At Instagram, we had many different functions that analyzed data. Some of the data job titles included: data scientist, analyst, researcher, and growth marketing.
There is often a lot of confusion between the roles of data scientist versus decision maker.
We had both on Instagram and they met different needs, so I thought I’d explain the key differences I see from my personal experience in the decision science role, working closely with my data science colleagues.
Data Science vs. Decision Science
The data scientist focuses on finding insights and connections through statistics. The decision maker is looking for insights that relate to the current decision. Examples of decisions include: age groups to focus on, the most optimal way to spend an annual budget, or how to measure a non-traditional media mix. For decision scientists, the business problem comes first; analysis follows and depends on whether business decision needs to be made.
Data Scientists vs. Decision Scientists: How to Think About Data
Thinking drives action, so I’ll compare each role by looking at how data scientists and decision scientists differ in thinking about data.
Data Scientists
Data is the Tool for Improving and Developing New Products Based on Robust Statistical Methods
Data scientists want to understand, interpret and analyze in order to build better products. To see also : The case against the United States Supreme Court. Therefore, data quality, statistical accuracy and measurement perfection are often their trademarks.
For data scientists, analysis, statistical accuracy and understanding come first. Business challenges come second.
Data scientists think about data in terms of data patterns, data processing, algorithms and statistics. Often, data scientists conduct in-depth analysis and experimental statistics. They are obsessed with finding causal links.
Data scientists have a strong focus on data quality in relation to their product area, because better data quality results in a more thorough statistical analysis.
Data scientists frame data analysis in terms of algorithms, machine learning, statistics and experiments. They want to create order in big data to find insights and lessons related to their product or focus area. They have a statistical lens for everything they do.
Data scientists’ north star goal: Use high-quality data and robust statistics to support product development.
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Decision Scientists
Data is the Tool to Make Decisions
Decision scientists frame data analysis in terms of the decision-making process. Read also : HIV testing before and during the spread of COVID-19. They look at the different ways to analyze data related to a specific business question posed by their stakeholder(s).
Other names for this role may include analytics, analyst, and applied analytics.
The data scientist focuses on finding insights and connections through statistics. The decision maker is looking for insights that relate to the current decision. Examples of decisions include: age groups to focus on, the most optimal way to spend an annual budget, or how to measure a non-traditional media mix. For decision scientists, the business problem comes first; analysis follows and depends on whether business decision needs to be made.
The decision maker must therefore have a 360-degree view of the business challenge. They should consider the type of analysis, visualization methods, and behavioral insights that can help a stakeholder make a specific decision.
In other words, decision scientists must make insights useful. They must be able to work with a variety of data sources and inputs – each selected based on their ability to help answer the business question. This means that a decision maker must have strong business acumen and a robust analytical mind. You can’t have one without the other in a decision science role.
Sometimes the measurement will not be perfect. Business tactics are not always neat and tidy. For example, there’s almost no clean way to do a test and check for viral or celebrity marketing, but these are both legitimate marketing approaches and the decision maker has to agree. Companies should take an action not to measure it, but because it is the right thing to do; measuring comes next.
Sometimes a clean, causal experiment is possible and sometimes not. Decision scientists need to have a keen sense of when it’s appropriate to move forward with a correlation-based decision and when to push for a clean experiment. It all comes back to the business context and the decision at hand.
Decision scientists’ north star goal: use data and statistics to support business decision making, budgeting and marketing spend.
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Data Science vs. Decision Science: In the Real World
In my own experience at Instagram, each data scientist was dedicated to one specific product or product feature. They spend a lot of time making sure the data recording is accurate for that product area by performing statistical analysis of trends and using complex images to represent their type of analysis. On the same subject : The instrumental AI solution for manufacturing optimization for the high-tech industry is now part of the SAP® Store as part of SAP’s Industry Cloud portfolio. They have an in-depth knowledge of their product, but not the ecosystem.
If the product changes or if we launch new features associated with their product, the data scientist is responsible for both logging the new data and measuring adoption of the new features.
