Almost 69% of projects fail due to a lack of precise requirements and communication.
You need to define clear business goals and objectives and determine what data is necessary to achieve those goals.
Kyle Garrett, who is in charge of Sococo's Sales and Marketing Operations, saw early on that the company needed to develop unique KPIs to help the marketing, product, and sales teams work toward specific business goals. The teams wanted more information than the corporate health Business Intelligence dashboards could give them (YTD revenue, Inbound Leads Generated this Month, and New Seats).
Garrett said, "We wanted a way to make sure that changes to the product weren't unnecessary." "Every change to the product made people's problems disappear." They decided that the best information to get would be a simple example of what their customers were worried about when it came to pain. Grow’s dashboard Business Intelligence helped the company see through it.
Collaborating with business users is key to ensuring your data models are aligned with business requirements, identifying crucial data elements that might have been overlooked, and avoiding data modeling pitfalls.
By involving business stakeholders in the data modeling process, you can promote buy-in and adoption of the model. When stakeholders feel involved and have a sense of ownership over the model, they are more likely to use it and trust the data it provides.
Organizations that involve business stakeholders in data modeling are 2.5 times more likely to have success with analytics initiatives than those that do not involve stakeholders.
With its collaborative BI tools, including marketing dashboards, Grow ensures each of the critical decision-makers gets a say in how data is collected, processed, and viewed at the end.
Defining a standardized data modeling process is crucial to ensure data models are consistent and accurate. Many researchers report that data standardization can lead to almost half the time reduction required to integrate new data sources.
A standardized data modeling process creates a model that can be easily reused across different applications, platforms, and databases. This reduces the time and effort required to develop new models and ensures data consistency across different systems.
For example, you might use an ER (Entity-Relationship) modeling tool to create your data model. ER modeling is a widely accepted standard for creating data models, and using an ER modeling tool can help you create consistent and accurate models.
Defining clear data definitions and standards are crucial for creating accurate and reliable data models. Data collected without proper standards can result in inconsistent, incomplete, or incorrect information. This can lead to inaccurate analysis and decision-making, which can have significant consequences for an organization.
For example, if The Younique Foundation in its finance dashboard did not have clear definitions for what constitutes a donation or how to record different types of expenses, its financial and administration dashboards may not accurately reflect the organization's financial health. This could make identifying areas where the foundation could reduce costs or optimize its operations difficultly.
Additionally, having clear data definitions and standards can help ensure consistency across different organizational departments or teams. This can make combining data from various sources, such as financial and donation records, and create more comprehensive reports and analyses easier.
Create a data dictionary that defines all the data elements used in the model. You might also create diagrams that show the relationships between data elements.
E.g., a possible data model for The Younique Foundation's finance and admin dashboard may include the following components:
By making clear documentation, you can ensure that everyone in the organization understands the data model.
Data governance refers to the processes, policies, and standards for managing data assets.
Grow's dashboard Business Intelligence tool can help incorporate data governance principles in several ways:
Data Catalog: Grow's BI tool can act as a centralized data catalog, allowing you to easily manage and organize all your data assets in one place. Maintaining a detailed inventory of all your data assets ensures that all data is appropriately classified, labeled, and tagged, a fundamental principle of data governance.
Data Lineage: Our advanced BI tool can help you trace the origin of data elements and their lineage throughout the organization. With data lineage, you can ensure that data is not being used unauthorizedly or violating any data privacy regulations.
Data Quality: Using Grow's Business Intelligence dashboards, you can identify data quality issues, such as missing data, incomplete records, or data inconsistencies. By identifying these issues, you can take steps to remediate them and ensure that your data is accurate, consistent, and reliable.
Role-Based Access: With our BI tool, you can implement role-based access controls, ensuring only authorized users can access and manipulate sensitive data. By controlling access to data, you can protect against unauthorized access and maintain your data's confidentiality, integrity, and availability.
To learn more about Grow’s BI tool, read Grow Features & Capabilities GetApp and experience our advanced in-built data governance yourself!
By soliciting input from business users, data modelers can identify and correct errors or omissions in the model, refine its scope and assumptions, and better align it with the needs and expectations of the intended users. This can lead to more accurate predictions, better decision-making, and improved business outcomes.
For example, you might survey users to get their feedback on the usefulness of the data model. You might also track usage statistics to see which data elements are used the most and which are rarely used.
According to a study by IBM, poor data quality costs U.S. businesses an estimated $9.7 million annually.
Testing the model on a separate dataset can help identify any errors or biases in the model, as well as assess its accuracy and generalizability. Validating and testing your data model increases your confidence in its accuracy and reliability.
Ensuring scalability and flexibility is crucial to ensure that your data model can accommodate changing business needs. By doing this, you can avoid redesigning the model in the future.
For example, you might create your data model using scalable data storage solutions in marketing dashboards to allow for future growth. You might also develop your data model to allow flexibility by incorporating data elements relevant to multiple business areas.
With Grow’s in-built data warehousing, scaling can be easy for your business operations.
Implementing change management processes is essential to ensure that changes to the data model are appropriately documented, tested, and communicated.
For example, you might require that changes to the data model go through a formal approval process and be tested and validated before implementation. By offering accurate data analysis, customizable dashboards, collaboration tools, and flexibility, Grow's Business Intelligence dashboards can make implementing change management processes more accessible and practical.
Transform your data modeling game with Grow's powerful Business Intelligence tool! Say goodbye to manual data processing and hello to streamlined insights that empower better decision-making.
Try Grow today and experience the power of accurate, real-time data at your fingertips.