Organizations today have access to data about nearly everything related to their business. This includes customer preferences, operations on the production line, movement of goods through the supply chain, location of field service personnel and an ever-expanding host of other data from numerous sources. The challenge for any organization is how to ensure it will take advantage of all this data to improve operational performance and the bottom line. Doing so clearly requires a data-driven culture throughout the organization. Creating such a culture requires a combination of leadership and technology.
The initial catalyst for a cultural change is a shared view of the importance of data-driven decision- making. Executives can deliver this message directly, but an organization also communicates the priority it places on data-driven decision-making through the data-related actions it takes. For instance,
management should ensure that the relevant data useful to the various line of business functions is available throughout the organization. After all, you can’t very well expect data-driven decision making without access to data. And this data should be shared between management and staff so they have access to the same information and can effectively collaborate during the decision-making process.
To enable data-driven cultures, organizations also must provide technology that enables and supports collaborative analytic functionality, including the ability to work jointly on analysis, share data across business units, and coordinate decisions and actions among cross-functional team members. Our research finds that nearly four in 10 organizations support their analytics with collaboration and another 52 percent expect to at some point in the future. Analytic insights embedded in collaborative exchanges provide instant decision-making guidance, and these exchanges can link to live views of the data to allow further data-driven exploration. Leaders can encourage data-driven collaborative processes by providing to those who participate rewards that can range from social recognition to monetary rewards for the most active participants.
Data must be easily accessible. For example, large monitors in prominent locations can display relevant information throughout the workplace. In the customer service department, data about the number of outstanding service requests, average time to resolution and whether these are trending up or down can be displayed for all to see. And easy data access can be provided via mobile devices – not just in the form of tables and charts of information, but also through data-driven alerts and notifications. Making mobile devices a key part of the mix can engage a broader section of the workforce, particularly those who are not always sitting at a desk or workstation.
Another way to make data not only accessible but integral to business processes is to embed analytics into the operational applications. Remove the friction or context switching that is required to go “look at the data.” Bring the data and analytics to the line of business personnel in the applications they use every day. Nearly three-quarters (72%) of organizations in our research reported that embedded analytics was important to them. After all, the purpose of performing analytics is ultimately to improve operations, so why not take steps to embed the analytics directly into the operational application? This makes the analytic insights easier to access, which in turn provides more opportunities to use them to influence the organization’s operations.
The next step beyond data accessibility is to build meaningful metrics using this data. Well-constructed metrics will provide context for the reported values by including not only the value but the target range for that value and a measure of the variance from the target. They should also include a comparison to the prior period or periods to show whether the metric is improving or deteriorating over time. Finally, for aggregated metrics, there should be an indication of the status of lower-level metrics. For example, within the metrics reported by a customer service department, the number of outstanding requests might be within the target range, but the number of cases opened and closed during the period may have exceeded the target range significantly.
As the saying goes, “To manage something you must first measure it.” Work with your organization to agree on what should be measured and how it should be measured. Publish those metrics so everyone has the same view of performance. Embrace an analytic architecture that enables your organization to easily share this information and move to embed these capabilities into your business processes and allow for collaborative interaction. Encourage the use of these tools and techniques not only with social or monetary rewards, but also by your example. You are working toward a goal that won’t be achieved overnight, but this approach puts you on the path to creating a data-driven culture that will improve your operations.