Data has become a cornerstone of digital success for modern businesses. According to a report by the International Data Corporation (IDC), the global datasphere is expected to grow to 175 zettabytes by 2025, up from 33 zettabytes in 2018. This exponential growth is driven by the increasing digitization of business processes, the rise of eCommerce, and the proliferation of internet-connected devices. Future-ready and innovative organizations are now heavily leveraging extensive and sophisticated data to unlock real business opportunities that were simply not possible using traditional business models.
Data lakes play a powerful role in this data-driven push for digital success, with the global market size for data lakes is expected to grow from USD 13.74 billion in 2023 to USD 37.76 billion by 2028. Data lakes are enormous centralized repositories built for organizations to store structured and unstructured data at any scale. Companies like Uber and Airbnb use data lakes to store tremendous amounts of data, from ride details and user profiles to property listings and personalized recommendations.
By tapping into the transformative power data lakes offer, organizations can rapidly analyze data to unlock hidden potential, supercharge growth, and wow their customers. The ability to store and analyze such vast amounts of data in real time provides businesses with very tangible and impactful insights that can be leveraged to reduce operational costs, improve customer experiences, and innovate rapidly in response to ever-changing market demands.
Data cataloging: Taking data from chaos to clarity
At its core, data cataloging is the process of organizing, classifying, and indexing vast amounts of data stored in data lakes. This clear-cut organization makes it easier for organizations to find, access, and manage their data. For instance, in the finance sector, data cataloging powers efficient organization of transaction records, customer data, and market trends. This well-defined and organized data can be quickly accessed for tasks like fraud detection or credit risk assessment, as well as to inform real-time decision-making.
Several technologies play a role in effective data cataloging. Tools like Apache Atlas or Alation have become staples for many organizations. Apache Atlas offers a framework for managing metadata, ensuring data governance and security. Alation, with machine learning, automates data discovery and promotes collaboration among users. These types of tools emphasize data quality, with features that track data lineage, ensuring users can trace a dataset’s origins and transformations – proving to be essential for organizations to access accurate and relevant data.
Once data has been cataloged, organizations can shift their focus over to observing and tracking usage analytics to generate meaningful insights on how, where, when, and by whom, data is interacted with.
Data usage analytics: Discover actionable data insights
Data usage analytics is the incredibly important process of analyzing how datasets within a data lake are accessed, used, and interacted with by users, teams, software applications, etc. By monitoring and tracking these interactions, organizations can build a clearer view of which datasets are deemed most valuable in the real world. These powerful and actionable insights can then be used to optimize data management strategies, prioritize data enhancement efforts, and drive more informed business decisions based on actual data usage patterns such as which data is accessed frequently, monitoring user interactions, and understanding application dependencies.
Identifying high-value datasets
Organizations accumulate masses of datasets, and not all of these datasets are created equal. Some hold more value than others, directly impacting business outcomes and strategic decisions. Identifying these high-value datasets helps organizations start to tap into the full potential of its data and usage insights. Here’s how to use data usage analytics to pinpoint these invaluable datasets:
Monitoring data access patterns
By using tools like Apache Ranger or AWS CloudTrail, organizations can track which datasets are accessed most frequently and by whom. This information can provide a clear view of which datasets are in high demand, which can then be traced to discover where each of the frequently accessed datasets are most impactful.
Evaluating dataset relevance
Once you know which datasets are accessed often, the next step is to determine their relevance. Tools like Collibra or Alation can help organizations catalog their data and align frequently accessed datasets with current business goals and projects. For best results, organizations need to have clearly defined goals, objectives, and relevant KPIs to track and compare with data access patterns and other key usage analytics.
Correlating with business metrics
It’s essential to understand the impact of datasets on both the broader business (strategic view) and specific projects, applications, and even teams. By integrating data usage analytics with business intelligence tools like Tableau or Power BI, organizations can see if the datasets that are accessed often have a direct correlation with key business metrics such as sales, customer retention, or operational efficiency, and used to arrive at accurate action points.
