This means providing wide access to data, rather than keeping analysis close to the chests of the data scientists. While the goal of disseminating data analysis widely so every team member can benefit from the insights gleaned is good, there can be some drawbacks. Noorzad notes that this ethos can come with an assumption that data scientists aren’t important. It can be hard for data scientists to gain the expertise needed to design and engineer internal data dissemination systems. In addition, she notes that “dashboards aren’t data science.” It’s important to recognize the value of analysis by trained data scientists and the interaction between data scientists and others on the product team.

How to Build a Data Science Team

Behind the most successful data science projects you’ll find a well-organized data science team. Data scientists need to not only be proficient in data mining and analysis of big data; to advance in the field, you also need to possess the talent to build a great data science team.

The team-building skills you may be called upon to use in your career as a data scientist include collaborative problem-solving, active listening, clearly-written communication, and flexibility. In addition, you’ll need to demonstrate to your team members that you are dependable and honor your commitments. Your ability to nurture new talent will also be a key factor in your ability to build your team and complete data science projects.

To build a successful data science team, you also need to demonstrate leadership skills. Key leadership skills include willingness to continue learning, effective follow-through on decisions, being the biggest booster for the members of your team, understanding your own strengths and weaknesses, being a good communicator, and putting the goals of your team ahead of your own personal goals. Data scientists bring all these leadership qualities and more to bear as they assemble successful teams to tackle data science projects.

You’ll also need to focus on bringing out the best in all your team members. That means being generous and gentle with feedback, recognizing outstanding contributions, developing personal connections with your team, and making sure that the data scientists in your team have well-defined roles.

Fortunately, team-building and leadership are abilities that you can develop. The UVA Master of Science in Data Science program includes more than courses in computation and analytics. Students are trained in communication and problem-solving tools that data scientists need to succeed.

Elements of a Successful Data Science Team

At the top of a data science team, you might find a chief data officer (CDO) or chief analytics officer (CAO). Your data science team should also include software engineers to handle coding, data engineers to maintain databases, and, of course, data scientists. Whether or not your unit is an integrated data science team, you may also want to include a product manager to make sure that the data analytics completed by your data science team further the goals of the product development team.

Depending on the demands placed on data scientists at your organization, your team may include members who specialize in artificial intelligence, machine learning, data mining, or predictive analytics.

The data science team’s role at many organizations extends well beyond making sense out of big data. Others in your company may look to the data scientists on your team to provide insights and guidance based on their analytics. In many businesses, leaders look to the data scientists as the drivers of innovation.

Benefits of an Online MSDS from the University of Virginia School of Data Science

If you want to build a career in the fast-growing field of data science, an online MSDS from the University of Virginia is a great choice. You’ll be able to complete the courses to receive your degree in two years, without leaving your current job.

UVA’s multi-disciplinary curriculum is building the data scientists of tomorrow. With your MSDS, you’ll be highly qualified for a range of careers, including director of data science, deployment strategist, or engineering fields like systems engineers, machine learning engineers, and software engineers. Data scientists who have graduated from the UVA program have found positions with some of the top corporations in the US. If you’ve been searching for a career that will give you the opportunity to keep learning and growing, consider getting your MSDS and becoming a data scientist.