Five Practices to Improve the Value of Data Science

Five Practices to Improve the 

Value of Data Science 

Optimizing the Effectiveness of Data Science Teams 

Data science efforts are only as good as the results they deliver. In this Big Data 

world, executives are relying on data scientists to help them move their company 

Customer Experience Management 

Best Practices Study Results 

forward. Here are five ways companies can improve the value of data science. 

How to improve the effectiveness of your CXM 

Program 

Bob E. Hayes, PhD 

bob@analyticsweek.com 

@bobehayes 

Bob E. Hayes, PhD, Chief Research Officer 

email: bob@appuri.com 

phone: 206.886.0893 

twitter: @bobehayes

Improving the Value of Data Science | 2 

The value of data is measured by what you do with it, and organizations are relying on 

data scientists to extract insights from their data. Many data professionals, pundits and 

bloggers have asserted their opinions about how companies can leverage data science to 

their advantage. While these experts have made good points, it’s important to note that 

their assertions are simply opinions that need to be verified by data. I conducted a 

survey of data professionals to better understand what it means to be a data scientist 

and how to best leverage their unique skill set to unlock the value of business data. 

Based on that research, I discovered a few things that can help improve the effectiveness 

of data scientists and the work they do. 

Data Scientists and the Practice of Data Science 

Data scientists have many diverse 

skills. While we measured 25 

distinct skills across five general 

skill types, a factor analysis of 

proficiency ratings of the 25 skills 

resulted in three distinct skill 

types. These skill areas included: 

1. Business (Subject Matter 

Expertise) 

2. Technology / Programming 

3. Statistics / Math 

Additionally, we found that there 

are four distinct job roles among 

these data professionals: 

1. Developer (e.g., developer, 

engineer) 

2. Researcher (e.g., researcher, 

scientist, statistician) 

3. Creative (e.g., Jack of all 

trades, artist, hacker) 

4. Business Management (e.g., 

leader, business person, 

entrepreneur) 

Figure 1. Data Scientists have different skill sets. From: 

Investigating Data Scientists, their Skills and Team Makeup 

appuri.com | 206.886.0893 | info@appuri.com | @AppuriCorp 

Copyright © 2016 Appuri Inc


Data professionals in 

different job roles have 

different skill sets (see 

Figure 1). Not 

surprisingly, data 

professionals who 

identified as Developers 

reported the highest levels 

of proficiency in 

Technology and 

Programming skills 

compared to their 

counterparts. 

Additionally, Researchers 

reported the highest levels 

of proficiency in Statistics 

and Math while data 

professionals who 

identified as Business 

Management reported the 

highest levels of 

proficiency in Business. 

Finally, data professionals who Figure 2. From: Data science skills and the improbable unicorn 

identified as Creative reported 

moderate ratings across all skill sets, suggesting they are indeed jack-of-all-trades. 

Finding a data scientist who is proficient in all data science skill areas is extremely 

difficult (see Figure 2). Data professionals rarely possess proficiency in all five skill areas 

at the level needed to be successful at work. In fact, the chance of finding a data 

professional with expert skills in all five data science skills is akin to finding a unicorn; 

they simply don't exist. 




Five Ways to Improve the Success of Data Science Projects 

Given that there are different types of data scientists with unique skills sets, how can 

companies leverage data scientists to extract insights from their data? Following these 

five practices is a good start. 

1. Adopt a team approach for your data science projects 

We found that data professionals who worked with other data professionals who had 

complementary skills were more satisfied with their work than when they did not work 

with another data professional. For example, Business Management professionals were 

more satisfied with the outcome of their work when they had quantitative-minded 

experts on their team (e.g., Math & Modeling and Statistics) compared to when they did 

not have them on their team. Also, Researchers were more satisfied with their work 

outcome when they were paired with experts in Business and Math & Modeling. 

Developers were more satisfied with their work outcomes when paired with an expert in 

Business. Creatives’ satisfaction with their work product is not impacted by the presence 

of other experts; this finding is likely due to the fact that Creatives are not able to 

contribute sufficiently to teamwork success because they are not highly proficient in any 

of the data skills. 

2. Employ the scientific 

method for data-intensive 

projects 

Scientists have been getting insight 

from data for centuries using the 

scientific method. Formally defined, 

the scientific method is a body of 

techniques for objectively 

investigating phenomena, acquiring 

new knowledge, or correcting and 

integrating previous knowledge. The 

scientific method includes the 

collection of empirical evidence, 

subject to specific principles of 

reasoning. The application of the 

scientific method helps us be honest 

with ourselves and minimizes the 

chances of us arriving at the wrong 

Figure 3. The scientific method is a good approach to extracting 

value from data. 




conclusion. The scientific method plays a critical role in understanding any data, 

irrespective of their size or speed or variety. 

The scientific method follows these general steps (see Figure 3): 

1. Formulate a question or problem statement 

2. Generate a hypothesis that is testable 

3. Gather/Generate data to understand the phenomenon in question. Data can 

be generated through experimentation; when we can’t conduct true experiments, 

data are obtained through observations and measurements. 

4. Analyze data to test the hypotheses / Draw conclusions 

5. Communicate results to interested parties or take action (e.g., change 

processes) based on the conclusions. Additionally, the outcome of the scientific 

method can help us refine our hypotheses for further testing. 

When I map the three data science skills against the five steps of the scientific method, 

it's clear why data science skills are so important in extracting insight from data (see 

Figure 4). Proficiency in each of the three data science skills is required to successfully 

implement the scientific method as a way to get insights from data. Business knowledge 

is necessary to help formulate the right questions, generate hypotheses, gather data and 

communicate results. Technology/Programming skills are needed to gather/generate 

data and analyze data/test hypotheses. Finally, Statistics/Math skills are necessary to 

gather data, analyze data/test hypotheses and communicate results. 

