3.5 Steps To Build Your Data Capabilities

#Data.

Do you have it?

Do you know what to do with it?

Bet we can't have a business convo these days without “data” thrown into it. But how many of us know what is takes to build a solid data capability in our company? From my experience here are 3.5 steps you can take to build a data-driven organization.

1) Define your data strategy

Before you start building your data capabilities, have a clear understanding of why you are collecting data and what you want to achieve with it. Start by identifying the business problems you are trying to solve, the questions you are trying to answer. Then determine what data you need to collect and analyze to answer those questions. Create a #roadmap outlining your data #strategy, including short-term and long-term goals.

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When 宝洁 started their #digitaltransformation journey they outlined a clear data strategy. Two key focus areas were identified: improving #supplychain #efficiency and better understanding #consumer behavior. This allowed them to put teams and tools in place to analyze sales data, listen to #socialmedia conversations, and even do #trend analysis on consumers looking for #sustainable and chemical-free products etc.

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2) Build a solid data foundation

Once you have a data strategy in place, you need accurate and #reliable data to make informed business #decisions. This should be done by creating a data #governance framework to outline how data will be collected, stored, secured, and shared across the organization. For companies that have access to large amounts of data, it is advisable to establish data #quality standards and processes to ensure data accuracy and completeness. Many data management tools and technologies are available to help store, manage, and analyze data more efficiently.

When the 密西根大学 acknowledged that data was critical to their success, they established a Data Governance Council with a charter to focus on data availability and sharing, data quality, and #simplification of data #stewardship structure including #roles, #responsibilities, and #appointments. By standardizing data definitions and ensuring everyone uses the same data, they were able to improve data accuracy and reduce reporting errors.

3) Start small and iterate

Trying to tackle everything at once is a big mistake. It is better to start by focusing on a few specific use cases where you can make the biggest #impact. Identify quick wins that can demonstrate the value of your data capabilities to the rest of the organization. Once you have established some momentum, #iterate and expand your data capabilities to tackle more complex #usecases.

施耐德电气 is a pioneer in digital transformation who recognized the importance of data capabilities to stay competitive. The approach it took was to start with a few specific use cases such as optimizing #energy usage in buildings. By collecting data from a few pilot sites and analyzing it to identify opportunities for improvement, the team was able to roll out into a broader program across all buildings - to iterate and expand data capabilities, leading to new use cases such as #predictive #maintenance and energy #forecasting. This approach allowed Schneider to improve operational efficiency, reduce costs, and significantly improve customer satisfaction. If you are in Europe I encourage you to visit IntenCity, Schneider's flagship energy autonomous building, which consumes 10 times less energy than the average #European building.

3.5) Build a data-driven culture

Encourage your #employees to embrace a data-driven #culture by providing them with the necessary #training, #tools, and #incentives to work with data effectively. #Communicate the importance of data to business, and ensure that data is accessible and usable for decision-making at all levels of the organization. This will increase data #literacy, foster #collaboration, and drive #innovation.

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When 雀巢 established a global data and analytics team that worked to centralize data across the organization and build a culture that valued data-driven decision-making, it allowed them to use data effectively to optimize supply chain, improve product quality, and personalize marketing efforts.

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You may not have #bigdata.

You may be starting small.

No matter where you are on your transformation journey, by defining your data strategy, building a solid data foundation, starting small and iterating, and fostering a data-driven culture, you can successfully build your data capabilities and be competitive in this #digital world.