Understanding the ways data science can amplify your content intelligence

Data science has been increasingly part of the conversation at companies—no matter the industry—over the past many years.

But what is data science, and how can it improve your organization? More specifically, how can the insights of a data scientist drive results for a business that's publishing hundreds, if not thousands, of pieces of content each year?

Let's take a closer look, because the combination of the human intelligence of a data scientist and extensibility of a publishing platform like Brightspot CMS make for a powerful formula, indeed.

Data science + Brightspot CMS = Publishing power

What is data science?

Data science describes the use of domain expertise—combined with math and statistics, systems and processes—to help make sense of the relationship between data in order to extract knowledge and better understand outcomes.

In that way, everyone is a data scientist—for example, if you tell your roommate or spouse that you’ll be home in 30 minutes, you’re crunching numbers to make that prediction. Variables like time of day, weather conditions, traffic, route—all of these get taken into consideration before hypothesizing the number of minutes it will take to get from Point A to Point B. Once you’re home, you know how many minutes it took, which you then use to understand how right or wrong your prediction was. Over time, you adjust your mental model as your sample size grows more robust, and improve your predictions going forward.

This is data science: the process of using tools, math, and algorithms to generate predictions, understanding the relationship between different variables that go into the number you provide, and then quantifying how right or wrong you are.

Like traditional science, data science uses the scientific method, beginning with creating a hypothesis, then gathering data, experimenting with various data models, understanding results, making tweaks, and proving or disproving the hypothesis.

Data science has a broad range of applications and is analogous to machine learning, predictive analytics and big data.

How does data science help content businesses?

Data science is a business-oriented practice, and business begins with working toward shared goals. Common goals may be increasing your bottom line, enhancing the customer experience, or optimizing the way your workforce operates.

Take optimizing the way your workforce operates. A data scientist can analyze data from your organization's timekeeping tools and project information to discover gaps or room for optimization. Using statistics, one could derive whether people are misaligned on projects, or if they have additional room to bill, or if the organization needs to hire more employees to better service the workload. Data scientists can present this information to the appropriate personnel at the organization and show how they arrived at their conclusions and relay suggestions.

Some companies may leverage data science to enhance the customer experience. By studying product usage data, data scientists can use statistical analysis to discover ways to provide a better experience. That may include making their users' processes more efficient when using the software, or even building new features on top of what you already have to provide a smarter, more robust experience.

What does a data scientist's day-to-day look like?

As with many roles, the duties of a data scientist can vary each day. One day may be spent on problem discovery. Another might be spent framing solutions and gathering requirements. And another day beyond that may include prototyping and working with developers, or measuring and reacting to the adoption of the features they have helped create. It is also within the data scientist's scope to educate their organization on the benefits of data science, establishing the appropriate data environment to be able to do it.

Regardless of the duties of a given day, engaging stakeholders and setting and aligning to goals remains important each day. Being as specific as possible with those goals is paramount. Otherwise, it's impossible to make the decisions about modeling, methods, technology, and people resources that your organization needs to make.

What tools does a data scientist use?

Data scientists leverage a number of tools to perform their duties. JIRA is popular for ticket management and due to its ubiquity across organizations of all industries. Many data scientists enjoy proofing solutions out in code, so text editors and integrated development environments are important. For aligning on problems and establishing goals, a data scientist may leverage Confluence or Google Docs. If a lot of data is sourced, stored, managed, and modeled, then cloud computing and microservices are often used.

What programming languages are popular with data scientists?

Python is a popular programming language that data scientists use, particularly for machine learning or predictive tasks. R is also popular, especially for statistically-oriented tasks, or those with research and academic backgrounds.

Often, large technology companies predicate which languages are the most popular to use merely through adopting it and institutionalizing it. Google is heavily invested in Python, for example, from using it for their own purposes as well as creating libraries that do very specific tasks that they have open-sourced, like TensorFlow. So, in that way, Python becomes a good language to know if data scientists are working with Google products.

Brightspot and data science

At Brightspot, the importance of leveraging data to create predictable and beneficial outcomes is paramount. Data scientists, beyond their daily tasks, also have a complementary overlap with other fields, like data visualization and data engineering. As a result, Brightspot employs data science methodologies when necessary to get a greater return from existing resources.

When it comes to Brightspot CMS, the result is a rich set of in-CMS tools and features that help editors and publishers make actionable, real-time decisions about what content is performing (and underperforming); topics that are trending that may need coverage; and granular insights right down to which contributors are driving the greatest engagement or publishing efficiencies.

The data is there. An integration-ready platform like Brightspot can let it flow. And a skilled data scientist will help bring the story to life in new and innovative ways.