{"id":20143,"date":"2018-03-28T08:30:13","date_gmt":"2018-03-28T15:30:13","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=20143"},"modified":"2018-03-29T08:44:15","modified_gmt":"2018-03-29T15:44:15","slug":"data-scientists-guide-communicating-results","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2018\/03\/28\/data-scientists-guide-communicating-results\/","title":{"rendered":"A Data Scientist’s Guide to Communicating Results"},"content":{"rendered":"

In many ways, the field of data science is as unclear and vague as a muddy lake. While most terms and concepts include a legitimate definition, it\u2019s all too easy to get bogged down in technical jargon. As such, some ideas mean different things from company to company and even \u2014 in some cases \u2014 from project to project.<\/p>\n

1. Translate Data Into Actual Business Terminology<\/b><\/p>\n

You likely spend a lot of your time trying to explain complex technical terms to senior- and executive-level staff members. These professionals tend to be from older generations. Not only are they unfamiliar with some of the most basic terms and concepts of IT, but many of them aren’t even interested in learning.<\/p>\n

To make the strongest appeal among this group, translate all your actionable data into business terms<\/a> and ideas that are familiar. Instead of touting the efficiency of the next big software suite, compare it to systems they already use and explain the upgrades it provides.<\/p>\n

2. Provide Real-World Examples of Benefits<\/b><\/p>\n

Nearly every professional wants to know how new technology makes their lives easier. To achieve this, provide real-world examples of the benefits of next-gen IT or advanced data sets. While it’s easy to fall into the trap of listing intimate details on every step of the process, there’s a lot to be said for brevity \u2014 especially in a role such as this.<\/p>\n

Although it’s best to demonstrate how new technology or information benefits the specific company, you can use case studies to provide supplemental predictions and forecasts<\/a>. Doing so is less effective than producing models based on the company’s internal processes or dataflow, but it still provides an excellent way of making your point as quickly and completely as possible.<\/p>\n

3. Solicit Questions and Feedback<\/b><\/p>\n

Before ending your report or finalizing your project, try to solicit direct feedback from your audience. It doesn’t matter if you have to prompt them to ask questions or if they’re champing at the bit to put your knowledge to the test \u2014 this kind of interaction is crucial when trying to improve your communication skills and build a successful career<\/a> as a data scientist.<\/p>\n

4. Use Visualizations Whenever Applicable<\/b><\/p>\n

Charts, graphs, illustrations and spreadsheets<\/a> can work wonders for an otherwise boring or drab report. Today’s computers, including laptops and even many smartphones, come pre-loaded with intuitive design software for this very purpose. Use these documents to your advantage whenever applicable. They make it easy to display various datasets, highlight different stats and draw attention to the most important points you’re trying to make.<\/p>\n

5. Focus on the Result<\/b><\/p>\n

Try not to get bogged down with the technical details of any specific project. More importantly, don’t overload your client or manager with information from the start. Instead, begin by drawing attention to the result and work backward from there. Instead of explaining the technical specifications or requirements of a new application or process, describe the final benefits. Not only does this capture your audience’s attention immediately, but it might be enough to mitigate any other concerns and gain the necessary approval.<\/p>\n

Start the Project on the Right Foot<\/b><\/p>\n

If you’re an established data scientist in the industry, it’s critical you explain your thought processes and ideas in a clear and straightforward manner. Instead of leaving advanced concepts open to interpretation or for later clarification, tackle the subject immediately<\/a>. Not only does this ensure you’re on the same page with your clients, bosses and co-workers, but it gives everyone a clearer understanding of the entire project scope and any specific, individual requirements.<\/p>\n

About the Author<\/strong><\/p>\n

\"\"Contributed by: Kayla Matthews<\/a>, a technology writer and blogger covering big data topics for websites like Productivity Bytes, CloudTweaks, SandHill and VMblog. <\/em><\/p>\n

\u00a0<\/em><\/p>\n

Sign up for the free insideBIGDATA\u00a0newsletter<\/a>.<\/em><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

In this contributed article, technology writer and blogger Kayla Matthews discusses the field of data science is as unclear and vague as a muddy lake and it is critical to properly communicate results. While most terms and concepts include a legitimate definition, it\u2019s all too easy to get bogged down in technical jargon. As such, some ideas mean different things from company to company and even \u2014 in some cases \u2014 from project to project.<\/p>\n","protected":false},"author":10513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[182,87,180,56,1],"tags":[613,96],"acf":[],"yoast_head":"\nA Data Scientist's Guide to Communicating Results - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2018\/03\/28\/data-scientists-guide-communicating-results\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"A Data Scientist's Guide to Communicating Results - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"In this contributed article, technology writer and blogger Kayla Matthews discusses the field of data science is as unclear and vague as a muddy lake and it is critical to properly communicate results. 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