Business Intelligence Etl Tools – Imagine you are a business analyst at a fast fashion brand. Your job is to understand why the sales of a new clothing line in a given region are declining. Your task is to increase sales while achieving the desired profit benchmark. Some of the variables to consider are the buyer’s customer personality, website reviews, mentions on social media, sales data per store location by day and time, holiday or other event, expected payment dates at local businesses, and even heatmap data for each store and up-to-date planogram. . .
That’s a lot of different data stored in different formats. You have to extract them from different systems, or sometimes manually collect the missing data. Then, explore and visualize data by defining interconnections between events and data points to bring your data into a single warehouse. Too many dimensions, too much data to process. There must be a data management strategy and there must be an IT infrastructure to implement that strategy. This is what business intelligence (BI) is all about.
Business Intelligence Etl Tools
Business intelligence is the process of accessing, collecting, transforming and analyzing data in order to reveal knowledge about the company’s performance. And then use that knowledge to support decision making.
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Yes, process. This means that BI includes activities, tools and infrastructure that support the transformation of data from its raw form into readable graphs. This process can be seen as a chain of successive phases:
Phase 1. Data Extraction – Connecting to and extracting original data sources. Data sources can be internal (databases, CRM, ERP, CMS, tools such as Google Analytics or Excel) or external (order confirmations from suppliers, reviews from social networks, public repositories of data sets, etc.).
Phase 2. Data transformation – placing data in temporary storage called staging area. Formatting data according to specific requirements and standards to make it suitable for analysis.
Phase 3. Upload Data – Transfer standardized data to the final storage destination – database, data lake, or data warehouse. If necessary, creating data marts – subsets of data warehouses to store information from each unit of the company, e.g. HR or sales department.
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Steps 1, 2, and 3 are combined into ETL (extract, transform, load) operations. The ETL process defines how heterogeneous data is extracted from various sources, transformed into a form suitable for analysis, and loaded into a single destination. We won’t spend much time explaining it here as we did it in a dedicated ETL developer article.
Earlier, we wrote about the steps to implement the Business Intelligence strategy, where we touched on data integration tools and data warehouses. In this article, we will delve into the tools and services needed to establish and maintain a system-to-system data flow with further analysis and visualization.
You need the entire BI infrastructure. If you want to set up a BI process from scratch, consider vendors whose analytics solutions include modules with ETL, data warehouse, data analytics, and visualization services.
Your goal is to build a custom BI. You have tech people on board who can develop a custom BI platform or some parts of it, but are looking for building blocks. In this situation, we suggest looking at libraries, frameworks and tools that allow you to perform any stage of data processing.
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First, let’s look at some A-Z solutions from leading vendors. These BI platforms include ETL and data storage services, as well as visual analytics and reports.
We checked the Gartner 2019 Magic Quadrant for Analytics and BI Platforms (as of January 2019) and the list of the best BI programs by G2 Crowd.
Sisense is a business analytics platform that powers all BI operations, from modeling and data mining to creating dashboards. Supports on-premises, cloud, and hybrid deployment scenarios.
Data acquisition. There are two ways to get data with Sisense: importing it into ElastiCube, the solution’s proprietary database, or linking directly to the sources. The second option is called live connection and works well for frequently changing data.
Big Data / Etl And Business Intelligence/data Visualization
Links to additional unsupported sources can be obtained from the Sisense community. See the documentation for a full list of supported data sources and types.
Data transformation. The platform provides many functions for data transformation. For example, it parses the spelling of attributes in tables, groups them by similarity to help users standardize attribute names. The interface makes manipulating data models simple.
Data visualization. Dashboards are made on the web. Users can add widgets to dashboards. The choice of reporting options is wide: sunburst widget, calendar heatmap, scatter map, as well as line, pie or bar charts, boxplot, polar plot and many more.
Sisense comes with tutorials, videos and documentation which are a huge help in understanding how to get the most out of the platform. To receive a price offer, please fill in the form.
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Microsoft Power BI is a self-service cloud-based BI solution for exploring and analyzing visual data on-premises and in the cloud. The platform enables real-time data monitoring on almost any device across all major operating systems, with the ability to enable mobile alerts for any changes.
Function: Users can combine all files in a specific folder if they have the same file type and structure, such as the same columns. After users combine files, they can apply additional transformation or extraction steps as needed.
It is also possible to specify data categories for columns, thanks to which Power BI generates accurate visualizations for them. Columns with such ambiguous data may contain geographic abbreviations: CA for California and Canada, or Georgia for a US state and country.
Standard features are also available, such as filtering field rows, converting text records to numeric records, renaming columns or tables, or setting the first row as headers.
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Data visualization. Customers have plenty of visualizations to choose from. The tool will upload custom visuals, pin the visuals to the dashboard as a site, or change their styles to see which one fits your data better. Here you can browse the list of available visualizations. The platform also has ready-made dashboards and reports from popular providers, such as Salesforce or Google Analytics.
Users can choose between a free version (1 GB of storage with daily refresh cycles) or a professional version (10 GB of storage from $9.99 per user per month). To start using the tool, you just need to register.
Tableau, which has its place among the leaders of the Gartner quadrant, offers several products to support the end-to-end analytics workflow.
Tableau Server is one such solution. It is an enterprise analytics platform that can be deployed on premises (Windows or Linux), in a public cloud (AWS, Microsoft Azure or Google Cloud Platform) or fully hosted by a provider.
Etl In Business Intelligence: What You Need To Know
Data acquisition. The platform connects to both cloud and on-premises data sources via an online data connector and APIs.
Data transformation. Users can plan and manage dataflows using another tool, Tableau Prep Conductor, which is part of the data management add-on to embed dashboards in other applications and combine different datasets. In addition, the solution allows users to manage dashboard extensions – web applications that allow users to interact with data from other applications directly in Tableau via the Tableau Extensions API. We have mentioned a few ways to manage your data, but there are certainly more.
For example, it has an Ask Data feature that allows users to ask questions from any published data source in natural language (by typing) and get answers in the form of visualizations. This feature is based on algorithms “for automatically profiling, indexing and optimizing data sources”.
Tableau Server is included in three software packages: Tableau Creator ($70), Tableau Explorer ($30), and Tableau Viewer ($12). Price is per user per month if billed annually. The seller offers a free trial.
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Sure, other top-ranked providers are worth considering. For example, Qlik, Looker, ThoughtSpot, products such as SAS Business Analytics, Salesforce Einstein Analytics and SAP BI solutions. To learn more about Tableau BI tools, check out our article.
Then we will deal with the second scenario – building a custom BI using specialized tools. Let’s start with data integration.
To make decisions that will positively impact business in both the short and long term, companies must have full visibility into their operational data. ETL, which involves collecting and integrating data from different sources into a common destination, helps you get a complete picture of your business data.
Now let’s take a look at the tools you can use to build an ETL pipeline to solve the data puzzle. The Gartner Magic Quadrant for Data Integration Tools was one of the sources we relied on for this list.
Business Intelligence Concept With Olap, Data Mart, Etl (extract Transform Load), Realtime Reporting, Master Data Stock Illustration
PowerCenter is the enterprise data integration platform from Informatica – a provider of data management, quality, security and integration solutions. One of the features of the platform is the metadata support layer, which ensures better data quality and consistency. For example, with the ability to verify the provenance of data, analysts can “track what data was changed, when and by whom to support business and regulatory requirements for data validation and auditing.” Metadata can be exchanged with various applications.
The platform supports batch and real-time data processing. It connects to both on-premises and cloud-based data sources via REST APIs.
Reviewers at Capterra and Gartner Peer Insights note how easy the tool is to use, even for humans
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