Story image

Integrating data into decision-making from the ground up

20 May 2019

Organisations today collect more data than ever before, and whether or not they use that data to drive business decisions can be the difference that makes or break them in a fast-paced environment.

In order to implement a change that can be sustained, organisations need to find a way to embed it into their culture.

Tableau shares four steps organisations can take to build a data-driven culture from the ground up.

Step One: Build consensus on the value of data and insights

Getting people to buy into a data-driven approach is critical to embracing a data-centric culture in your organisation.

Employees must understand that data is fundamental to the company's value and success and that organisations that are better equipped to make sense of their data will do better than those who are not.

Where there is resistance to using data to make decisions, there will be barriers to new technologies that aid analysis.

Organisations can start by focusing on making data widely available across the company.

Make analytics capabilities available at every level and reinforce the importance of making every decision data-driven.

Reinforce the behaviour by bringing data and analytics directly into decision-making meetings and answer questions in real time.

Measure how data is used.

Understand its impact.

Finally, build a community that evangelises it, including with executive support to reinforce its importance.

Step Two: Demystify smart analytics

Often people will avoid what they don’t understand, and they hate to look foolish by not understanding something.

We need to help people realise that most of us don’t really have a grasp on smart analytics.

It is a relatively new field and we’re all still learning.

Education and transparency are key to wider trust.

As algorithms and models become more sophisticated, it’s critical they don’t become incomprehensible.

The concept of “explainable AI” is a powerful one—I should be able to understand the operations and logic that were applied to come up with an answer.

This helps build my conviction that the answer is right.

AI techniques need to expose their inner workings, while at the same time helping us acknowledge and avoid the biases that humans tend to introduce to analytics. 

This combination will help leverage the best of both worlds—human and machine.

Step Three: Help people see smart analytics can help them, not replace them

People will not trust something if they believe it endangers their livelihood.

However, people should view smart analytics as a way to help them perform better, instead of a threat to replace them.

We collectively need to quell misconceptions like “AI is going to replace my job” and help people understand how machines learn from data—not experiences.

Smart analytics can help employees make better decisions to increase efficiency, automate, personalise the customer experience, differentiate versus competitors, and more.

Step Four: Promote data literacy

Tools and technology are certainly important parts of the greater movement, but employees must also learn to think critically about data.

They need to understand when it’s useful and when it’s not.

Acting on the wrong data—or wrong recommendations from a “smart” machine—will lead to bad decisions and wasted resources.

This is where data literacy, critical thinking, and people development come in.

An impactful data education requires both practical and creative skills.

Introducing smart analytics into business processes will require trust in these technologies alongside good judgment from the workforce.

Even more experienced data scientists may have hesitations—why, if they have tried and true experience, should they trust a machine?

Less experienced users will need to learn how to interact with and validate smart technology recommendations, or to interject human knowledge to correct course.

Find out how you can build a culture of self-service analytics in your organisation today.

Check Point announces integration with Microsoft Azure
The integration of Check Point’s advanced policy enforcement capabilities with Microsoft AIP’s file classification and protection features enables enterprises to keep their business data and IP secure, irrespective of how it is shared. 
Blockchain: New Zealand needs to get up to speed
"The technology can traverse every business domain and can have far reaching impacts on society as we know it."
Why AI will be procurement’s greatest ally
"AI can help identify emerging suppliers, technologies and products in specific categories."
Five key ways an IT professional can keep their body and mind healthy
Sitting in the same place facing an artificially lit screen for 6-8 hours a day can have a negative impact on your overall health if you don’t offset it with diet and exercise.
Are AI assistants teaching girls to be servants?
Have you ever interacted with a virtual assistant that has a female-based voice or look, and wondered whether there are implicitly harmful gender biases built into its code?
Google 'will do better' after G Suite passwords exposed since 2005
Fourteen years is a long time for sensitive information like usernames and passwords to be sitting ducks, unencrypted and at risk of theft and corruption.
Commission warns Spark for misleading in-contract customers
The warning follows an investigation into representations Spark made on its website and in emails in August and September 2018.
Optic Security Group celebrates Axis accolade
Auckland-based business security systems provider Fortlock has picked up an award at Axis Communications’ annual Oceania Axis Partner Summit 2019.