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The power of data: How data science can help you lead

Jul 16, 2021 - 8 min read
Joshua Ahn

As of 2021, Facebook’s valuation sits at $1 trillion with 98% of revenue generated from advertisements. With nearly 2.8 billion users, Facebook’s target base of small businesses understands the value of Facebook’s user data when it comes to targeted ads. While their advertisement targeting accuracy is up for debate, Facebook’s machine learning algorithms and big data produce granular character profiles that assist small businesses in gaining new customers and retaining frequent customers.

In 2012, a controversial story about Target’s ads targeting pregnant women circulated the web through an article discussing the potential and results of algorithmic ad targeting. Despite some debatable and questionable statements in the article, Target’s yearly revenue increased by over $20 million between the time period of 2002 and 2010 when Andrew Pole was hired to manage a team in marketing data analytics. There’s nothing to say that access to big data was the sole reason for this success, but people started to ask themselves the question of “what if?”.

There are numerous stories and articles that often overestimate the potential for data science and artificial intelligence to create a grandiose facade for its predictive ability. While data science and data analysis aren’t silver bullet answers to every company’s issues, what are the realistic steps to lead with data science, and what is the importance of data science?

Managing Expectations

Evidence-based Decisions

Product Optimization

Efficient Recruitment

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Managing Expectations

Data science is not the answer to everything. Before moving forward with business decisions, align expectations between your organization’s leaders and your data team. A common narrative involves a misunderstanding of what a data scientist and data analyst can offer, which results in a top-heavy structure. Unreasonable expectations from leaders at the top meet realities faced by the data team below.

In many cases, leaders start off with an expectation similar to “bailing out the ocean with a bucket”, while data scientists bite off more than they can chew. In these cases, business results appear insignificant and cause disappointments regarding the role of a data scientist.

To make sure both parties understand each other’s limitations and goals, imagine a large circle with polka dots scattered within the perimeter. Each polka dot represents problems or questions to solve within the parameters of the company’s vision.

Measuring expectations takes a more granular approach by imagining each polka dot to have its own set of polka dots representing data systems required to be developed by data scientists to better understand the question or problem at hand. Those granular sets can further face roadblocks such as legacy data sets or misaligned internal relationships.

Due to the dynamic nature of each problem and question, business leaders need to formulate prioritized parameters for their data team to create measurable metrics and manageable results.

Evidence-based Decisions

Rather than relying solely on intuition or the “feeling in your gut,” data science moves the arrow towards providing business decisions grounded on quantifiable data. Data scientists build the structures necessary to gather and filter data your company collected or needs to collect. As a result, your leaders can then use quantitative data to make a case for moving your company forward.

Here is a step-by-step process to better understand how your data scientists approach evidence-based decisions. For this process, let’s use Uber as an example.

1. Define a question or problem

  • Reducing frustration in customer wait times to improve customer experience

  • How valuable are reduced wait times for our customers?

2. Acquire Data

  • What kind of data do we need?

  • Where do we collect the data from?


  • Location of travel

  • Time

  • Cost per ride

  • Wait time

  • Time of transportation arrival

  • Time of transportation scheduled

3. Cleaning Data

  • This is the longest part of the process for data scientists

  • How can we filter inconsistent or unstructured data?

4. Analyze Data

  • What relationships can we find in our data?

  • How can we provide useful data visualization?

5. Make Predictions

  • What inferences can we make based on the data we collected?

  • What models can we create for better decision-making?

In 2018, Uber released a function to give discounted rides with higher waiting times. While this example significantly undercuts the data processing behind deciding to discount longer wait time, the decision-making process used an evidence-based approach through their data team.

Understanding how your data scientists approach complex issues manages expectations to provide evidence for making big business decisions.

Product Optimization

Learn to use data science in addressing your product’s areas of improvement. Data science creates the structures necessary to pull digital data from large data sets that may indicate areas of improvement or areas to surpass your competitors.

Building a product facilitated through the use of data demands cross-team collaboration and evaluation. To manage product improvement through data, follow a three-step process of opportunity identification, product development, and evaluative iteration.

  • Opportunity identification

  • Product development

  • Evaluative iteration

For example, the benefits of data science and team collaboration can be seen clearly in the healthcare industry. Data scientists and data engineers were able to use a large amount of data to make more accurate diagnoses of medical imaging and ECGs.

Product improvement through data uses a two-pronged approach. Using “The Castle Analogy,” often coined to describe security, imagine a castle surrounded by a moat. Your product’s two-pronged method involves improving the castle while fending off competition by developing the moat. Data helps to manage all three steps of this end-goal process.

Opportunity identification:

Proper opportunity identification involves clear communication between the product team and the data team. Clear communication starts by having each team understand the needs and prioritizes for the business team and data team to align target audiences and goals.

The best way to start this process is by upskilling your product team on data literacy to better equip them for collaboration with your data team.

Next, align your company’s direction by increasing the visibility of data across all teams.

With greater accessibility to data, all teams reap the benefits to make opportunity identification a more efficient and normalized process.

Product Development

There’s a careful balance between technical validation and product-market validation.

To address this balance, start simple and develop a series of MVPs. Maintain strong communication between the data team and product team to address both sides of the coin while moving forward with your product development. It’s more than likely that some sacrifices will be made on either end of the development process, but future iterations can optimize your development process.

Evaluative Iteration

After launching the product, consider several questions to evaluate the performance and decide if the product should move forward with iterations. What low-hanging iterations can improve the product? How quickly can these iterations be implemented? Once again, maintain constant communication between teams when considering the layers for the product.

Efficient Recruitment

Recruiting and retaining talent maintains a top-of-mind subject for any leader. Recruitment consumes time and resources from both HR and industry leaders. When thinking of the window of opportunity for recruitment, leaders need to reduce the time spent on three stages of recruitment.

  • Sourcing

  • Screening

  • Feedback


Data-driven recruitment provides data on the performance of various recruitment channels. Data on performance indicators such as retention, quality of applicants, and conversion success rates give actionable insights into which recruitment channel you should invest your time in.


Companies adopt automated forms of screening to recruit higher-quality candidates, reduce hiring time, and improve candidate experience. Data science incorporates multiple data sources, including social media, corporate databases, and job sites, to create a comprehensive candidate profile.

Utilizing the power of data, you’ll find candidates who fit your job description and company culture with greater efficiency and precision.


In traditional forms of recruitment, the usefulness of an applicant tracking system dwindles after hiring the candidate. Hiring managers and recruiters leave the data in the applicant tracking system and hardly look back.

With cloud services pushing new products, the time for when data between the recruitment and offboard cycle stores itself onto one system draws near. Having all the data in one place opens the door for data science to provide effective feedback and insight for industry leaders to improve the hiring process.

Data-driven recruitment hinges on the growth of data science and analytics. Looking forward, it provides a window for optimizing the recruitment process for higher retention, better-fit candidates, and lower recruitment costs.

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Wrapping up

The rise of data science jobs indicates the importance of data science and the competitive edge provided by deep learning and machine learning models. Companies understand the importance of data science, but actually using data science to make better decisions is met with the growing pains of onboarding a new team. While managing your expectations, set your company up for success by looking into ways to use data science in making:

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