Making friends with Big Data Transforms Your Business
What on earth is Big Data?
Used for solving business problems and making informed decisions with advanced analytics including machine learning, big data goes by datasets being huge in volume and growing rapidly over time.
Which challenges Big Data poses to businesses?
- Integrating from different sources
Ingesting data from different website logs, call centers, enterprise apps, social media streams, email systems and webinars into a single repository and then transforming it into a unified format for analysis tools requires an investment into extract, transform, load (ETL) tools and data integration tools. - Governance
Reassuring that records reconcile and are usable, accurate and secure is very much related to data integration: it’s not uncommon that pieces of information obtained from different systems don’t agree, for example sales figures from a company’s CRM system may be different than those recorded on their eCommerce platform. - Security
Data gathered from external sources should not be assumed safe and in compliance with organizational standards. If security isn’t built in at an early stage of architecture planning, it’s almost impossible to “bolt-on” a comprehensive protection later. High-level Big Data security best practices include creating access and authentication policies, vetting cloud and technology providers as well as using encryption and threat intelligence to safeguard data in transit. - Organizational resistance, finding and keeping best Big Data talent
Growing leaders and nurturing a culture inside the organization, hiring experts from IT consultancies and audit firms and investing into sufficient built-in AI and machine learning tools – Big Data impacting your revenue line requires rethinking business and reinventing organization.
What are the top trends in Big Data?
In “The Data-Driven Enterprise of 2025” report, Quantum Black AI by McKinsey seven main characteristics are listed:
- Data embedded in every decision, interaction, and process
- Data is processed and delivered in real time
- Flexible data stores enable integrated, ready-to-use data
- Data operating model treats data like a product
- The chief data officer’s role is expanded to generate value
- Data-ecosystem memberships are the norm
- Data management is prioritized and automated for privacy, security, and resiliency
Big Data crucial in FinTech: Lerpal & Credibly
Financial sector crucially depends on data and it is never allowed to stop. For Credibly to achieve impressive results such as enabling raising funding from $5K to $400K in 24 hours and receiving multiple awards for providing over $2 billion in financing to more than 30 000 small businesses, Lerpal team alongside with Credibly experts integrated the data related to various loan management financial operations, tortured raw data to turn them into insights while creating a robust and accurate system for creditworthiness evaluation(scoring). Throughout the entire process, Lerpal has been securing data privacy and transaction integrity.
Big Data transforming HealthTech: Lerpal & Evolution Nutrition
Financial success and tech innovations are impossible without a healthy society. Evolution Nutrition not merely connects gyms and fitness coaches with clients, it reinvents human lives through fostering them rethinking habits. One of numerous data-oriented improvements Lerpal did was integrating Trainerize into the platform, helping trainers to effortlessly transfer client data to Evolution Nutrition and generate personalized plans for existing Trainerize users. Trainers can now save tens of hours each week and focus on what matters most – their clients. With more than 1,000 gym and trainer partnerships now, the portal plays a pivotal role in redefining wellness and nutrition practices worldwide.
Big Data fuelling Digital Transformation: Lerpal & Coastal Marinas
Coastal Marinas is a win-win ecosystem created by a US company serving a unique experience of signature boats and yachts rental and maintenance. System works like a Swiss watch thanks to the comprehensive dashboard for managing listings with detailed information and photos, bookings and user accounts, thus empowering renters to reserve boats/yachts based on availability, provide feedback and rating. Lessors maximize their return on investment resting assured their possession is being treated properly.
Big Data helping brick and mortar beat E-Commerce: Lerpal & LocalZone
LocalZone is an app-based platform enabling small retailers, entrepreneurs and service providers to sell a virtually unlimited range of products in every industry, niche or interest category. Stores can sell products without handling physical inventory. Based on priceless insights, Lerpal introduced a brand new product built from scratch in an unknown niche within a highly competitive industry and built a digital ecosystem being CMS, CRM and marketplace – all at the same time, storing huge volumes of data.
Big Data securing safe travels: Lerpal & SafeExplore
Safe Esteem/SafeXplore is a truly big data-native unique Travel Risk Intelligence (TRI) empowering holistic assessments on both personal and corporate level. Crucial value proposition is made possible thanks to taking numerous factors into account, including crime, accidents and health risks for local and international locations by regions. The product leverages mathematical model including both unsafe vs secure locations(e.g. Police stations) and events (e.g. virus outbreaks) and implements in-app risks analysis in real time, logging reports for both personal and managerial usage.
How Lerpal gets things done with Big Data?
Tackling Big Data challenges while cooperating with the external Software Development partner is powered by joining interdisciplinary forces of analysts and engineers and merging agency’s methodology with client’s corporate culture.
Want to unleash the hidden potential of Big Data for your organization? Click “Contact Us” and let the change begin!
FAQ
Big data analytics is the intricate process of examining large and diverse data sets, commonly known as “big data,” which are generated from various sources such as eCommerce platforms, mobile devices, social media, and the Internet of Things (IoT). This process involves integrating different data sources, converting unstructured data into structured formats, and extracting meaningful insights using specialized tools and techniques. These tools often distribute data processing across a network to handle the vast scale involved.
As the amount of digital data continues to grow rapidly—doubling every two years—big data analytics has emerged as the solution for managing and analyzing these extensive data sources. While traditional data analytics principles are still relevant, the scale and complexity of big data require innovative methods for storing and processing the vast amounts of structured and unstructured data involved.
