Why do Business Analytics Projects still fail in 2021?

Table of Contents

Unrealistic Expectations
Weak Promises
Lack of Methodology
Companies are Not Ready
Change Management
Closing Note

The hype of Big Data Analytics has stormed by now quite a lot of Businesses. Only by looking at the amount of literature generated around the topic you can figure out how relevant has become.

Source: Knowledge Graphs and Big Data Processing by Valentina Janev, Dea Pujić, Marko Jelić, Maria-Esther Vidal¹

Companies quests for 360 customer view, next best action algorithms, self-service BI, AI and alike Analytics Projects had mostly failed.

Based on a prediction from Gartner², through 2022, only 20% of analytic insights will deliver business outcomes.

But what are the reasons for what a well funded, sponsored and envisioned project can fail to leave companies not only with a few million less in the bank but also with an empty promise?

Unrealistic Expectations

Analytics requests often include “mission impossible” questions that not even God is ready to answer. Linked to that, companies’ immediate need for seeing returns on their analytics projects concludes with upsetting results. As much as Data & Analytics is a powerful tool, it needs time and patience.

Half of surveyed Senior leaders think that Marketing Analytics did not have the expected influence for the business. Source Gartner³

There is no magic button that you can press and tell you what to do with 100% of accuracy. If that would have existed, the COVID pandemic, for example, shall have never happened.

Weak Promises

We have the system that your company need; this is the future. These are common vendors claims. The fact is that they need to sell and they don’t doubt to offer you something that they know will not work.

Big Data” and other buzzwords created by sales/marketing/BI folks have completely bamboozled companies into creating new teams and positions. “Big Data” is not just a large data set. Around 90% of the professionals pitching Big Data and Social Analytics do not have a grasp of this area.
Vijay Gupta for KDNUGGETS⁴

Overengenieered, unusable products are now part of companies infrastructure. They were expensive, they don’t work, and it will be even more costly to dismantle them.

Lack of Methodology

The fact that a company have the money, the technology and even the people doesn’t mean that they will succeed using analytics. That is particularly true for corporations with complex structures. The disconnection between functions, divisions, departments, countries, time zones and most crucially, objectives is widespread.

This part is were tools like the GIDAR Analytics Canvas can help put everyone on the same page.

Companies are Not Ready

That includes the large group of:

We don’t have the skills
We don’t have a budget
We are busy with other priorities.

And probably the worst one, We don’t have data!. (Yes some companies haven’t collected and sorted their data and yet want to do AI).

Image of a chart with Top Reason why analytics is not used in Business — Conflicting course of action and Data quality are big problems at the time of using data to inform decisions. Source: Gartner⁵

According to Venturebeat only one out of every 10 Data Science projects actually makes it into production⁶.

Change Management

We spoke about technology, processes, and other known issues, but let’s not forget that is people who do analytics in the end. In many cases (self-service BI is a flagrant one), employees are thrown into a buggy system containing wrong and incomplete data and are asked to solve all company problems.

Employees stress levels based on Change parameters. Source: Gartner⁷

Companies must understand that we are in a speedy period of humanity and that people need time and support to embrace change.

Closing Note

The Big Data Hype will not go away anytime soon. It will probably be substituted by other buzz words like Artificial Intelligence or Machine Learning and vendors will still market it as Saint Grial: The capability that will make your company take over the rest.

Make sure that you have the right foundations (Goals, Culture, Skills) to make your next analytics projects a success.
Santiago Tacoronte

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