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Today, data serves as a gateway to new possibilities. The volume of data produced is exponentially increasing. This surge is mainly driven by the growing number of connected devices and the rising percentage of the global population gaining access to the internet.
Did you know?
- Research by BARC revealed that businesses using big data experienced an 8% increase in profit and a 10% reduction in costs.
- GlobalDMA found that 49% of marketers use data to enhance the customer experience.
- Additionally, a recent study by the Centre for Economics and Business found that 80% of businesses boosted their revenue by leveraging real-time data. This study encompassed 1,200 companies across 12 countries in four key industries: telecommunications, finance and insurance, manufacturing, and automotive.
- McKinsey’s recent findings indicate that data-driven companies outperform their competitors by up to 20%.
What is a data-driven organisation?
A data-driven organisation bases its strategic decisions on data analysis rather than relying solely on intuition, assumptions, or gut feelings.
It systematically measures and analyses various aspects of its operations, leveraging data to guide decision-making processes. This approach helps identify areas for improvement and drive growth.
A data driven organization is known for its clear focus on facts and figures. Its business leaders rely on real-time data to optimise operations and make strategic decisions.
Such organisations see data as a major asset, so they invest in the technologies and expertise needed to acquire, store, and access it efficiently.
Learn about the benefits of big data analytics in FinTech.
Furthermore, for an organisation to be truly data-driven, it should
- Have access to reliable data from various sources, such as customer feedback, market research, and sales figures.
- Possess proficiency in data analysis, using specialised tools and techniques to extract valuable insights and identify patterns.
- Integrate data into all operational aspects, breaking down departmental silos and ensuring universal access to the same information.
- Demonstrate a commitment to continuous learning and improvement by analysing data and identifying trends.
Use cases: data-driven organisations that stand out
The value of a data-driven approach has been demonstrated through various use cases, contributing to growth and ongoing improvement for numerous companies, including large corporations.
Amazon
It proactively uses big data and predictive analytics to explore consumer patterns and anticipate customer demands, enabling timely warehouse replenishment and increased sales. Amazon also analyses data to optimise pricing based on user behaviour and relevant competition.
Bank of America
It employs big data to detect and mitigate high-risk accounts. The bank’s data scientists use various methods, including logistic regression, clustering, and decision trees, to wisely segregate customers appropriately and mitigate possible security risks. This approach has minimised fraud by identifying anomalies in consumer behaviour and their purchasing patterns.
Coca-Cola
Coca-Cola smartly uses image recognition technology and data analytics to target users based on the photos they share on social media. This approach provides insights into who consumes their products, where they are, and why their brand is being mentioned. The personalised ads generated through this method achieved a click-through rate four times higher than other targeted advertising methods.
How to become a data driven organization
Adopting a data-driven approach involves significant changes to a company’s internal processes. This transformation includes redefining the way your company handles data by prioritising data governance practices while ensuring data quality and security.
It also requires considerable investment in your IT infrastructure to optimise the process of data collection, storage, and processing.
Before adopting this approach, it’s reasonable to assess the company’s technical, business, and human resource capabilities and establish clear goals and objectives for further transformation. Such an assessment will help formulate a strong business case and ensure the organisation truly benefits from the ongoing change.
Once you decide to adopt a data-driven approach, identify key areas where it will benefit most and promote the transformation in those fields.
Equally important is defining a key performance indicator system to track the efficiency of your transformation aspirations.
A successful transformation requires process flexibility, a shift in corporate culture, and strong commitment from the organisation’s leadership.
Do you wonder how to use the data from your app? Read more.
Establish a data-driven culture
Creating the right culture within the business is a top priority, and the right culture appreciates the value of data. Data only becomes valuable when it is actively used company-wide.
The best technology leaders play a key role in helping the business understand how to use data to achieve better outcomes, often by asking questions the business doesn’t yet know to ask.
There is a need to transition from data science to decision science within the organisation.
With this new focus, the organisation aims not just to deliver analytics or insights but to provide clarity on the decisions being made, which requires a sharp understanding of the desired outcomes.
Every organisation can foster new perspectives on data management among employees by adhering to the fundamental four E’s rule of data culture.
Therefore, establishing a data culture is not a one-off task but an ongoing effort that demands continuous learning and adaptability.
Additionally, a strong data culture ensures sustained data awareness within the organisation.
Employees who are data-literate can efficiently and timely process, analyse, and interpret data, facilitating informed decisions across all levels of the organisation.
Prioritise data lifecycle management
As you know, data goes through various stages during its lifecycle, and companies must ensure it is appropriately protected at each stage.
Wisely integrated data lifecycle management practice provides the necessary tools to maintain data security, integrity, and accessibility from its creation to its eventual destruction.
Implementing effective DLM requires continuous efforts in several key areas:
- Data governance: Establishing frameworks and company-wide policies ensures appropriate levels of data management, control, and accountability. It’s reasonable to assign the employees responsible for data collection and storage, establish data best practices, and make all data lifecycle processes transparent.
