Key Factors Affecting the Time to Insights

This report provides an overview of the key factors affecting the time to insights, including the benefits of BI and the need for tailored solutions.

In a world where businesses operate blisteringly, leveraging data assets is the best bet for staying ahead of the competition. Analyzing data is an invaluable practice that generates insights to help enterprises to steer in the right direction. Fast-moving market dynamics and stiff competition means generating those insights is more important than ever. However, data analytics is not the differentiator; time leverage is. 

After all, data isn’t valuable until it can be synthesized into reports, dashboards, charts, or graphs — in short, consumable insights. As a result, it can be challenging for organizations to make informed decisions, especially when data comes in different formats from myriad sources. 

This is where Business Intelligence (BI) comes into the picture. The conventional definition of BI is broad, encompassing tools and techniques to transform raw data into meaningful and useful information.

Key Factors Affecting the Time to Insights
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Especially when it comes to large volumes of data and diverse data sources, achieving effective BI soon becomes an expensive and time-consuming task requiring significant computing resources and advanced software capabilities. As a result, organizations looking to reap the benefits of BI must invest in BI infrastructure, software, and maintenance. Additionally, it takes significant expertise to process, analyze, and interpret the data accurately. These costs become compounded when working with large data sets, which place higher demands on computing infrastructure. Because of the significant investment necessary to make BI useful for a business, it’s essential to understand the potential return on investment clearly. 


Standard vs. Tailor-made BI Solutions


Existing techniques and tools have long provided standard procedures to generate insights. Every organization faces unique challenges. They often resort to customized BI solutions that cater to their industry and help them stay ahead of the competition. TailoreAs a result, tailored solutions generate maximum business value from the most actionable insights and accelerate innovation and growth. 


Not Just Insights, Time to Insight Matters as Well


User behavior is changing dynamically, making it both mandatory and challenging to make timely and appropriate business decisions. Success hinges on timely insights in identifying key issues affecting business growth, areas of opportunity, emerging market trends, or new revenue streams. With this in mind, it’s no surprise that data analysis proves to be most effective and helpful to the business only when the insights are generated quickly.

The duration to turn data into actionable insights is an important KPI to gauge the efficacy of analytics functions, commonly called Time to Insight. It is also vital to underpin the meaning of insights since data analysis must always be accompanied by the value generation that enables business decisions. 

Various factors can slow down the time it takes to make sense of the data, such as 

  • Data Volume and Complexity: The larger and more complex the data set is, the more time it will take to clean, process, and analyze it.
  • Data Quality: Poor quality data, such as missing or inconsistent information, slows down the process of turning it into insights.
  • Availability of Tools and Infrastructure: The availability and effectiveness of tools used for data analysis, such as software and hardware, impact the time to insight.
  • Skills of the Individuals Involved: A BI expert can speed up the time to generate insights compared to a newbie.
  • Efficacy of Experiments: Inefficient and outdated methods of logging experiments face the risk of missing necessary details or being unrecorded, leading to a longer insights cycle. 
  • Quick Iteration Ability: An organization’s ability to quickly iterate through experiments spurs growth by giving birth to innovative ideas. 

Other non-technical factors can also hinder the speed of generating insight for any organization, such as:

  • Organizational culture: The culture and practices of an organization may facilitate or hinder the ability to turn data into insights quickly.
  • Data Governance: Policies and processes that govern data access, data quality, data security, data lineage, or data retention can also impede the rate at which insights come to life.

These factors are often organization-specific, but some industry-wide trends affect analytics and BI on a much broader scale. 


Industry Examples


To speed up BI initiatives, an ideal platform should allow users to extract insights quickly by facilitating automated data cleaning and analysis. It must spare the developers from dealing with performance-related issues that come with the data-processing platforms necessary for effective BI so that they can focus on the features.

Let’s look at some examples of where businesses benefit from blazing-fast BI results: 


Retail Company


Let’s say a medium-sized retail business is interested in gaining insights into customer behavior and preferences to improve its sales and marketing strategies. They have a large amount of data from multiple sources, including sales, customer, inventory, and social media data. BI tools can help this retail business process and analyze its large data sets to determine, for example, which products are most popular among different customer segments or which store locations have the most loyal customer base. These powerful insights will help the company make informed decisions on product development, marketing, and store operations.


Healthcare Provider


A family doctor and their support staff get data from disparate sources like electronic health records (EHRs), lab results, and insurance claims. They aim to identify data patterns that can predict and prevent chronic diseases. By its very nature, the healthcare domain falls under the high-risk category and requires extensive experimentation to build robust models. The data insights can help the healthcare staff identify high-risk patients, find cost-effective treatments, and pinpoint patients most likely to be readmitted to the hospital. These valuable insights help the practitioner make informed decisions about patient care, treatment plans, and preventative measures.




The ever-increasing need for BI in the modern world is not enough to help businesses stay ahead of the competition if it takes a long time to generate insights from data. The post discussed the challenges and trends affecting the pace at which insights are generated. An effective BI strategy that includes data processing automation, custom data analysis, and the maintenance of scalable solutions can prove useful in generating insights timely. 
Vidhi Chugh is an AI strategist and a digital transformation leader working at the intersection of product, sciences, and engineering to build scalable machine learning systems. She is an award-winning innovation leader, an author, and an international speaker. She is on a mission to democratize machine learning and break the jargon for everyone to be a part of this transformation.