Effective business management and growth is a multifaceted endeavour, that requires input from, understanding of and experience with numerous specific disciplines and industry corners alike.
Much accolade is given to the business leader that charts a course through unknowable crises, from domestic financial instability to global market disruptors.
However, while many outside of, or new to, the business world might understand these leaders as uniquely inspired, or possessing unusual intuition, it is not the ‘gut feeling’ that guides a business to success.
Many more businesses fail by attempting to intuit their growth than succeed.
Data analysis is an undeniably crucial aspect of serving a growing business’ best interests, through the provision of an objective and evidence-based foundation for a new risk management strategy.
But the field of data analysis itself is fraught with difficulty, as internal data management processes tend to favour the obsolete.
Business budgets understandably prefer tangible growth, with the unintended consequence of handicapping what could prove to be their most effective value-building asset.
Seeking field-specific advice should be the first port of call for any business looking to overhaul its approach, but what are some of the leading factors you should understand before starting your own business down the path of analytics-based risk management?
Meaningful analysis can only be achieved with access to data. The more data your business can access, the more you can learn.
With the continued ubiquity of internet connectivity and the near-universal adoption of digital tools for many internal and external business processes, it is easier than ever to access and harvest relevant information relating to key areas of risk in your business and industry.
Targeting your approach to data collection is a challenge, though, especially for new leaders with little understanding of data analytics.
Enterprise Resource Planning systems, or ERP for short, are uniquely useful in harvesting and categorising data from systems – enabling you to engage more meaningfully with the information collected.
This brings us to the evolution of data analysis tools in general. There are many ways to engage with collected information, with deep statistical analysis able to discover trends and risks that a cursory eye could not reveal.
As such, contemporary data analysis tools are indispensable to modern-day risk management.
Back-end analysis programmes can use algorithms to sift through data sets and minimise man-hours spent interpreting individual points. Front-end visualisation software enables you to render your findings in digestible formats, illustrating new trends in simple terms.
Regulation and Growth
But why go to these lengths? For some, drilling down into data can seem a counter-intuitive exercise in comparison to the drafting of simple contingencies. The sheer importance of analysis-led risk management is illustrated by the pace of regulation in modern business.
New regulatory frameworks pose their own systemic risk to businesses, especially long-running ones with older structures and processes. Robust, analytical approaches to risk management enable businesses to better understand themselves and shift their processes before compliance becomes a problematic undertaking.
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