ca. 30 Minuten
nach Wunsch auch mit VideocallComprehensive Transparency - but how? This is what this blog-series is all about:
Tableau, Looker, Incuda, Power BI or Supermetrics - they all do some kind of magic with data and often this is labelled as Business Intelligence (BI), but where exactly is the BI? The selection of software solutions is large and now it has become difficult to keep track of all the offerings. This guide provides a remedy in the form of 4 consecutive blog articles - based on our comprehensive BI-centered consulting and development activities in the online sector. For everyone who wants to understand the differences. And especially for online retailers.
Episode directory
- Business Intelligence - see the big picture. Retailers have to consider this in their BI decision
- Comparing Business Intelligence Software - Tableau vs. minubo
- Looker vs. minubo - Business intelligence software compared (coming soon)
Episode 3
Supermetrics vs. minubo - Who Does What With Data Here?
In this third episode of the Business Intelligence blog series, we take a closer look at Supermetrics. The aim is to find out where the differences between Supermetrics and minubo lie, which requirements the software covers and what needs to be known and considered during the BI project, and the setup of the infrastructure for the respective solution.
For Marketing (and beyond)
Many of you will know this already: in a data landscape, the relevant data is often distributed across multiple tools, applications, systems and sources in various departments or across the company. These data silos do not provide a consolidated and complete view of the business across all channels. It is therefore impossible to rely on your data and create detailed analyses as a basis for decision-making.
This is where Supermetrics and minubo come into play - but with a different approach and scope. If the focus at Supermetrics is on bringing together all marketing data from all conceivable channels and campaigns and making it available in self-service for the marketing department for further analysis, minubo adds to this the integration of shop, ERP, PIM (and many more), as well as data from marketing, to form a holistic basis and thus enables comprehensive and company-wide transparency, and data-driven processes in all departments.
Supermetrics and minubo in Comparison
Supermetrics - “We get Data”
Supermetrics “gets Data” - but what exactly does that mean? If you research on the Internet, you can find various names for this software provider: reporting tool, ETL solution or data connector. It doesn't seem very clear under which generic term Supermetrics is now being classified. Not to worry, because the use and function of the software can be described relatively clearly.
Supermetrics collects marketing data from over 60 different data sources, such as Facebook, Mailchimp, Google or Bing, and integrates them into a preferred reporting, analysis, and storage platform - regardless of whether it is a BI tool, a spreadsheet, data visualization, data lake or data warehouse.
Supermetrics is thus a fully managed, code-free marketing data pipeline, which can be implemented easily, even without specific IT knowledge. A gain for every marketing department, because Supermetrics makes it possible to skip the often time-consuming and error-prone steps of data collection, and to automatically access the relevant data using self-service via a user-friendly interface. The integration of the data in a visualization tool (such as Data Studios) enables central monitoring of the overall performance as well as in the individual channels.
minubo - The Complete Business Intelligence Solution
The minubo Business Intelligence solution, which is specialized in retail, covers a wide range of requirements in the data value chain. In addition to the extraction of data from marketing, data from all relevant data sources (POS, ERP, online, store, etc.) of a company is consolidated into a holistic database in its own data warehouse and, based on the integrated data model, is modeled.
The data model now includes almost 1,000 key figures, attributes and metrics from the retail environment and thus saves you time and effort you might have spent developing these key figures and logic yourself. The data is then available for analysis and visualization in your own minubo front end or it can also be flexibly integrated into third-party systems (marketing systems, commerce platforms, AI & machine learning modules, etc.) to establish intelligent processes and automation.
minubo thus enables comprehensive transparency and a company-wide, data-driven work culture in which every employee can access the data relevant to him or her using self-service, and make decisions based on it.
If you haven't read it already, I recommend Article 1 of this blog series “Business Intelligence - Retailers have to consider this in their BI decision”. This conveys a general understanding of the structure and the different components of a BI solution to better classify requirements coverage and criteria for functionality, because ...
…as always, the difference between the two solutions lies in the architecture
Let's take a look at the BI infrastructure, which we usually divide into four levels: Data Sources, ETL process, Data Warehouse (DWH), and data Visualization and Analysis. Based on the graphic, it becomes quite clear that both solutions cover a different part of the data value chain. At Supermetrics, the main focus is on the integration and provision of data, primarily marketing data. This is only a part of the entire Business Intelligence setup, which minubo covers as a complete solution, in addition to other requirements. What does that mean exactly for functionality?
Requirements Coverage in Detail
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Supermetrics |
minubo |
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Level 1: Data Source |
As a marketing data pipeline, Supermetrics can extract data from 60+ different data sources and then load it into any other data preparation or BI tool or database for further processing via an interface to Google Sheets, Google Data Studio, Google BigQuery or via the Supermetrics API. However, the focus is clearly on marketing data. Only very selected sources are available for systems from the commerce sector. If the desired system is not included, this means for the user that they have to extract the data themself with the help of additional software solutions - a process that not only requires technical expertise but also time. |
minubo has connectors to all common operational systems. Thanks to a large number of standard interfaces and individual data pipelining, data can not only be obtained from marketing systems, but from a wide variety of source systems (such as CRM, shop systems, web tracking, ERP or merchandise management, etc.) using a “plug and play” approach can be integrated into minubo and further processed directly.