Figure 4. You need all three data science skills when you adopt the scientific method 




3. Educate all data science team members on statistics 

Different data science job roles require different data science skills for success. For 

Business Managers, they need to be savvy in statistics, machine learning and big 

and distributed data. For Developers, their skills need to include product design 

and development, systems administration and back-end programming. For 

Creatives, their skills need to include math, business development and graphical 

models. For Researchers, they need to possess skills in statistics, algorithms and 

simulations and product design and development. 

Figure 5. Top data science skills across different data scientists include statistics. From: 10 Data 

Science Skills You Need to Improve Project Success 

It’s clear that data science skills are necessary for successful analytics projects. While 

top skills varied by data science job roles, it is interesting to note that skills in statistics 

and math dominated the top 10 drivers of project outcomes (see Figure 5). In fact, the 

most important data science skill to project outcome was Data Mining and 

Visualization Tools; if you are a data scientist, it doesn't matter if you are a Developer 

or Researcher or any other any other kind, it would benefit you to learn tools that help 

you mine and visualize data. The more proficient in these tools you become, the better 

you will feel about the outcome of your analytics projects. 




4. Integrate your data silos 

Data scientists’ work is fueled by the data that are available to them. Unfortunately, data 

are often housed in different systems, making it difficult to collect and integrate them. 

For example, data scientists can use Google Analytics to understand customers’ search 

behavior. They leverage Mixpanel to learn how customers use their applications. They 

rely on Marketo to track the effectiveness of different forms of communication. They 

rely on Salesforce to track customer interactions across throughout the lifecycle. The use 

of these separate tools results in data silos, each one housing a particular piece of the 

customer puzzle. While each data silo contains important pieces of information about 

your customers, if you don't connect those pieces across those different data silos, you're 

only seeing parts of the entire customer picture. 

Analyzing each data source separately is limited by the variables in each data set. To get 

the complete picture of your customers, you need to connect the dots across the data 

silos. By integrating all your data, you will be able to analyze all your data to extract 

deeper insights into the causes of customer churn. 

Siloed data sets prevent business leaders from gaining a complete understanding of 

their customers. In this scenario, analytics can only be conducted within one data silo at 

a time, restricting the set of information (i.e., variables) that can be used to describe a 

given phenomenon; your analytic models are likely underspecified (not using the 

complete set of useful predictors), decreasing your model's predictive power / increasing 

your model's error. The bottom line is that you are not able to make the best prediction 

about your customers because you don't have all the necessary information about them. 

The integration of these disparate customer data silos helps your data science team 

identify the interrelationships among the different pieces of customer information, 

including their purchasing behavior, values, interests, attitudes about your brand, 

interactions with your brand and more. Integrating information/facts about your 

customers allows you to gain an understanding about how all the variables work 

together (i.e., are related to each other), driving deeper customer insight about why 

customers churn, recommend you and buy more from you. 




5. Adopt Machine Learning Capabilities 

While it is true that the value of all of your data is greater than the value of each data silo 

taken alone, extracting that value from the combined data in your data platform can be 

overwhelming. As the number of variables grows, the more statistical tests you are able 

to run. Consequently, data scientists are utilizing the power of machine learning to help 

them get insights from extremely large data sets. 

Iterative in nature, machine learning algorithms continually learn from data. The more 

data they ingest, the better they get. Based on math, statistics and probability, 

algorithms find connections among variables that help optimize important 

organizational outcomes. For our customers, their outcome of interest is customer 

churn. We apply machine learning on their data to understand which customers are 

likely to churn and why. By identifying their at-risk customers, our clients are able to 

decrease customer churn through proactive retention management efforts. 

Summary 

Based on a study of hundreds of data scientists, we learned a lot about how to improve 

the effectiveness of data scientists. The practice of data science requires proficiency in a 

handful of specific data skills, including business acumen, technology / programming 

and statistics / math. Different data professionals report vastly different proficiency 

levels across these skills. Because data professionals tend to specialize in only one or two 

skill areas, organizations have a better chance of extracting value from their data when 

they adopt a team approach consisting of data scientists who have complementary skill 

sets. 

Following the scientific 

method in our data projects 

helps us keep our biases in 

check and minimizes the 

chances of us arriving at the 

wrong conclusion. Through 

trial and error, the scientific 

method helps us uncover the 

reasons why variables are 

related to each other and the 

underlying processes that 

drive the observed 

relationships. 




Additionally, it is important that your data science team understands statistics and data 

mining. Not only do Researchers get value from understanding statistics, but other 

kinds of data scientists (i.e., business management, creative, developer) also benefit 

greatly by knowing statistics. 

Finally, we recommend that businesses integrate their data silos and apply the power of 

machine learning to help them connect the dots across their disparate data sources. The 

more you know about your customers, the better you are able to meet their needs and 

ensure they are receiving value from your solutions. 

About Appuri 

------ 

Appuri provides an enterprise-grade data platform for businesses to transform their 

customer data into deep customer experience insights. The Appuri platform helps 

businesses integrate their data silos and leverage the power of machine learning to 

analyze billions of events to better predict the causes of customer loyalty. These insights 

help business deliver a better customer experience to decrease customer churn, acquire 

new customers and deepen their relationship with existing customers. 

Appuri, Inc. 

119 Pine St., Ste. 300 

Seattle, WA 98101 

info@appuri.com 

sales@appuri.com

Five Practices to Improve the Value of Data Science

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