Executives are investing heavily in data collection and analytics. But despite widespread enthusiasm for data science in business, more than half of organizations are struggling to develop a data-driven culture. The problem isn't technological. Augmented by AI, business intelligence tools are more powerful—and more readily accessible—than ever. There's no lack of motivation either. Data-driven businesses have been known to achieve greater efficiency, reduced operating costs, and more success attracting and keeping customers.
So, what's standing in the way, if not intention or capability? It may come down to vision. Impactful data initiatives typically begin with a clearly-defined organizational goal, and a strategy to support that goal through the collection, analysis, and sharing of relevant information. But too often, businesses amass data without any real plan for its application. In the best case scenarios, this results in missed opportunities. However, it can also lead to low ROI in BI tools, mishandling of sensitive information, and threats to security and compliance.
Identifying the root cause of dark data
Dark data can create a significant barrier for organizations seeking to become more data-driven. Unfortunately, more than half of the data held by the average organization is considered "dark." Unleveraged (and frequently unstructured), dark data takes up storage space, increases energy consumption, and creates a significant compliance and security liability. It's also indicative of a larger threat to org-wide data initiatives: pervasive data silos.
Silos can emerge for any number of reasons, such as inadequate collaboration tools or a lack of organizational alignment. In companies where silos are common, visibility often suffers and high-value data is rarely used to its fullest potential.
Consider a support team that receives a high volume of questions regarding the company's pricing model. If they share that information with the sales and marketing teams, the company can improve the clarity of its model and prevent customer frustration down the line. But if the communication is simply logged and forgotten, it will likely become dark data, taking up space in the company's system without serving any tangible purpose.
Designing a framework for strategic data management
As companies prepare to grapple with data silos and transform their org-wide data policies, a sharp and granular focus on data processes will be essential. Has the company invested in data tools that are not only capable, but user-friendly, easy-to-integrate, and flexible? Is there a plan in place to train employees on the most effective implementation of those tools? Have data access permissions and security protocols been established to prevent breaches and misuse?
Developing a detailed and transparent transformation roadmap can go a long way toward boosting employee confidence and encouraging buy-in for new systems and tools. It's helpful to bring stakeholders into this process. In fact, research has found that organizations that consider the perspectives and feedback of frontline employees are 80% more likely to implement new and better systems than their peers.
It is, however, important to keep in mind that a plan for utilizing data is only effective when the data is valid and trustworthy. Addressing flawed, incomplete, and outdated records in your systems can significantly improve the usefulness of your data, paving the way for more accurate interpretations and more precise insights.
Data quality is especially important when your data is used to train AI-driven BI tools, as erroneous data may cause bias and hallucinations. In addition to having adverse impacts on organizational decision-making, bias and hallucinations can erode employee trust in AI systems and limit buy-in for new tools and processes.
Setting measurable, long-term goals
Short-term goals and "easy wins" can be good for morale—but when companies lack long-term, measurable plans for their data, enthusiasm and buy-in can quickly fizzle out. Setting specific, big-picture goals helps encourage an ongoing commitment to data-driven processes, which in turn, affords the organization more time to fully develop its data-driven culture. It could be beneficial, for instance, to set a benchmark for the number of departmental decisions that will be backed by data within the next year or establish a time frame for fully incorporating BI into the sales funnel.
Whichever goals your organization chooses to pursue, it will be crucial to monitor progress on an ongoing basis (a task that will become easier the more you invest in quality data and analytics). This will give decision-makers an opportunity to assess their available technology, processes, and resources, and make adjustments when needed. If the results are falling short of expectations, it may be a sign of a broader issue, such as the company's software lacking critical functionalities or employees requiring more comprehensive data literacy training.
Embracing flexibility at every stage
It's important to keep in mind that a data-driven culture doesn't develop overnight. It takes time for employees to become adept with new analytics solutions and to determine which features, reports, and dashboards are best suited to their needs. A lack of immediate results should not be viewed as a reason to abandon data initiatives, but rather, a reason to reassess and readjust the path to achieving them.
By nature, a data-driven culture is a flexible culture; a sign of an organization that is willing to evolve as the data demands. Bringing flexibility into the earliest stages of cultural transformation will ensure that the company's data strategies are the right fit for its stakeholders' needs, and are designed to support the company in its plans for long-term growth.
