Business Intelligence, Analytics: Why It Is Necessary In A Company

How and why to choose a platform to model, analyze, share and manage the data available to support decision-making and the definition of business strategies. Organizations today must be able to derive maximum benefits and value from available data to manage contingent and future challenges arising from increasingly erratic scenarios. It follows that Business Intelligence and Analytics platforms become precious levers, able to model, analyze, explore, share and manage the data available and support the decision-making process and the definition of business strategies.

Business Intelligence And Analytics Platforms: Key Features And Benefits

Exponential data growth is both a significant challenge and opportunity facing organizations today. That is a challenge as it involves finding the most efficient ways and means to acquire, manage and analyze data, an opportunity regarding the data’s potential for more objective decision-making and a deeper understanding of context. 78% of IT professionals – according to what can be seen from the 2022 report of the American Data Analytics solutions provider OCIENT – believe that faster data analysis contributes to both an increase in revenues and greater profits.

The Business Intelligence and Analytics products on the market are numerous and varied, and it follows that organizations, before making a choice, must carefully consider analyzing the various characteristics and functions to identify the most suitable BI and Analytics platform. Able to meet expectations. Let’s see what considerations to make in the selection process.

Understand and articulate business objectives in terms of data. To meet your objectives, you must check whether the Business Intelligence and Analytics model has the necessary analysis, visualization and customization capabilities. Knowing who will use the BI and Analytics tool and for what purpose is also essential.
Assess implementation and maintenance requirements – This involves understanding the costs and time required for implementation, system integration, employee training and adequacy of skills, hardware or software costs regarding servers and cloud storage, updates over time, after-sales conditions and customer support.
Check the general characteristics that the platform must possess, i.e. cloud and on-premise distribution; personal and group work areas and ways of collaborating and sharing analysis results; scalability over time, degree of customization and technical skills needed by users.
The data collection capability – structured and unstructured data, big data, operational data, streaming data and self-service data ingestion.
Data management – self-service data preparation, pre-built ETL, support of complex data models.
Reporting and visualization methods – interactive dashboards, predefined and customizable images, scheduled and ad hoc reporting, embedded reports, reports for mobile devices, geospatial data mapping, and data “narration”.
The Analytics capability – online analytical processing (OLAP), data mining, predictive analytics, ML and AI-based automated insights, and real-time data analysis capabilities.
Security – security at the report, workspace and row levels; availability of flexible authentication options; control of application activity and Notifications and Alerts functionality in real-time and based on established parameters.

5 Business Intelligence And Analytics Platforms Chosen For You

Microsoft PowerBI

It is the platform preferred by millions of users capable of providing an analytical service equipped with interactive data visualization and BI features. Power BI is cloud-based and deployed in the Azure cloud. There are also on-premise capabilities for individual users or when advanced users create complex data mash-ups using internal data sources.

Pros-Different products are available with varying price ranges. It is the most convenient product on the market compared to others. The platform allows users to perform data preparation, discovery and dashboards with the same designer tool. Furthermore, it integrates with Excel and Office 365. The interface is simple to use, allowing the end user to create their reports in real-time, carry out predictive analyses, clean big data and prepare datasets faster using Azure data lakes. Integrating the platform with existing applications allows you to analyze and create structured and customized dashboards and connect them to hundreds of data sources. It requires neither particular IT support nor particular coding knowledge and guarantees frequent updates. Furthermore, it can be used on multiple devices and has responsive Customer Service.
Cons-The user interface is awkward and challenging to navigate when you go beyond simple views. Furthermore, repetitive tasks cannot be automated and require significant manual effort on the user’s part. The visual configuration is limited compared to other tools.

Tableau

It is considered the leading visual analytics platform suitable for business and academic use. It is an evolving platform and, therefore, constantly updated. The platform can provide suggestions in the face of changes in the context, send alerts for emerging trends, and make predictions based on data using machine learning. Additionally, thanks to conversational analytics, users can ask the platform questions in everyday language and get in-depth answers.

Pro – It is available in several subscription versions. Additionally, drag-and-drop functionality enables the creation of high-performance visualizations. It requires no coding and is capable of handling large volumes of data as well as being able to be connected to multiple data sources. Furthermore, it guarantees support for mobile devices, and users are given the option to include Python or R scripts. Tableau is available on-premise or in the cloud and offers built-in analytics capabilities. Additionally, users can view and share data with Tableau Public.
Cons – It appears to be particularly expensive. Different subscription options are available, and prices vary considerably depending on the number of users, the cloud server used or the local implementation. It is not particularly easy to use, especially for beginners. It has neither report scheduling functionality nor custom formatting of multiple fields that must be done manually. Again, it does not have a custom visual import feature, unlike other platforms like Microsoft Power BI, and when data values change, the parameters need to be updated manually.