Feedback from teams
Direct feedback from teams using the data can be invaluable. Platforms like Slack or Microsoft Teams can be used to gather input from various departments about which datasets they find most valuable for their operations and decision-making. This review and feedback process should be built into each relevant team’s standard workflow to build a winning team culture.
The business landscape is ever-evolving, and so is the value of datasets. Regular reviews using data governance platforms like Informatica or Talend can help organizations keep their list of high-value datasets updated as business needs and priorities shift – and as organizations innovate to push for a digital competitive edge.
Maximizing business impact with high-value datasets & usage insights
Making decisions based on data is key for business success in the intensely competitive digital space. Using the right datasets effectively can set an organization apart and lead to better outcomes. As industries evolve and markets shift, businesses that harness the power of their most valuable data can adapt faster, spot trends earlier, and position themselves as leaders in their fields.
Strategic decision-making with real-time insights
By integrating high-value datasets with Business Intelligence tools like Tableau or Power BI, organizations can visualize data in more meaningful ways. This allows for real-time insights that can guide strategic decisions, from product launches to market expansions, supporting these decisions with data that actually matters in the real world, and not just at the conceptual level.
Netflix used a localization strategy to accelerate its growth across the globe. By analyzing real-time data on viewer preferences, Netflix was able to tailor its content to different markets, leading to successful product launches.
Enhancing customer experience
High-value datasets can be used with Customer Relationship Management (CRM) systems such as Salesforce or HubSpot. By understanding customer behaviors and preferences, organizations can tailor their offerings, leading to improved customer satisfaction and loyalty. An iterative approach here can ensure that customer experience is gradually and incrementally improved as datasets are better defined and their usage insights practically leveraged.
Optimizing operations and reducing costs
When integrated with Enterprise Resource Planning (ERP) systems like SAP or Oracle, these datasets can provide insights into operational inefficiencies. This can lead to streamlined processes, reduced waste, and significant cost savings, especially when adopted in the long-term view. It’s easy to overlook these savings, especially while changing business goals, objectives, KPIs, and new project launches.
High-value datasets can be a source of exciting innovation. By feeding this data into Machine Learning platforms like TensorFlow or Azure Machine Learning, organizations can develop predictive models, automate tasks, and even create new product offerings based on who, what, when, and where datasets are most used and interacted with.
Risk management and compliance
With the help of platforms like Palantir or IBM OpenPages, organizations can use high-value datasets to monitor and manage risks. This not only ensures compliance with regulations but also safeguards the brand’s reputation and assets.
Case Study: Coca-Cola Andina’s data-driven transformation with AWS Data Lakes
Coca-Cola Andina, serving over 54 million consumers across Chile, Argentina, Brazil, and Paraguay, embarked on a transformative journey to harness the power of data. They faced challenges due to data being scattered across different systems, making it difficult to derive meaningful insights.
Building a unified data lake
To address this, the company built a data lake on Amazon Web Services (AWS). This initiative enabled them to consolidate 95% of their business data into a single, accessible platform. AWS’s suite of services, including Amazon S3, Amazon QuickSight, and Amazon Athena, played a pivotal role in this transformation.
Impact on productivity and decision-making
The results were profound. The productivity of Coca-Cola Andina’s analytics team soared by 80%. This data-driven approach allowed both the company and its customers to make decisions grounded in reliable data. The benefits extended to various facets of the business, from improved promotions to reduced stock shortages. Consequently, the company witnessed a surge in its revenue.
With the foundation of a robust data infrastructure in place, Coca-Cola Andina is poised for further innovation. They plan to expand their AWS infrastructure, exploring new applications and solutions to continually enhance their business operations.
As businesses continue to digitally transform, the rapid growth of data generation and their expansive storage in data lakes can be both a treasure trove and a challenge. Effectively cataloging this data is the first step, but truly understanding its value requires diving deeper. By employing data usage analytics, organizations can pinpoint datasets that are not only frequently accessed but also have a significant impact on business outcomes.
Recognizing and leveraging high-value datasets can lead to informed decision-making and drive successful digital transformation. As we navigate an increasingly data-driven landscape, it remains extremely important to capitalize on these insights, and ensure that the data organizations prioritize aligns with overarching business goals and deliver tangible results.