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Level 2: ETL |
Supermetrics extracts marketing costs and interaction data from a wide variety of marketing-relevant sources and loads them into the desired third-party system for further use (data warehouse, BI solution, data visualization, machine learning applications, etc.). In order to ensure a compliant format, the data can be converted into a uniform data scheme after export (without any coding). The marketing manager thus has automated direct access to the marketing data. |
minubo has its own ETL process, which was built in Apache Spark, an analytics engine for big data processing. This guarantees high performance and efficiency and, above all, full scalability. For the customer, this means no additional effort and access to reliable data in correctly validated form, which is automatically made available every morning. |
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Level 3: DWH |
Supermetrics has special data warehouse products in its portfolio, combined with other software solutions, into which the desired marketing data can be integrated with just a few clicks via an interface: Supermetrics for BigQuery, Azure, Snowflake or Amazon S3. If you decide on a different storage system, an interface can be configured with the Supermetrics API product. |
As a complete BI stack, minubo is equipped with a flexible analytics database as a data warehouse as well as an integrated data model, which now has almost 1,000 key figures, attributes and metrics - tailor-made for the reporting and analytics needs of commerce companies. Based on elasticsearch, a particularly fast search engine technology, minubo has developed the analytics database, which guarantees outstanding performance at all levels of data query - from the highest aggregation to the raw data level. This not only ensures that minubo can offer a wide variety of data tools, but also enables the data from minubo to be used flexibly in third-party systems. |
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Level 4: Visualization & Analysis |
Supermetrics itself does not have its own reporting tool. In addition to connectors to storage solutions, the product portfolio also includes interfaces to visualization and analysis tools. The data can be integrated via the Supermetrics Connector to Google Data Studio, Google Sheets or Excel in order to create dashboards or reports and analyze data. In addition, data can be easily integrated into visualization tools such as Power BI or Tableau via the Supermetrics API. |
minubo itself has its own strong front end, which, thanks to its ease of use, enables not only analysts but the entire organization to establish a better, data-driven decision-making culture and intelligent process automation. The comprehensive web-based tool set not only includes a variety of different visualization options, such as dashboards and reports, but also offers access to a high-performance data warehouse and thus the possibility of operationalizing data and using other third-party systems (e.g., marketing systems, commerce platforms, external analytics & Reporting, external AI & machine learning modules, etc.) via standard interface. Users can establish a real data-driven work culture with tools such as segmentation, proactive alerting or flexible ad-hoc analyses. |
Comprehensive Transparency - but how?
Anyone who wants comprehensive transparency over their company data cannot avoid a holistic data infrastructure, for example, in the form of a BI setup. As always, the question arises: 1) do I build my BI infrastructure with my own tech stack and Supermetrics as a sub-component or 2) do I use a standardized BI solution that covers most of the data value chain? We looked at a very detailed overview of the advantages and disadvantages in terms of costs, flexibility, time expenditure and risk of a self-made BI solution vs. a complete solution in Article 1. I recommend looking at it again. For our scenario, two basic factors can be emphasized again in addition to the general aspects.
Supermetrics is a powerful tool when it comes to providing marketing data. It can be implemented quickly and easily - without specific IT knowledge. Everyone, from marketers to non-technical analysts, can set up the data transfer in a few minutes, which drastically reduces the time required for implementation. Supermetrics is aimed at marketing managers and marketing analysts who want to bring their relevant data into a common picture. The tool enables the user to query, manipulate and automatically provide the data in a third-party system.
A big gain for every marketing department, but for no others. Because Supermetrics alone is not enough to generate a holistic, consolidated database - the basic requirement for data-driven decisions. Why? Supermetrics is “just” a data integration for marketing systems. A pretty good one in this specialized area, because the tool has connectors to the majority of all relevant marketing systems and thus enables quick access, but there are no 1) connectors to, for example, ERP or shop systems such as Shopify, Magento and so forth, which are essential for eCommerce companies. If an online retailer wants a holistic assessment of marketing activities, this data must also be integrated in a holistic data pool. For this, interfaces must be built independently and/or stacked on other systems in the tool landscape.
And 2) Supermetrics only covers part of a BI infrastructure and therefore only works in combination with other software and storage solutions, such as Data Studio, Azure or Power BI, in order to be able to view, analyze and visualize data holistically. The advantage is that you can flexibly assemble your tech stack according to your needs and requirements. The disadvantage: the more tools that are used in the infrastructure, the more technical expertise is required and the greater the number of potential sources of error. As already mentioned, you can read a detailed insight into the advantages and disadvantages of “Build vs. Buy” in the first article of this blog series and in the blog from data expert Lennard Stoever.
In contrast to Supermetrics, with minubo, you get a standardized and complete solution, that is, you purchase an entire Business Intelligence infrastructure from a single source. Thanks to the holistic approach - tech & tool in one solution - minubo covers everything from data integration, ETL, databases and data models to visualization, analysis, and automation. No further software is required to be able to rely on meaningful and reliable results based on data.
So why go for more effort, costs and a not as complete solution in the end, when it can be done easily, quickly and by a single source?
If you are interested in learning more about the basic approach to a BI project or its most important success factors, I recommend reading the following: The Commerce Intelligence Blueprint.
