The Allure of Transformation and Big Data Strategy
Today's enterprises face the challenge of deciding how to invest in their future big data strategy. While it may be tempting to quickly develop a transformation strategy, the basic steps of automation and investment often get lost in the glamour of "transformation". Before outlining infrastructure and data management costs, talent acquisition in data analytics and engineering, and the necessary strategic commitment, you should first determine if your organization is well-suited to actually take advantage of its data. This can be achieved through automation (improving operational efficiency and productivity), the development of new information (investing in new data to align business decisions with telemetry), or business transformation (the development of new products and services to drive innovation and new revenue streams).
Automation: Streamlining Processes and Reducing Costs
Automation can generate ROI by streamlining business processes and improving operational efficiency, thus reducing costs. While Amazon dominates markets where it can use its automation to create efficiency at scale, Walmart, too, is flexing its muscle redefining responsibilities using scanning robots and other automated technologies. The practice of automation goes beyond reducing manual labor and can be leveraged to further enable analytics and operations teams. LogZilla's engine assesses the severity and next steps based on data gathered from external systems and takes appropriate actions to automatically bring the affected system back into service. Using machine learning, this detection process can be automated and made more accurate over time, freeing up resources to perform more strategic analysis.
Generating New Information: The Path to Transformation
Generating new information to improve business operations should be your top goal, and using data to create new value builds on automation's virtues and puts an organization on the path to transformation. The commitment to achieving more information value from data requires that more data be collected, stored, and analyzed. According to Tech Pro Research, 61% of companies aren't using any big data solution, thus potentially missing out on insights that could inform business decisions. LogZilla's deduplication feature is the industry's only solution that can pre-process data at 800,000 events per second, or in easier terms, 36TB per server, per day – never hitting server limits, or worrying about leaving data 'out in the cold.'
Are You Ready?
Organizations striving for transformative use of data are inherently data-driven: they ingest more and different types of data than counterparts, and develop a culture rooted in agility. Retail marketing, for example, has transformed the industry with geolocation which can be used for presenting ads, relevant content, promotions, or coupons creating more tailored and relevant marketing for potential customers.
In the journey to transformation, ensuring your data operations infrastructure has the speed, scale, and capacity to harness operational insights – while adapting to the complexities of your evolving business – is critical.
Ensure you can meet your organization's challenges by having the ability to scale data operations to extract insight – and value – from both real-time streaming events and historical analytics.
Leveraging LogZilla's Deduplication Feature
LogZilla's deduplication feature is a game-changer when it comes to managing and analyzing vast amounts of data. By pre-processing and deduplicating data, LogZilla allows greater efficiencies without the risk of data ingestion limits or escalating server costs. This enables organizations to focus on analyzing the data that truly matters, without being bogged down by duplicates or extraneous information.
Data-Driven Organizations: Are You Ready?
As the business world evolves and becomes more competitive, it's essential for organizations to adapt and become more data-driven. By leveraging data analytics and automation, companies can make better decisions, optimize processes, and even develop new products and services. However, the journey to becoming a data-driven organization can be challenging and requires a clear vision, commitment, and the right tools.
Scaling Data Operations for Real-Time and Historical Analytics
To unlock the full potential of data analytics, organizations must be able to scale their data operations effectively. This means being able to process and analyze large volumes of data in real-time, while also having the capability to perform in-depth historical analysis. By investing in scalable solutions like LogZilla, organizations can ensure they have the infrastructure necessary to support their data-driven goals.
Real-World Use Cases:
- Insurance: An insurance company leveraged data analytics to identify fraudulent claims, resulting in significant cost savings and improved customer satisfaction.
- Banking: A bank utilized real-time data analytics to enhance fraud detection and improve the customer experience.
- Healthcare: A hospital implemented data analytics to optimize patient care, reduce readmission rates, and streamline operations.
- Retail: A retail chain used data analytics to optimize supply chain management, reducing costs, and improving product availability.
- Energy: An energy company employed data analytics to optimize the performance of its power plants, reducing downtime and improving efficiency.
- Agriculture: A farming cooperative utilized data analytics to optimize crop yields and improve resource management, leading to increased profits and sustainability.