{"id":15982,"date":"2016-12-02T14:46:58","date_gmt":"2016-12-02T14:46:58","guid":{"rendered":"https:\/\/thedatascientist.com\/?p=15982"},"modified":"2024-02-29T07:06:37","modified_gmt":"2024-02-29T07:06:37","slug":"data-science-architect","status":"publish","type":"post","link":"https:\/\/thedatascientist.com\/data-science-architect\/","title":{"rendered":"A new role in data science: The data science architect"},"content":{"rendered":"\r\n
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Rise of the data science architect<\/h2>\r\n\r\n\r\n\r\n

I believe there is a new role in data that businesses need to start taking into account, that of the\u00a0data science architect<\/strong>.<\/p>\r\n\r\n\r\n\r\n

What is a data science architect? It is a mix between a\u00a0data scientist and a data engineer<\/strong>.\u00a0Data science<\/a>\u00a0is (according to wikipedia):<\/p>\r\n\r\n\r\n\r\n

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Data science<\/strong>\u00a0is an interdisciplinary field about processes and systems to extract\u00a0knowledge<\/a>\u00a0or insights from\u00a0data<\/a>\u00a0in various forms, either structured or unstructured,[1]<\/a><\/sup>[2]<\/a><\/sup>\u00a0which is a continuation of some of the data analysis fields such as\u00a0statistics<\/a>,\u00a0machine learning<\/a>,\u00a0data mining<\/a>, and\u00a0predictive analytics<\/a>,[3]<\/a><\/sup>\u00a0similar to\u00a0Knowledge Discovery in Databases<\/a>\u00a0(KDD).<\/p>\r\n<\/blockquote>\r\n\r\n\r\n\r\n

The role of a\u00a0data engineer<\/a>\u00a0is<\/p>\r\n\r\n\r\n\r\n

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A\u00a0data engineer is a worker whose primary job responsibilities involve preparing data for analytical<\/a>\u00a0or operational uses. The specific tasks handled by data engineers can vary from organization to organization but typically include building data pipelines to pull together information from different source systems; integrating, consolidating and cleansing data; and structuring it for use in individual analytics applications.
As you can see below, data engineers need to have a broad range of technical data skills. Given much of this is based on databases, learning to use SQL<\/a> and non-SQL databases is a key foundational skill which they can then build on.<\/p>\r\n

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The\u00a0data science<\/strong><\/a>\u00a0architect<\/strong>\u00a0(DSA) sits in between the two. The DSA deals with the design of the\u00a0data collection<\/a>, storage and analysis processes, while taking into account time and cost trade-offs and business requirements.<\/p>\r\n\r\n\r\n\r\n

Some example problems are:<\/p>\r\n\r\n\r\n\r\n

1) What variables should be stored?<\/strong><\/p>\r\n\r\n\r\n\r\n

This is mostly an early stage company problem\u00a0which I have already discussed in my article about\u00a0data science<\/a>\u00a0strategy.<\/p>\r\n\r\n\r\n\r\n

2) What issues might arise regarding data quality?<\/strong><\/p>\r\n\r\n\r\n\r\n

Should additional measures be taken in order to ensure that the appropriate data is in place? What these measures can be and at what stage of the architecture (e.g. a data firewall, or filling missing values during the analysis).<\/p>\r\n\r\n\r\n\r\n

3) What are the different options for a database, and which suits the company the best at this and at future stages?<\/strong><\/p>\r\n\r\n\r\n\r\n

Is it more important to go for a solution that makes storage easy, but it is is more difficult to query, or a relational database might be a better choice?<\/p>\r\n\r\n\r\n\r\n

4) Are there any concerns regarding the choice of a database, programming language, the data being collected and different technologies?<\/strong><\/p>\r\n\r\n\r\n\r\n

E.g. A particular type of analysis might be easier to do with a library that exists only in R. However, there might not be anyone in the company that can use R, so a second best has to be found in Python.\u00a0The DSA needs to decide on the best way to adapt and move forward.<\/p>\r\n\r\n\r\n

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\"data<\/a><\/figure><\/div>\r\n\r\n\r\n

So, what does a data science architect do?<\/h2>\r\n\r\n\r\n\r\n

So, a DSA starts by analysing a company\u2019s needs having the end goal in mind:\u00a0using data to generate value<\/strong>. From that goal, the DSA designs the architecture<\/a> and the analytics pipelines while taking into account appropriate time frames, and costs.<\/p>\r\n\r\n\r\n\r\n

The DSA is a more relevant role for startups, since all startups that deal with data will have to make these decisions<\/a>.<\/p>\r\n\r\n\r\n\r\n

Now someone might argue that the DSA is not so much a separate role as it is a separate function within a\u00a0data scientists<\/a>\u00a0repertoire. I think this could be right, but it is still\u00a0important to stress out the existence of this function. A\u00a0data scientist<\/a>\u00a0is valuable when the data is already in place. A\u00a0data engineer does not have the appropriate skills and knowledge to design the architecture<\/a>\u00a0in a way that maximises value for the long run. A\u00a0data science<\/a>\u00a0architect<\/strong>\u00a0enters the scene in the early stage and then paves the way for the other two.<\/p>\r\n\r\n\r\n\r\n

How to set up the right data strategy<\/h2>\r\n

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Understanding how to best structure your\u00a0data strategy, and the roles within an organisation is not an easy task, but a\u00a0data science<\/strong>\u00a0architect<\/strong>\u00a0can be of great a<\/p>\r\n

ssistance. I have written in other articles about the importance of a\u00a0data strategy<\/a>\u00a0and a\u00a0data-driven culture<\/a>. Make sure to\u00a0get in touch<\/a>\u00a0if you\u00a0have any questions!<\/p>\r\n\r\n\r\n\r\n

Do you want to become data scientist?<\/h2>\r\n\r\n\r\n<\/div>\r\n<\/div>\r\n\r\n\r\n\r\n

Do you want to become a\u00a0data scientist<\/a>\u00a0and pursue a lucrative career with a high salary, working from anywhere in the world? I have developed a unique course based on my 10+ years of teaching experience in this area. The course offers the following:<\/p>\r\n\r\n\r\n\r\n