Companies that, sooner or later, diligently collect analytical data on their products, marketing campaigns and customers cannot avoid having their own data warehouse, in which data from various sources are consolidated and prepared for evaluation. ETL (Extraction, Transformation, Loading) solutions, with which these processes can be automated, are becoming increasingly important. We present professional alternatives for start-ups and SMEs.
- In Which Ways does a Data Warehouse enable business users to be more effective?
- Xplenty: ETL for the cloud age
- Segment: Focus on customer success
- Stitch: New alternative with great potential
- Panoply: ETL and data warehouse in one tool
- Data Warehouse for Small Businesses FAQS
In Which Ways does a Data Warehouse enable business users to be more effective?
In the PC era, small businesses were content with Word, Excel and other rudimentary office tools to manage their daily tasks. Larger companies that could afford it used an integrated set of ERP from SAP, IBM or Oracle, which had different modules for each department. Cloud computing has changed all that. Today, it’s no longer management or IT that decides which software solutions are used in the enterprise.
Thanks to the simple, fast and cost-effective deployment that cloud services make possible, the purchasing decision is increasingly being made by department managers. Instead of an all-in-one platform, companies are using solutions from different providers, each focusing on a specific problem area. According to a study by Siftery, a portal where companies list the applications they use, an average of 37 different software solutions are in use today; for large companies, the figure is almost 90.
With all the advantages that this “breed improvement” approach brings, there is one decisive disadvantage that must be accepted: data silos. Since each team works with its own favorite tools, important data is often inaccessible to other departments. This can quickly become a problem, especially in the analysis department. Because companies that want to better understand their customers or optimize their business processes have to bring together KPIs (Key Performance Indicators) from different departments and data sources and evaluate them centrally. Redundancy, inconsistency and incompatibility of data are inevitable.
Modern ETL solutions from the cloud promise a remedy. They allow companies to gather data from the most diverse sources, whether it is the numbers of visitors to the company’s website, customer information from the helpdesk system, or user actions recorded in a separate application. The data is extracted from the source, transformed through various tasks and then exported to a database, a data warehouse such as Google Bigquery, Amazon Redshift or a Data Lake.
There it is processed for analysis. Best of all, these ETL processes can be fully automated. This saves time and resources, because you no longer have to manually export all your data from the various systems and bring it into the data warehouse. Developers also benefit from ETL tools because they don’t have to implement data integration via the various solution providers’ APIs themselves.
Below are a number of modern ETL services from the cloud that follow different approaches and promise an easy start.
Xplenty: ETL for the cloud age
Xplenty is a comprehensive ETL platform developed in San Francisco that enables the integration and data processing of a variety of data warehouses and SaaS applications. These include local servers, private cloud environments and public clouds, as well as over 100 popular online services, from analytical tools such as Mixpanel and Google Analytics to productivity applications such as Slack, Asana and Basecamp, and comprehensive business solutions such as Salesforce. With the help of a modern user interface, users create their data pipeline by simply dragging and dropping.
The extraction, transformation and loading processes can be configured according to individual needs and fully automated. For example, the program can be configured to extract, prepare and export data from Google Analytics every three hours every day to Bigquery.
Segment: Focus on customer success
The segment is another data integration platform that was also developed in California. Unlike Xplenty, the start-up, which has raised about $100 million to date, focuses on customer data. Renowned companies such as Levis, Trivago and IBM rely on Segment to achieve a consistent understanding of their customers across the enterprise.
Segment is marketed as a “Customer Data Infrastructure” platform that enables companies to bring all customer information, no matter where it is created and stored, to a common denominator. The service enables automated data integration with more than 200 systems. Developers can also integrate the Segment API into their applications and capture all user actions and records.
Regarding pricing: The entry plan costs as little as $120 per month and is limited to tracking a maximum of 10,000 users per month. With the Free Plan, developers can use the solution for free with up to 1,000 users per month.
Stitch: New alternative with great potential
Stitch is a high-performance, easy-to-use ETL service that was launched just two years ago. It is designed to allow developers to deliver data to analysts and other departments in minutes, not weeks. It supports over 80 online services as a data source, including productivity applications such as Jira and Trello’s project management solutions and the harvest time tracking tool, which is not found in other ETL vendors.
Convenient: Instead of storing data in a separate data warehouse, it can be exported directly to business intelligence solutions such as Chartio or Klipfolio.
Stitching is also offered in a freemium model. Whoever wants to get a first impression of the possibilities of the solution can use it for free with up to five data sources, as long as the limit of five million rows of data per month is not exceeded. Companies that want to process between five and 250 million rows per month should put between 100 and 1,000 dollars per month on the table.
Panoply: ETL and data warehouse in one tool
Panoply proves that innovative software solutions do not necessarily have to come from Silicon Valley. The service developed in Tel Aviv is presented as an intelligent data warehouse that combines ETL tools and a data warehouse.
This means that users can not only connect different data sources and automatically extract and transform data, but can also store this data directly in Panoply. The extracted data can be used to create tables that are clearly arranged, freely configurable and ready for immediate use. Therefore, users who choose Panoply do not need to export the data to Redshift, Bigquery or any other data store. Popular BI tools such as Bime or Tableau software can be integrated to analyze the data.
Monthly prices range from just under $250 (Initial plan with 25 million rows and 12.5 gigabytes of storage) to $750 per month (Business plan with 200 million rows and 100 gigabytes of storage).
The most successful companies have a clear and detailed understanding of their customers and continually adapt as customer behavior changes. However, it is easier said than done to achieve this understanding and make it available in every department. If you want to understand the entire customer journey, you need to build an infrastructure to collect customer data from each channel, load it into a data warehouse, and then analyze it with a business intelligence tool.
Modern ETL cloud-based solutions that automate and accelerate these complex processes are becoming increasingly important. As we have demonstrated in this article, it is not only large companies that can benefit from this.
Data Warehouse for Small Businesses FAQS
How can big data help small businesses?
Businesses successfully mining Big Data are cross-referencing their internal information-pricing histories, customer traffic patterns-with multiple outside sources to increase revenue by understanding customers’ behavior better, reducing costs by eliminating inefficiencies and human bias, strengthening client bonds by anticipating clients’ needs, enriching service offerings with new knowledge, and giving employees new tools to perform their jobs better.