Common Business Intelligence Pitfalls: Everything You Need to Know

Business intelligence (BI) has long been the preferred tool of nontechnical employees to take a shot at the stored data and derive information from it relevant to their needs.

However, one of the common business intelligence pitfalls since the emergence of BI in the 80’s till 2015 has been their limited reach when it came to detailed data analysis. Data was stored in small, siloed channels and their analysis did not go much beyond answering simple, straightforward questions. They lacked sophisticated analysis and were employed to answer basic questions such as how much sales a company generated during a certain time period, or what sales were made in the previous year.

For decades, Moore’s Law has held true, and the cost of computing and memory has fallen further and further. Moore’s Law is an observation made by American engineer Gordon Moore in 1965, and revised by him in 1975, that states that the number of transistors per silicon chip doubles every two years while the cost per transistor falls.

In fact, they have become so cheap in the last few decades or so that it has become rather easy for businesses to collect all the data they can lay their hands on. Companies as such are now equipped to store and analyze huge amounts of data quickly and inexpensively.

Businesses that understood the power of data analytics and how the information derived from processing huge amounts of data could help them obtain competitive advantage, started to put together specialized data teams to answer new, more complex business questions, as the gaze gradually shifted away from traditional business intelligence. These companies are way past the basics of answering simple questions in the ever-changing landscape of modern businesses.

Dispelling the myths around traditional and modern BI covers that shift directly, and how big data analytics changes decision-making picks up where the simple questions run out.

Data Analytics and BI solutions are being used nowadays to keep a track on the internet of things (IoT), knowing consumers’ preference or finding the best delivery route to name a few that helps organizations to make data driven decisions, ensuring a faster time to market and a unique customer experience.

Revolutionary companies like Netflix, Facebook, Uber and Amazon were able to march ahead at a precipitous pace as they realized early the advantage, they could derive from using data to solve advanced business problems. They started a data revolution which allowed them to emerge as big winners in their respective industries.

Five ways IoT big data could help your business is the companion read on the device side.

And amidst all these, the role of BI has been constantly shrinking. Business intelligence can still answer sales and marketing questions within small data sets, but those answers are increasingly losing significance in comparison to the value provided by deep analysis of mammoth data sets. As such, businesses that are still relying solely on traditional BI are losing market share. The data landscape has progressed to a point where BI on its own isn’t likely to suffice.

Here we cover the most significant business intelligence pitfalls we encounter during a BI implementation and how to effectively overcome or mitigate these major challenges of implementing business intelligence.

Before you implement any BI project, it is important that you properly assess and analyze your overall data and analytics strategy.

First and foremost, you need to find answers to relevant questions such as: Do you have a plan in place? How does this project complement your overall analytics goals? In what way it is going to aid you with your company’s overall corporate strategy? How is your business going to benefit from data analytics?

It is strongly recommended that first and foremost you develop a sound analytics plan, so that your analytics activities are in sync with your overall corporate goals. This is important as it will help you assess the current state of your analytics program which in turn will aid you in prioritizing your efforts and build a long-term plan so that you can start deriving meaningful insights from the data at your disposal.

The groundwork that strategy depends on is covered in why data integration matters for enterprise success, choosing between a data lake and a data warehouse and whether your data is ready for generative AI.

We often have heard stories of a company purchasing an expensive software package and expect it to be a silver bullet. Executive teams are often duped by the hype and hoopla associated with the software and they are lulled into believing that shiny new BI software package will take care of all the relevant issues.

Implementation will be as easy as sliding a hot knife through butter and user adoption will quickly ascend. Businesses mistakenly expect the software to take care of all their data issues.

If you are at the shortlisting stage, the top five BI solutions and seven ways BI can improve your business set realistic expectations of what the category does and does not do.

After spending a lot of time, money and other valuable resources, you realize to your horror that the people you thought would help you with budgetary issues, adaptation and implementation exert little or no influence. This scenario often plays out when projects are developed without it being shared widely within the organization.

In order to ensure that this does not happen, it is important to tackle the project one step at a time and reveal the results of every step to the widest audience possible. This will help you find out the key decision makers who could help you with the project.

The role of data analytics and BI in organizational success is a useful piece to circulate when you need that wider audience to care, and how smaller businesses use BI is the version for leaner teams.

Most importantly, it needs to be understood in the very beginning that companies that claim to make “data-driven decisions” with sole reliance on BI are living in a fool’s paradise. Big data challenges are immense and any organization that fails to build a formal data analytics team can’t be really serious about jumping on the ‘data revolution’ bandwagon. It is just akin to trying to pull a cart without a horse. They may be a victim of circumstances, but unfortunately this is not going to turn their goals and objectives into a reality.

The loftier objective of successfully dealing with emerging issues in business intelligence demands a team of well-trained data professionals, and not just a tool whose functionality does not extend beyond answering a few basic questions.

Organizations may opt to take services of a data analytics and business intelligence solution provider, who would help you in deriving business benefitting decisions from your data through their data analytics and BI capabilities.

Ultimately, the real worth of BI is an extension of the value of the data analytics team. If they manage to create data sets where anyone can answer their own questions, they’ll have time at their disposal to deal with the issues of successful business intelligence implementation, which in turn can actually add value to a business.

On building that team and what it works with: data engineering and AI collaboration, Apache Kafka in modern data pipelines, machine learning operationalization, augmented data analytics and augmented data management. For where the value lands, see how business analytics fosters growth and the sectors making big in data analytics.

Impressico Business Solutions — business intelligence, data engineering and analytics consulting for organizations that want more than a dashboard.