![]() In our 2016 survey, the rate of success was 20 percent in 2014, 26 percent and in 2012, 20 percent. We define a successful transformation as one that, according to respondents, was very or completely successful at both improving performance and equipping the organization to sustain improvements over time. Years of research on transformations has shown that the success rate for these efforts is consistently low: less than 30 percent succeed. Transformations are hard, and digital ones are harder These categories suggest where and how companies can start to improve their chances of successfully making digital changes to their business. These characteristics fall into five categories: leadership, capability building, empowering workers, upgrading tools, and communication. The results from respondents who do report success point to 21 best practices, all of which make a digital transformation more likely to succeed. While our earlier research has found that fewer than one-third of organizational transformations succeed at improving a company’s performance and sustaining those gains, the latest results find that the success rate of digital transformations is even lower. Yet success in these transformations is proving to be elusive. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. Of them, 1,521 have been part of at least one digital transformation in the past five years at either their current or previous organizations. The online survey was in the field from January 16, 2018, to January 26, 2018, and garnered responses from 1,793 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. In a new McKinsey Global Survey on digital transformations, more than eight in ten respondents say their organizations have undertaken such efforts in the past five years. PlotDigitizer is easy to use, great if you want to manually select your points.As digital technologies dramatically reshape industry after industry, many companies are pursuing large-scale change efforts to capture the benefits of these trends or simply to keep up with competitors. However, it is not so easy for occasional usage. ![]() Engauge is great for automated extraction, complex plots. The file I got needed some extra processing before I had the ame.Ĭonvert <- ame(x=as.numeric(mydf),ĬonclusionThe programs complement each other. ![]() While I see the advantage of a file including documentation, it would also be nice to get the data out of the file. It is also possible to cipy-paste the results. There is also a possibility to trace a line on screen and it will add points it detects there. The modern interface allows manual adding/removing/moving of points. It had no problems with the large photo, except that it could not scale that photo enough to fit on the screen. PlotDigitizerPlotDigitizer looks much more modern. Probably in a colored plot automatic detection would work better, you have some settings to guide it. Engauge has the ability to do point detection, to use that it is probably best to crop the figure as much as possible, Engauge has no qualms finding points in text, black blobs, axis labels and such. Engauge comes with a manual so everything can be resolved. Initially I copied-pasted the results to a spreadsheet, later I managed to create a. ![]() For instance, it took me quite some time to figure out how to export the results. It is clearly the program which can handle more exotic plots. It was not able to import my original figure (2992*2992 pixels, 694 KB) but had no problems after resizing to 500*500 pixels, 55.9 KB. It is many features, but looks a bit outdated. Engauge DigitizerEngauge has been there for quite a while.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |