How to create a data culture in your company

To improve their efficiency and competitiveness, organizations need to capitalize on analytics. According to Accenture’s “Closing the Data-Value Gap” report, data-driven companies see an average of 27% annual revenue growth. But not everyone knows how to interact with data. The same report says that 91% of companies cite people and processes, not technology, as the biggest obstacles to doing so. Employees who are supposed to process information often have lack experience, act inconsistently, and are not familiar with best practices and not willing to share knowledge – especially with representatives from other departments. There are also problems with data: it can be incomplete, inaccurate and require lengthy manual processing; it can be difficult to search for, and the quality can be difficult to verify.

A systematic approach

All of this wastes time and resources, hampers decision making, and affects financial performance. Many organizations try to close the data gap by hiring a savvy chief digital officer-but that’s not enough. It must be approached systematically: changing the corporate environment, transforming critical elements of the operating model, launching targeted initiatives – and ultimately creating a data culture within the organization.

The Stages of the Big Journey

Creating such a culture is a multi-step process. To begin, an organization or team should project its “target state” – to understande what goals need to be achieved and for what purpose. Goals can be broken down into three main categories. 1. Improving the quality of management decisions – for example, achieving a comprehensive approach to supply chain management, necessary to identify bottlenecks and so on. 2. Reducing resource costs – say, labor for data retrieval and management reporting, or the cost of running automation projects. 3. Reduction of risks – for example, associated with incorrect implementation of automation projects, with non-compliance with regulatory requirements, with manual data processing, and so on. In addition to these, companies in different industries may have their own business goals that are unique to them.