Organisations that want to be truly data-driven need to change their approach depending on the scenario, as not all data projects are equal in the eyes of company leaders.
A recent SAP report, Challenges for Analytics as Data-Driven Enterprises Chase Scale, explains how it is easier to get approval for customer-facing projects rather than internal infrastructure projects.
But internal infrastructure projects are vital if customer-facing projects are to succeed.
However, these organisations do not have much choice, the report argues. For example, those companies that need to embed Artificial Intelligence (AI) or machine learning into their products and service offering have little choice but to build those capabilities.
This lack of choice leads to another unresolved debate: while analytics, machine learning and AI are often conflated as disciplines, is this really accurate?
It’s a question that must be explored, and the stakes for large enterprises could not be higher. Analytics executives say this is an existential issue for them because if they fail, they will be unable to compete in the future.
Companies in the ASX top 20 are likely to make radical shifts in the next couple of years.
The growing popularity of true data science as a discipline has made matters worse.
According to the report, the failure to recognise that data science is not just the next evolution of business intelligence — but an entirely different discipline — creates its own problems.
The difference between data scientists and business analyst is the former delivers the most value when dealing with the unknown or running experiments. The latter normally describe current realities using well-understood, structured data.
Leap from business intelligence to AI
Data science and predictive capabilities are still emerging, and Australian enterprises might need to make the leap from business intelligence to AI.
Both business leaders and analysts need to change how they work to reflect the new realities of analytics-led decision-making.
The authors of the report explain that analysts need to modify their approach and develop separate processes depending on the seniority of the executives they are helping.
Self-service tools may be enough — and even preferable — for many middle managers, but this is not often the case for general managers and senior executives who need to make quick decisions. These managers want the analysts to be able to tell the story of their data in simple and clear ways.
Therefore, the ability to speak the language of business is critical to the success of analytics. The report notes that analysts need to be able to explain how economic and quantitative models work in simple terms.
Opportunities remain compelling
Even with the challenges, the opportunities presented by analytics and related models remain compelling.
The authors demonstrate that many new-generation businesses unimpeded by existing structures are reaping the rewards of data, analytics, machine learning, AI and other disciplines.
For example, one globally recognised business is using data to create more opportunities and influence the behaviour of its workers.
They can make more money while meeting increased demand due to dynamic, demand-responsive pricing.
See how the best in the world are embracing intelligent technology and innovation. Register for e’ffect at Carriageworks in Sydney on August 8.
About this author
Athina Mallis is the Editor of the Which-50 Digital Intelligence Unit of which SAP is a corporate member. Members provide insights and expertise for the benefit of the Which-50 community. Membership fees apply.