The 5 characteristics of a data-driven organization

Change doesn’t happen overnight, and certainly not in the world of data and analytics. Organizations are constantly looking for ways to transform, including how they use data and analytics. Ultimately, AI technology should become a permanent part of the decision-making process. But what does such a data-driven organization look like?

Experimentation, error and adaptation… They are all factors that are part of the journey to a truly data-driven organization. A journey that cannot possibly be in a straight line. Like a captain setting course for an island paradise, he encounters currents, waves, storms and technical obstacles that divert his ship. In the end, you might not get to the exact place you originally envisioned, but the destination will be tropical for a while. In digital transformation, this island paradise equals a true data-driven approach.

But what exactly does the data drive mean? Below, we review five universal characteristics of data-driven organizations, regardless of the specific market characteristics they face.

1. Every decision is supported by predictive and prescriptive analytics

In fact, data has always been an important part of the decision-making process, even before computers existed. The biggest innovation lies in the sophisticated systems we now use to extract insights from data, make predictions and make informed decisions.

Although the technology exists, predictive systems in practice remain quite rare and experimental. There are several reasons for this: The technical complexity, lack of awareness in the boardroom, tools and technologies that do not work together, company culture, etc.

Successful organizations succeed in incorporating the necessary insights, predictions and recommendations based on data into every decision-making process.

Successful organizations succeed in incorporating the necessary insights, predictions and recommendations based on data into every decision-making process. For example, every point of contact with a customer is an opportunity to personalize services, offer more relevant products and services and avoid switching customers to competitors. These companies do not dwell on predictions (predictive analytics), they assess how likely it is that something can happen and determine the best possible course of action based on this (prescriptive analytics).

2. Insights lead to real-time decisions and process automation

To improve the customer experience and generate better business results, it is important that data can be processed for insights and decisions in real time. Think of the online store manager who manages to identify when a valuable customer is at high risk to go to the competition and launch an effective counteroffer in time.

Not only do companies make better decisions, they can also automate and streamline those decisions in certain processes. For example, an insurance company can process claims without the need for human intervention. Or a bank can react immediately when a customer comes to apply for a loan. Automated decisions reduce organizational costs and provide customers with a seamless digital experience. To enable such real-time analysis, companies must be able to easily combine data from various sources with business data.

3. DevOps principles help industrialize the entire analytics lifecycle

Today, organizations are doing only a fraction of what they could achieve with analytics. Often their approach is still experimental, and each step of the process – from preparing data to building and monitoring models – takes too much time and work. Furthermore, there is no guarantee that these experiments will lead to a production-worthy solution. According to IDC, only 35% of analytical models go into full production. And often it can take months or even years.

Forward-thinking organizations have therefore fundamentally changed the way they produce and implement analytics. They combine different methods, such as DataOps and ModelOps. They work with clear roles and responsibilities for effective collaboration between teams. And they proactively monitor models so they don’t waste resources on daily maintenance tasks. The basis for such an industrialized approach is a unified analysis platform that provides a reliable environment for the development and implementation of innovative projects.

4. Democratization of analytics ensures high levels of data literacy

Data has become a product where consumers can easily browse and access a catalog to explore data without the involvement of a data warehouse team. For this, they rely on point & click interfaces that take into account security and privacy rules and also prevent data sets from being replicated endlessly in local databases.

The skills gap bridges a successful business by complementing open source programming with low-code/no-code functionalities.

The skills gap bridges a successful business by complementing open source programming with low-code/no-code functionalities. This creates a wider community of citizen data scientists. “Analytics translators” are the missing link between the business and data science teams.

In addition, a Data & Analytics Center of Excellence can proactively identify use cases, promote collaboration, support innovation, simplify the operationalization of models in the decision-making process and drive competence development through training programs.

5. Proactive AI management ensures tangible and accountable business results

While every organization has the ambition to become data-driven, only the most innovative companies have a proactive, organized and strategic approach to data and analytics. What exactly does that mean? First, they have formalized their data & analytics strategy in the form of a document that is spread across the entire organization.

In addition, the execution of that strategy is supported throughout the company. It could be the job of a Chief Data Officer (CDO) or a Center of Excellence. In any case, there is at least some form of governance structure linking departments and lines of business with clear areas of responsibility.

In the most regulated sectors, such as financial services and pharmaceuticals, compliance already requires some governance. These organizations often have an advantage if they can expand that foundation for digital innovation. A governance framework should make data and analysis more productive and usable for organizations. It also provides an ethical overview so that companies can use AI in a responsible way.

How can you accelerate your digital transformation?

Ready to become a data-driven organization yourself? So remember the following tips:

  • Provide an integrated approach

Data is meaningless without strong analytics, but you will get nowhere without reliable data with analytics itself. Therefore, embrace the entire process and connect all steps – from providing data to building models. And to create real value, you need to implement analytics in places where important decisions are made.

  • Start with what really matters

Many organizations put their energy into the early stages of the race (access to data) and are exhausted when it starts to get really interesting. Then run the race in the opposite direction and start by defining goals and what results you want to achieve. In a next step, you can decide what insights and data you need for this.

  • Democratize analytics for a broad audience

Often, organizations spend a lot of time on basic data with a low value. By democratizing analytics, you can make more effective use of available talent and skills. This also allows your organization to more easily focus on more advanced, high-quality analytics.


This is a submitted contribution from SAS. Would you like to delve deeper into this text? So be sure to watch the webinar “Take Analytics to the Next Level and Future-Proof Your Organization”.

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