Data driven decision making (DDDM) provides a significant competitive advantage for enterprises by allowing organizations to make informed decisions based on data rather than intuition or gut feeling.

In the digital age, businesses are building significant amount of data, making it challenging for decision-makers to make informed choices. The rise of big data, cloud computing and advanced analytics has made it possible to leverage data in new and meaningful ways. According to a survey by NewVantage Partners, 92.2% of executives reported that their organizations have increased their investments in big data and AI over the past year. (2021)

A study by Dresner Advisory Services found that 49% of organizations are currently using data and analytics to make decisions, up from 32% in 2016. (2020).

Why Data Driven Decision Making

1. Improved accuracy: Data driven decision making enables organizations to identify patterns and insights in data, leading to a more precise definition of the problem and accurate predictions and decisions.
2. Better customer experience: Customers talk to organisations through data. When the organisation adopts DDDM, they are getting access to a more specific view of the customer behaviour and preferences thereby allowing organisations to build personalized customer experiences.
3. Improve efficiency: Using advanced analytics, organisations can automate generation of actionable insights for faster decision making and improved speed-to-market for products.

A study by PwC found that 90% of companies that use customer data analytics, report an improvement in customer satisfaction. (2019)

How can organizations bring DDDM into practice

1. Set up the building blocks –
   1. Determine the data needs of the organization, understand the quality of the existing data.
   2. An important aspect is to ensure that there is a continuous process of collecting and storing the required data.
   3. Build insight driven analytical process – this ensures time is not lost in analysing scores of data and dashboards and business rule driven insights are available for business users.
2. Foster a data-driven culture –
   1. Ensure data is a part of all the decision making in the company.
   2. Encourage business users to focus on insights provided to get faster view of problems and wins.
   3. Build a feedback loop for business users to define the current challenges and future data needs.
3. Continuously monitor and improve –
   1. Continuously monitor the use of data in decision making and make improvements as needed.
   2. Measure the impact of new and existing projects through predefined metrics
   3. It is also important to establish metrics to measure the data quality, data governance and regularly assess progress against them.

Also read: Empowering Data Quality Excellence through SSOT Implementation

Challenges observed in implementing DDDM

The challenges faced by organisations in transforming themselves into data driven organisation are typically –

1. Data management challenges
   1. Ensuring the quality and accuracy of data is a critical challenge. Poor data quality can lead to unreliable decision-making, which can lead to issues for the organisation. To mitigate this risk, establishing data quality checks – cleansing and validation ensures accuracy of data.
   2. Integrating data from several sources in an ecosystem of modern and legacy systems can be a challenge. But it has been seen that by building an SSOT through standards like data governance, clearly defined integrations can lead to minimal risk.
   3. Data security is another key area to be focused on as it is one of the key factors for success. As a part of the data ecosystem, the protocols around data security like access control & data encryption should be implemented.

2. Organisational challenges
   1. Initiating and managing a culture of data driven decisions requires a significant change management in the organisation. Organisations should invest in data literacy training for employees to ensure they can work with data efficiently.
   2. Bringing in a culture of measuring the impact of a business initiative through data also would need management push. Organisations should only define future road-maps based on measurable success of an initiative.
   3. Finding the right skilled associates or building these skills within the organisation is also expensive or a time-consuming process. Depending on the size and need of the organisation, they can collaborate with contractors for data management initiatives.

Data-driven decision making can provide significant benefits for enterprises, including improved accuracy, increased efficiency, better customer satisfaction, and increased competitiveness. By assessing data needs, developing data infrastructure, fostering a data-driven culture, and continuously monitoring and improving the process, enterprises can successfully adopt data driven decision making and reap the benefits.