Data processing capacity is limited, i.e., before loading, data must be “prepared” and “cleaned” using a separate tool, such as Power BI, Excel or Alteryx. Users must have coding know-how to exploit the potential of the platform entirely.

Qlik Sense

The platform enables users to generate insights, offering real-time end-to-end data integration to bridge the widest gaps between insights, data and actions. Additionally, it can provide suggestions for every change in context, send alerts for emerging trends, and make data-driven predictions using machine learning. The conversational analytics feature allows you to ask the platform questions in everyday language and get in-depth answers.

Pro – Offers excellent color visualization of data and allows the analysis and interpretation of complex data, which can be shared between different teams and functions. It does not require IT support and is equipped with low-maintenance software. It also guarantees rigorous data security, fast analysis times and an excellent ability to understand the data. It is possible to perform direct and indirect searches of the data. It is characterized by broad implementation flexibility (i.e. on-premise, cloud, multi-cloud).
Cons – Unfortunately, it offers poor real-time data analysis and requires more RAM space. Furthermore, application development requires SQL skills on the end user’s part as well, considering that it is not easy to integrate or incorporate it with other applications. Furthermore, further investments are necessary if we want to guarantee full exploitation of the platform. There is no drag-and-drop function, and another sore point is the relatively poor online customer service.

SAP BusinessObjects BI suite

This is the on-premise BI layer for SAP’s enterprise technology platform, providing centralized reporting, visualization, and data-sharing capabilities.

Pros – The platform can create exciting visualizations with drag-and-drop functionality and API integration. The web interface is easy to use, and it is possible to upload data to Excel for sharing. Another feature of the platform is the scalability and the possibility of connecting to different data sources such as OLAP and XML and other integration points such as SharePoint, Java, Microsoft Office and NET.
Cons – The platform is expensive, and the licenses are expensive. Furthermore, it is “heterogeneous” (i.e. SAP has acquired many companies over the years, which means that product integration is sometimes uneven) and presents problems in terms of performance and reporting quality. Furthermore, the server can suffer interruptions due to heavy loads, and software updates are particularly challenging.

Sisense

Sisense is marketed as an easy-to-use BI and analytics tool for managing large, complex datasets and is recognized by Gartner as one of the leading API-first cloud platforms that help build analytics apps.

Pro – The platform is characterized by quick implementation and has numerous drag-and-drop features. It offers interactive dashboards to facilitate collaboration and supports data export to PDF, Excel and CSV. Furthermore, it stands out with its remarkable ability to work with massive data sets (terabyte scale) and provides on-premise and cloud-based options. The web-based dashboard allows access to multiple devices. The platform features custom in-chip technology that optimizes computation to use CPU cache rather than RAM, resulting in 10-100x faster computing power . Elasticube – i.e. Sisense’s high-performance analytical database – allows users to take “snapshots” of data—excellent ability to integrate data from sources such as Adwords, Google Analytics and Salesforce.
Cons – Although Elasticube gives access to massive data sets, it is not easy to use and requires significant SQL skills. The application is heavy as it requires more RAM space, server resources and time for installation and configuration. Dashboards are interactive on the web only and cannot schedule reports for sharing via email.

Conclusions

As seen from the above, organizations in a market that offers numerous BI and Analytics tools must be able to identify the most suitable solution for their objectives. Above all, once again, it is a question of starting from the awareness that it will be used by employees whose skills – in most cases – need to be sufficiently adequate to exploit the value of the investment fully.

Therefore, it is more necessary than ever to remember that data analysis goes beyond the ability to use tools, code and generate reports. It is based on the ability to “analyze”, read and interpret data. This data awareness must be instilled at all levels of the organization, particularly in decision-makers, who will need to be able to examine, verify and interpret the BI and Analytics outputs resulting from the various dashboards, graphs and reports.

Business Intelligence and Analytics platforms – like any technology, in the term’s etymological meaning – must convert into a strategic tool/lever to help organizations manage their business with better planning and execution, using in-depth data. However, it will also be fundamental to understand which paradigm shifts to implement within the organization to guarantee a structured digitalization and innovation process.