In today’s competitive business environment, companies are looking for the cutting edge they can get to stay ahead. One of the ways to beat the competition is by establishing a culture of data-driven decision making. In this blog post, we will explore how to create a data-driven culture that values data analytics and provides actionable insights into what needs to be done next in order to create a future-ready digital organization.
What is data-driven culture?
Data-driven culture is about creating an organization that is data-driven, where everything from business processes to culture supports the need for data-based decision making. In other words, every step of a business process must be supported by data and actionable insights that can help make important decisions and take related actions to create positive business outcomes. Data-driven culture is key to digital transformation. The key to data-driven culture is setting up data and analytics practices / COE while creating data-driven business processes that offer guidance to employees and teams on how they should be executing their day-to-day business activities. Data analytics provides the required information, insights, trends, patterns, etc., which can help make informed decisions about what needs to be done next so an organization stays relevant in today’s competitive business environment.
How to create a data-driven culture in your organization?
The following are the six key pillars of a data driven culture.
- Establish data management practice: Data management is key to any data-driven culture. Data management involves data acquisition, data governance and the processes that govern how an organization handles its information assets. In order to create a culture of data-driven decision making it is important to establish clear data management practices as well as metrics which can help measure their effectiveness. One of the most important aspect of data management is to establish a data governance model and set up the right tools, systems and processes that increase agility, reduce costs & risks associated with compliance, security (data confidentiality), etc., while ensuring there are no barriers to effective decision making with actionable insights from relevant business data. Another key step is setting up data quality framework which ensures high quality data while ensuring data reliability, data democratization (accessibility) and data security. High quality data is needed to ensure the successful analytics projects including dashboards and advanced analytics / machine learning projects. In order to achieve all of this, what is also needed is a data management team which consists of staff members having good experience and expertise with data management skills.
- Establish trust: With data management being set up, next important pillar is to establish trust by successful execution of analytics projects. The analytics projects can range from descriptive analytics (dashboard) projects to advanced analytics (machine learning) projects. The trust gets established when the analytics projects deliver value to the business. In other words, what is needed is value-centric approach to analytics project execution. It is important that the business analytics team ensures value-driven approach by delivering actionable insights from relevant business data and through a successful dashboard implementation. This helps gain more visibility into what’s happening in an organization with respect to key metrics which can help make informed decisions about next steps for taking actions. In order to establish value-centric approach, what is needed is a prioritization processes which can be used to identify prioritized portfolio of analytics projects. What is very important to establish trust is an analytics team which consist of staff members having expertise with visualization, automation (ModelOps, MLOps etc) and advanced analytics (data scientists). A great analytics team delivering prioritized analytics portfolio will help in winning trust of the organization including leadership and help establish a culture of data-driven decision making.
- Establish technologies: Data and analytics technologies play a key role in establishing a data-driven culture. The technologies can vary depending on the business and industry verticals, but typically there are some key technologies that play an important role in creating a data-driven culture like: data lake, visualization tools (Tableau, Qlik), big data platforms (Hadoop/Spark etc.), analytics databases (Vertica) and other advanced analytics platforms (Jupyter, R, Amazon Sagemaker) etc. One of the key aspect of technologies’ implementation is usage of cloud services. There are several advanced analytics cloud services which can be used on-demand to build data management and analytics solutions. Check out my related blog on Amazon machine learning services.
- Establish methodologies: With data management and analytics practice set up, what is important is to set up methodologies comprising of prioritization, governance, development methodology such as Agile SCRUM and engagement model. Organization aiming to adopt data-driven culture will need these methodologies to work or engage with each other in well-defined manner. Prioritization can be based on cost-benefit analysis as well as on some other criteria such as risk, ROI and time. Governance of projects represents projects / product success metrics. Engagement model is about setting up well-defined RACI for team to engage together in a well-defined manner.
- Establish common communication / language: Another important aspect of data-driven culture is common thinking or communication process based on which organization will interact or execute projects. This is where data literacy can play a crucial role by establishing common communication process based on data language. It is very important to set up this common data language which can help in increasing collaboration among different departments of an organization, between business analytics team and top management as well as external vendors / partners/ consultants etc who are engaged with an organization for execution of projects.
- Gather leadership support: Finally, what is most important is to get leadership support to help in building a data-driven culture. Leadership support can be obtained by making them understand the value of data analytics, how it helps to gain visibility into what’s happening in an organization and drive better decision-making process which is based on relevant insights gained from business data through well implemented dashboards as well as advanced analytics projects (including data science projects). For gaining leadership support, it is important to start by executing some successful data analytics projects. This will help in showing the value of data-driven culture and how business decisions can be improved based on insights gained from relevant data obtained through well implemented dashboards as well as advanced analytics project execution.
Having defined the above six pillars of the data-driven culture, it will be good to create data analytics maturity model which can help in determining “where an organization is” with respect to its data-driven culture. Having maturity model will also ensure that efforts are put on right things at the right time based on which overall business analytics practice as well as cultural transformation initiative of an organization keeps moving forward towards establishing a strong data-driven culture for better decision making.
Why data-driven decisions are important?
Data-driven decisions are important because of some of the following reasons:
- Faster, better and informed decisions thereby helping organization drive digital transformation initiative which is an important business priority for most of the organizations.
- Help in improving customer experience by providing relevant personalized offers to customers based on data insights gained through dashboards as well as advanced analytics projects.
- Gain better visibility into what’s happening inside and outside organization resulting in improved process execution capability, increased ROI etc.
- Enhanced data accessibility: Data-driven culture also helps in improving accessibility of data by its users across an organization resulting in improved collaboration between business and IT teams.
Read about greater details on my another related post: Data-driven decision-making: What, Why & How?
In order to create a data-driven culture, it’s important to have transparency and trust in your decision-making. Data analytics can help you make better decisions by providing relevant insights for different departments of an organization as well as external vendors / partners/ consultants etc who are engaged with the company. It’s also very important that leadership supports this culture because without their support, there will be no change or improvement. To gain leadership support, start by executing some successful data projects so they see what type of value these projects provide. If you want to learn more about data-driven decision-making, read my other blog post: “Data-driven decision making: What, why & how?”
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