Pdf Microsoft Business Intelligence Tools For Stand Out Experts – Imagine you are a business analyst for a fast fashion brand. Your task is to understand why sales of a new clothing line are declining in a particular region. Your job is how to increase sales while achieving the desired profit benchmark. Some variables to consider include customer buyer personas, website reviews, social media mentions, daily and hourly sales data for different store locations, holidays or other events, expected pay dates from local companies, and even the popularity of each store. graph data as well as current sales data. planogram. . .
This is a lot of different data stored in different formats. You have to extract it from various systems or sometimes manually collect missing data. Data is then moved to a single store, explored and visualized, defining the interconnections between events and data points. Too many dimensions and too much data to process. There must be a data management strategy, and an IT infrastructure must be in place to enforce that strategy. This is what business intelligence (BI) is all about.
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Business intelligence is the process of accessing, collecting, transforming and analyzing data to reveal knowledge about a company’s performance. This knowledge is then used to support decision making.
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Yes, process. This means that BI consists of the activities, tools, and infrastructure that support the transformation of data from raw form into readable graphics. This process can be seen as a series of successive stages:
Phase 1: Extract Data – Connect to the original data source and retrieve data from it. Data sources can be internal (databases, tools such as CRM, ERP, CMS, Google Analytics, or Excel) or external (order confirmations from suppliers, reviews from social media sites, public dataset repositories, etc.).
Phase 2: Transforming the data – The data is placed in temporary storage called the staging area. Format data according to specified requirements and standards to make it suitable for analysis.
Phase 3: Load Data – Move the normalized data to the final storage destination – database, data lake or data warehouse. If necessary, create a data mart—a subset of the data warehouse that stores information from each corporate department, human resources, or sales.
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Steps 1, 2, and 3 are combined into an ETL (Extract, Transform, Load) operation. The ETL process specifies how to retrieve heterogeneous data from different sources, transform it into a form suitable for analysis, and load it into a single target. We won’t spend too much time explaining it here as we explain it in a dedicated article for ETL developers.
Previously we wrote about the steps to implementing a business intelligence strategy involving data integration tools and data warehouses. In this article, we’ll dive into the tools and services needed to establish and maintain data flows between systems, with further analysis and visualization.
You need your entire BI infrastructure. When you want to build a BI process from the ground up, consider a provider whose analytics solutions include ETL, data warehousing services, data analytics, and visualization modules.
Your goal is to build custom BI. Your technical staff can develop a custom BI platform or some part thereof, but are looking for building blocks. In this case, we recommend looking at libraries, frameworks, and tools with functionality to perform any data processing step.
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First, let’s investigate a few solutions offered by some leading providers. These BI platforms include ETL and data storage services, as well as visual analytics and reporting.
We checked Gartner’s 2019 Magic Quadrant for Analytics and BI Platforms (as of January 2019) and G2 Crowd’s list of the best BI software.
Sisense is a business analytics platform that supports all BI operations from data modeling and exploration to dashboard building. It supports on-premises, cloud and hybrid deployment scenarios.
Data Sources. There are two ways to get data with Sisense: import it into the solution’s proprietary database, ElastiCube, or connect directly to the source. The second option is called a live connection and is suitable for frequently changing data.
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Connectors from other unofficially supported sources are available from the Sisense community. A full list of supported data sources and types is in the documentation.
Data conversion. The platform provides many data transformation functions. For example, it analyzes how attributes are spelled in a table, grouping them by similarity to help users coordinate attribute names. The interface makes manipulating the data model easy.
Data visualization. Dashboards are created on the web. Users can add widgets to the dashboard. The choice of reporting options is extensive: sunburst widgets, calendar heatmaps, scatterplots as well as line charts, pie or bar charts, boxplots, polar plots, and more.
Sisense provides tutorials, videos, and documentation that are a great help in understanding how to get the most out of the platform. To get a quote, please fill out the form.
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Microsoft Power BI is a cloud self-service business analytics solution for visual data exploration and analysis on-premises and in the cloud. The platform allows real-time data monitoring on virtually any device across all major operating systems, with the ability to turn on mobile alerts for any changes.
Feature: User can merge all files in a specific folder if they are of the same type and structure (e.g. same columns). Once users combine files, they can apply additional conversion or extraction steps as needed.
It can also assign data categories to columns so that Power BI can generate accurate visualizations for them. Columns with such ambiguous data can contain geographic abbreviations: CA for California and Canada, or Georgia for US states and countries.
Standard features are also available, such as filtering field rows, changing text records to numeric records, renaming columns or tables, or setting the first row as headers.
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Data visualization. Clients have a variety of visualizations to choose from. The tool will upload a custom visual, pin the visual to the dashboard as a site, or change its style to see which style works better for the given data. You can browse the list of available visualizations here. The platform also features pre-built dashboards and reports from popular providers like Salesforce or Google Analytics.
Users can choose between a limited free plan (1 GB storage, daily refresh cycle) or a Pro plan (10 GB storage, starting at $9.99 per user per month). To start using the tool, just sign up.
Tableau is a Leader in the Gartner Quadrant, offering multiple products to support end-to-end analytical workflows.
Tableau Server is one of these solutions. 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 the provider.
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Data Sources. The platform connects to cloud and on-premises data sources through web data connectors and APIs.
Data conversion. Users can use Tableau Prep Conductor, another tool in the data management plugin, to schedule and manage data flows, embed dashboards into other applications, and combine disparate datasets. Additionally, the solution allows users to manage dashboard extensions – a web application that allows users to interact with data from other applications directly in Tableau through the Tableau Extensions API. We mentioned a few ways to manage data, but there are definitely more.
For example, it has an “Ask Data” feature that allows users to use natural language (by typing) to ask questions from any published data source and get answers in the form of visualizations. The feature is based on algorithms that “automatically analyze, index, and optimize data sources.”
Tableau Server is included in three packages: Tableau Creator ($70), Tableau Explorer ($30), and Tableau Viewer ($12). If billed annually, the price is per user per month. The provider offers a free trial.
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Of course, it’s worth considering other providers with the highest ratings. For example, products like Qlik, Looker, ThoughtSpot, SAS Business Analytics, Salesforce Einstein Analytics, or SAP’s BI solutions. To learn more about Tableau BI tools, check out our linked articles.
Next, we’ll cover the second scenario – building custom BI with specialized tools. Let’s start with data integration.
In order to make decisions that positively impact short- and long-term business, companies must have a comprehensive understanding of their operational data. ETL needs to collect data from different sources and integrate it into a common destination, which helps in obtaining a complete view of business data.
Let’s now look at the tools available for building ETL pipelines to bring data puzzles together. The Gartner Magic Quadrant for Data Integration Tools is one of the sources we relied on to compile this list.
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PowerCenter is the enterprise data integration platform from Informatica, a provider of data management, quality, security, and integration solutions. The metadata service layer ensures better data quality and consistency and is one of the features of the platform. For example, with data lineage verification capabilities, 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 different applications.
The platform supports both batch and real-time data processing. It connects to on-premises and cloud data sources through a REST API.
Reviewers for Capterra and Gartner Peer Insights point to the tool’s ease of use, even for the average person
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