Making data-driven decisions is more important than ever; that’s why a business intelligence (BI) tool is usually a game-changer for almost any organization. Not only can it analyze crucial data, but it can also provide conclusions and support actions, so both people and organizations can function at optimum efficiency.
How can one BI tool do all that? By offering specific capabilities that are designed to help organizations make informed decisions. Each BI tool has all kinds of unique offerings, and it can seem overwhelming to go through all the details; but a few of the capabilities stand out above the rest. If a business intelligence platform can get these five factors right, it could end up being a great fit for your organization.
1. What data storage solution is used?
Most of the capabilities offered by BI tools don’t have an equivalent already in place, but data storage solutions are the exception. Many organizations will already have their cloud solution or on-premises data storage in place by the time they’re implementing a BI tool; if a dedicated cloud solution is included with the platform, it could feel redundant. On the other hand, a ready-to-go cloud solution could make the integration process much smoother for businesses that are working with limited data storage.
Tableau – This platform comes with the most limited data storage solution; it can only be deployed from Salesforce’s cloud solution.
Power BI – Organizations can opt to deploy this platform from their on-premises servers, or they can use the Azure cloud solution from Microsoft. The on-premises capabilities are different from what you’ll get on the cloud – just something to remember.
Qlik Sense – This platform can be deployed from private or public clouds, from on-location servers, or a combination of the three. This BI tool is cloud-agnostic, so it doesn’t matter which cloud solution is used.
2. Are robust embedded analytics offered?
BI tools are usually integrated into established organizations. This means that by the time the tool is being introduced to users, they’ve already developed and optimized their own workflows. If a BI tool lets users implement embedded analytics, this will minimize the disruption to workflows, while providing in-depth insights. Since the embedded analytics will be implemented across applications, portals, and workflows, it should feel as organic as possible for users.
Tableau – Users can embed dashboards throughout their workflows; other objects, like metrics or individual values, aren’t available for embedding.
Power BI – Since users at almost any skill level can embed a variety of objects, they have choices for when they need insights. However, Power BI doesn’t prioritize API, so some users might have some difficulty adjusting at first.
Qlik Sense – Metrics, individual values, numbers, and entire or partial dashboards can be embedded within edge devices, portals, and workflows. Since Qlik Sense puts API first, users should find it easy to start using this feature quickly.
3. Are broad use cases supported?
A data analytics interface that can be used on multiple products or devices can be just as essential as embedded analytics. Not only should users be able to use the BI tool on any application or process, but also use the tool for whatever purpose they need, in any context. Whether they’re exploring or visualizing data, performing BI reporting, or completing any other task, the BI tool should support the use cases that users need.
Tableau – Multiple use cases are available, but most of them have to do with data visualization. Experienced business authors would be more comfortable with this capability than most end users, as some skill is necessary.
Power BI – There are more comprehensive offerings than Tableau, and they use Microsoft products to deliver on their range of use cases.
Qlik Sense – One interface is provided for all use cases, so users are able to familiarize themselves quickly. As they move forward, the consistency helps them maintain efficiency throughout their workflows.
4. How is the data managed?
You don’t want data dictatorship, where access is restricted to just a few authors; and you definitely don’t want data anarchy, where there are too many people creating content, and not enough people validating it.
Tableau – Users build workbooks on their hard drives, and then send them to a single server for IT to double-check.
Power BI – Self-service access is available to authors, and other users can explore their published content with limited interactivity. As new content is completed, it gets sent to a central server first for validation; after that it’s available for circulation.
Qlik Sense – End users have fewer limits on what they’re able to do; authors, end users, and everyone in between work on the same server. With this arrangement, IT can validate and manage content without the intermediate step of having authors send each completed workbook from their hard drives.
5. How does the data engine work?
Without a powerful data analysis software, a BI tool wouldn’t be able to generate in-depth insights. The data engine should give an organization the solutions that are most likely to drive growth. In some cases, a linear approach would be better – users make a query, and the data engine searches predetermined data sets for the solution. In other cases, a more creative approach is required.
Tableau – They use a query-based approach, working on an SQL database.
Power BI – They also operate over an SQL database, giving solutions from query-based searches.
Qlik Sense – Their data engine is associative rather than linear; the data sets that get explored are much broader, so users can sometimes see connections or patterns that a typical data engine would probably have missed.
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Thanks to the sophisticated data analytics technology used by business intelligence tools, organizations are able to see connections, identify risks, and find new opportunities that would have gone unnoticed otherwise. Once an organization is matched with a solid business intelligence platform, there’s no telling how far it could go.
Author Bio: Justin Davis is a lover of tech and big data. When not writing articles or editing copy, he is usually found winding down the river in a canoe with friends.
Disclaimer: The information contained in this article is for general information purpose only. Product features and information are subject to change. This information has been sourced from the websites and relevant resources available in the public domain of the named vendors as on 18, April 2021. Wire19 makes best endeavors to ensure that the information is accurate and up to date, however, it does not warrant or guarantee that anything written here is 100% accurate, timely, or relevant to the website visitors.