The (second) A in GIDAR Analytics: Actions

When an individual or an organization wants to fix something, test a new idea or embarks on any mission, normally what they are looking for is to change something. 

 A person stand on top of 2 arrows with habbits and changes

Change and Disruption

To produce change, you need to take actions, because if no steps are taken, things will continue being as they were until you or your business get disrupted. Disruption is something that you don’t necessarily want to see in your life or company or let’s better say at least that if you get disrupted, you want to be somehow ready.

Today, being in constant change is a must for most of us (look at 2020 if you want some examples).

Mosaic of the Remarkable events in 2020

The most distinct feature of the GIDAR Analytics Framework is the insistence on Actions, on producing change.

Who is Responsible for Change?

Suggesting what to do next or how to change has been traditionally out of the scope of Business Analysis and Insight teams. Unfortunately, business sponsors will trigger changes using less, let’s say, scientific approaches like HIPO or eventually not change until disruption comes.  

Fundamentally, we need to take actions to meet our goals. The base for these actions will be the Information, Data and the Analysis that we did in previous steps.

As Benjamin Disraeli would say: 

Action may not always bring happiness, but there is no happiness without action.

Benjamin Disraeli

Types of Action

In a normal situation (sometimes also in extraordinary circumstances) there are three types of action:

  1.  Removing something that is not working: Closures, laid offs, cost-cutting, etc.
  2. Replicating something that worked well before: Products, Campaigns, Offers, Teams.
  3. Experimenting with something new: New technology, a new type of product. Something that we haven’t done before.

Inducing Action

A critical human factor of the GIDAR Methodology is proactivity. It is crucial that you come to the Actions workshop with ideas or suggestions, and that you approach this session as a brainstorm.

Regardless you are an expert or not in the topic; you have business domain knowledge, you are Junior or Senior, after collecting Information, Data and doing Analysis you (your team) must have some ideas.

The second part of this step is to get more initiatives from the sponsors/stakeholders.

Finally, assess which one of them are feasible and which ones we cannot do now.

By the end of this step, you need to have one or more actions that will be put in motion to meet the goal.

Some (basic) examples:

  • Increase, reduce prices
  • Change the opening hours
  • Implement new policies
  • Change a process
  • Target a new segment
  • Launch a campaign
  • Add new products
  • Remove products
  • Launch a new sales channel
  • Implement a lead acquisition tactic

Summary: No Action, No Change, No Result

Actions differentiate a good Business analysis from an excellent one.

Actions are also the most challenging step of the GIDAR framework because you need to produce one of the hardest things for humans: Change. We are creatures of habit, and we hate changes.

Man stopping the domino effect

Changes at work usually mean “more work” or simply moving out of the comfort zone. Get buy-in from stakeholders and decision-makers. If they are reluctant, Produce accurate estimates if possible on what the change could result, back up every suggestion with your analysis, so there is no space for ambiguity.

Your objective in this step is to bring everyone on board. When you change your direction, you frequently find waves ahead, and everyone should row in the same direction. 

The best part is that you are only one step away to see Results.

Why do Business Analytics Projects still fail in 2021?

Table of Contents

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

References:

  1. https://link.springer.com/chapter/10.1007/978-3-030-53199-7_9
  2. https://blogs.gartner.com/andrew_white/2019/01/03/our-top-data-and-analytics-predicts-for-2019/
  3. https://www.gartner.com/en/marketing/insights/articles/gartner-marketing-data-analytics-survey-2020-analytics-fail-expectations
  4. https://www.kdnuggets.com/2014/02/comments-why-your-company-should-not-use-big-data.html
  5. https://www.gartner.com/en/marketing/insights/articles/gartner-marketing-data-analytics-survey-2020-analytics-fail-expectations
  6. https://venturebeat.com/2019/07/19/why-do-87-of-data-science-projects-never-make-it-into-production/
  7. https://www.gartner.com/en/corporate-communications/insights/change-communication