How to Choose a BI tool

 How to choose a BI Tool             

             

             

In the process of choosing a Business Intelligence (BI) tool, here is a list of seven key capabilities that are recommended to consider. These capabilities will help you evaluate and select the best BI tool that aligns with your specific business criteria and goals:             

             

Data integration             

Data management             

Data warehouse             

Data analysis             

Data visualization             

Automation             

Data security             

Licensing Cost             

Self service BI             

Ease of user adoption             

Sharing Capability             

             

             

             

What is a business intelligence (BI) tool?             

Business intelligence software provides an interface to your raw data, giving you the ability to easily model, analyze, and report on disparate data. Through automation and visualization, these BI tools should augment decision-making processes and provide a better understanding of your organization's key metrics and drivers of those metrics.Business Intelligence develops methods, techniques and tools with which you collect and analyze data about your organization, goals and processes. The ultimate goal is to consistently make data-driven decisions at operational, tactical and strategic levels. So that you can optimize your processes and improve your results. You easily create (new) KPI dashboards, reports and data analyses with BI tools. You put all relevant data ready for the user. BI saves you a lot of time, creates one version of the truth and makes your decisions data-driven.             

             

             

             

             

             

             

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As per a recent study by Gartner entitled “The Future of Decisions”, 53% of respondents agree that there is a higher expectation for them to justify or explain their decision. Further, 65% of respondents agree that making decisions has become more complex now, compared to 2 years ago. It can thus, be concluded that organizations are becoming increasingly data-centric, which in turn is driving increasing demand for modern Business Intelligence and Data Analytics (BI) platforms.

Data generation and consumption patterns have transformed over the past few years. Analytics & Business Intelligence is evolving rapidly from simply “What happened” viewpoint to “Why it happened” and “What must I do in future” endpoints. It’s no longer enough to churn out reports and publish them to users in the hope that users will be able to find answers in those reports. Traditional BI architectures do not lend themselves seamlessly to modern data generation, consumption, and publishing requirements. Hence, the need for advanced Analytics and BI platforms and tools."             

             

             

             

             

             

             

             

             

             

Considerations for choosing a BI tool             

"BI tools are geared primarily toward either business users or data teams. Some tools, first and foremost, enable business users to explore data, while others enable real analytical powerlifting for deep business insights, but require more technical prowess. Advanced analytics platforms enable you to track metrics and KPIs according to very specific business requirements.

When considering implementing a BI tool, first think about who will be both maintaining it and using it. (Of course, any tool can and should be used and maintained by both groups—this distinction simply refers to the primary audience.)"             

             

             

             

             

             

Top Critical Analytics & BI Capabilities             

             

Security – To enable security, authentication, auditing of platforms, and administration of users.             

Governance – For tracking usage and managing the creation of information -right from prototype to production.             

Cloud-enabled Analytics – Building, deploying, and managing analytics in the cloud -depending on data on-premises and the cloud.             

Data Source Connectivity – Allowing users to connect while leveraging data in different storage platforms.             

Data Preparation – Delivering support for a user-centric, drag-and-drop combination of data from multiple sources along with the creation of analytical models.             

Data Catalog – Displaying content to make it simpler to observe and consume.             

Automated Insights -Ability to make use of ML or Machine Learning techniques towards automatically generating insights for end users.             

Data Visualization – Supporting highly interactive dashboards along with exploring data through understanding chart images.             

Natural Language Query – Allowing users to query data with terms either spoken or typed in the search box.             

Data Storytelling – Generating new styles of data stories depending on the ongoing monitoring of observations.             

Natural Language Generation – Automatic creation of descriptions that are linguistically rich and found in data.             

Reporting – Offering pixel-perfect, paginated, and parameterized reports that can be scheduled.             

             

"Which data sources can you get started with

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Internal company data: retrieve data from systems such as SAP, Microsoft Dynamics, Oracle, AFAS, etc. You usually retrieve this data via ODBC links.             

Social media data: monitor and analyze trends or sentiments on Twitter, Instagram, Facebook and Mastodont. You usually retrieve this data using a REST API.             

Open Data from the government , Commercial parties i.e Data Aggregators             

Web Data: contains data about visitors to your website and how they use it.             

Zero Data: data that you do not record but which, in combination with other data, can generate very valuable insights. For example, you can quickly see what your customers didn’t buy.             

Dark Data: data that is not or has never been used in a BI system or data warehouse but can be very valuable. Read more about Dark Data here.             

Big Data: unstructured data such as photos, emails, documents or videos, or ephemeral data such as sensor data. This often also involves very large amounts of data. Read more about Big Data Analytics here.             