- Metadata management: Organisations manage different types of data, each requiring clear differentiation. Efficient data governance starts with a focus on metadata classification and management. Neglecting metadata management can lead to misinformation among teams, delays in delivery or updates, and ultimately impact data quality, increasing expenses and time needed for system optimisation.
- Data quality: Continuous improvement ensures that data remains valid. Data validation, cleansing, and standardisation methods promote data accuracy across different sources. Additionally, data monitoring helps identify anomalies and patterns that indicate potential data quality issues.
- Data infrastructure: Investing in robust data infrastructure is the first step for efficient data collection, storage, organisation, and retrieval. Of course, your infrastructure must be accessible, ensuring your team can use data at any time without experiencing downtime.
- Data security: Protecting data from breaches and leaks involves incorporating relevant security measures, including encryption, data backup, access controls, and disaster recovery plans.
Explore the AI adoption framework: technology/privacy/ security/data point of view and EU AI act
Provide a unified data access
Typically, data driven organizations use a centralised data system as a single source of truth, allowing all departments to access necessary information.
It involves democratising data access and minimising silos across different departments and existing systems. Your data should not be restricted to specific teams.
It is wise to share information with employees, encouraging them to use it in their daily tasks when needed to enhance efficiency in their daily operations.
Monitor and promote continuous improvement
Conducting systematic process assessments and optimisations is a must to maximise the value of resources you’ve invested in transformation initiatives.
While change planning and implementation are critically important, one can’t foresee all emerging pitfalls on the way to a data-driven organisation.
Therefore, companies should always remain flexible, be ready to identify any inconsistencies, and smoothly pilot through the change.
An efficient tracking process and established metrics help companies to track and improve their performance, stay updated with trends, and implement necessary improvements promptly.
Challenges that get in the way of becoming data-driven
Lack of data-driven culture
Becoming a data driven company requires more than the right business strategy and technology. A data-driven culture must be nurtured, where decisions are based on data-driven facts rather than assumptions.
Unfortunately, there is often distrust in accepting data as the primary resource for gaining insights. Many businesses and stakeholders consider data solely within the IT domain and hesitate to collaborate in the data management and analysis process, which can limit the adoption of data analytics tools.
It is important to consistently promote data awareness among employees by:
- Creating and integrating a change management program to illuminate the role of data management in routine processes.
- Arranging specific training sessions to clarify priorities, establish training preferences, and gather teams’ feedback for further improvements.
- Sharing results and achievements of data-driven decision-making by own example
- Encouraging continuous improvement to drive a cultural shift toward data-first decision-making.
Low-quality data
The quality of data significantly impacts decision-making. Low-quality data is a key reason organisations struggle to use it effectively for business decisions. Insights based on incomplete and inaccurate information are often faulty.
Many organisations still rely on manual data maintenance procedures instead of modern automated data management systems.
The absence of a centralised data system further limits data accessibility, making it difficult for decision-makers to collaborate and plan cohesively.
It’s important to maintain data quality by:
- Developing a clear data governance framework that defines roles, responsibilities, and processes for data management.
- Appointing data stewards responsible for managing data quality, integrity, and usage.
- Implementing tools for correcting data errors, standardising formats, and removing duplicates.
- Regularly assessing the quality of data through audits and validation checks.
- Monitoring data quality using automated tools that provide alerts for anomalies.
- Ensuring consistent data entry practices across all departments.
- Maintaining documentation of data quality policies, procedures, standards, and metrics.
- Ensuring that data is protected against unauthorised access and breaches.
- Using advanced analytics and AI to detect and correct data quality issues.
Becoming data-driven is a shared journey
Data is the most valuable asset in today’s digital-first age. Establishing a data-driven culture can significantly enhance business outcomes by enabling better decision making across all levels of your organisation.
However, cultivating such a culture is not an overnight process; it requires a systematic, habit-forming strategy that needs to be consistently implemented and reinforced.
Although establishing a data-driven culture can be challenging at first, strong support and commitment from leadership can make the transformation possible.
Early buy-in from top management is crucial, as it sets the pace and provides the necessary resources and accountability for the cultural shift to occur.
Once the groundwork is laid, the organisation can expect measurable improvements in various areas, such as operational efficiency, customer satisfaction, and overall profitability, as data-driven insights guide strategic decisions and optimise processes.
How can we help you?
It’s high time to make informed business decisions with insights gained from advanced AI research services. At Altamira, we make the best use of AI and ML data, uncovering trends, forecasts, and benchmarks that shed light on market dynamics and future directions.
What problems do we usually resolve?
- Data quality, accessibility, and fragmentation-related issues
- Underused data, not leveraged for internal insights or monetisation purposes
- Datasets validation, cleansing, and augmentation before utilising advanced AI/ML solutions
- Data management processes integration to ensure proper data quality for future usage
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