I have seen the growing importance of decision-driven data analytics in business. Companies that can effectively leverage data to inform their decision-making can gain a significant competitive advantage. However, implementing decision-driven data analytics can be challenging, and companies need to ensure that their initiatives are aligned with their business goals, have the right talent and technology in place, and are effective in driving better decision-making.
In this article, I will explore the challenges companies face when implementing decision-driven data analytics, as well as strategies for promoting a culture that supports this approach and measuring the success of decision-driven data analytics initiatives.
Date-Driven Decisions Vs. Decision-Driven Data
Data-driven decision-making has been touted as the holy grail of modern business practices. The idea of using advanced analytics and big data to augment human decision-making has been marketed as a way to create smarter decisions and drive business performance. However, despite the significant investment in data initiatives, many companies have been left disappointed with the results.
According to a recent Accenture survey, only 32% of companies reported realizing tangible and measurable value from data. This begs the question: why are companies failing to see the benefits of data-driven decision-making? The answer, according to Bart de Langhe and Stefano Puntoni, is that companies are taking a backwards approach to data and analytics. Instead of starting with the decision that needs to be made, many companies focus on finding a purpose for the data they have or trying to extract value from available data. This leads to answers to the wrong questions and misleading insights.
De Langhe and Puntoni propose a different approach, called decision-driven data analytics. This strategy is anchored on the decision that needs to be made and works backward to find the data that will best deliver for that particular business objective. By starting with the decision, companies can ensure that they are asking the right questions and using the right data to drive their decisions.
In their discussion, the professors provide context for why the predominant data-driven decision-making approach often fails to meet expectations. They also explain how organizations can reverse course to let business objectives and decisions drive data analytics.
The first problem with the traditional data-driven decision-making approach is that companies tend to put data on a pedestal without thinking critically about how the data was generated. This can lead to jumping to conclusions based on flawed or incomplete data. The second problem is that companies are often asking the wrong questions. Starting with the decision that needs to be made can help ensure that companies are asking the right questions and using the right data to inform their decisions.
Decision-driven data analytics requires a shift in mindset and approach, but the benefits can be significant. By starting with the decision that needs to be made, companies can ensure that they are using data to drive their decisions, rather than letting the data drive their decisions. This approach can lead to better decision-making, improved business performance, and a more efficient use of resources.
Data-driven decision-making has been marketed as a way to create smarter decisions and drive business performance. However, many companies have been left disappointed with the results. According to Bart de Langhe and Stefano Puntoni, the problem is that companies are taking a backwards approach to data and analytics. By starting with the decision that needs to be made, companies can ensure that they are asking the right questions and using the right data to drive their decisions. Decision-driven data analytics requires a shift in mindset and approach, but the benefits can be significant.
Examples of Companies that Have Successfully Implemented Decision-Driven Data Analytics
There are several companies that have successfully implemented decision-driven data analytics.
One example is Netflix, which uses data to drive its content decisions. Netflix collects a vast amount of data on its users' viewing habits and uses this data to make decisions about what content to produce and acquire. By starting with the decision to create content that will appeal to its users, Netflix can use data to inform its decisions and ensure that it is producing content that its users want to watch.
Another example is the online retailer, Amazon. Amazon uses data to drive its pricing decisions. By starting with the decision to optimize pricing to maximize profits, Amazon can use data to inform its pricing decisions. Amazon collects data on customer behavior, competitor pricing, and other factors to determine the optimal price for each product.
A third example is the healthcare company, Humana. Humana uses data to drive its healthcare decisions. By starting with the decision to improve health outcomes for its members, Humana can use data to inform its decisions about which programs and interventions to implement. Humana collects data on its members' health status and behavior, as well as data on the effectiveness of different healthcare interventions, to determine which interventions will have the biggest impact on health outcomes.
In all of these examples, the companies start with the decision that needs to be made and use data to inform their decisions. By doing so, they can ensure that they are asking the right questions and using the right data to drive their decisions. This approach can lead to better decision-making, improved business performance, and a more efficient use of resources.
Challenges Companies Face When Implementing Decision-Driven Data Analytics
While decision-driven data analytics can be a powerful tool for driving business performance, there are also some challenges that companies may face when implementing this approach.
One challenge is data quality. For decision-driven data analytics to be effective, companies need to ensure that they have high-quality data that is relevant to the decision they are trying to make. This can be a challenge, as data may be fragmented, incomplete, or inconsistent across different systems and sources. Companies may need to invest in data cleaning, integration, and governance to ensure that they have the right data for their decision-making needs.
Another challenge is organizational culture. Decision-driven data analytics requires a shift in mindset and approach, as it requires starting with the decision that needs to be made and working backward to find the data that will best inform that decision. This can be a significant change for organizations that are used to a data-driven approach, and it may require changes in how decisions are made and how data is used.
A third challenge is talent. Decision-driven data analytics requires a combination of business acumen and data analytics skills. Companies may need to invest in training and development to ensure that they have the talent they need to implement this approach effectively. They may also need to rethink their hiring practices to attract and retain talent with the right skills and mindset.
Finally, technology infrastructure can be a challenge. Decision-driven data analytics requires a robust technology infrastructure that can handle large volumes of data, support advanced analytics, and enable integration across different systems and sources. Companies may need to invest in new technology or upgrade their existing infrastructure to support this approach.
Decision-driven data analytics can be a powerful tool for driving business performance, but it also comes with some challenges. Companies may need to address issues related to data quality, organizational culture, talent, and technology infrastructure to implement this approach effectively. By addressing these challenges, companies can unlock the full potential of decision-driven data analytics and drive better business outcomes.
How Companies Can Change their Organizational Culture to Support Decision-Driven Data Analytics
Changing organizational culture to support decision-driven data analytics can be a significant challenge. However, there are several steps that companies can take to promote a culture that supports this approach.
