Why Data Culture Matters
Diaz, Alejandro; Kayvaun Rowshankish, and Tamim Saleh. “Why Data Culture Matters.” McKinsey Quarterly, September 2018.
Dr. Allan Gray, Executive Director and Professor
The authors identify seven key takeaways from interviews with executives at companies considered to be leading the data analytics revolution. Those seven key takeaways are summarized as:
- Data culture is decision culture: The fundamental objective in collecting, analyzing and deploying data is to make better decisions.
- Data culture, C-Suite imperatives and the Board: The top of the organization must engage in an ongoing, informed conversation with those who lead data initiatives throughout the organization.
- The democratization of data: To create a competitive advantage from your data you need to stimulate a demand for data from the grass roots.
- Data culture and risk: Data can be a double-edged sword of benefit and risk. Risk management within your organization should act as a smart accelerator, by introducing analytics in to key processes and interaction in a responsible manner.
- Culture catalysts: To get complete buy-in, you need people who can bridge data science and on-the-ground operations. Usually, the most effective catalysts are not digital natives.
- Sharing data beyond company walls? Not so fast: Data leaders are building cultures that see data as the crown jewel asset, and data analytics is treated as both proprietary and a source of competitive advantage in a more interconnected world.
- Marrying talent and culture: competition for data talent is unrelenting but, striking the appropriate balance for your business between injecting new employees and transforming existing ones is at least as important as finding data talent.
What this means for Food and Agricultural Business
Data analytics has increasingly become more than just a buzzword; it is a fact of life in the food and agribusiness industry. Not only is it affecting what we do in production agriculture from cost management to productivity enhancement to environmental footprints, but data is also increasingly driving efficiency and productive at all levels of the food and agribusiness value chain. Those companies that can more creatively utilize the data have to stand an excellent chance of being able to create and capture value above their fewer data capable competitors. For example, it seems that data analytics has a potentially high impact role to play in better managing input supply inventories at the manufacturer, wholesaler, and retailer levels in both crop and livestock input segments. Another example may be in utilizing data to redesign pickup/delivery routes to create both safer and more efficient routes for drivers while also increasing timeliness and customer satisfaction. Still, further impact could be experienced through analysis of employee data to enhance training and development efforts, reduce workflow redundancies, and improve employee engagement. In short, the opportunities to dramatically change the business data analytics have only begun to be identified.
But as the authors of this article point out, to get the full potential from data analytics a company must embrace a data culture. The article does not suggest that data should replace experience and critical decision-making but that data analytics is a critical tool to make the whole organization more capable of making better decisions. To get maximum advantage from data you need to think about the benefits that can come from the data and the risks; the skepticism from employees before they buy-in and the excitement once they do buy-in; the need for flexibility, and the insistence on common frameworks and tools. Most importantly, we want to think about the competitive advantage that can be unleashed by a culture that brings data talent, tools, and decision making together. The exhibit below, really helped me visualize what we are talking about when we think about a culture that brings these three elements together. How well is your business culture embracing the data culture recipe of business skills, technology skills, and analytics skills?