How to Develop and Manage a Data-Driven Culture?

Introduction 

Are you looking for fruitful results and actionable insights from your data assets in order to improve the quality and rationality of your business decisions with data-driven decisions? Embrace the changes dictated by the valuable insights from the correct data analysis if you want to make your business a purely data-driven entity. 

You’ll be able to target your efforts more efficiently if you use data to gain a broad understanding of customer behavior, preferences, and priorities within a highly competitive and dynamic market. An ROI boost can be achieved through data-driven marketing and sales practices. 

Data-driven organizations can outperform their competitors by 6% in profitability and 5% in productivity, according to a PwC study. It has been found that data-driven businesses outperform their revenue goals by 162%. Compared to their competitors, who are not data-driven, they are 58% more likely to hit their revenue goals. The report also shows that 81% of businesses believe data should guide all corporate decisions. 

As discussed, creating a data-driven culture will trigger a revolutionary chain reaction for your organization, improving ROI, engagement, and brand reputation. 

Let’s take a closer look at each step. 

What Is a Data-Driven Culture? 

Data on a daily basis surround us. Numbers, spreadsheets, pictures, and videos are among the many forms in which it is represented. As data becomes more accessible, companies are leveraging it to grow and make an impact. The importance of data today cannot be overstated. Having a data-driven culture is essential for organizations’ survival and growth. 

How does data-driven culture differ from traditional culture? Essentially, data-driven meaning describes the use of data throughout the organization to make decisions. Using facts and assumptions instead of gut feelings is the essence of a data-driven culture. 

There is no guarantee that a company has a data-driven culture or is data-driven merely because it collects a great deal of data. In order to make informed decisions, organizations need to leverage data. 

Types of Data in an Organization 

A structured data record consists of a very fixed field of data. Relational databases, spreadsheets, and other documents can contain this type of data. The term “unstructured data sources” encompasses any data that cannot be easily categorized, such as photos, graphics, videos, streaming instrument data, web pages, PDF files, PowerPoint presentations, emails, and data processing documents. There is also the possibility of semi-structured data being a cross between these two types of data. However, it lacks the strict structure of a data model. In an organization, there are several types of data: 

  • Master Data
    A master record contains key information that is used to create transactional records. The application-specific feature means its uses are specific to the business process it is associated with, e.g., the HR application forms, stores, and maintains the employee’s master data.
     
  • Dark Data
    There exists a huge amount of potential in dark data that is still ignored by businesses in a corporation. It’s the information hidden within an organization’s internal networks. It is also possible to move a variety of relevant information to the information lake and use it to generate valuable insights and growth for your business. Digital information that lies dormant and is not being used is known as dark data. Untapped, it is one of the best-hidden resources in many organizations.

  • Transactional Data
    This type of knowledge describes core business activities. Purchasing and selling activities may be included for a trading company. Transactional data includes information related to the hiring and firing of employees at a very basic level. Compared to the opposite styles of data in a company, this type of knowledge contains a very large amount. Operational applications such as ERP systems usually create, store, and maintain this data.

What Goes Into Building a Data-Driven Culture? 

In order to develop a data-driven culture, four components are required: Data Maturity, Data-Driven Leadership, Data Literacy, and Decision-making Process. The 4Ds are essential to building a culture of data-driven decision-making. 

