Key lessons from corporate data leaders on how to become 'data-driven'

Read any business plan today and you are guaranteed to see the words “data-driven”. Every single organisation knows that to compete and grow today, they need to be using their data strategically. 

Yet, it seems that despite the growing investment in data and AI initiatives each year, many companies are struggling to derive value from their investments and become data-driven. Even the really big ones.

In a 2021 survey of the Fortune 1000 industry-leading firms, only 24% of respondents said that their organisation was data-driven. Companies reported slow and stagnant progress on managing data as a business asset, forging a data culture, and using data and analytics to drive innovation and compete.

What is at the root cause of this stagnation? Being a truly data-driven organisation takes more than having great technology and quality data, according to JJ Phillips, the Country Manager at Alteryx ANZ.

“In many cases, cultural challenges rather than technological challenges are often the biggest obstacles to realising business outcomes from data analytics initiatives. Many organisations are encumbered with legacy, hierarchical legacy cultures, as well as legacy skillsets, that are resistant to change,” he said. 

“Cultivating a data culture starts by embedding an understanding of the value and impact of data science within an organisation, from frontline workers right to the very top with business leaders.”

“This needs to be supported by programs that improve data literacy and skills, and internal processes where data is shared widely and consistently so employees are empowered to act on data.”

So how can we create the right environment to unlock the tremendous potential of data? Here are some key lessons that corporate data leaders shared at the recent Alteryx Inspire event.

Reexamine the key requirements of data used by your organisation
Often the hunt for data you need to deliver a complex business objective requires long, tedious searches across different systems, data repositories and file locations.  Rather than performing the same “fetching” routine for the information time and time again, consider how you can best optimise this process.

Some organisations choose to design governance and quality into the source system right away to create the perfect data export, whilst some other business may choose to do this via a scheduled transformation process.

“With an increase of complexity in the subject matter, transforming dirty data from disparate locations in an efficient manner that also maintains the quality is imperative to successful client services. This can only be achieved by utilising technology that is fit-for-purpose that to ensure output is of high-value,” said Jessica Farthing, Director of Data Transformation and Analytics at Grant Thornton Australia.

Farthing also believes it requires the forethought can create big benefits downstream.

"At the start of our data analytics engagements, we design what structure we need to add to the native data sources to achieve our overall objective and what automated tools we can apply to efficiently streamline these tasks.  Our clients are generally very happy when “data” efforts are managed well and we spend the majority of our time on the “analytics” that gets to the heart of the business problem they need solving."

Make sure everyone in the organisation has a shared source of truth 
Organisational silos also prevent resources and information from being shared among people, teams and departments. Without the flow of information, organisationss minimise the opportunity for innovation.

“Collaboration is vital when integrating data across an organisation. It helps every individual understand the importance of analytics and the value of data, in particular the value of integrating data across different silos and how that benefits the organisation as a whole,” said Brad Kane, Manager of Procurement Analytics at NSW Department of Planning, Industry & Environment.

“Capturing information (in silos) failed to build a strong strategy that shared insights across key shared service data. Once we integrated teams, it resulted in multiple cultures coming together and multiple business units merging into each other, that is when we were able to build on the insights,” said Kane.

To derive more value for customers, the organisation is also looking to democratise data science.

"Before, only my data and analytics data team used it our analytics automation solution. Now, we are upskilling the whole organisation to help with a one team model, deliver the same message and visualise the end goal of keeping our customers in mind,” said Kane.

Focus data initiatives on meeting real-world demands
Companies can also benefit by focusing their data initiatives on clearly identified high-impact business problems or use-cases. This can help companies build credibility and momentum in their data investments.

“Data within the education industry mostly comes down to meeting real-world demands, particularly for our students,” said Chris Logie, manager of marketing analytics at Deakin University, Australia.

“What we found in our data is more than 20% of inquiries going to universities were unanswered,” said Logie.

By using real-time to understand the needs of students, the university has been able to drive positive operational improvements and overall a better student experience.

 “With real-time data, we were able to achieve our goal in responding to every inquiry within a 24-hour time period, and as result, increased student applications and acceptance of offers,” said Logie.

Data holds significant potential to improve service, innovation and efficiency, but it needs the right environment to thrive. Organisations that are ready and willing to embrace cultural shifts and cultivate new skillsets are on the right path to become truly data-driven.

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