Five Practices to Improve the Value of Data Science

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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. appuri.com | 206.886.0893 | info@appuri.com | @AppuriCorp Copyright © 2016 Appuri Inc
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. appuri.com | 206.886.0893 | info@appuri.com | @AppuriCorp Copyright © 2016 Appuri Inc
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Five Practices to Improve the Value of Data Science

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