5 key shifts for successful data-driven decision making

From data to insights to action

Tableau and AWS recently presented at IDC’s 2021 European Data and Intelligence Summit.  The audience consisted of C-level decision makers responsible for the analytics, AI and digital transformation agenda at their organisations.  We used this forum to share insights and best practices around how enterprises are putting data to use. 

For highlights from the session, watch the post-event fireside chat moderated by IDC analyst Neil Ward-Dutton: Data. Insights. Action—Unlocking the Data Value Chain.

The theme of the presentation—From Data to Insights to Action—centered around how to bring data to all of the people, across all levels of the organization and into every workflow to maximize the impact on business.

AWS and Tableau have worked together as partners in thousands of joint customers to solve difficult and really important problems.  We have worked together to help companies and government agencies respond to the pandemic, help companies meet their sustainability objectives and respond to the climate crisis.

And what we have learned is that data-driven businesses are more resilient.

• 82% of data-driven companies have reported critical business advantages during the pandemic 
• Insights-driven companies are 23x more likely to acquire new customers

So it is no surprise that 85% of businesses want to be data driven, but very few have achieved this. In fact, only 37% have been successful.

In advance of the summit IDC conducted research with the attending CxOs and the results are telling:

• Only 1 in 6 believed analytics is used consistently at all levels of their organization
• Only 6% have embedded analytics in all their key workflows and applications
• People—and their resistance to change—is seen as the single biggest challenge faced when trying to use data more effectively across their organization

Given this context, during the CXO roundtables that followed, we shared our own research and experience on unlocking data-driven decision making in complex enterprise organizations. Providing valuable advice and best practices.

“What we found was that a lot of data projects in the past were designed as monolithic multi-year initiatives so there wasn’t a lot of focus on delivering specific business outcomes.  Organizations often jump straight to the data they have or don’t have, rather than stepping back and thinking about the outcomes they’re trying to drive.” Francois Zimmermann, Tableau

We’ve identified five shifts that are critical to help organizations become more data driven and successful in their decision-making:

Shift 1: From data focused to outcomes driven

From data as the starting point of the discussion, to centering on the business and people outcomes you want to enable.

Most organizations make the mistake of trying to start by creating a data lake, defining the taxonomy first and only then moving on to the consumption of data.  

Successful organizations start with defining specific outcomes, they start with the data consumer in mind and then work backwards from that.  

Shift 2:  From complete data to curated sets

From complex, multi-year efforts to collect, store, clean ALL data, to focused efforts to integrate and enable the data sets necessary to answer your question.

Many organizations often feel paralyzed by their data.  It’s never clean, complete, and ready to go.  But that’s okay.  All companies struggle with this.  

Successful organizations start with curated sets of data to help them answer the question they’re seeking to answer and build their data strategy from there.  They don’t wait for their data to be perfect.  Instead they focus on curating high quality assets with decentralized data ownership, making sure that the platform enables all parts of the business to access these and then combining data assets to unlock value.

Shift 3:  From reactive insights to proactive sensing

From lagging reports that read out what’s happened in the past to leading analytics that enable real-time exploration and predictive guidance.

Most organizations recognize that historical data and lagging reports are not enough and don’t help organizations anticipate what’s to come for the business.  

Successful organizations are able to combine predictive, prescriptive and descriptive data to drive forward looking insights.  For example, by bringing in AI recommendations and prescriptive next best actions to frontline personnel to help them sense customer needs and anticipate where the market is going.  

Shift 4:  From linear processes to cycles of learning

From stage-gated ways of working, resulting in one-off initiatives, to a learning mindset of small bets and continuous sense and response.

Most organizations run on linear ways of working with stage-gated decision making and one-off initiatives.  

Successful organizations adopt a learning mindset, continuously learning from their actions and bringing in additional data sets and insights to inform how they move forward, pivot or conduct the next set of experiments.

Shift 5:  From mostly “top-down” to mostly “front line” decision making

From a few big decisions coming down from the top to data empowering more decision makers at the front lines.

For most organizations, decisions tend to happen at the top and data sits within the IT department.  

Successful organizations use data to empower more decision makers right down into the front line, empowering all lines of business.

When these five things are deployed, insights generation becomes an iterative process.

It is no longer sustainable to say “I’m going to spend six months bringing all the data into my data lake, and the next six months developing insights”.   It all needs to happen iteratively.  How efficiently you can move back and forth between tasks in the insight generation loop is a critical success factor.   

To achieve this, we believe organizations need to focus on three key pillars: 

1. Unlocking Data Assets 
2. Empowering Everyone 
3. Acting on Data

The cloud has such a critical role to play in this evolution.  Modern data systems on the cloud allow more data to be stored, and actively used, than ever before, helping to generate insights that are more accurate, faster and more relevant to the business.  The cloud provides the platform for data-driven decision making which is why Tableau and AWS have such a close collaboration.

Why Tableau and AWS?

Tableau and AWS have a very deep relationship on multiple levels.  Tableau is a customer of AWS, and AWS is a customer of Tableau’s.  Tableau’s SaaS offering, Tableau Online runs on AWS and Tableau helps Amazon see and make sense of their data.  Critically, AWS and Tableau have worked together as partners in thousands of joint customers to solve really difficult problems.  

Listen in to the Fireside Chat to hear perspectives and best practice tips from three industry leading firms: Tableau, AWS and IDC on achieving success in unlocking your data, empowering everyone and acting on your data—Data. Insights. Action - Unlocking the Data Value Chain.

To find out more about joint AWS and Tableau solutions.