- Driving better decisions.
- Reducing repetitive tasks.
- Streamlining time-consuming processes.
- Optimizing pricing.
- Enabling practitioners to forecast outcomes accurately.
Enterprises today are understandably eager to harness the power of data. In all industries, data science’s transformative powers include:
However, organizations must commit to a developmental voyage destined for data maturity to achieve these data-driven gains.
What Exactly is Data Maturity?
Data maturity is how an organization understands—and can make effective use of—its data resources. An entity has achieved data maturity by cultivating a workforce with the right toolset and mindset to support and sustain a complete data transformation. The ideal climate for optimum data usage is where data resources flow seamlessly between departments and into organizational processes and create positive outcomes.
Why is a Data Assessment Important?
Before an organization begins its quest for data maturity, it must establish a clear baseline of its current data maturity level. Determining an enterprise’s data maturity level requires examining specific indicators that signify its collective capacity to extract, analyze, and communicate data-driven insights that are meaningful and relevant. It is also important to gather insights into the workforce’s perceptions of data science technologies and the value they might contribute to different departments and functions. Understanding the degree to which a workforce is equipped to achieve these benchmarks helps organizations formulate a realistic roadmap of the most appropriate data science pathways to follow as they progress toward data maturity.
DataDNA Assessment
To provide organizations with a handy instrument for evaluating their data maturity level, Data Society has developed the DataDNA assessment tool. This survey probes an organization’s capabilities for effectively gathering, leveraging, governing, and applying their data assets. The DataDNA assessment captures a complete, multidimensional view of the workforce’s relationship with data tools and technology by aggregating responses that reflect employees’ perspectives across roles and departments. Thus informed, DataDNA ultimately produces a detailed description of the organization’s data maturity status and a prescription for reaching its data maturity goals. An AI-driven tool, the DataDNA assessment determines DataDNA type by distilling the critical components of data maturity into two strands, culture and actions:
The Culture Strand
The Culture Strand analyzes an organization’s existing conditions to evaluate its ability to develop a comprehensive data strategy and support sophisticated data programs. This strand includes:
- Infrastructure : An organization’s data infrastructure is its foundational framework for data management, including processes and policies for the collecting, storing, securing, and sharing data resources.
- Skills and Knowledge : Data skills and knowledge gauge an organization’s understanding of the importance of data, its familiarity with potential applications of data science, and its command of data science tools and techniques.
- Leadership : The data leadership rating for an organization reflects the presence and influence of stakeholders who are aware of data’s potential to transform the company’s future and are willing to champion the programs and procedures necessary to realize this vision.
- Sharing and Access : The scope and efficiency of data sharing and access are indicators of an organization’s progress in developing a cultural foundation that supports the smooth flow of data across the enterprise.
The Actions Strand
The Actions Strand examines a workforce’s application of data science tools and techniques to produce measurable results. These applications fall into four categories, including:
- Decisioning : The extent to which an organization acts upon data-driven insights, using data to inform operational and strategic decisions, measures its data decisioning position.
- Innovating : An organization’s ability to leverage data-driven insights toward creating and enhancing products, services, and marketing initiatives determines how it rates on the data innovating scale.
- Productizing : Effective data productizing or deploying data science technologies in product offerings for tracking usage and supporting customer service is a significant indicator of an organization’s level of data sophistication.
- Improving : Assessment of an organization’s use of data science tools to monitor quality, efficiency, and compliance provides insights into its capacity for data improvement to increase the efficacy of processes and services with data science applications.
DataDNA generates organizational data maturity profiles based on scores in each category. In addition, the assessment is sensitive to the significance of the interactions between these variables, producing a detailed view of an organization’s habits and climate with regard to data usage, access, sharing, and literacy.
How Do Organizations Use Their DataDNA Results?
By understanding its DataDNA type, an organization is in a strong position to begin developing a targeted data strategy. The DataDNA assessment results provide thorough narrative explanations of where an organization stands on the data maturity spectrum, the metrics on which results are based, and recommended steps to charge ahead toward data maturity. For most companies, an essential part of this strategy will entail ongoing data science training of staff across departments.
meldR, Data Society’s new Learning Experience Communications Platform (LXCP), offers organizations an effortless way to translate their DataDNA profiles into customized learning pathways. The first learning platform to incorporate course content, progress tracking, and the benefits of a community of practice into a single environment, meldR addresses the critical areas identified in the DataDNA assessment by delivering a comprehensive and personalized educational experience.
The Data Society team developed this platform to meet all organizational goals and needs, equipping it with features that foster communication between and among learners, L&D departments, instructors, mentors, and supervisors. Driven by the AI-powered meldR Engine, meldR also enables users to create personalized learning pathways based on organizational datasets, offering the educational benefits of relevant materials, collaboration, and individualized curricula.
Destination Data Maturity
There is now widespread recognition of the critical role data science technologies play in securing competitive edges in all industries. For this reason, many organizations are wisely implementing data science programs. However, isolated and fragmented measures will produce limited results. Instead, organizations committed to achieving the transformative impact of data must invest in a long-term vision that begins with assessment, progresses into training, and endures through sustained data maturity.
Unlocking Data-Driven Culture and Actions
DataDNA is an assessment tool that will help you get a clearer picture of your organization’s data maturity status. Data Society created this assessment to help organizations evaluate how effectively their data resources flow across departments and into the processes where they have most impact. The assessment uses two lenses – culture and actions – to gauge an organization’s data maturity, what we call its DataDNA.
