Data-driven Organization & How to Become One

While the digital transformation direction is gaining momentum, data has become a valuable asset for businesses. In this article, we elaborate on the concept of data-driven organizations and explore the key facets that contribute to their success. You will learn about the essential steps to becoming data-driven, major challenges that businesses face on the go, and actionable recommendations to overcome them.

Data is the new power source for business growth across all industries – from marketing to technology. According to McKinsey, data-driven organizations are 23 times more likely to acquire customers. They also use data analytics solutions to better retain customers and become more profitable as a result. For example, companies that build their strategies based on customer data analysis achieve profitability levels almost 19 times higher than the market average.

Data Lifecycle Management

Organizations relying on a data-driven approach have bigger chances of success. Although data-driven has already become a buzzword, many people still find it hard to formulate what this actually means. Let’s explore this in more detail.

What is a Data-Driven Organisation?

Organizations that use data insights obtained through comprehensive analysis to empower business sustainability and growth are called data-driven. The fundamental principles of such organizations include a strong commitment to data-driven decision-making while treating data as a strategic asset. Such companies use data not only to power effective decisions but also to build business development strategy, identify business opportunities, mitigate risks, and optimize overall performance. Data analytics allows organizations to reveal new patterns, trends, and correlations that can further be used in their daily routine, thus improving outcomes for businesses.

Data-driven approach has proven its value across numerous use cases, fostering growth and continuous improvement for hundreds of corporations, including such major ones as:

  • Amazon: Big data and predictive analytics allow the company to study consumer patterns and predict peak customer demands to timely replenish warehouses and get higher sales. Amazon analyzes collected information to optimize prices based on user behavior, competition, and availability.
  • Bank of America: It has successfully used big data technology to detect risk accounts. The bank’s data scientists employ numerous data methods (clustering, logistic regression, decision trees) to segment customers appropriately and mitigate security risks. As a result, the bank minimized the fraud level by spotting anomalies in consumer behavior and purchasing patterns.
  • Uber: The company matches users with suitable drivers and charges by using data analytics and maintaining a vast database of drivers, customers, and records. Uber collects customer request data at specific locations and times to predict possible load peaks while drivers use this information to find where the customers are likely to be. As a result, this effective use of data-driven insights improves user experiences and facilitates the development of pricing strategies.

How to Become a Data-Driven Organization

The decision to move toward a data-driven approach entails multiple changes to the company’s processes and workflows. This includes transforming the way a company treats data by prioritizing data governance practices, ensuring data quality, security, and privacy metrics. Company-wide transformation also requires proactive investment in IT infrastructure and data management systems to streamline the collection, storage, and processing of big volumes of data.

Before adopting a data-driven approach, it is reasonable to analyze the company’s technical, business, and human resource capabilities and set clear goals you want to achieve with such transformation. This will help you formulate a business case and make sure your organization needs the change.

Once the decision is taken, you’ll need to identify key areas where the data-driven approach will bring the most value and pilot the transformation there. Another no less important aspect is KPI (Key Performance Indicators) system definition since you’ll definitely want to measure the progress and efficiency of your transformation initiative. Smooth transformation requires significant process flexibility, shift in corporate culture, and strong commitment from the organization’s leadership.

It’s worth noting that becoming a data-driven organization takes time and systematic effort. It is reasonable to prioritize specific directions, gradually transform core activities, and catalyze change within the company.

Steps to Becoming a Data-Driven Organisation

Becoming a truly data-driven organization encompasses many aspects, including but not limited to developing relevant data strategy, upskilling employees, implementing new systems to handle data, etc.
Here are key steps leading to the successful adoption of a data-driven organizational approach:

Establish a Data Culture

The idea of data culture embraces the shared behaviors and beliefs within an organization that prioritizes the value, practice, and promotion of data usage to improve decision-making. Every organization can encourage their employee’s new perceptions of data handing by following the fundamental four E’s rule of the data culture:

Data-driven organisation

Data culture implementation is not a one-time activity. It is an ongoing initiative that requires continuous learning and readiness to adapt to changes. At the same time, data culture helps maintain data awareness in the organization. Data-literate employees process, analyze and interpret data more efficiently. Thus, your organization can make effective business decisions at all levels.