This demand for faster processing speeds and greater storage capacities has led to the development of new storage solutions, including data warehouses, data lakes, and nonrelational databases like NoSQL. Additionally, big data analytics leverages advanced technologies and frameworks such as Apache Hadoop, Spark, and Hive, which are crucial for managing and processing large-scale data.
By utilizing advanced analytic techniques, big data analytics can process and analyze enormous data sets—ranging from terabytes to zettabytes—comprised of structured, semi-structured, and unstructured data from various sources.
Before the advent of Hadoop, the technologies behind modern storage and computing systems were relatively basic, confining most companies to analyzing “small data.” Even this simpler form of analytics was often challenging, particularly when integrating new data sources.
Traditional data analytics relies heavily on relational databases, such as SQL databases, which consist of tables of structured data. To analyze raw data in these systems, every byte must be formatted in a specific way before being ingested into the database—a process known as extract, transform, load (ETL). This ETL process must be repeated for each new data source, making it a time-consuming and labor-intensive task.
The primary issue with this three-step approach is the significant amount of time and effort required. It can take data scientists and engineers up to 18 months to implement or modify, which greatly hinders agility and responsiveness in data analysis.
- Web data. Customer level web behavior data such as visits, page views, searches, purchases, etc.
- Text data. Data generated from sources of text including email, news articles, Facebook feeds, Word documents, and more is one of the biggest and most widely used types of unstructured data.
- Time and location, or geospatial data. GPS and cell phones, as well as Wi-Fi connections, make time and location information a growing source of interesting data. This can also include geographic data related to roads, buildings, lakes, addresses, people, workplaces, and transportation routes, which have been generated from geographic information systems.
- Real-time media. Real-time data sources can include real-time streaming or event-based data.
- Smart grid and sensor data. Sensor data from cars, oil pipelines, windmill turbines, and other sensors is often collected at extremely high frequency.
- Social network data. Unstructured text (comments, likes, etc.) from social network sites like Facebook, LinkedIn, Instagram, etc. is growing. It is even possible to do link analysis to uncover the network of a given user.
- Linked data: this type of data has been collected using standard Web technologies like HTTP, RDF, SPARQL, and URLs.
- Network data. Data related to very large social networks, like Facebook and Twitter, or technological networks such as the Internet, telephone and transportation networks.
Big data analytics helps organizations harness their data and use advanced data science techniques and methods, such as natural language processing, deep learning, and machine learning, uncovering hidden patterns, unknown correlations, market trends and customer preferences, to identify new opportunities and make more informed business decisions.
A database is an organized system for storing, searching, and reporting on structured data from a single source. Databases are generally classified into two main types: relational and non-relational.
Relational Databases (RDBMS):
- Structure: Relational databases use schemas and are best suited for structured data. They store data in tables and manage it using SQL (Structured Query Language).
- Examples: MySQL, PostgreSQL, and Oracle.
- Use Cases: Ideal for applications requiring structured data with predefined schemas, such as traditional business applications and data analysis.
Non-Relational Databases (NoSQL):
- Structure: Non-relational databases handle unstructured or semi-structured data and do not require a fixed schema. They can store data in various formats, including documents, key-value pairs, graphs, or wide columns.
- Examples: MongoDB, Cassandra, and Redis.
- Use Cases: Suitable for applications with flexible data models or when dealing with varied data formats and large volumes of data.
Databases are straightforward to create and are available as both open-source and proprietary solutions, allowing for flexible deployment both on-premise and in the cloud. While relational databases are excellent for structured data and traditional applications, their rigid schema makes them less suitable for integrating diverse data sources. Non-relational databases, on the other hand, offer the flexibility needed for handling varied data formats and are ideal for modern applications that require scalability and adaptability.
A data warehouse is a centralized repository designed to store large volumes of structured data from multiple sources. It allows organizations to consolidate their data, enabling advanced analytics, reporting, and supporting business intelligence efforts. By centralizing data, a data warehouse helps generate valuable insights that drive informed decision-making.
Setting up a data warehouse involves significant planning and design, particularly in examining and organizing data structures. While the initial setup can be costly and complex—often due to proprietary software and storage requirements—the return on investment is typically well worth it, thanks to enhanced data analysis capabilities.
Popular data warehouse platforms include Amazon Redshift, Google BigQuery, and Snowflake. These solutions offer powerful tools for storing and analyzing large datasets, with features like scalability and advanced data management to meet the evolving needs of organizations.
A data lake is a centralized repository that can store structured, semi-structured, and unstructured data from a variety of sources. Unlike databases and data warehouses, a data lake allows organizations to store raw data without the need for processing or transforming it at the time of ingestion.
Key Features of a Data Lake:
- Flexible Storage: Data lakes can accommodate a wide range of data types, including logs, videos, images, and social media content. This flexibility supports advanced analytics, machine learning, and big data processing.
- Cost-Effective: Storing data in a data lake is generally more economical than in a data warehouse, making it an attractive option for managing large volumes of data efficiently.
- Schema-On-Read: Data lakes enable data scientists and analysts to process and transform data as needed, rather than requiring a predefined schema before data is ingested. This approach facilitates the creation of new data models and allows for more dynamic analysis.
Limitations:
- Not a Replacement: While data lakes offer significant flexibility and cost benefits, they do not replace data warehouses or relational databases. Data lakes typically lack the performance, reporting capabilities, and ease of use provided by data warehouses.
- Governance and Management: Effective use of a data lake requires robust governance and management practices to ensure data quality, security, and accessibility.
Data lakes are popular in modern data architectures due to their versatility and lower costs, but they complement rather than substitute traditional data management solutions.
