Data solutions are designed to empower organizations with transparency, standardization, and enhanced utilization of their collected data. At CTI Data, we focus on developing and implementing enterprise data solutions that yield substantial returns on investment by focusing early on the underlying characteristics of a successful platform and a strategic roadmap defining how to get there. These solutions enable organizations to optimize operations, make informed decisions, and drive efficiency across various departments.
While implementing an enterprise data solution entails some costs, these costs are far outweighed by the benefits of that implementation. Developing a strategic, detailed, and executable strategic data roadmap may require an initial investment, but it sets the foundation for a robust solution. The architecture design, infrastructure development, and data governance management can be time-consuming and demanding of skilled individuals. However, organizations can break down these tasks into smaller projects to achieve early wins gaining support from end users. Although these projects require time and resources, hiring or training can address any necessary skill gaps. Provisioning tools for developing and using the end product is essential for success.
These projects often receive high visibility and executive-level prioritization within organizations. While this may involve allocating top-level resources in planning meetings, it also fosters collaboration and alignment among business units. Introducing an enterprise data implementation program stimulates conversations and creates an environment of future-oriented thinking. While this may temporarily divert attention from daily work, it paves the way for innovation and dedication to improve regular business activities. The allocation of budget and resources to data initiatives represents an investment in the organization’s future growth and success.
Although each organization’s data implementation is unique, there are common factors that contribute to successful data solutions.
The success of a data solution hinges on obtaining buy-in from the entire organization, not just the Data or IT teams. Collaboration among business and technical roles is crucial for building functional use cases, identifying organizational goals, and sharing knowledge. By creating a centralized solution, data sets are no longer siloed within individual business units. Instead, they become widely understood and utilized. Standardized definitions ensure clarity and alignment across the organization, facilitating effective communication and decision-making. With organizational buy-in, data solutions become comprehensive, well-utilized, and representative of the organization’s needs.
A successful data solution strikes a balance between business-driven initiatives and technical expertise. While business units may be enthusiastic about implementing data solutions, their lack of data expertise can hinder progress. In such cases, a cohesive technical plan is essential to transform intentions into actionable steps. Collaborating with Data and IT teams ensures a well-structured technical stack and efficient implementation. By combining business objectives with technical proficiency, organizations can avoid wasting time and resources on projects that do not materialize into functional products.
When planning a data project, it is essential for organizations to assess their current position on the data analytics maturity lifecycle. While predictive modeling and machine learning are exciting trends, they require a certain level of data maturity to yield optimal results. Organizations can prioritize foundational projects that enhance data quality, governance, and infrastructure by acknowledging their current data maturity stage. This approach ensures that when the organization is ready to delve into more advanced topics, it can do so swiftly and accurately.
By embracing effective data solutions, organizations unlock immense potential for growth, efficiency, and informed decision-making. The positive impact of these solutions far outweighs any initial challenges or costs, creating a solid foundation for future success.
Enterprise data solutions should be approached with a “walk first” mentality, focusing on establishing a solid foundation. The initial step in any data program is understanding the data you have. If data is not being collected, it’s essential to start collecting it. If there is data collection but lacks a centralized infrastructure, building that infrastructure becomes a priority. Similarly, if a centralized infrastructure lacks data quality, data cleansing should be addressed. Each step is crucial to properly building a house that meets building codes. A solid foundation ensures stability and longevity.
Data literacy plays a vital role in the success of any data implementation. At an organizational level, data literacy aligns with data maturity. However, assessing the overall data literacy of your organization and individual employees during the early stages of enterprise data implementations is crucial. Employees unfamiliar with most effectively using data may not fully engage with the final product, impacting productivity and quality.
Integral to a successful data implementation is a robust data training program. This program should cater to individuals at all skill levels and be data-driven, utilizing surveys to gauge employees’ current and desired data capabilities. By tailoring training to meet employees’ needs and providing them with the necessary knowledge, engagement and usage of data will increase and drive positive outcomes.
Before constructing an enterprise data solution, it is common for different teams or business units within an organization to maintain and use their own individual data sets. Some data sets may exist within the organization’s systems but are no longer managed by the original employee, making them inaccessible to others. This fragmented approach often results in inconsistencies, inaccurate definitions, and data hoarding.
Addressing this issue requires thorough data source discovery and subsequent centralization. The project team must diligently uncover all data sources and uses during the planning phase. Missed data sources can lead to a lack of standardization and incomplete metadata. Integrating siloed data sources into a centralized and standardized system requires agreement on terms and documented definitions to ensure accuracy and consistency. Over time, a cultural shift toward data collaboration and sharing will foster greater standardization and improved data literacy among end users.
Data solutions hold immense organizational potential, driving efficiency and accuracy through centralized data, standard definitions, and increased transparency. By reducing the time spent searching for and cleaning data, end users can focus more on utilizing data to achieve their objectives.
To unlock these benefits, it is crucial to approach data projects with a step-by-step mindset. Avoid an all-or-nothing approach that can drain resources without tangible outcomes. Instead, prioritize goals, identify measurable wins, and work in shorter sprints. This incremental approach allows for continuous improvements and ensures that end users can witness and support the positive changes that data solutions bring. Moreover, it enables early margin growth throughout the project, rather than waiting for significant changes months or years later.
Implementing a data solution is undoubtedly complex. Organizations can develop functional data solutions that provide exponential results over time by creating detailed plans, defining tangible goals, working in short sprints, and embracing continuous improvement. Regularly assess your progress, compare it to your overall goals, and be open to making necessary adjustments. These priorities will help your organization stay relevant and innovative in an increasingly data-driven world.
Contact Us for a consultation on our approach to designing and implementing enterprise data solutions.
Amanda Darcangelo (she/her) a Senior Consultant at CTI in our Data and Analytics Practice.
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