Focus on Data Lifecycle Management

Data passes various stages through its existence, and companies have to make sure it’s appropriately safeguarded on each of those. Well-implemented Data Lifecycle Management (DLM) gives you the right instruments to sustain data confidentiality, integrity, and availability all the way from its creation to destruction. Apart from setting up unified data standards, DLM requires continuous investment of efforts in the following aspects as well:

  • Data Governance: The establishment of data governance frameworks and policies helps to ensure proper management, control, and accountability of data assets. It is necessary to define roles and responsibilities over data collection and storage activities, set data standards, and clarify data lifecycle processes.
  • Metadata Management: Organizations handle diverse data types, and this process requires clear differentiation. The first step to efficient data governance is a consistent approach across the entire data lifecycle with a focus on metadata classification and management. When a company neglects metadata management during system lifecycles, it may lead to misinformation among the teams, and potentially prolong feature delivery or updates. As a result, inappropriate metadata management impacts data quality, thus increasing costs and the time needed for system adjustments.
  • Data Quality: A continuous data quality improvement helps a business to ensure the data is valid and reliable. There are many methods and approaches to secure data quality. For example, data validation and cleansing promote data accuracy while data standardization secures consistency across different sources and systems.  At the same time, data profiling and monitoring help to identify data outliers, anomalies, and patterns that indicate potential issues with data quality.
  • Data Infrastructure: It is important to invest in data infrastructure hence it enables efficient collection, storage, organization, and retrieval of data. It must be scalable and accessible to make sure employees can easily work with data with no downtime.
  • Data Security: Organizations seek to keep their data protected from unwanted breaches and leaks by implementing appropriate security measures. This includes access controls, encryption, data backup, and disaster recovery plans. The balance between confidentiality, integrity, and availability of data is critical to eliminate privacy issues.
  • Scalability: Since each company has distinct requirements, analytics solutions should align with their specific needs. It is important to consider whether the chosen data management solution is future-proof so that it could be customized to address both current and future needs.

As a rule, data-driven organizations have a centralized data system that serves as a single source of truth enabling all company departments to work with the necessary information. This involves democratizing data access and eliminating silos between data stored in different departments and systems. Data should not be confined to specific teams or limited to analysts only. It is important to securely share information with a broader range of employees and empower them to use it through routine processes to be more efficient in their daily operations.

Data analytics gives value to the collected data and transforms it into an effective source for decision-making. It is a tool that helps to optimize business performance, reduce costs, improve customer experience, or even develop new product features. The selection of the appropriate data analysis tool depends on several factors:

  • Business Objectives: First of all, it is important to identify the organization’s primary goals and compile a list of desired organizational outcomes to be reached by data analysis.
  • Adjustability: There are many data sources with different types of data (structured, unstructured, semi-structured, raw), so the platform needs to be adaptable to connect to all of them. The company should facilitate the data transfer since you will need to connect new data sources to your data platform as you scale your data-driven approach to the whole organization. The organization should have a clear understanding of how to adjust the integration process with systems and platforms to minimize risks of data loss and breaches.
  • Accessibility: Organizations can take advantage of an array of easy-to-use low code/no code platforms that don’t require technical skills (e.g. Microsoft Power BI, Tableau) to complete regular tasks, including preparing, analyzing, storing data, and integrating third-party software. These platforms can be implemented by an in-house team or an external vendor.

Companies may also require a custom data platform to address more complex tasks and meet specific business requirements, so then it is reasonable to engage Data Engineers. Apart from designing custom data platforms in line with business objectives, Data Engineers can help scale your data infrastructure and provide insights to optimize overall data management performance.

