Five Practices to Improve the Value of Data Science

Transcription

1 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 forward. Here are five ways companies can improve the value of data science. Customer Experience Management Best Practices Study Results How to improve the effectiveness of your CXM Program Bob E. Hayes, Bob E. Hayes, PhD, Chief Research Officer phone:

2 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

3 Improving the Value of Data Science 3 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.

4 Improving the Value of Data Science 4 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.

5 Improving the Value of Data Science 5 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

6 Improving the Value of Data Science 6 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.

7 Improving the Value of Data Science 7 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.

8 Improving the Value of Data Science 8 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.

9 Improving the Value of Data Science 9 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 info@appuri.com sales@appuri.com