On the other hand, I was in the decision science function group. My team and I supported the marketing group and marketing leadership in making decisions about marketing budgets and priorities.
I relied heavily on the tables, logging and analysis of my data science colleagues as the basis for our marketing activities. I then supplemented their work with my own analysis to help our marketing leadership make decisions about where and when to spend the marketing budget.
My visuals are designed for consumption and business action and therefore had a different purpose than the data scientists’ goal to use visuals to represent complex analytics.
Because data scientists only focus on one product area, my analysis mostly looked at relationships between products and the impact of demographics on product behavior at the company level.
My decision science team is the only team that regularly looks at the entire ecosystem, because marketing decisions revolve around wanting to understand how one behavior interacts with another.
As you can hopefully see, there are some subtle but important differences here.
The decision maker sits hip to hip with decision makers and management to help them make the best decisions for the business. Decision scientists are equal business leaders and data analysts.
The data scientist is hip to hip with data and statistical accuracy. Data scientists are relentless about quality and in-depth analytics that ensure products are scaled and developed based on usage data.
Every role is necessary and critical.
Decisions need to be made quickly to move the business forward based on what is now known. This is the decision maker’s job.
The company also needs to grow, scale and build better products. In-depth product knowledge, a high standard of data quality and statistical accuracy ensure they get the best insights so that product leaders understand their domain. This is the job of the data scientist.
A company must both make progress in making decisions and improve its products over the longer term, so the decision maker and the data scientist both contribute to the health of the company.
Data science is the field of applying advanced analysis techniques and scientific principles to extract valuable information from data for business decision-making, strategic planning, and other uses.
What is decision science at Facebook?
The Decision Science team provides insightful analysis of consumer perceptions of the Facebook experience as an advisory partner to our marketing team, helping us better connect our users to Facebook and our products.
Who gets paid more data scientist or computer scientist?
Today, data scientists earn more on average than computer scientists, due to the high demand for professionals who can handle the growing amounts of data generated by companies. The national average salary paid to data scientists is $116,654 per year.
What is harder computer science or data science? Data science is easier to summarize than computer science. This discipline focuses almost entirely on collecting, organizing and analyzing data and can be described as a mix of mathematics, statistics and computer science.
Are data scientists the highest paid?
The demand for data scientists has only increased in recent years, meaning that salaries are likely to rise compared to others in the tech industry and other industries in general. Data scientists in the US earn an average of $117,000, while software engineers earn an average of $108,000 in comparison.
What is the salary of decision scientist?
The decision scientist salary in India ranges between ₹7.0 Lakhs to ₹9.0 Lakhs with an average annual salary of ₹7.3 Lakhs. Salary estimates are based on 476 salaries received from Decision Scientists.
What do decision scientists do? A decision maker is a technology professional who focuses primarily on making technologies work for decision-making and entrepreneurship. However, the term “decision scientist” really makes sense when you compare it to another similar position called “data scientist” or “big data scientist”.
What is a scientist salary?
Life scientists (all others) had an average annual salary of $81,500. This is an all-encompassing career category for life scientists.
What is the salary of a scientist in one month?
The average salary for a research scientist in India is 7.1 Lakhs per year (€59.2k per month).
Is decision scientist a good career?
The expertise of decision scientists is useful in many sectors. These include driving retail sales, delivering value to the banking industry, transforming the airline industry, contributing to healthcare, transportation, communications, education, and much more.
Is data scientist an IT job?
Data Scientist is a Job with IT Support As most IT jobs focus on helping their organization use a particular technology, Data Scientists focus on helping their organization use data.
What does an IT data scientist do? In simple terms, a data scientist’s job is to analyze data for actionable insights. Specific tasks include: Identifying the data analysis issues that present the most opportunities to the organization. Determining the right datasets and variables.
Is data science IT or science?
Data science is a new scientific field that thrives to extract meaning from data and improve understanding. It represents an evolution of other analytic areas such as statistics, data analytics, BI and so on.
Is data science an IT field?
It uses techniques and theories from many fields within the context of mathematics, statistics, computer science, information science and domain knowledge. However, data science is different from computer science and information science.
