How can you answer questions about the business with siloed data?
We design and build data architecture to help you connect information with the right tools and generate insights to improve decision-making.
Having data centralized in the right place allows you to efficiently model the relationships, perform analytics, and generate insights automatically and at scale.
Our experts help you build the right data architecture so everyone in your organization can access the data they need to perform at a high level. Even your non-technical teams will be empowered to make better, data-driven decisions.
Instead of a predefined process, our experts meet you where you’re at—whether you need help figuring out your architecture or already know your requirements and need us to deploy and optimize.
Our Visioning Program provides strategic consultation to help you formulate, refine, and game-plan your data vision for success. We bring our wealth of industry experience and technical expertise to solve highly complex infrastructure challenges.
Once we define your vision, we identify data sources and design architecture to support and streamline your data processes. We create a strategy to align with your objectives, priorities, budget, and timeline.
Many people throw good money after bad because they don’t have the right tools to support their vision and architecture. Not with us. While we often recommend the Microsoft Data Stack and Azure Data Factory, we’re also experts in Python, Snowflake, and Databricks.
Timely reports and visualization empower business users to turn data insights into action. Our Power BI experts help you build and automate reporting so everyone in the organization can access the data they need, whenever they need it.
Even resourceful organizations struggle to keep up with the ever-evolving BI and data architecture technology landscape. You can stay agile with Dedicated Support, which gives you exclusive access to our team of experts.
You probably have a wide range of systems throughout your organization, like your CRM, ERP, and financial systems. Data architecture centralizes these various data sources to support the efficient storage, management, and access to data and insights.
How It Works:
Centralization: Data from different sources is centralized into a data lake, which can store raw data in its native format.
Structuring: The data is then structured into a data warehouse using tools like Azure. This process involves organizing data into a schema that facilitates efficient querying and analysis.
Access and Analysis: Analytics tools like Power BI can be layered on top of this architecture. These tools enable data visualization, reporting, and advanced analytics, including AI initiatives.
The goal is to democratize data, making data accessible across all departments so teams can easily leverage data-driven insights for enhanced decision-making.
Not that simple.
Data architecture is a combination of technologies designed to solve specific problems. Our team has competence in many different tools, so we can help you combine them to meet your needs.
While Azure is great for many enterprise teams because it strikes the right balance between user-friendliness and access to advanced features and customization options, we’re by no means a one-trick pony.
Besides Microsoft, we’re competent in Python, Databricks, and Snowflake so we’re well-equipped to build the best solution. We may use multiple elements from Azure or combine your existing on-prem applications with some cloud services.
A great starting point when designing your architecture is Collectiv’s Visioning Program. Through Visioning, we’ll help you choose the right tools for the job or figure out the optimal way to work with your existing architecture.
Data architecture has many moving parts. Here are more details on how it can support your organization.
If you have additional questions or you’re ready to move forward, reach out to the Collectiv team.
As the leading Microsoft consulting firm, organizations partner with Collectiv for their most important strategic initiatives by building a strong data architecture, improving analytics processes, and fully leveraging AI’s capabilities.
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