8CIOReview | | NOVEMBER 2022The data revolution is changing businesses and industries in permanent ways. Revolutions seldom move backward; they continue marching ahead. The scale at which parts of the revolution advance may differ, but it is hard to ignore the movement. And it is quite the same with data. For example, the growing propagation of the data culture signals the age and maturity of data science.Today, organisations and businesses are increasingly dependent on data to understand trends, drive insight and help streamline efforts to make sound business decisions that can be transformed into the right actions to help achieve business outcomes that make all the difference, especially when navigating times of uncertainty and change. Companies that succeed in their data-driven efforts look for patterns everywhere; tie decisions back to the data and continually discover and learn. They understand that creating a data culture is a diligent quest and magic bullets do not deliver results. To adapt a famous quote from Thomas Edison, becoming data-driven is 1% inspiration and 99% perThe following are five lessons that future leaders need to understand to be successful with data and analytics and sustain a culture with data at its core. Data-driven culture starts at the top companies with strong data-driven cultures, set an expectation that it's not an exception for decisions to be tied to data, it is standard. And these practices propagate downwards. The example set at the top of the business stimulates significant shifts in company-wide norms.Companies need to embody data in their DNA and ingrain data fundamentals and robust processes to evolve data from a raw ingredient to a finished good. This requires the right focus, commitment, and investments to follow through on commitments. As well as an understanding of what data can be used for value creation.· Remove frictionWhen developing and implementing a data culture or a transformation agenda it is imperative to stay true to the business problem. Focus on the business objectives and outcomes and then look at the landscape of data and what insight is required. Quickly act on it, whilst maintaining quality, iterate, and deliver the analytics back to the customer. Use the feedback as an accelerator for improving the capability and/or service of the data product to make better decisions more often.A good starting point is to look at places across the business where people are attempting to make decisions. Review their processes and attempt to identify gaps, for example, time to obtain the data, the effort to evaluate the data, find insight, or make a decision. Put simply, start by attempting to remove friction from an existing decision-making process. · Data Yin and YangIntroduction of good data management and data governance ­ master data management, data dictionaries, transparent logic and rules, data health metrics are all examples of fundamental Natalie JakomisCREATING A DATA CULTURE IS A DILIGENT QUEST. MAGIC BULLETS DO NOT DELIVER RESULTSBy Natalie Jakomis, Global Director of Data Science and Analytics, CoatsIN MY OPINION
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