Adopting a Data-driven Decision-making Approach in your Organization

Companies continue to be data-rich and information-poor. Robert wrote this Waterman in his book In Search of Excellence. He pointed out thirty-five years ago that companies churn large bundles of data without any insight. Lack of reporting and analytics to derive insights from valuable data creates incongruence between products and consumers. This struggle translates to hit-and-miss business strategies, lukewarm conversion rates, a case of unhappy customers, and unsuccessful products.

Therefore, businesses need to tailor the digital experience to the customer needs with bold marketing strategies and realize their digital transformation requirements more accurately. They also need to set a vision for how they wish to serve clients and retain them. It is the inability to assess data where most businesses fall short. And another issue arises when enterprises are unable to equalize their data efforts with their business needs.

What does data-driven decision-making mean?

Adapting to a data-driven decision-making approach means organizations would have to trust data and gain more digital literacy. Managers are so confident in their abilities that they overlook how data fits so well in making business decisions. So, when a business risk becomes apparent under the microscope of robust predictive analytics, they become wary. Hence, they conclude that becoming a data-driven organization would require changing their mindset and adapting to the culture of “trusting the statistics.”

All of these changes in approach need process upheavals that are both tedious and challenging. But, such managers do not understand that business processes need automation. And that can only get done through data-driven decisions. Such decisions boost a company’s journey toward digital transformation. Also, it ensures businesses can understand customers and serve their digital needs by providing a relevant user experience.

Why it leads to business success?

Business analytics can be essential when deploying strategic decisions. For instance, the ride-hailing company Uber upgraded its COTA (Customer Obsession Ticket Assistant) to a tool that utilized machine learning and natural language processing to enable agents to enhance speed and response to tickets. Here prescriptive analytics helped make more informed decisions. The idea turned out to be successful as it led to faster service and more active resolution, and higher customer satisfaction scores.

Companies can generate higher dividends by embracing the shift toward data and analytics. A McKinsey study suggests organizations that invest in big-data analytics witness an average increase of 6% in profits. Thus, the financial payoff of investing in big data is because of a robust business analysis strategy.

What is the purpose of big data?

Big data came into existence to enable the analysis of massive amounts of information. However, big-data analytics is not an easy task. And it is time-consuming as well as challenging for most organizations because they have not adopted data-driven decision-making. What’s more, software with data analytical capabilities can organize and analyze data into well-compiled information suitable for making business decisions.

Therefore, business analytics allows organizations to automate their entire decision-making. Also, it presents the relevant information to professionals to simplify decision-making. All around, data-driven decisions enable businesses to build strategies that ensure the success of their products. Moreover, by building business strategies through accurate reporting and forecasting, companies can create products tailored to their customer requirements.

How can organizations improve the customer experience?

One can utilize data to generate actionable insights from a broad range of applications to detect unseen patterns in key processes. And they can derive meaningful information to make business decisions that will guide their businesses towards growth with KPIs.

Businesses need to recognize that data science helps identify opportunities and pinpoint risks. And software performs various functions critical for customer retention. Also, it can enable organizations to utilize their knowledge to improve customer experience by creating relevant products, effective business strategies, and efficient design thinking.




CMMI Level 5 custom software development company. A global leader in developing user-centric and mission-critical software solutions.

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Amity Software Systems Limited

Amity Software Systems Limited

CMMI Level 5 custom software development company. A global leader in developing user-centric and mission-critical software solutions.

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