Common Mistakes that Complicate a Business Analytics Problem (and How to Avoid Them)

We do many reviews of industry-centric case studies from the business analytics world and found out the most common types of mistakes made in business analytics. In this article, we will highlight a few of those with the intention to help you benefit from business analytics certification and ensure these classical mistakes are not repeated again.

It’s OK to mistakes—everyone makes one. But if it is going to cost your company millions of dollars in this recession era, you better watch out for making a mistake. In fact, go a yard ahead and stop others in the company from making one too—as it could make a decisive impact on how the company balance sheets look at the end of the year, and how you are appraised for your gritty strategic ideas that saved the company from financial loss. Unfortunately, this doesn’t come easy—it could take some serious training and education, which comes from tuning up with the best analysts and trainers in the business intelligence world. But, even that may not solve the problem – or worse, could even worsen the problem with irreparable consequences. 

Mistake 1: Business analytics will solve all your problems, even if you don’t have data

The first mistake business analysts make in their job is that they think their techniques will solve all types of problems. For some problems, there are no solutions – these are open and shut cases. For example, the problem of bias in data analysis is a real problem, and it is accepted to a certain extent by business analysts as part of the anomaly detection and error management principles. If you are trying to find a way around removing these anomalies, you would need a highly sophisticated machine learning platform – which again requires training with supervised data labeled for training and analysis processes. 

Business analytics problems can solve many problems, but if you don’t have the right data, you would never make it far. In certification courses for business analysis, data management for training algorithms used in the business analysis must be taken very seriously. You could be learning with Python and R to understand how data management and Machine learning could come together to provide you with some really cool insights extracted from hidden patterns.

Mistake 2: Managers know everything! Because they don’t in some cases

Many young analysts think their senior team members and even managers know the outcome of the business analysis processes. This assumption could backfire in your career, and worse, render your skills obsolete because everyone understands the problems, but not everyone gets the solution, or worse, if they have a solution, they may not understand how to display the solution in an intelligent manner so that all who don’t know the answer can also analyze the solution. This could be done using a simple Excel table or an advanced Business Analytics dashboard that is part of the modern BI culture in the organization.

Irrespective of the tools you use, remember this – nobody knows the answer, because data treatment can deliver varied outcomes. If your outcomes are different, be proud of them and test them further for validation from leading business analysis tools and techniques.

Mistake 3: Not using charts for data presentation

Insights could be portrayed in many different ways. Business analysts learn the data visualization and reporting aspect of business analysis only when they are asked by decision makers to do so. The lack of experience in working with data visualization could be detrimental to the pace at which decisions are made in an organization. Data visualization can solve many problems at different levels of decision making.

Mistake 4: Ignoring the data culture of the organization and the customer

Culture is a big part of the business analysis world. The lack of effective communication could push BI teams into making mistakes that may not be absolved by the corporate culture and the data security policies of the company and the customer. Ignoring these would result in the failure of the business analytics plan and loss of interest among customers and prospects. 

Certification ensures these mistakes are addressed intelligently and backed by data. Best business analysts understand the problems and provide their recommendations to the stakeholders based on the data inferences. If you are able to display data intelligently and also provide insights and recommendations based on your analysis, you are playing a very important role in taking your BI team to the next level of success.