Making 'data-driven' more than an aspiration

Becoming data driven is stated as an aspirational goal by many business leaders, but often lacks the underpinning strategy and executed roadmap to make it a reality.

My experience from my 4? years leading the set up and scaling of advanced analytics across the Wesfarmers Group taught me that just recruiting a top-talent team and delivering Use Cases with strategic impact is only part of the role.

Below are four guiding principles (adapted from a recent MIT Sloan Fortune 1000 CDO conference) and my thoughts on the key questions that need to be resolved by organisations if the transformational potential of data and AI is to be realised.?

1. Develop and execute a data strategy that mirrors your business strategy and vision.

• What is the business owned roadmap for how data/ AI will accelerate the delivery of strategic goals? What are the opportunities to use data/AI to shape new strategic priorities?
• What aspects of your organisation’s data should be a source of competitive advantage? Rather than just relying on the data as generated by customers or operational processes – is there an approach to augment its value through internally generated attributes and features?
• Is the plan based on building the technology and governance enablers first, and then trying to prove its value, or is the business plunging in to pilots without considering what is required to scale and productionise? Does your execution roadmap strike the right balance between delivering value and forward solving for the barriers that will limit future use?

2. Relentlessly communicate the context, application, and value of data up and down the organization.

• Is data seen as something to be defensively protected, or also recognised as a business asset that needs to be enriched and applied for value?
• Is there a healthy tension between its defensive protection and its offensive value-driving use? Are conversations overly dominated by a risk/ governance based cautiousness, or are business leaders equipped with the arguments to challenge and secure the right balance?

3. Boldly engender trust in your data to build business buy-in, credibility, and momentum.

• The value and issues with data/ AI are only surfaced when its used and then stretched in its application. Do business users understand a pragmatic ‘art of the possible’, and also have a grounded view of which are 'high-risk/ low likelihood moonshot' aspirations to be avoided?
• Are there the beginnings of a data governance virtuous circle? I.e. if business users see the value of the using data and also limitations caused by quality gaps, they are more inclined to recognise the importance from taking ownership of improving data quality.

4. Create a compelling career path for becoming data driven within your organization

• The challenges of attracting technical data and analytical talent are well known. Whilst deeper technical specialism or team leadership is one career path, are opportunities sought for data experts to expand into business roles and so champion what being data-led looks like?
• If a business is to become data driven, are there business roles where 'analytical literacy' should be a recruitment selection criteria? Do HR or the recruiting manager sufficiently understand its importance and how to select the right candidate?

Through applying my experience of setting up and scaling advanced analytics teams at Wesfarmers, Falabella (South America), Quantium/ Woolworths and Sainsbury's (UK), at Adaptive Data our passion is helping businesses prove and accelerate the value from data so that its transformational potential can be realised.

To what degree does Thomas Edison's quote "Vision without execution is hallucination" apply to your business's data-driven ambitions? How are you going to champion its execution to make dreams a reality?