Use Data to Drive Decisions

Businesses need to make decisions based on facts rather than assumptions. Efficient analytics helps to improve business strategies and development plans, securing timely and ongoing adaptation to changes.  Businesses that use data also become more agile and adaptive, capable of quickly responding to market changes and customer demands.

Monitor and Promote Continuous Improvement

It also makes sense to conduct regular process assessments and optimization to get maximum value from all resources invested in the transformation initiatives.

Transformation planning and change implementation is critically important for any business. However, you can’t predict all potential bottlenecks and challenges you may face during the transition to a data-driven approach. That’s why organizations have to remain flexible, be ready to spot process inconsistencies and areas for improvement, and smoothly navigate their way through the change.

An effective tracking process and clear metrics allow the company to monitor its performance, keep up with the trends, and make required improvements immediately.

Challenges to Becoming a Data-Driven Company

The transition to a data-driven approach brings multiple challenges for companies as any other complex transformation initiative, including agile decision-making, improved employee engagement, and customer experience. Businesses face many operational and technical challenges on their way toward a data-driven model implementation:

Challenge 1: Education

Major roadblocks on the path to a data-driven organization are related to data literacy, technical skills, and cultural shifts among employees. Unfortunately, many companies underestimate the importance of these aspects, and this becomes an obstacle for a successful transformation.

Solution: It is important to remain consistent in promoting data awareness among its employees by:

  • Developing and implementing a change management program to elaborate on the importance of data within a company
  • Scheduling phased training sessions to capture goals, clarify priorities, establish training preferences, and collect feedback to integrate further improvements
  • Engaging data stewards to populate data among the team, help enforce regulations on data handling, and implement further data initiatives
  • Demonstrating value for stakeholders and business users by sharing results and achievements
  • Implementing clear procedures and policies within a company to facilitate the transformation process
  • Promoting continuous improvement to gradually drive a cultural shift toward data-centric decision making

Challenge 2: Excessive data 

Organizations find it difficult to manage and extract meaningful insights from the vast amounts of data. Large amounts of data can overwhelm data processing and analysis capabilities, risking to turn into a data swamp. Lack of strategy on large data volumes storage, prioritization and optimal structure can eventually lead to increased management and maintenance costs that strain the budget.

Solution: Organizations can address the excessive data challenge by:

  • Establishing data governance framework to safeguard that data is managed efficiently and complies with relevant laws and regulations
  • Conducting regular audits & cleansing procedures to identify and eliminate excessive data, safeguarding balanced data repositories and data integrity
  • Integrating new technologies, such as machine learning and artificial intelligence, to automate data processing, analysis, and pattern recognition tasks

Challenge 3: Lack of Technical Expertise 

Sound data management, software, and systems engineering require strong technical expertise. Unfortunately, the in-house teams may lack experts who could design and develop the data ecosystems compatible with the organization’s scalability, security, and availability requirements. As a result, slow response times, data inconsistencies, system vulnerability, and limitations can decrease the organization’s ability to safely derive valuable insights from data.

Solution: Organizations can overcome this challenge in several ways. They can focus on developing in-house tech capabilities among the team, including hiring and training the right talents. Otherwise, companies can engage external subject-matter experts who will help to adopt modern data management and analytics tools, as well as streamline all data processes.

To sum up, any company can become data driven. It is just a matter of having clear reasons to go through this transformation and prioritizing data investments. As any comprehensive initiative, it is a multi-year project that requires thorough planning. The good news is that it can be done step-by-step, and you can start with smaller changes, gradually scaling the initiative over time.

In case your company requires external consultancy to implement necessary changes in data processes or build data infrastructure, contact Sigma Software experts. With over 20 years of experience on the board, we will help you efficiently integrate analytics into your business and set up processes for maximized data value and data-driven business decision-making.

Share article: