Developing Valuable Business Insights From Data Driven Strategies

Reliance on credible data sources that define actionable insights a business should indulge in for streamlined operations and revenue generation has seen centuries and centuries. Analytic investments are increasingly gaining ground in every aspect of different businesses.

According to a report from Mckinsey Global Institute, Those organizations that use data-driven strategies not only gain 23 times more customers but manage to retain their potential customers more than 6 times, as a result of that they are 20 times more profitable as well comparing those companies who don’t use data-driven strategy. 

It’s obviously coming out that the most powerful tool for solving problems is data. Huge data and analytics are burgeoningly climbing to the top of the corporate agenda. They pose business intelligence by promising to transform the way companies do business. The question is, how most effectively can you utilize data analysis to your business advantage?

As data-driven strategies continue to redesign organizations’ core processes, they interestingly grow into an important point of business competitive differentiation. No acclimatized organization can deny that consolidating data points yields great business insights that work in unison to streamline operational efficiencies. 


The resultant effects of obtaining ardent operational guidelines from brand measurement and analytical market performance, whether short or long-term, are increased efficiencies in operations, diminishing costs of running businesses, and enhanced revenue generation.

Creatively Opting for the Right Data

In the past few decades, the volume of information and the opportunities to expand invaluable insights by combining the collected data have simultaneously accelerated rapidly. Bigger and more fine-tuned data have given many organizations more granular views to their business environment. Upping the game in sourcing data creatively, with necessary IT support, has seen working strategies given birth to, business operations improved, and customer experiences get better.

Companies may readily have the data necessary in tackling business challenges, but managers or operational executives may simply not know how to utilize the information in making key decisions. Getting specific with the business problems and opportunities needed to be addressed can encourage a more comprehensive look at data.

This should not be biased against the potential of external or new sources of data. For instance, social media generates terabytes of unstructured data that should be among the valuable external sources to prompt broader thinking of what decisions can be made from all the information available.

Transforming Business Capabilities from Data-Driven Models

Many times, data has proven essential in building models that predict and optimize business outcomes. This should not make organizations blind to the fact that performance improvements and competitive advantage emanate from analytics that allows operators to predict and optimize outcomes.

Hypothesis-led modeling where managers identify a business opportunity and establish how data-driven models can improve performance has proved to generate faster outcomes. The model is deeply rooted in practical data relationships readily understood by my management.

One important aspect of data-driven business improvement is developing models that readily synchronize and permeate the organization at all levels. A mismatch between a company’s existing culture or capabilities and the models to exploit analytics successfully may develop a grave disregard for the potential business insights.

The analytical tools, whether generated by experts in business modeling, should be geared towards being easily understood by the management and people on the operational frontlines.

As much as advanced statistical methods indisputably develop better business-oriented models, the modeling exercise may pose an inherent risk.

The experts who design models from the collected data may, at times, develop strategies that are too complex to execute. Coming up with the least complex model that would improve business performance without exhausting the organization’s capabilities should be the plan.