  • Data Maturity
    Data culture begins with solid data maturity. Management of raw materials, such as data, is at the heart of what it does. Good data maturity is characterized by high data quality standards that are maintained by checks. Data maturity requires metadata management and alignment with KPIs to ensure good data quality. The Data Lineage must also be recorded, which provides insight into how the data has changed since its origin. Furthermore, employees should have access to data according to their decision-making needs, and a solid data governance structure should be in place. The usability of data, ease of access, and the scalability and agility of infrastructure also play a role in data maturity. For instance, a company with outdated infrastructure would find it difficult to access its data. The organization will not use data that is not easily accessible in such cases. In addition, companies spend more time validating and building alignment if the KPIs are not aligned than analyzing the impact. 
  • Data-Driven Leadership
    An organization’s culture is defined by its leaders. Leaders must lead by example in order to establish a data culture. Data-driven leaders ask the right questions and hold their teams accountable for using data in a structured manner. Leading with data is a key priority for a data-driven leader who sees data as a strategic asset. In one example, an organization plans to make an app’s default subscription fee monthly rather than annually. The leader should ensure that teams make data-driven decisions here. The teams will make the decision based on an experiment — that the sample size is met with correct planning. In addition, the experiment should show if the uptick in the difference by changing the subscription plan is statistically significant. 
  • Data Literacy
    Reading, analyzing, digesting, and interpreting data are all aspects of data literacy. When it comes to data literacy for an organization, it does not necessarily mean that employees have a good understanding of how to interpret and use data. Everyone should have a certain level of data literacy according to their job role and the decisions they must make. A key requirement is to ensure that there are no skeptics when it comes to data. A company with a high level of data literacy is more likely to use data to learn more about its customers as well as how they use the product. 
  • Decision-making Process
    In order to get the most value from data, it must be an integral part of the decision-making process. Do you have a planning mechanism for choosing which projects to work on? Is there a mechanism for reviewing decisions after they have been made? It can be said that data is used to make decisions when, for example, marketing budgets are allocated based on expected returns. In most organizations, decision-making is not based on systematic, data-driven processes. 

How to Build a Data-Driven Culture? 

In order to create a data-driven decision-making culture, you need to replace gut feelings with data-derived facts, such as revenue, profits, or analytical results. Each department in an organization can leverage insights from its data. 

Why Is Data Culture Important? 

An organization with a data-driven culture will be able to achieve success in many ways. The following are some of the most influential ones. 

  • Makes Decisions More Efficiently:
    Using reliable data as a basis for decision-making is encouraged by a data-driven culture. A business must be capable of collecting and interpreting relevant data using critical thinking. A data-driven culture, however, helps organizations determine when it is appropriate to make data-driven decisions and when it is not. The performance of the company is constantly monitored to ensure continuous improvement. Using insights and analytics makes it possible to make better business decisions. 
  • Provides Progress Tracking Support:
    Data-driven cultures help organizations move beyond generating reports and tracking progress. To better understand an organization’s performance, it supports the creation of transparent reports, such as 360-degree views. 
  • Coordination is Increased:
    A data-driven culture can coordinate marketing, sales, and operational efforts. It improves consistency in the products/services/processes delivered and creates a more precise analysis of the market’s and audience’s future needs when the various teams work together more closely.  

A data-driven culture facilitates data democratization. The data can be owned by anyone who can see it, as there are no gatekeepers. It is possible for departments to gain a deeper understanding of customer needs via data democratization without having to engage the customer directly.

Data analytics is the future; if a company leverages the data properly, it can identify potential opportunities. 

Why Do Companies Struggle to Build a Data-Driven Culture? 

There have been mixed results for many businesses trying to move toward data-first operations. The reason behind this is that despite the numerous reasons to work toward developing a data-driven culture, many challenges must be overcome. A data-driven business strategy faces no technology challenges, according to research. 

Moreover, it doesn’t help that data-driven businesses are becoming more and more difficult to operate. It is no longer simple for companies to collect and quantify unstructured data, such as text, sensor data, pictures, signals, or other forms of unstructured data. 

Cultural Dynamics 

Businesses are finding it more difficult to change to more data-driven methods as a result of cultural dynamics like self-service and the 2020 global health pandemic. Data and information are distributed decentrally today to consumers on how and when they want them. With this approach, consumers have the freedom to choose what social media platforms they use, which news outlets they follow, and what information they trust. 

As data is created exponentially, it’s no longer shocking that many organizations struggle to become data-driven when you factor in the structural aspect. The task becomes increasingly challenging as time goes on. 

Data management and ownership are rapidly emerging concerns for organizations today. In other words, it ensures that data is used ethically and responsibly. There has been a lot of discussion and writing on this topic recently, and critics have been focusing on it. 

Collecting and analyzing data can be overwhelming without the proper data sets, analytics tools, and data specialists to help. 

