If you have basic questions about implementing a Data Strategy, you can find many answers quickly by doing a Google search...


The tricky part is figuring out how to solve the challenges in each phase of the Data Life Cycle:


Data, Technology, Analytics, People, and Culture.

These are the most common challenges that arise when executing a Data Strategy: 

(Data) The data is not accessible, traceable, secure, or democratized. Companies make a common mistake by investing in technology without first taking the data and the business needs and objectives into account. They end up adapting the business to the technology instead of applying the best technology according to the needs and goals. It's crucial to implement technology that best suits your organization.

(Technology) There's isn't a scalable model. There is more data to manage and, therefore, more unstructured data. When implementing technology, a common mistake is developing a bespoke solution thinking it's the best solution just because it's tailor-made. The reality is that a bespoke solution will not be scalable because it only addresses the short or medium-term needs. It's better to use a market solution from a technology partner because they have already studied the different business cases and they have a long-term product roadmap. 

(Analytics) The report-to-report model is not suitable. Reporting on something after it's happened needs to end if you want to get the most out of data. It's essential to have a long-term, global view of the data strategy to correctly plan resources, equipment, budget, and to predict possible setbacks. The recommended analytics model is a predictive model with a good analytics strategy to influence processes. A model like this will strengthen the strategic map because it will line up with what lies ahead, and the company will not suffer from shortsightedness.

(People) The company focuses on analytics, but it doesn't pay attention to the needs of the business. It's essential to be clear about peoples' capabilities and to train them so they can carry out the data strategy. A common mistake is to focus the plan too much on the data, losing sight of the business. It's better to cross analytical thinking with business vision and create "Citizen Data Scientist" roles that are a hybrid between a Data Scientist and a Business Analyst.

(Culture) There is no data culture. Data isn't accessible; therefore, people aren't working with it. The entire organization needs training on how to read, work, analyze, or discuss data. Culture is key to launching a data strategy.