Whether for performance reporting or projecting future strategy, almost every major organization employs BI and analytics today. When firms expand their capabilities, usability, functionality, and scalability are key factors to keep in mind.
These aspects and awareness of BI-friendly predictive analytics advantages are crucial in choosing the best option. Read on to learn more.
Current position and goals
Assess your position on the analytics curve as a first step. Are you planning advanced analytics, like prescriptive analytics, to define the organization’s future? Are you employing backward-looking BI to diagnose previous performance and predictive analytics to estimate future performance? Important queries include:
• Are you prepared to move forward?
• Do you have a sorted plan of action for getting there?
• Do you have the dedication required to see it through?
You can proceed to the following stage as soon as your organization knows its goals. This will entail identifying which BI and advanced analytics elements are pertinent and necessary for attaining these objectives and which features are unnecessary.
Ease of use
Ease of use is considered the primary driver of BI and analytics success. Excellent usability helps:
• Increase revenue growth significantly
• Lead to higher data confidence
• Encourage staff to use BI and sophisticated analytical tools
Usability is the most crucial factor, even more so than functionality. This translates to speedy results, the ability to delve deeply into data, simple collaboration, and straightforward functionality for business users.
From an IT standpoint, simplicity in deployment, the ability to access many data sources, scalability, user-friendly user interfaces, and automated reporting and processing are all examples of ease of use.
Efficiency and effectiveness
The level of BI or advanced analytics is closely tied to the amount of capability required, such as:
• Descriptive analytics: Using Ad Hoc and conventional BI reports that describe past performance.
• Diagnostic analytics: Simple BI tools that pinpoint the causes of previous performance.
• Predictive analytics: High-tech analytical techniques that forecast demand, quantities, and pricing.
• Prescriptive analytics: High-end simulation software for advanced analytics that simulates real-world performance to identify the best possible business solutions.
The degree of functionality must be suitable for your company’s current and future needs.
Relying on IT or data scientists to make minor tweaks and revisions infuriate BI or advanced analytics consumers more than anything else.
While IT support for analytics is necessary, especially for predictive and prescriptive ones, users must be able to query, iterate, and analyze data independently. Employees are more likely to use BI and advanced analytics effectively when they have user autonomy than when they don’t.
There are many BI and advanced analytics platform alternatives accessible, such as cloud-based packages, on-premise options, and even straightforward desktop programs. The level of analytics you plan to conduct and your firm’s IT and data science capabilities are key factors in making the optimal choice.
Scalability frequently doesn’t get enough attention. The ability to sustain users and processing capacity are the initial main concerns. However, as your capacity increases and more staff members participate, two new difficulties appear.
The first requirement is to support and instruct less experienced consumers. The capacity to manage rising data quantities and link to various data sources comes in second. You need a platform that enables you to scale as per your changing requirements.
Overall cost of ownership
BI and advanced analytics solutions’ total ownership costs must account for:
• Price of a first license
• Conversion expenses
• Validation and model design
• Ongoing model creation
• Support expenses
There are several strategies to reduce these expenditures. However, data indicates that choosing the lowest option as the basis for an investment decision frequently results in poorer total economic benefits.
By keeping the aspects mentioned above in mind, you will be able to find a BI-friendly analytics platform that suits your requirements and budget. You will realize actual and palpable economic benefits if you establish clear, achievable goals, assemble a solid team, carefully choose your provider, and appropriately budget the project.