Six Components for your Data strategies
By Ramy Sedra and Annie Veillet
In the digital era, we face a never-ending stream of data — it’s everywhere we look! We may collect and compile it in vast quantities, but do we really know how to benefit from it? In order to put data to better use and address a variety of business issues, PwC has designed a six-part analytical framework designed to shed light on the concept of data profitability.
- Business decisions and analytics
To identify the goals we wish to achieve by tapping into our data, we must first and foremost align our overall business strategy with that the specific operating segment in question. We must then identify and prioritize certain business issues (based on a profitability analysis) to map out which analytical solutions should be implemented.
2. Data and information
Based on the identified business priorities, we must extract and store the data we need for analytical purposes (ideally a large volume of both structured and unstructured data). Identifying the data requirements should take into account the organization’s internal and external data (statistics, behavioural data, etc.). Needless to say, data confidentiality must be ensured and managed at all times.
3. Technology and infrastructure
To address this aspect, we must select scalable platforms encompassing the entire data ecosystem. Open source tools, particularly those that accommodate artificial intelligence, are among those that should be considered.
4. Organization and governance
To ensure the right data structure and the right data governance tools, it is crucial to develop an operational model that specifies each stakeholder’s roles and responsibilities. This structure should also recognize and mitigate the human/technological risks associated with data sharing and/or regulatory compliance needs. For more information on data governance, please read this article.
5.Process and integration
Strategic data management should be agile and focused on value creation. The data management process should support this approach. As regards integration, interactions between analytical teams and business domain managers (owners) should be improved whenever possible.
6. Culture and talent
The final aspect to consider is culture and talent. There’s no doubt about it: we must select the right employees if we are seeking to develop a culture focused on effective data use and data profitability. To that end, we must hone our skills in the areas of data science, data engineering and product management. We must also put mechanisms in place that facilitate critical behaviours in support of cultural transformation.
Would you like to learn more about how your business could benefit from a more effective data strategy? Contact us!
