Dynamic infrastructure financing models available through data analytics
Enterprises today can develop more agile infrastructure financing models than ever before, thanks to the wealth of data available from social media, the Internet and a host of other sources. Modern analytical tools provide quick insight into this data, enabling enterprises to rapidly make adjustments to their infrastructure financing models to account for changes in customer demand, interest rates or other market factors.
According to a McKinsey Quarterly article, developing and launching a big data and advanced analytics strategy requires more than gathering and analyzing data to visualize trends. It should instead result in widespread changes to the way the company conducts its daily business. The results should be used to drive infrastructure financing models that enable the company to react immediately to detected changes.
Analytics drive continuous forecasting
Data analytics can lead to continuous forecasting, as tracking shifts based on changing data input enables companies to constantly revise their infrastructure financing models.
If demand for a product or service is higher than expected, for example, then the budget can be adjusted accordingly to produce more supply, supported by shifting funds from other areas. If the potential profits are strong enough, the company might opt to borrow money for additional inventory and support.
On the other hand, if demand is lower than expected, the budget can be similarly adjusted to reduce support and inventory and perhaps even to eliminate the product or service from the company's offerings. Funds no longer used for inventory or support could then be used to accelerate payments on business loans.
Faster results, quicker strategy shifts
Shifting resources to meet demand or other market factors is not a new concept. But traditionally, such shifts were a quarter or more in the making because it took months to collect and analyze the appropriate data using spreadsheets or older electronic tools.
However, today's hardware and software have the power to collect and analyze this data nearly instantaneously. These tools augment the information gathered from inside the enterprise with data available from social media. So while internal data may show that the company's sales are down in one area, the social media chatter may indicate why customers aren't buying. Perhaps the price point is too high for that region of the country, where wages are typically lower.
So rather than abandoning the product altogether in that area, the company can alter its infrastructure financing model to reflect a price low enough to induce additional sales while still earning a profit. This information also allows the business to forecast how different price changes would likely impact profits and revenues.
Improved risk recognition
The impact of real-time data analysis could be even more impactful in the financial industry, enabling banks, brokerages and other financial industry firms to better manage risk.
In a Data Center Knowledge blog, Patrick Lastennet, director of marketing and business development, financial services segment for Interxion, writes that better, quicker data analysis might have helped Lehman Brothers better understand how much risk it had in its portfolio. Better understanding of the risk could have led to improved decisions, possibly avoiding what was called "the Pearl Harbor moment of the U.S. financial crisis."
While most companies are unlikely to incur the risks and eventual collapse suffered by Lehman Brothers, the ability to quickly collect and analyze data enables firms to quickly adjust their infrastructure financing models in response to market conditions on a national, regional, local and store-to-store basis.
To learn how to deliver deeper insights to help your organization make faster, better business decisions, visit the IBM Financial Performance Management webpage and sign up for a free trial today.