CIOReview | | 9 NOVEMBER 2022data tools to help enable data-driven businesses. The other side ­ `Data Yang' is more of the data analysis, data science, and innovation. If you don't have a strong footing, you can't use the data. If you have a solid foundation but are not being ingenious with the data, you're not growing. The successful blending of these two is a key challenge for any industry. You must combine both, it's a pre-requisite for data success. · High impact learningA learning culture is what enables some companies to identify problems in their products and fix them quickly. It is what enables some companies to `out-innovate' their competitors. It is what enables other companies to grow at faster rates. And a lack of learning culture prevented many now non-operational companies from embracing changes in their markets and evolving their products. High impact learning requires experimentation, which involves accepting positive and negative results. It is important for leaders in companies to keep an open mind, learn from the experiences of others, their failures, and successes -- and look beyond the four walls for inspiration and success models. · Failure is an optionLeveraging data is both aspirational and risky; if you're not failing, then you're not pushing the boundaries enough.At the beginning of a data science initiative, there is no guarantee your project will be successful. Iterative trial-and-error is part of the data science project lifecycle. Science is messy and often many people do not appreciate the ratio of failures to successes. Sometimes developing an idea through to completion can be unsuccessful. The model does not work, or the idea is incorrect and needs to change. Data Science is more about experimentation and iteration than building up solutions from paper to production.By only knowing what doesn't work and why, can we determine what will work? Failing in a controlled way as part of an experiment creates value. If this isn't acknowledged it can become new friction and damage any innovation or experimentation.To conclude culture can be a multifaceted problem or a multifaceted solution. When an organisation's data mission is detached from the business strategy and core operations, it should come as no surprise that the results of analytical initiatives may fail to meet expectations. But when excitement about data analytics permeates throughout the entire organisation, it becomes a source of energy, drive, and momentum. Ultimately, today's technology is incredible. Imagine how far it can go with a data culture to match. Companies that succeed in their data-driven efforts look for patterns everywhere; tie decisions back to the data and continually discover and learn
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