What is & How Do You Become a Data Driven Organization?

In today’s modern industry, data is a key component in every company’s strategy. Most companies, especially large enterprises, are investing heavily in the collection, storage, and analysis of data. An organization that follows these practices is called a Data Driven Organization.

Companies follow these practices because they have realized how valuable data is and the role that it plays in the success of any business. Due to this, most companies have resorted to the use of data to gain game-changing insights. Research shows that data-driven companies are 6 times more likely to retain their current customers, 23 times as likely to acquire new customers, and 19 times as likely to make a profit. 

This article aims at discussing the best strategies to build and thrive as a Data Driven Organization. 

What is a Data Driven Organization?

The term “Data Driven Organization” is not a new concept. It’s simply any business that makes decisions based on facts rather than based on opinions, gut feelings, and emotions. In such an organization, data-driven decision-making does not happen at the senior-level management only but across all levels of the organization. 

A Data Driven Organisation makes sound decisions in a continuous data-driven business cycle. 

Understanding the Cycle to Become More Data Driven

This cycle requires these 3 capabilities:

• Tech-Savvy (Data creation and Integration): The organization should be capable of creating and collecting all relevant digital data, then integrating and structuring the data into information. 
• Data Fluency (Business Intelligence and Analytics): The organization must have the ability to extract insights and intelligence from data and information.
• Data Literacy (Decision Management): The organization must possess the ability to make decisions and take actions based on insights and intelligence extracted from data. 

Data-Driven Culture in Organizations

When workers are aware of the Driver Metrics they are accountable for and how those metrics affect the Key Performance Indicators/KPIs, a company is said to have a data-driven culture. Data democratization is required, which means that the information must be available to the common person. Employees must be able to comprehend and apply facts in order to make decisions that are relevant to their jobs. It requires citizen analysts who can perform basic analytics without relying on the data team. When people make judgments based on the same set of data, the firm needs a Single Source of Truth. Ensuring data uniformity, accuracy, usability, and security requires data governance and Master Data Management.

The more data-driven an organization is, the better these indicators are. While they appear to be difficult, they are not. These variables will fall into place after you begin your data culture journey and complete it appropriately. It’s crucial to remember, though, that data culture isn’t a one-time job that can be abandoned once completed.

Data culture is a journey in which you must constantly work on it in order for it to improve. Below are the four key traits of Data Driven Organization.

Key Traits of a Data Driven Organization

1) Data Literacy

The capacity to read, manage, understand and debate with data is referred to as Data Literacy. Data literacy will be a primary focus for all employees in a data-driven company. Some data-driven businesses have implemented initiatives to assist employees to become more data literate. It has been proven that efforts to make data literacy more common have a positive impact on participation.

Employees with data literacy abilities are also better able to assist customers with data-based customer support inquiries. A Data Literacy adept employee, for example, may be able to answer a client’s question about social media platforms or direct them to the appropriate person if the customer has a data culture question.

2) Data Democratization

When individuals within an organization are denied access to the appropriate data, the entire business suffers. Unfortunately, even companies that profess to have a data driven organizational culture may fail to provide appropriate data access to their staff. Employees must seek information through an inefficient formal process, which is another component of an antiquated data strategy. A formal process that requires employees to collect data requests and approve each individual might be a huge waste of time.

Employees may also believe that their company does not trust them if company data is not available. Executives must ensure that the right data is provided to staff as rapidly as possible, ideally instantly, to best foster data literacy and a business being data driven. The democratization of data management within enterprises requires accessibility. Employees with varying levels of technical expertise must be able to access data in a data-driven company.

3) Bussiness Leaders

You’ll need folks who can see the “big picture” and know how employees can use data to better the company. This encompasses marketing, sales, and customer data analysis, but it doesn’t stop there. Internal operations can benefit from data-driven decisions, such as improving customer service and support and reducing inventory costs, for example. And it all starts with employing people with a vision who are open-minded about what the statistics will tell them about the path forward.

4) Freely Collaborate

When it comes to suggesting methods to use data, all employees should feel comfortable taking the initiative. Of course, this mindset encompasses much more than just statistics. If you create a workplace where all employees are free to express their opinions—as long as they are backed up by data—even if those opinions contradict senior executives’ assumptions, you will create an environment where the best ideas will naturally rise to the top, keeping you competitive in even the most volatile markets.

Why It’s Important to Become a Data Driven Organization

Data is one of an organization’s most precious assets. It’s a gold mine and a strategic, priceless resource with the potential to change the world, improve how we live, develop a business, and make business operations cheaper, faster, and better.
Data-driven Organizations, unlike traditional businesses, do not scale linearly, and I believe that one of the reasons why companies like Google and Amazon consistently achieve exponential growth is that their business models are essentially based on data:

• Data-driven Organizations may outperform their competition by 6% in profitability and 5% in productivity, according to this PwC article.
• In addition, data-driven organizations are 162% more likely to exceed revenue goals and 58% more likely to beat revenue goals than their non-data-driven counterparts, according to the report.