             

             

Perform a proof-of-concept             

"Doing a proof-of-concept (PoC) is essential for choosing a Business Intelligence tool that suits your organization. So you can test the solution in your own IT environment, and get an idea of the functionality, connectivity, usability and performance of each Business Intelligence tool.

Define beforehand what has to be done, what the results should be and what data should be used. Be sure that the data in your source systems is accessible. In general, a proof-of-concept can be done in three to five days."             

             

             

             

             

             

             

Business-user-focused BI tools             

Business-user-focused tools require some initial technical setup to make the data queryable for the business user (i.e., clean up messy data and create logic that maps complex data to familiar business terms, like customer or revenue). After set-up, fewer technical chops are needed on an ongoing basis. The tools allow a business stakeholder—say, the head of marketing—to build their own analyses and get actionable insights without writing Structured Query Language (SQL). These tools include a user-friendly interface and more robust end-user functionality—like drag-and-drop, filtering, drill-downs, and computed fields—to enable this.             

             

             

             

             

             

Analyst-focused BI tools             

"Analyst-focused tools SaaS like Periscope Data and Mode, on the other hand, are ""code-first,"" data exploration layers. These tools are generally used by analytics teams; although business users can refresh existing reports, code is generally needed to create and customize them. With these tools, analytics teams are empowered to be data explorers instead of reactive, database-maintainers. In those cases when business users may not know the right questions to ask, data analysts can step in to investigate more deeply using SQL, Python, or R.

While more of these analyst-focused tools are starting to add more functionality for business users, to truly get the most value out of the tools a SQL pro teammate is required."             

             

             

             

             

             

             

Cost factor             

Depending on the budget, size of organization, number of users and size of data that needs to be processed decision needs to made. Hardware, Software, License, BI Resources, Training, Implementation, Ongoing Support & Maintenance are key contributors to TCO. It is common mistake to make BI tool decision purely based on User License cost alone             

             

             

             

             

             

Deciding which BI tool to Use             

"As you evaluate which BI tool will work best for your company's data needs, we encourage you to consider who will be responsible for initially setting up the tool (along with a data warehouse and ETL process, if you don't already have one). Whoever maintains the tool, will also need to be proficient in SQL for any of these options. You'll also need to decide whether you want to enable business users to create custom analyses within the confines of a pre-defined environment or whether you want to give your analytics team the horsepower to answer any sophisticated, ad hoc business questions that may arise in real-time. Keep in mind, some companies choose to set up a mix of these tools so they can pair a business-user-focused one with an analytics-first platform.

While we only reviewed a few key players in the ecosystem, there are many BI different BI tools.

When picking an analytics tool, the idea of future state roadmap or anticipated growth in that platform should be a key consideration. A tool with a limited product roadmap should be considered a red flag. As an Example a client select's to choose a tool on the clear downswing because they were given a sweetheart deal on licensing. To no surprise, a year later they have to go through the process of ripping out that tool as it did not meet their future state growth goals.

Selecting a business intelligence tool does not need to be a daunting task. Just be sure to go into the selection process knowing your use case, and how the tool will be used to meet your analytics needs.

Evaluation Criteria 

Reporting, dashboarding & analysis in one package

Zero foot-print (web-based)

ETL feature

Role based reporting & dashboarding

Support for Mobile BI

Communication facilities (notes/comments/likes)

Real-time aware

Basket, advanced & predictive analysis

Usability (ease-of-use, ease-of-learn)

Portal integration

Designs (insights) are reusable across BI applications

Sharing of reports to users"             

             

             

             

             

             

             

             

             

             

             

List of BI Tools             

"✪ Tableau 

✪ Qlik Sense 

✪ Microsoft (Power BI) 

✪ SAS (Visual Analytics) 

✪ SAP BI 

✪ Oracle BI " "✪ IBM (Cognos, Watson) 

✪ TIBCO 

✪ DataRobot

✪ Sisense 

✪MicroStrategy 

✪ Alteryx 

✪ Metabase" "✪ Domo 

✪ Zoho 

✪ Looker 

✪ InetSoft 

✪ Yellowfin 

✪ Infor BI 

✪ Superset " "✪ Dundas  

✪ Hitachi BI

✪ ThoughtSpot 

✪ GoodData 

✪ Incorta 

✪ Insightsoftware 

✪ Datapine" "

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2022 Gartner Magic Quadrant for Analytics & BI Platforms             

"Leaders

Microsoft Power BI

Salesforce

Qlik" "Challengers

Google Looker

Domo" "Visionaries

ThoughtSpot

SAP Analytics Cloud

Oracle Analytics Cloud

Sisense" "Niche Players

Amazon Web Services

MicroStrategy

Alibaba Cloud"