Business intelligence can be referred to as different technologies, applications, and practices that are widely used by organizations for the collection, integration, analysis, and presentation of business information. Business Intelligence has only one aim and that is to support better business decision making. It provides all the means to analyze data and give companies a clearer understanding of how their business processes are actually executed. The system beneficiaries include a group of users like specialists in financial reporting, marketing and salespeople, supply chain, operations, logistics, and of course members of the board. Thus BI is an important tool for any organization that helps them in decision-making.

Business Intelligence , data analytics, and Business Analytics are Almost the same term and All the 3 terms are used Interchangeably. Tools used for them is Tableau. It is very useful and boost’s one career.

Checkout our Tableau Certification Preparation Videos for details as well as Tableau Certification Dumps for Desktop (Specialist & Certified Association) and Tableau Server. So, we can conclude our topic by saying that Tableau is a useful tool for data scientists, and those companies or data scientists who are unaware of the benefits they can enjoy using Tableau must read this topic and get an idea about it.

Other Related Articles:

Although the majority of the organizations rely on BI and its tools for doing a lot of work, yet there are some practices that are considered wrong in this field and that organizations should stop doing it. So, below we will be talking about some of the worst practices that are being followed and should be avoided.

8 worst practices in business intelligence:

Below are the 8 worst practices in business intelligence that we think everyone should avoid doing.

Ignoring important data sources:

It has been found out that the majority of the business users only focus on information in data warehouses, databases, ERO, and CRM. What they miss are many other data sources that might contain information that could be crucial to the organization like mobile, web monitoring data, and social medial. Only one-third of the unstructured data is being used by businesses for decision-making which is quite wrong. So, companies must make sure that they use all the available data whether it is unstructured or structured.

Undervaluing the data preparation process:

Bad decisions in a company can be made at any time. But why does it happen? Well, it can happen due to the inadequacy of data. When you apply bad data in BI, it will for sure cause disaster no matter how compelling analytics you have, you’ll still face problems. So what should be done here is not undervalue the data preparation process and closely pay attention to the data preparation steps like cleansing, matching, and profiling.

Bureaucratic structures:

Following a bureaucratic or organizational structure can slow down the implementation of BI strategies and discourage all kinds of enthusiasm in other users to go for a new approach. In other words, what we are trying to tell is that the team should be given the freedom to try new approaches and think out of the box and not just focus on what the organizational structure says.

Thinking BI projects to be complicated:

Many organizations are to believe that BI tools are very sophisticated to use and can lead to a lot of problems instead of getting things done faster. It requires high IT technical skills to implement and in doing so the team forgets the actual goal. But this is truly not the case as the BI tools are not that complicated and demand a little understanding to use them. So instead of becoming reluctant to use the BI tools and drag the implementation of the project, be open to it and pay a little attention to how to use them, and everything will start making sense to you.

Doing inaccurate estimates:

As we have talked about earlier, inaccurate data can delay the project scope but so does incorrect estimates. If you make inaccurate estimates, then this can hold back the implementation cycle in cases where the estimates differ when the BI project is implemented. For example, if a project proposed that BI tool can deliver nine conversions but in reality, it required 20, then this can not only change the project scope but affects the implementation process as well. So never make inaccurate estimates let alone rely entirely on them.

Consultants not being able to understand the BI scope:

In many organizations, there are consultants sitting behind the desk who just want to make money and would lengthen the implementation period. What they do is instead of finding what is more important to change to speed up the productivity, they just tend to increase the implementation. This all also happens due to a lack of understanding of the goals and culture of the organization.

Underestimating user training:

There are many organizations that don’t even bother to give training to their business users as to how they can use the BI tools. And that is why they end up facing issues when implementing the new solution. So what should be done here is to offer a complete training to stakeholders, end-users, and other personnel, and all those involved in the implementation of the project.

Time-consuming BI development:

Any business would think that BI development is going to take a few weeks to complete. The development of an interface for achieving a business milestone takes a lot of time and effort which not everyone can understand. With that, there also can be transactional level delays that can add more weeks to the implementation of the BI strategy, thereby, taking more time than what was estimated by the planners. So, never rely on the estimates that are in front of you because every time you think that the work will be done within the given estimate, something else will pop out that will delay the implementation strategy. Thus it is best to not rely on estimates.

Best Tableau & Data Science Free Online Courses with Certificates

Fundamentals of Visualization with Tableau
Data Visualization and Communication with Tableau
Essential Design Principles for Tableau

Visual Analytics with Tableau
Creating Dashboards and Storytelling with Tableau
Data Visualization with Tableau Project

Conclusion:

So these are some of the practices that should be avoided in business intelligence. And if you are that person who follows these practices or your organization, then now is the time to stop. Read this topic carefully and then figure out how you and your team is dealing with the BI tools and what changes should be made, what to follow, what to avoid, and much more, and never stop learning.

8 Worst Practices to Avoid In Business Intelligence