The Expert's Handbook for Successful Big Data Implementation

Implementing Big Data Projects: What Is the Purpose?

No matter what purpose big data systems are used for, the ultimate goal is still to achieve meaningful insights. With the right approach, big data analysis helps businesses reveal hidden patterns that strengthen decision-making and improve overall performance. However, this technology’s list of benefits does not end there.

• Better Customer Satisfaction and Retention: As customers use mobile applications daily, the amount of raw data about their digital footprints increases over time. By analyzing user behavior and preferences, businesses easily custom their products and services according to individual needs, leading to higher levels of customer satisfaction, loyalty, and retention.
• Targeted Promotions and Sales Effectiveness: Marketing campaigns only succeed when they reach the right target customers. Instead of traditional market research methods such as interviews, big data sources obtained from automating the data collection process create a premise for analyzing customer trends and help increase the effectiveness of marketing efforts by reaching the right people with the right message.
• Enhanced Fraud Detection and Security: As customer data is collected every second, fraudulent activities are easily detected in a matter of time. This makes it easier for developers to prevent potential cyber security risks and unexpected events before they happen, thereby proactively mitigating them and creating a smoother user experience.
• Competitive Advantages: Big data allows companies to leverage the superiority of predictive analytics to anticipate future trends, market dynamics, and customer behavior. Understanding changing needs and trends ahead of competitors helps in proactive decision-making and staying ahead of market changes, ultimately creating a competitive advantage.
• Scalability and Flexibility: When the amount of data exceeds a certain limit, data storage can become overloaded, and manual handling by humans may no longer be possible. In such scenarios, big data solutions can be the ideal alternative to ensure data collection continues seamlessly. These solutions store the data generated from various sources safely until the data is used.

Proven Big Data Implementation Strategies for Businesses

Big data implementation is a complex process that requires careful planning and execution for successful commitment. While each project has unique characteristics and requirements, no perfect strategy exists. However, the key components below can provide you with a sample structure to rely on.

#1 Data Integration

In order to ensure that businesses do not miss any potentially valuable data that may contain meaningful insights, it is recommended to integrate various data sources and consolidate them into a unified format for analysis. When all data is stored in a centralized and comprehensive dataset, companies can easily reach wanted data in a snap, saving time focusing on core competencies.

However, due to the diversity of data sources, data may exist in different data structures, formats, or access methods cannot be ruled out. Extracting the data, which is a key aspect of data integration, requires experts to understand the use of appropriate techniques and source systems to retrieve the data accurately and efficiently.

#2 Data Storage

Users use types of software products every day. That is the reason why data collected and stored from sources always increases over time. To efficiently manage and handle this data, businesses need to choose a storage system that suits their technical requirements.

Currently, there are countless options available to help companies solve data storage problems. Among them, distributed file systems, cloud-based storage solutions, and NoSQL databases are the three main solutions.

• Distributed File Systems: This option allows businesses to store large volumes of data across multiple machines in a distributed manner by breaking down files into smaller blocks and distributing them across a cluster of machines. Hadoop Distributed File System (HDFS) is an example of this type.
• Cloud-based Storage Solutions: By applying advanced technology called cloud computing, this option is an effective method to help businesses store data completely in the cloud environment in a highly scalable manner without worrying about hardware infrastructure.
• NoSQL Databases: Unlike traditional relational databases, NoSQL databases such as Apache Cassandra or MongoDB are suitable for handling large-scale, unstructured, and semi-structured data from social media analytics and IoT applications for real-time ingestion.

#3 Data Processing

Data processing that barely relies on human effort and tools, including spreadsheets and calculators, is currently considered an outdated and ineffective method. Instead of processing data in the traditional way, businesses gradually recognize the significance of automated systems and turn to distributed data processing frameworks. These frameworks have the ability to collect data and process them across a cluster of machines while limiting errors and increasing speed and efficiency.

Choosing which distributed data processing frameworks depends largely on the specific needs and programming languages of the business. Currently, there are three popular tools, including Apache Hadoop, Apache Spark, and Apache Flink, gaining popularity in the data world. Despite each offer’s unique features and capabilities, they are all extremely well-suited frameworks for handling large-scale data processing tasks.

#4 Data Transformation and Loading

Whether a business takes advantage of existing frameworks or not, data needs to go through the transformation and loading process first to serve further analysis. Why?

Because data is generated from many different sources, it often exists in various heterogeneous structures such as text, images, and sounds, making it difficult for businesses to analyze. In order to eliminate big data issues and help them reach the desired level of granularity for analysis, businesses need to transform data through the process of data cleaning, reformatting, and restructuring.

Once transformed, the data is loaded into a target system for easy querying, which is usually a data warehouse or data lake. Data warehouse relational uses database management systems to store structured data and perform analytics, business intelligence (BI), and reporting. On the other hand, the data lake does not enforce a predefined schema on the ingested data and supports exploratory analysis, big data processing, and data science.

#5 Data Analysis

Whether data has value or not depends on the results of the data analysis process. By utilizing analytics tools and algorithms, businesses can gain valuable insights from the available data.

To start the data analysis process, data analysts need to carefully analyze the specific objectives and data nature of the software applications. Depending on technical requirements, experts use corresponding supporting technologies such as natural language processing (NLP), graph analysis, or machine learning algorithms. The ultimate goal of this process is to unlock the hidden potential within software data and leverage it in decision-making as well as customer experience.

#6 Data Visualization and Reporting

Businesses need to engage in data visualization to help relevant stakeholders, even without technical knowledge, understand aggregated insights. Such a process involves using tools and frameworks to visually represent data through charts, graphs, and maps. Besides visualizing using traditional methods, the development team can employ interactive dashboards, data visualization libraries, or business intelligence (BI) tools to simplify visual representations quickly.

However, data visualization alone is not enough. It is the business’s job to create structured reports that synthesize all the findings from the analysis process to explain and interpret data insights. These important documents later play an indispensable role in bridging the gap between complex data analysis and decision-making, driving business outcomes.