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Data integration involves combining data from different sources while providing users a unified view of the combined data. This lets you query and manipulate all of your data from a single interface and derive analytics and statistics. You can also visualize and even migrate your combined data to another data store for longer-term storage and further analysis.
The vast majority of companies will be focusing on data-driven marketing this year. By reducing technical complexity and creating a single point of access to all of your data, you can achieve a better understanding of your data and the reality it depicts.
Traditional data integration approaches often involve several steps. First, wIth pivot tables, the user manually integrates data using spreadsheets like Microsoft Excel or Google Sheets. This works well for very small datasets, but becomes cumbersome the larger the number of rows, or even when the number of required analyses becomes too large. This is often supplemented with manually downloading, scrubbing, converting, and uploading data on marketing spend and customer journeys. This data may come from a variety of sources, each requiring a different treatment. Finally, querying a data warehouse (or several) for structured summary data must also be done where important data is already siloed in different warehouses.
Alooma identifies the following four data integration best-practices for marketing organizations:
The first involves using a single ID so that every user has an ID that is joinable across different systems. The next best practice is collecting all raw data. Many marketing platforms only provide access to aggregate data. This makes it difficult to combine data from different sources, because concepts like "earnings" may be defined, typed, and stored differently by different sources.
The third best practice stipulates that KPIs are defined. It's important to ask "What are we measuring, and how?" Finally, it's essential to enable real-time responses. Sales and marketing, for example, can achieve much better results if they can see and react to data in real time.
However your data is used, you'll need to establish a data pipeline. Alooma addresses the following five data pipeline needs:
Alooma allows for a variety of integrations, so that you can connect to different data sources. Our tools let you transform your data, enabling you to enrich data before it's ready for analysis. With our smart error handling, when you do have an error, you'll always get your reports in time! Your Alooma data pipeline can automatically scale to accommodate even meteoric business growth (and event load)! Finally, Alooma's data pipeline works in real time, giving you the power of insights from data that is both in motion and at rest.
A variety of tools, platforms, and sources can be used to ingest and store data. Getting everything into one place can be a major challenge. What's more, new data sources are introduced all the time. This means scale and schema flexibility are paramount.
Aggregated data is often inferior to raw data when deriving analyses and insights to make data-driven decisions. However, your raw data may be in different, barely-accessible silos and/or formats.
The opportunities missed from not having the right information at the right time can lead to money left on the table and inaccurate predictions.
Whether your data comes from SalesForce, MongoDB, Oracle, or any number of other data sources, Alooma's data pipeline as a service has you covered. Transform and analyze your data in real time on your desired output platform, such as RedShift, BigQuery, Snowflake, and more. You'll have real-time access to raw data, not just dashboards.
Data integration can be a challenging process, but Alooma gives you the tools you need to establish your data pipeline, consolidate your data silos, and securely query your data to gain actionable insights in real time.