Why make decisions with data?

Does data matter?

The volume of data available for decision-making has grown exponentially in the last few years. Information pours in from digital platforms, mobile phones, wireless sensors, etc—but why should businesses and change agents value data-driven decision-making?

And, perhaps more importantly, how can they persuade others that going data-driven is desirable, even necessary?

Data-driven decisions

Communication around data and its impact on business strategy are important, and companies that strategically leverage data to tackle business challenges are at the forefront of their industries.

Below are five reasons organisations should want to become analytically mature.

1. ‘Data and analytics are changing the basis of competition. Leading companies are using their data capabilities to improve business operations considerably.’[1] Digital platforms are playing a key role in creating a winner-take-most dynamic in most markets.
2. Data has become a critical corporate asset. Data captured via mobile phones, sensors, payment devices, etc are becoming increasingly commoditised. This emergent value of such scarce data accrues to the owners, which can be used to provide critical analytics.
3. Data and analytics disrupt industries. New types of data sets are disrupting the very way decisions are being made and how businesses respond to market changes and competition. A powerful example of this is that data insights can be used to personalise products and services. New analytical techniques also fuel discovery and innovation to improve profitability over time.
4. Advances in machine learning can be used to solve business problems. Systems supported by machines learning can manage logistics, analyse medical records, provide customer services, etc. These technologies can generate productivity gains, increase profitability, and improve quality of life.
5. Supports decision-making and minimises biases. Data and analytics can improve decision-making by bringing in more points from new sources—and the process can be made instantaneous by adding automated algorithms. So-called ‘smart cities’ are a great example where sensors are used to improve traffic flow and the internet of things (IoT) is used to enable utilities and reduce waste.

Disruptive technologies

The third point returns to the idea of new data technologies being ‘disruptive’, which can be the case, but a useful note here is that characteristics of certain markets—such as information asymmetries, human biases and errors, as well as in-efficient matching—open the door to disruption.

The pressure for organisations to become data-driven can, therefore, come from both inside and outside.

For a good overview of disruptive models and capabilities that are transforming certain industries, consider the following table from the McKinsey Global Institute:

(Click to enlarge this image)

Source: McKinsey Global Institute Analysis [1]

References

[1] The Age Of Analytics: Competing In A Data-driven World [Document]. McKinsey; 2016, Dec. Available from: https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Executive-summary.pdf