Installed Analytics Free Of Charge Business Intelligence Tools – Imagine you are a business analyst in a fast fashion brand. You are tasked with understanding why sales of new clothing in a certain region are declining. Your job is how to increase sales while achieving the desired profit standards. Some variables to consider are buyer personas, website reviews, social media mentions, daily and hourly sales figures at different store locations, holidays or other events, expected payment dates at local companies, even heat map data for every store, and current. Plan. . .
That’s a lot of data stored in different formats. You have to extract it from various systems or sometimes collect the missing data yourself. Then to move the data into a single repository, explore and visualize it, define connections between events and data points. Too much size, too much data to process. There must be a data management strategy, and there must be an IT infrastructure to implement this strategy. That’s what business intelligence (BI) is all about.
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Business intelligence is the process of accessing, gathering, transforming, and analyzing data to reveal knowledge about a company’s performance. and then use this knowledge to support decision making.
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Yes, the process. This means that BI encompasses the 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 stages:
Phase 1. Data Extraction – Connecting to original data sources and retrieving data from them. Data sources may be internal (database, CRM, ERP, CMS, tools such as Google Analytics or Excel) or external (order confirmations from suppliers, reviews from social media sites, public repositories, etc.).
Phase 2. Data Transformation – Placing data in a temporary storage area known as a staging area. Data formatting according to the requirements and standards set to make it suitable for analysis.
Phase 3. Data loading – moving standard data to the final storage destination – database, database, or data warehouse. If necessary, the creation of data marts – a subset of data warehouses to store information from each unit of the company, HR or sales department, for example.
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Steps 1, 2, and 3 are included in the ETL operation (extract, transform, load). The ETL process defines how heterogeneous data is retrieved from different sources, converted into a format suitable for analysis, and loaded into a single destination. We will not spend much time explaining it here since we did it in a dedicated article about ETL developers.
Previously we wrote about steps to implement a business strategy, where we touched on data integration tools and data warehouses. In this article, we will dive deeper into the tools and services needed to create and maintain the flow of data from system to system with further analysis and visualization.
You need all the BI infrastructure. When you want to set up a BI process from scratch, consider a provider whose analytics solution includes modules with ETL, data warehousing, data analysis and visualization.
You have a goal to create custom BI. You have a tech on board who can develop a custom BI platform or part of it but are looking for something to build. In this situation, we recommend looking at libraries, frameworks, and tools that are capable of performing any data processing procedure.
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First, let’s explore several A to Z solutions by some of the leading providers. These BI platforms include ETL and data warehousing services, along with analytics and visual reporting.
We checked the Gartner 2019 Magic Quadrant for Analytics and BI Platforms (as of January 2019) and the list of the best BI software by G2 Crowd.
Sisense is a business analytics platform that supports all BI operations, from data modeling and exploration to dashboard creation. It supports on-premises, cloud, and hybrid deployment scenarios.
Source of information. There are two ways of obtaining data with Sisense: importing it to ElastiCube, the proprietary database of the solution, or connecting directly to various sources. The second option is called live connection and works well for frequent data changes.
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Links to additional resources that are not officially supported can be obtained from the Sisense community. A full list of supported sources and categories is in the documentation.
Data transformation. The platform provides many features for data transformation. For example, it analyzes how attributes are spelled in a table, grouping them by similarity to help users match attribute names. Interfaces make manipulation with the data model simple.
Data visualization. Dashboards are created on the web. Users can add widgets to dashboards. The selection of reporting options is extensive: sunburst widgets, calendar heat maps, scatter maps, as well as line, pie, or bar charts, box plots, polar charts, and more.
Sisense has tutorials, videos, and documentation, which are a great help in understanding how to fully use the platform. To receive a quote, please fill out the form.
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Microsoft Power BI is a cloud self-service business analytics solution for surveying and Analyze visual data on location and in the cloud. The platform allows real-time monitoring of data on almost any device across all major OS, with the ability to enable mobile notifications of any changes.
Features: Users can combine all files within a specific folder if they have the same file type and structure, for example, the same column. When users combine files, they may apply additional transformation or extraction steps if necessary.
There is also the ability to specify data types on columns so that Power BI creates the correct visualization for them. Columns with such ambiguous information can contain geographic abbreviations: CA is the state of California and Canada or Georgia is the state of the United States and a country.
Standard features such as filtering rows of data fields, converting text records to numbers, renaming columns or tables, or setting the first row as a header are also available.
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Data visualization. Customers have many images to choose from. The tool will upload custom images, pin them to the dashboard as a site or change their layout to see what works better with the given data. You can see through the list of images available here. The platform also has pre-built dashboards and reports by popular providers such as Salesforce or Google Analytics.
Users can choose between a free limit (1 GB of storage with a page load cycle daily monitor) or professional version (10 GB of storage from $9.99 per user per month). To start using the tool, just register.
Tableau, which ranks among the leaders of the Gartner Quadrant, offers several products to support end-to-end analytics work.
Tableau Server is one of these solutions. It is an enterprise analytics platform that can be deployed on-premises (Windows or Linux), in the public cloud (AWS, Microsoft Azure, or Google Cloud Platform), or fully hosted by a service provider.
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Source of information. The platform connects to data sources both in the cloud and on-premises through web data connectors and APIs.
Data transformation. Users can schedule and manage the flow of data with another tool, Tableau Prep Conductor, which comes within the data management add-on, to embed dashboards into other applications, and integrate different data sets. Also, 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’ve mentioned a few ways to manage data, but there’s certainly more to it than that.
For example, it has an Ask Data feature that allows users to ask questions from any data source published in natural language (through typing) and receive answers in a visual format. The feature is based on an algorithm “to automatically profile, index, and optimize data sources.”
Tableau Server is included in three software packages: Tableau Creator ($70), Tableau Explorer ($30), and Tableau Viewer ($12). Pricing is per user per month if billed annually. The seller offers a free trial.
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Of course, it’s worth considering other top-rated sellers. For example Qlik, Looker, ThoughtSpot, products like SAS Business Analytics, Salesforce Einstein Analytics, or BI solutions by SAP. To find out more about Tableau BI tools, check out our related article.
Next, we will cover the second scenario – creating custom BI using special tools. Let’s start with data integration.
In order to make decisions that influence the business in both the short and long term, companies must have a complete view of their operational data. ETL, which includes the collection and integration of data from different sources into a common destination, helps to have a complete view of business data.
Now let’s take a look at the tools you can use to create an ETL pipeline to pull the puzzle data together. Gartner’s Magic Quadrant for Data Integration Tools is one of the sources we relied on when compiling this list.
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PowerCenter is an enterprise data integration platform by Informatica – a provider of data management, quality, security, and integration solutions. The metadata service layer, which ensures better quality and consistency of data, is one of the special features of the platform. For example, with the ability to audit data streams, analysts can “track what data was changed, when and by whom to support business and regulatory compliance for data audits and audits.” Metadata can be exchanged with different applications.
The platform supports batch and real-time data processing. It connects to both on-premise and cloud data sources through REST APIs.
Reviewers on Capterra and Gartner Peer Insights note the ease of use of the tool, even for people.
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