Now that many organizations have jumped on the analytics band wagon, the ability to assess the effectiveness of analytics in relation to business success has presented itself. The problem is, although there are many reasons for using analytics to understand and drive business growth, businesses feel that analytics isn’t working for them, and here’s why
1. Lack of Trust
When it comes to making business decisions many CEO’s rely on their gut instincts. With a deep knowledge of their business and industry this sometimes works, but isn’t optimal in large organizations. Combining instinct with analytical insights derived from data, moves the needle towards better knowledge-based decision making. However, a mistrust of analytics is widening the gap between analytics and business decision making.
A July 2016 study by KPMG showed that only 38% of survey respondents were very confident in the insights gained from Data and Analytics for customer insights. And only 34% reported being very confident in the insights gained from D&A for business operations. This means that over 60% of organizations surveyed are not very confident in the insights derived from analytics.
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This lack of trust in analytics insights devalues the benefits of decision making based on analytics, and might have businesses turning once again to historical insights based on financial statements and on gut instinct.
2. Lack of Understanding
Although many large businesses are well into the cycle of using analytics for better decision making, smaller businesses have been left behind, mainly because they lack in-depth understanding of what analytics can do for their business and how to use the information. They are also overwhelmed with the choice of platforms and lack the funds to hire data scientists and specialists.
The majority of small businesses know they need to use analytics in order to augment decision making, but aren’t sure what to do with the results, thus relying solely on historical financial analysis to make decisions about the future.
When we taught a client how to manage their ecommerce pricing decisions using simple mathematical and predictive analytics modelling in a spreadsheet program they were already familiar with, they were blown away. The model allowed them to determine the best pricing for each product to maximize revenue. It provided a simple alternative and showed that predictive decision making need not involve complex processes, and systems. Using advanced spreadsheet techniques with statistical modelling can lead to deeper insights without an excessive investment of time or money, and these techniques are easily learned.
3. Lack of Execution
Business decision makers drool over beautiful visualizations and effective models that provide deep insights, yet they fail to execute on the insights. Proving to a decision maker that production of a particular widget is affecting their P&L negatively is one thing, having the decision makers take the steps to change systems, increase efficiencies, or eliminate production of the widget, is another.
[bctt tweet=”A critical step in using #analytics is to implement the strategy developed through insights. ” username=”S_H_E_L_L_E_Y”]
One of the critical steps in using analytics to its best advantage is to implement the strategy developed through analytics insights. The analytics alone will not get you to business success, developing strategy based on the insights and executing the strategy using an implementation roadmap will.
There is no doubt that using the RISE approach is a launch pad to business success, and requires strategic execution of all four components.
Lack of, trust, understanding, and execution are the 3 foundational reasons why decision makers feel analytics isn’t working for their businesses. What do you think? Is analytics working in your business? Why or why not?
RISE 