{"id":1291,"date":"2018-01-10T06:00:54","date_gmt":"2018-01-10T05:00:54","guid":{"rendered":"https:\/\/staging.blogs.yokogawa.de\/chemical-pharma\/uncategorized\/analyze-your-data-success-in-5-steps\/"},"modified":"2022-06-17T09:28:08","modified_gmt":"2022-06-17T07:28:08","slug":"analyze-your-data-success-in-5-steps","status":"publish","type":"post","link":"https:\/\/www.yokogawa.com\/eu\/blog\/chemical-pharma\/en\/analyze-your-data-success-in-5-steps\/","title":{"rendered":"Analyze your data \u2013 Success in 5 steps"},"content":{"rendered":"

When a data analysis becomes a must<\/strong><\/h2>\n

Post no. 1<\/strong> \u2013 The familiar buzzwords big data<\/a> and Industry 4.0 inevitably play a part again here. You\u2019ve probably heard them so many times recently, but it makes sense to address the issues nevertheless. That doesn\u2019t mean you have to go for big data straight away, but at the same time it doesn\u2019t hurt to get to grips with whatever data you have \u2013 and in the future that will truer than ever. My advice is to start now with data analysis before it\u2019s too late!<\/p>\n

Data analysis in 5 steps<\/strong><\/h2>\n

Anyone who\u2019s ever carried out a data analysis needs no telling that the time and effort can often be considerable. Maybe not always, but certainly in a lot of cases, the benefits are well worth all the hard work. And if you take the time upfront to think about the quality of your data \u2013 and the most efficient way to collect it \u2013 you can save yourself quite a few problems later. Once again, experience is immensely important.<\/p>\n

You can find plenty of information in the VDI Big Data Expert Committee\u2019s<\/a> status report headed \u201cOpportunities with big data \u2013 Best practices”<\/a>. There are undoubtedly many different ways to represent the step sequence in data analysis projects. The VDI method corresponds very closely to my own experience, which is why I\u2019ll lean heavily on it in my description.<\/p>\n

\"Data
Data analytics in 5 steps<\/figcaption><\/figure>\n

The diagram above shows the various steps or phases in a data analysis project based on the well-known, and internationally used, Six Sigma DMAIC method<\/a>. The Define<\/a> \u2013 Measure<\/a> \u2013 Analyze<\/a> \u2013 Improve \u2013 Control toolkit is enhanced here with methods and procedures which are also suitable for processing large quantities of data.<\/p>\n

Stick with me \u2013 or rather, time to get started!<\/strong><\/h2>\n

You\u2019ve probably noticed that I\u2019ve been avoiding the term \u201cbig data\u201d and have tended to talk instead about data analysis. This workflow is ideal for big data projects, of course. It\u2019s equally suitable for smaller projects, though, and if you\u2019re new to this kind of thing, that\u2019s where I\u2019d recommend that you begin! Just follow me and I\u2019ll take you step by step through the five phases of a data analysis in this special series. Let me start by giving you a short summary of what awaits you.<\/p>\n

Step 1: D<\/span>efine: Is a data analysis really worth the effort and how should you structure your project?<\/strong><\/p>\n

Click here to read<\/strong><\/span><\/a>!<\/p>\n

Step 2: M<\/span>easure: What special points do you need to consider when selecting the data and where do the stumbling blocks lie?<\/strong><\/p>\n

Click here to read!<\/strong><\/span><\/a><\/p>\n

Step 3: A<\/span>nalyze: What aspects should you be aware of when you carry out a data analysis?<\/strong><\/p>\n

Click here to read<\/strong><\/span><\/a>!<\/p>\n

Step 4: I<\/span>mprove: How can the results of your analysis be implemented in efficiency-enhancing solutions?<\/strong><\/p>\n

Post will be available on February 7, 2018<\/strong><\/span>!<\/p>\n

Step 5: C<\/span>ontrol: How can you assess the efficiency of your solution and how can you estimate the potential benefits upfront?<\/strong><\/p>\n

Post will be available on February 14, 2018<\/strong><\/span>!<\/p>\n

Have we succeeded in arousing your interest? Stick with me for the next blog posts \u2013 I promise you won\u2019t be disappointed! Do you have any questions, observations, criticisms or suggestions? If so, just write us a comment. Your feedback is always welcome.<\/strong><\/p>\n

We look forward to hearing from you. We\u2019ll be in touch again soon.<\/p>\n

\n

Did you miss a blog post?<\/strong>
\nNo problem \u2013 all posts in this series can be accessed here:
\n<\/strong>
\n\u2013 Post no. 1: \u201cAnalyze your data \u2013 Success in 5 steps\u201d
\n\u2013 Post no. 2: \u201cAnalyze your data \u2013 Step 1: Define\u201d
\n\u2013 Post no. 3: \u201cAnalyze your data \u2013 Step 2: Measure\u201d
\n\u2013 Post no. 4: \u201cAnalyze your data \u2013 Step 3: Analyze\u201d
\n\u2013 Post no. 5: \u201cAnalyze your data \u2013 Step 4: Improve\u201d
\n\u2013 Post no. 6: \u201cAnalyze your data \u2013 Step 5: Control\u201d\u201d<\/a><\/p>\n

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

Anyone who\u2019s ever carried out a data analysis needs no telling that the time and effort can often be considerable. Maybe not always, but certainly in a lot of cases, the benefits are well worth all the hard work. And… <\/p>\n

Analyze your data \u2013 Success in 5 steps<\/span> Read More »<\/a><\/p>\n","protected":false},"author":115,"featured_media":1300,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"","footnotes":""},"categories":[53,56,57],"tags":[162,216,135,217,136,218,219,167,220,221],"coauthors":[222],"acf":[],"yoast_head":"\nAnalyze your data \u2013 Success in 5 phases - Yokogawa Industry Blog<\/title>\n<meta name=\"description\" content=\"Are you analyzing your data? 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Maybe not always, but certainly in a lot of cases, the benefits are well worth all the hard work. 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