A solid data management strategy is the foundation for everything in data analytics. Without good data management solutions in place, any effort to make your data useful and valuable will be ad hoc and limited in effectiveness.
Data management includes data centralization, data consolidation, and combining data from all your operational systems and databases. By bringing all your data together, you get a single, unified, comprehensive picture of your customers and operations. With that unified view, you can make decisions and deliver reports, dashboards, and insights to your customers and internal audiences.
Consolidate & Simplify Your Data
Navigate the challenges of data consolidation and data centralization with ease. Whether you need to consolidate and combine data from multiple sources, organize data in a structured manner, or ensure its accuracy, our platform provides the tools and expertise to streamline the process.
We leverage a combination of cutting-edge commercial and trusted open-source tools for centralizing data, including data warehouse software, to efficiently extract data from diverse sources such as SaaS applications, APIs, and databases. Our data management solution is centered around an approach of incremental data extraction, which not only reduces costs but also significantly boosts performance by focusing solely on new and updated data.
Data Management Solutions that Fit Your Needs
Our data management solutions are meticulously designed to cater to the distinctive requirements of businesses like yours. Setting us apart is our unique blend of advanced automated systems complemented by expert consulting and services. At the heart of our data management platform are industry-leading commercial tools, which include cutting-edge data warehouse software, ensuring that most integrations work seamlessly 'out of the box'. This flexibility allows Datateer to be integrated into specific areas of your operations, maximizing our impact.
Pricing with Datateer is transparent and scales with your needs. Costs are determined by the number of Data Assets under Management, granting you the flexibility to adjust the scope of what Datateer manages based on your evolving requirements.
Combine Data into a Unified View of Your Business
Datateer's data management solution combines data from various sources, centralizing it into a unified view of your business operations and customers. Built on leading data warehouse software, you have real-time access when you need it.
As part of our data management services, we centralize and consolidate data to give you a comprehensive view of your customers and operations.
All your data is stored in the data warehouse and can be easily queried using standard SQL. This means it's compatible with most BI tools, data management software, visualization platforms, and data query products. With Datateer, getting insights from your data is simple and efficient.
Scale Your Data Analytics
As your business grows, so does the complexity and volume of your data. At Datateer, we've engineered our platform to gracefully handle the challenges of scale. Our largest clients manage hundreds of billions of records spanning multiple terabytes, and while most aren't operating at that magnitude, our platform is ready for those who are. To ensure swift performance, we focus on extracting only new and updated data. Additionally, our dedicated Data Crew members are skilled at applying performance tuning to both database tables and queries, ensuring optimal responsiveness.
Integration is key in today's diverse tech landscape. Our platform is fully compatible with a broad spectrum of analytics tools, visualization products, and data management products. Whether you have a favorite BI tool or a custom-built solution, chances are, Datateer will work seamlessly with it.
In terms of handling demand, we're backed by the robust infrastructure of Google Cloud Platform and Amazon Web Services. The innate scalability of Kubernetes allows us to effortlessly scale to hundreds of parallel nodes for particularly expansive data sources, ensuring that peak loads and high-query demands are met with consistent performance.
"We created 100 different metrics very relevant to our customers. We have seen significant growth in our key accounts."
Paul Harty - Chief Strategy Officer @ Motion Recruitment
"We were data rich but information poor. When you are moving at the speed we are, you can't just throw people at that problem. Datateer gives us a full picture and saves us a ton of time.”
Kelsey Waters - Senior Director of Operations @ Equinix
"Datateer understood our data and consolidated all that information in a way that dramatically improved the speed and quality of client conversations."
Devin Mulhern - Managing Director @ Denver South EDP
FAQ (Frequently Asked Questions)
Data management is the formalized practice of treating data as an asset and managing it with the same rigor used for managing other financial assets.
Formal does not necessarily mean complicated or heavy-handed. Making formal decisions such as how to organize data sets and who can access them are both actions that fall under data management.
Data consolidation means bringing all data together in a way that it can be analyzed as a single, larger data set. This is especially valuable in modern businesses because they operate across many different products and tools. This reality results in many data silos.
With data living in many different places, it is impossible to get answers to comprehensive questions about your business or customers.
Data consolidation involves:
- Getting data out of its many siloes
- Centralizing it into a single database, warehouse, or lake
- Combining data into a single, unified model
Data centralization is bringing data from multiple silos into a single, central location. Data centralization is part of data consolidation and is an important step to unlock enterprise reporting and analytics.
Data management tools are built around the process of data centralization, as this is a key step in being able to report on consolidated data.
A data lake is a strategy for data centralization. It is a central location to store data that originated from various operational systems, databases, or other data silos.
Typically, data lakes are a place to upload files in a semi-organized way. Also typical is some process downstream of the data lake to actually make sense of and combine these data files.
Compared to a data warehouse, a data lake is not as structured. Data lake solutions are more of a “landing zone” or “dumping ground.” than a full solution to analyze centralized data.
A data warehouse is a database designed for analytical queries. This type of data analysis is sometimes referred to as OLAP, or online analytical processing. This is a helpful comparison with an application database that does OLTP, or online transactional processing.
Analytical queries often scan large swaths of data. They answer questions like “Show me ALL customers who fit profile X” or “Show me ALL sales for last month, by region.” These queries have to scan all the data, which can be large, so they require a special type of database design.
Comparing this to application databases doing transactional processing clarifies the difference. Transactional processing is typically concerned with small amounts of data that need to be returned very quickly. Such as “Update this one user’s last name to Smith.”
In the end, a warehouse is just a database. It can be queried like any other database. The difference is it is designed to handle large analytical queries.
Cloud technology enables better scalability, so most modern data warehouses are “cloud native” or built around cloud technologies.
A data warehouse performs analytical queries. It is designed to handle large amounts of data, so that you can ask broad questions like, “Show me ALL sales for last year, broken down by region and company size.”
A reporting and analytics strategy helps companies be data-driven and reap the benefits. An important part of that strategy is where the data will live that will drive all these analyses. A data warehouse is a purpose-built product to support this.
“Enterprise” is often misused by well-intentioned marketers. Its true meaning is that the product it is describing is meant to handle the concerns of a large organization.
Often, products are sold with different tiers, with the top tier being called “enterprise.”
The good news is that many products have lower-tier, “non-enterprise” plans that deliver all the functionality that most businesses need.
Both are strategies to centralize and consolidate data for reporting and analytics. A data lake is designed to be an easy way to get data from operational systems and other data silos into a central location.
In contrast, data warehouse solutions are designed to query and report on consolidated data.
Muddying the waters here is that often a data lake will provide some querying capabilities. And some data warehouses are easier than others to load data into. That means there is some overlap in their capabilities.
Nor are these two strategies mutually exclusive. If you have to decide between the two, a warehouse is a more foundational technology, while a data lake is more specialized.
At Datateer, we use both! The data lake makes it easy to get data into a central place. Our data lake is integrated into the warehouse, so we get the best of both approaches.