8 Best Practices to Build a Data Driven Organization

In order to build a Data Driven Organization, 3 pillars are to be satisfied:

• People: People refers to the employees of the organization. All the employees must have an enthusiasm to contribute to the organization and be keen on constantly learning new things. There needs to be a collaboration between all employees and everyone must be able to access the data to keep learning on the go.
• Process: Process refers to the steps involved in encouraging collaboration between different people in the organization. The processes also ensure that the decisions are data-driven and not made randomly. They also add value to the data being analyzed.
• Technology: Technology refers to the self-service tools that help build scalable platforms that perform a unified meta-data tracking and take care of the automation process.

Overall, by maintaining a balance between all the 3 pillars, any organization can become a Data Driven Organization.

The best strategies for becoming a Data Driven Organization are given below:

1) Cultivating a Data-Centric Culture

A data-centric culture is one where employees view data analytics as integral to the business strategy. If you’re the business leader, you must not understand every data analytics step in your organization. Consider using an employee app to help disseminate information across the organization, making it easier for employees to access relevant metrics and insights in real-time, ensuring data-driven decision-making at all levels.

2) Purchasing the Right Tools

A company that intends to become data-driven must accelerate the adoption of data analytics tools into its daily workflow. These tools can help your company create data quality assurance checks and provide automated recommendations from large data sets. An example of such a tool is a BI portal, which is a centralized data portal where the enterprise staff can access data and get recommendations. Such insights can help your IT staff to develop better end-user products and resolve issues with their current information architecture which internally paves a path to becoming a Data Driven Organization.

3) Industrializing Data and Artificial Intelligence (AI)

To manage and gain value from data, an organization must industrialize data and analytics. This involves encouraging a “Data First” approach in the organization by standardizing data-based processes and systems to support the continuous flow of data using technologies such as AI. This transforms the data from the initial analytic discovery to embedding prescriptive and predictive analytics into business operations, systems, and applications. 

4) Opening up Data Access

To become a Data Driven Organization, the organization must become data-hungry. You must open up your organization tools to access broader data pools that can provide insights for your business. For a marketer, this should involve targeting different channels and devices for marketing data. For IT staff, this should involve tracking product builds and implementing greater testing protocols and user reviews for more feedback.

5) Becoming Data Literate

Just having data is not enough for your business to become data-driven. In order to become a Data Driven Organization, you must define the metrics to track, and all members of the organization should recognize them. If you use a variety of tools, you’ll realize that data differs drastically among different sets, even when targeting similar users over the same period of time. Different APIs work differently, and that’s why employees should know these nuances in order to come up with more cohesive strategies reflecting the data being shared. 

To continue finding new ways to apply data and deliver new business insights faster, your organization must encourage a test-and-learn approach that allows for experimentation and learning from failures. If you support a continuous improvement approach in your analytics pipeline, your business will achieve its desired outcomes with greater speed and accuracy. This will improve your teams’ ability to work with data at scale and respond to business events quickly and make it a Data Driven Organization.

7) Aligning Data with Business Objectives

Business leaders should come up with data-centric goals. They should also track actionable KPIs (Key Performance Indicators) that add value to the business. From app retention metrics to conversion rates, organizations should use data in an actionable way that improves both the internal processes and end-user goals. From finance and sales to service-level and project management experiences, data must be rooted in goal-oriented tasks. 

8) Making the Right Decisions

Data harvesting is an expensive process, hence, it will be costly if the organization doesn’t use it to make the right decisions. Business leaders should promote a top-down approach to develop a data-centric culture by creating decision-making processes that reflect insights extracted from data, and empowering analytics centers to offer automated insights and harness data from multiple channels. This too matters when building a Data Driven Organization.

Overall, following all the above strategies in a systematic manner will ensure that there is a proper combination of technologies and employees working in a collaborative manner which makes building a Data Driven Organization easier.

Challenges of Building a Data Driven Organization

Turning your business into a Data Driven Organisation is a good way of steering the growth of your enterprise. However, this does not come easily because of the following challenges:

• Lack of Tech Employees: Data science is quite a recent field, hence, not many people have the right skills to work with data. Employees must be trained both theoretically and practically to extract valuable business insights from the data sets and make the organization more data-driven.
• Lack of an ETL Tool: This happens in some cases if companies are not able to correctly follow the ETL (Extract, Transform and Load) process. Hence, the right tool must be chosen to ensure that this process is optimized which will make the organization more data-driven.
• Inability to Collect Data in Real-Time: The organization may also end up making decisions from outdated data, which may result in making poor business decisions. This can cause a problem between the employees extracting the data for analysis and the data sets themselves. Hence, real-time analysis is crucial for building a Data Driven Organization.

Despite the challenges presented above, organizations that have a systematic plan put in place to address each of these challenges can easily overcome them and become Data Driven Organization.

Conclusion

This article gave a systematic approach to help organizations to become more data-driven. The importance of being data-driven was also discussed and a few challenges that organization’s can face have also been discussed. Overall, building a Data Driven Organization takes time, resources and patience from both the employees and the data sources. By building a careful and procedural environment, any organization can become data-driven.

If you are planning to make your organization data-driven, then Hevo Data is the right choice for you! It will help simplify the ETL process and management process of both the data sources and the data destinations.

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