{"id":29782,"date":"2023-09-05T10:03:39","date_gmt":"2023-09-05T09:03:39","guid":{"rendered":"http:\/\/www.turner.nl\/strategy-execution\/the-10-principles-of-data-analysis-and-innovation-both-hard-and-soft-2\/"},"modified":"2023-10-19T09:10:49","modified_gmt":"2023-10-19T08:10:49","slug":"the-10-principles-of-data-analysis-and-innovation-both-hard-and-soft-2","status":"publish","type":"page","link":"https:\/\/www.turner.nl\/en\/strategy-execution\/the-10-principles-of-data-analysis-and-innovation-both-hard-and-soft-2\/","title":{"rendered":"The 10 Principles of Data analysis and Innovation (Both hard and soft)"},"content":{"rendered":"\t\t
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Strategy = Execution<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The 10 Principles of Data analysis and Innovation (Both hard and soft)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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The 10 Principles of Data analysis and Innovation (Both hard and soft)<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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  1. Structure your data<\/strong>
    1. Otherwise you will spend unnecessary time recovering and cleaning Larger organizations can\u2019t do without a data manager.<\/li><\/ol><\/li>
    2. Clean your data<\/strong>
      1. Make sure your data are clean, recent and based on a single time point\/period. Remove any outliers, data entry errors, exceptional circumstances, etc. Save annotated scripts so others can reproduce your analyses.<\/li>
      2. Check the basics: data volume, variety, speed and uncertainty. Refresh as often as possible.<\/li><\/ol><\/li>
      3. Zoom out<\/strong>
        1. Preferably level up from your main interest. In other words, don\u2019t go for a quick fix. Scribbling numbers \u2018on the back of an envelope\u2019 might be useful as a fast check, but it is not the real deal. Take your time and make solid calculations. Include every variable in your calculations as a variable; don\u2019t hide them in assumptions.<\/li>
        2. Innovate by looking at the chain as a whole plus the wider context. Assess the impact on all partners in the chain, as this may be exactly where there\u2019s room for innovation.<\/li><\/ol><\/li>
        3. Start with the end in mind<\/strong>
          1. Keep an eye on the goals and the business case that this data analysis serves. Focus primarily on the data that affect output, and less on data affecting input or throughput (unless these are early indicators).<\/li>
          2. Check for relevance: (1) If the data don\u2019t cause a change of direction, they don\u2019t matter; (2) Analyze pertinent data only. If data do not directly impact a process, they are not pertinent.<\/li>
          3. Every analysis should be carried out in tandem by both a data analyst and a user.<\/li><\/ol><\/li>
          4. Collect targeted data. Too often supply drives demand<\/strong>
            1. Base your analysis on your hypothesis, but leave room for<\/li>
            2. Define \u2018known unknowns\u2018. Talk to employees to uncover issues and bottlenecks that could be solved by using data. Also, talk about \u2018unknown unknowns\u2018. Part of the value of data is latent; create a context of varied, coherent data that can accommodate possible future or external data.<\/li>
            3. Ask for a second opinion from an experienced data scientist, even at the earliest stages of the innovation process, for example for strategic priority setting.<\/li><\/ol><\/li>
            4. 1st-order data analysis: Be disciplined in your analysis (and watch out for bias)<\/strong>
              1. Guard against confirmation bias, stay open to all outcomes. Hypotheses are necessary to narrow the scope, but should not color your judgement. \u2018Presumptions are the mother of all evil\u2019.<\/li>
              2. Validate your results. Do a sanity check: evaluate whether the result can possibly be true and if not, why not (see Principle #1). What happens if you change the input? Explore the extremes. Include every single variable, check every single assumption, use estimates. Consult more than one source, expert and so on.<\/li><\/ol><\/li>
              3. 2nd-order data analysis: look for interdependence<\/strong>
                1. The power of big data resides not in key figures (0th dimension) or data sets (1st dimension), but in complex predictive, classification or explanatory models. Focus on the essentials \u2018before the comma\u2019. Don\u2019t spend too much time optimizing data models. Leave that to Kaggle.<\/li><\/ol><\/li>
                2. Set time apart for thorough reporting<\/strong>
                  1. Discuss your results with peers from various disciplines. Chuck your analyses in a corner, meditate for an hour, then take an empty mind map or piece of paper to write down the top 3 conclusions based on the problem you\u2019re trying to solve. What comes to mind? These conclusions lead to recommendations. Use the Minto pyramid to write from your client\u2019s POV, align with them. Think first, write later.<\/li><\/ol><\/li>
                  2. 50% of your output = visual management<\/strong>
                    1. Implement organization-wide best visualization practices. Set the bar high, because this is what people see. Aim high, set \u2018first time right\u2019 as your standard.<\/li><\/ol><\/li>
                    2. Use high-quality modern tools<\/strong>
                      1. Ignore legacy systems. Python\/R, Power BI, SAS, etc. And the classic: SPSS.<\/li><\/ol><\/li><\/ol>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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                        Strategy = Execution. Improve, Renew and Innovate Faster<\/h3>\n\t\t\t\t\t

                        How can organizations make strategy execution their number one priority? And improve, renew and innovate faster? This I describe in my book Strategy = Execution. Strategy = execution is based on the research that Turner started years ago into the success factors of strategy execution and innovation. We interviewed 60 directors and professionals and analyzed more than 75 cases, 300 relevant books and articles.<\/p>\n\t\t

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