Businesses are restraining themselves from adopting a data-driven strategy due to the following issues: 

  • Low-Quality Data
    There is no such thing as good data. Many companies are reluctant to use data-driven decision support because of low-quality data. In addition to harming business ambitions, bad data can also affect corporate decision-making overall. According to Gartner, businesses spend $15 million per year on poor data quality. The lack of systematic and continuous data collection affects the quality of insights based on incomplete, inaccurate, or bad data. Business decisions should never be based on assuming the accuracy of data.
  • Data That Is Inaccessible
    It is often difficult for employees to access data outside their departments or applications because they are trapped within functional silos. Traditional companies use manual data management methods rather than modern automated data management methods. Having access to centralized data makes it difficult for decision-makers to collaborate and plan together. 

Data sharing should be enabled between departments with the right infrastructure and tools. A single source of truth for all the data in an organization can also be created through data governance policies. 

How to Develop a Better Data-Driven Culture? 

New ideas can be backed up by solid evidence due to the explosion of data in corporations. For the past decade, companies have accumulated data, invested in technologies, and paid handsomely for analytical talent in hopes of better satisfying customers, streamlining operations, and clarifying strategy. Despite their value, data are rarely the foundation for every decision in many companies. 

Step 1: Lay Your Data Foundation  

To ensure data quality and accessibility across the organization, you must have the right data stack in place before doing anything else. 

You’ll pipe data into different analytics tools based on your company’s data sources. However, the Customer Data Platform (CDP) is the glue that holds your entire data infrastructure together. 

CDPs aggregate, clean, and resolve data from multiple sources into persistent customer profiles in conjunction with other downstream tools such as customer care, analytics, customer support, and data warehousing. 

Implementing a CDP successfully guarantees your team’s access to high-quality, real-time data across all the tools you use. Ideally, your solution should include the following features (at a minimum): 

  • Managing data quality, governing data, and resolving identity issues 
  • Non-technical stakeholders can access high-quality data without coding 
  • Various customer data sources can be used to collect data easily 

Step 2: Build data literacy and confidence  

When you have a solid foundation in place in terms of data, it is time to move forward. Next, we must develop the competencies, habits, and confidence that facilitate data culture in the workplace. Data-savvy teams may already be adept at handling data, but others—such as Marketing, Product, and Support—may have to develop these skills and develop confidence in using them. 

There are three things to consider: 

  • The addition of applied analytics tools that don’t require extensive technical expertise 
  • Ensuring that end users are onboarded and trained 
  • Stakeholders can learn to trust the data they have access to by maintaining the accuracy, consistency, and completeness of the data 

Step 3: Turn Data Into Action  

You must transform data into insights and action to build a data-driven culture. As a result, many efforts to instill a data culture stall out at this point-since changing behavior and making new habits are required. Beginning with the top down is crucial to establishing a mindset shift. Analytical reporting should focus on creating an expectation that data should drive key decisions. 

Every employee should be able to take actionable decisions with the right data stack, including: 

  • Segmenting audiences is easy 
  • Testing A/B in a hurry 
  • Analytics and reporting that are simple and digestible 

Step 4: Monitor and Refine Your Data Culture 

It’s not a set-it-and-forget-it exercise to foster a data-driven culture. Ensure your teams and data setups are consistently monitored: 

  • Everybody feels confident in using data to make decisions on a daily basis (and also balancing it with their own experience and intuition). 
  • A new hire’s expectations regarding the use of data and tools are explained to them. 
  • In addition to providing a flexible platform to support each team’s evolving needs, it supports adopting new technologies and practices, such as Artificial Intelligence (AI) and Machine Learning (ML). 

Conclusion 

Methodologies, machine learning, artificial intelligence, and relevant tools are not all that make up Data Science. Make sure your organization is ready to embrace a data-driven culture by modifying its existing ecosystem to adapt to this emerging field. Your business will surely reach new heights if your team’s objectives, mindset, and approach are aligned with data-driven decisions. We believe UNext Jigsaw’s IIM-Indore-certified Executive PG Diploma in Management & Artificial Intelligence can help you further.

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