In the modern workplace, workers are increasingly expected to make data-driven decisions, a task that is often easier said than done. Limited access and a lack of sophisticated tools makes it difficult to expand widescale user adoption of data analytics.
While many organizational leaders acknowledge the benefits of a data-centric culture, today only 37% currently leverage real-time streaming, 36% leverage embedded analytics, 35% leverage augmented analytics, 23% leverage low-code/no-code tools, and 22% leverage natural language querying, according to a recent report from Logi Analytics.
The Data Literacy Continuum
Setting out to reach data fluency is impossible without an understanding of where your organization currently stands on the data literacy continuum. This concept was recently explored by Logi Analytics and serves as a helpful guide to understanding the data literacy levels of organizations and individual employees. In the diagram below of the Data Literacy Continuum, most companies’ end users tend to fall into at least one of the following categories:
To make better sense of which quadrant applies to your organization, here is a brief explanation of each term:
- Data challenged – Companies and individuals where access to data is limited and analytics capabilities are rather rudimentary, leaving the organization unable to make business decisions based on data. This general points to a siloed work environment and a lack of a data leader willing to prioritize building a data culture.
- Data literate – Companies and individuals that have widespread access to data, but may not necessarily have the skills or self-service capabilities to use that data to make importance businesses decisions. Businesses and workers that are data literate often are in an ideal position to work towards becoming data aware or data fluent.
- Data aware – Companies and individuals that developed a good handle of their data analytics capabilities, but access to data is limited to select job roles internally. These organizations might offer a broad rollout of analytics to their customers, but it’s difficult to determine what can be done with the information in the workplace.
- Data fluent – Companies and individuals with widescale access to robust and sophisticated data analytics capabilities. These organizations have built a strong culture that prioritizes data-driven decision making.
Transforming your company’s data culture begins by understanding which quadrant your organization is currently in, then investing time and resources to make realistic improvements along the dimensions. Achieving data fluency won’t happen overnight; your organization must make a concerted effort to boost analytics capabilities and access, the two important stepping-stones to reaching data fluency.
Depending on their starting point, organizations may find themselves in need of a specific approach to data fluency. Here are some paths they might consider:
The Typical Path to Data Fluency
Majority of organizations find themselves in the data-literate or data-aware quadrants, trying to find their way towards becoming completely data fluent. This means they either have low levels of access to analytics, or lag behind on analytics capabilities. Powerful analytics tools and more access to such tools are two critical elements to reaching data fluency. In building a data culture, it’s imperative that data be available to as many employees as possible, and combined with a robust set of customizable capabilities that enables every user harvest insights from that data.
The transition to data fluency from either of the adjacent quadrants is still highly dependent on the organization’s strategy of applying technology, process and human resources. However, organizations can combine executive support, skills enhancement, data governance, analytics best practices and integration of data analytics into end-user workflows as ways to begin the transition to data fluency.
Path to Data Fluency for the Data Challenged
If your organization is currently data challenged, reaching data fluency is still possible. The most common path to data fluency starts with moving from data challenged to data literate, and then aiming to become more data aware before ultimately becoming data fluent. This means a broad rollout of basic analytics to expand access, followed by boosting analytics capabilities for a targeted set of users with the goal of optimizing capabilities for the rest of the organization over time.
Keep in mind, modern workplaces typically cannot move from data challenged straight to data fluent. It’s an unrealistic goal and there are multiple elements that must be successfully implemented before total data fluency so there must be a level of patience to move your organization forward in a meaningful manner.
This approach offers a realistic roadmap based on different maturity levels, creating the quickest path for most organizations to reach data fluency.
To achieve this goal, it’s important to implement actionable processes, apply flexible technologies that are future-proof, and onboard employees throughout the organization as data champions. These people are able to increase awareness, share data literacy education, and help recognize where data-driven actions and decisions can make the biggest impact.
Meanwhile, collected data must be used in the decision-making process, with analytics best practices driving the path forward. Collected data cannot be siloed, with robust data governance used to help employees collaborate on decision-making.
Although it may seem daunting, there are strategies to help your organization become data fluent. Company leaders must dedicate resources to create a digital transformation strategy powered by data-driven decision making. A disorganized analytics effort is a missed opportunity that hinders the organization from leveraging the people, processes, and technology that can push their business to the next level.
To learn more about taking your company’s data culture to the next level, read the latest report from Hanover Research’s recent survey, 2021 State of Analytics: How Data Literacy Improves Decision-Making, which explores where end users fall on the data literacy continuum and how well applications are supporting employees with different levels of data literacy.