First, companies need to ensure that decision-driven data analytics is a top-down initiative. Leaders need to communicate the importance of this approach and model the behavior they want to see in their employees. Leaders should encourage a culture of experimentation and data-driven decision-making, where employees are empowered to take risks and learn from their mistakes.
Second, companies should invest in training and development to build the skills and mindset needed for decision-driven data analytics. This could include training on data analytics tools and techniques, as well as training on critical thinking and problem-solving. Companies should also provide opportunities for employees to work on cross-functional teams and collaborate with data analysts and other experts.
Third, companies should create incentives and recognition programs that reward employees who embrace decision-driven data analytics. This could include bonuses or performance evaluations based on data-driven decision-making, as well as recognition programs that highlight employees who have made significant contributions to the organization through data-driven initiatives.
Finally, companies should foster a culture of continuous improvement, where employees are encouraged to learn from data and use that learning to inform future decisions. This could include regular data reviews, where teams analyze the results of past decisions and identify opportunities for improvement.
Changing organizational culture to support decision-driven data analytics can be challenging, but it is essential for driving better business outcomes. Companies can promote a culture that supports this approach by ensuring it is a top-down initiative, investing in training and development, creating incentives and recognition programs, and fostering a culture of continuous improvement. By doing so, companies can unlock the full potential of decision-driven data analytics and drive better business performance.
Measuring the Success of a Decision-Driven Data Analytics Initiatives
Measuring the success of decision-driven data analytics initiatives is essential for companies to understand the impact of their efforts and make informed decisions about future investments. There are several key metrics that companies can use to measure the success of their decision-driven data analytics initiatives.
First, companies can measure the impact of their decision-driven data analytics initiatives on business performance. This could include metrics such as revenue growth, cost savings, or customer satisfaction. By tracking these metrics over time, companies can determine whether their decision-driven data analytics initiatives are having a positive impact on the business.
Second, companies can measure the effectiveness of their decision-driven data analytics initiatives in driving better decision-making. This could include metrics such as the number of decisions made using data-driven insights, the percentage of decisions that were successful, or the time it takes to make a decision. By tracking these metrics, companies can determine whether their decision-driven data analytics initiatives are helping them make better decisions.
Third, companies can measure the ROI of their decision-driven data analytics initiatives. This could include metrics such as the cost of the initiative, the time it takes to see a return on investment, and the overall value delivered to the business. By tracking these metrics, companies can determine whether their decision-driven data analytics initiatives are delivering a positive ROI and whether they are worth the investment.
Finally, companies can measure the impact of their decision-driven data analytics initiatives on employee engagement and satisfaction. This could include metrics such as employee satisfaction with the decision-making process, the level of engagement in data-driven initiatives, or the number of employees who have received training on data analytics. By tracking these metrics, companies can determine whether their decision-driven data analytics initiatives are helping to create a more engaged and satisfied workforce.
Measuring the success of decision-driven data analytics initiatives is essential for companies to understand the impact of their efforts and make informed decisions about future investments. By tracking metrics related to business performance, decision-making effectiveness, ROI, and employee engagement, companies can determine whether their decision-driven data analytics initiatives are delivering the desired results.
Ensuring Decision-Driven Data Analytics Initiatives Are Aligned with Business Goals
Aligning decision-driven data analytics initiatives with business goals is essential for ensuring that companies are making informed decisions that drive business performance. There are several steps that companies can take to ensure that their decision-driven data analytics initiatives are aligned with their business goals.
First, companies need to clearly define their business goals and identify the key decisions that need to be made to achieve those goals. This could include decisions related to product development, marketing, pricing, or operations. By starting with the business goals and working backward to identify the decisions that need to be made, companies can ensure that their decision-driven data analytics initiatives are aligned with their overall strategy.
Second, companies need to identify the data that is most relevant to the decisions they need to make. This could include data on customer behavior, market trends, or internal operations. By focusing on the data that is most relevant to the decisions that need to be made, companies can ensure that their decision-driven data analytics initiatives are focused and effective.
Third, companies need to ensure that they have the right talent and technology infrastructure in place to support their decision-driven data analytics initiatives. This could include hiring data analysts or investing in data analytics tools and software. By having the right talent and technology in place, companies can ensure that they are able to effectively analyze the data and make informed decisions.
Finally, companies need to regularly assess the impact of their decision-driven data analytics initiatives on their business goals. This could include tracking metrics related to business performance, decision-making effectiveness, and ROI. By regularly assessing the impact of their decision-driven data analytics initiatives, companies can make informed decisions about future investments and ensure that they are driving business performance.
Aligning decision-driven data analytics initiatives with business goals is essential for ensuring that companies are making informed decisions that drive business performance. By defining business goals, identifying relevant data, having the right talent and technology in place, and regularly assessing the impact of their initiatives, companies can ensure that their decision-driven data analytics efforts are aligned with their overall strategy.
Conclusion
Decision-driven data analytics can be a powerful tool for driving business performance, but it also comes with some challenges. Companies need to address issues related to data quality, organizational culture, talent, and technology infrastructure to implement this approach effectively. By promoting a culture that supports decision-driven data analytics, investing in training and development, creating incentives and recognition programs, and fostering a culture of continuous improvement, companies can unlock the full potential of decision-driven data analytics and drive better business outcomes. By measuring the success of decision-driven data analytics initiatives and ensuring that they are aligned with business goals, companies can make informed decisions about future investments and drive better business performance.
Jonathan H. Westover, PhD is Chief Academic & Learning Officer (HCI Academy); Chair/Professor, Organizational Leadership (UVU); OD Consultant (Human Capital Innovations). Read Jonathan Westover's executive profile here.
