Big Data: Applications and Examples

In today’s digital age, data is as valuable as gold. However, managing and utilizing this massive information, known as big data, is no easy feat. This article is here to help you understand big data by discussing best practices for implementing big data and real-world examples that have shown remarkable results in various fields.

Curious about the comprehensive definition of big data? Check out this link: Big Data: The Key to Business Future

Best Practices for Implementing Big Data

In today’s digital age, big data processing is the key to gaining a competitive edge. However, with the vast amount of information available, managing and extracting insights can be a challenge. Therefore, it is essential to implement best practices to ensure that your big data implementation is successful and delivers actionable insights.

Align Big Data with Business Goals

The more data you have, the more insights you can draw. However, to ensure that the investment in new talent, organizations, or infrastructure yields tangible benefits, a strong business context is key. Ensure that big data implementation can support and align with the company’s business and IT goals.

Big data can be used to understand online market behavior by analyzing the web, researching social media sentiment and customer interactions, and understanding the statistical correlations and relevance between customer data, products, manufacturing, and engineering.

Measure Skill Requirements with Standards and Management

A major challenge in implementing big data is the lack of expertise. This can be addressed by integrating big data technology, considerations, and decisions into your IT management program. This standardization helps manage costs and maximize resources.

Organizations implementing big data solutions need to conduct regular skill requirement reviews early on. Skill gaps can be addressed through employee cross-training, new recruitment, or partnering with consultants.

Aligning Unstructured Data with Structured Data

The value of big data analysis is undeniable, but it is further enhanced when integrated with existing structured data. Gain deeper business insights by connecting low-density big data, such as customer, product, equipment, or environmental data, to core data and analytical summaries.

Big data analysis processes and models can be performed by both humans and machines. These analytical models encompass a variety of techniques, including statistics, spatial analysis, semantics, interactive discovery, and visualization. With analytical models, diverse data types and sources can be linked to uncover meaningful associations and insights.

Planning Data Research

Extracting insights from data is no easy task. In fact, sometimes we ourselves are unsure of what we are looking for. Both the management and IT teams need to support this data extraction process, even if the direction is still unclear or the requirements are not yet defined.

Close collaboration between analysts and data scientists with the business team is crucial to understanding business needs. A high-performance workspace is also essential to facilitate interactive data exploration and statistical algorithm experimentation. Ensure that this sandbox environment is adequately supported and well-managed.

Aligning Big Data with Cloud Operating Models

Big data users require a variety of resources to conduct experiments and daily tasks. These resources include transactional and core data, reference data, and summary data. To assist data teams in experimenting and developing new solutions, a dedicated analytics sandbox should be created. Effectively managing these resources is critical for controlling data flow, from pre-processing to analytical modeling. The right cloud strategy, whether private or public cloud, is essential to meet these evolving needs and maintain data security.

Real-World Applications of Big Data

In today’s rapidly evolving digital age, data has transformed into an invaluable resource. However, processing and unlocking the hidden potential within this vast ocean of data is akin to finding a needle in a haystack. This is where big data analytics emerges as a revolutionary solution.

This technology harnesses the power of sophisticated computing to extract valuable insights from massive datasets. This empowers organizations to make smarter decisions, optimize operations, and gain a competitive edge. Let’s explore how big data is being applied in the real world.

Product Development

Big data can be leveraged to better anticipate customer demand. Armed with available data, predictions for new products and services can be made. This is done by classifying the attributes of products or services that have been launched in the past and present. Then, these attributes are linked to the commercial success of those offerings.

Product development can utilize analysis from various sources such as focus groups, social media, test markets, and early store launches. This can streamline the planning, production, and launch of new products.

Predictive Maintenance

Data gathered from devices, including structured data like year, make, and model, as well as unstructured data like logs, sensor data, error messages, and machine temperature, can provide early predictions of mechanical failures. By analyzing these indicators, organizations can implement more cost-effective and proactive maintenance strategies, maximizing uptime for equipment and components.

Customer Experience

In today’s customer-centric world, understanding the customer experience is more important than ever. Big data applications can empower businesses to gather insights from social media, website visits, call logs, and other sources to enhance customer interactions and maximize value delivery. By leveraging big data, businesses can personalize offerings, reduce churn rates, and proactively address customer issues.

Fraud and Compliance

Security threats no longer stem solely from individual hackers but rather from organized teams of experts. Enterprises are challenged to stay ahead of the curve in evolving security and compliance requirements. Big data can identify patterns in data that indicate fraudulent activity and gather vast amounts of information. This significantly aids organizations in enhancing security and compliance. Regulatory reporting becomes much faster and more efficient, allowing organizations to focus on more strategic matters.

Machine Learning

In this era of sophistication, machine learning is one of the most highly anticipated technologies. One of the driving forces behind this is data, especially big data. Now, we no longer need to program machines for monotonous tasks, but can train them to do so. This is made possible by the availability of abundant big data to train machine learning models.

Operational Efficiency

Often overlooked, operational efficiency is an important aspect that can be significantly optimized by utilizing big data. In-depth analysis of production data, customer feedback, returns, and other factors enables the identification and reduction of bottlenecks that disrupt operations. Big data also plays a role in predicting future market demand, allowing companies to adjust strategies and decision-making in a timely manner.

Drive Innovation

Big data analysis can lead to the discovery of previously unexpected patterns and relationships, opening the door to innovation. Insights from data can help you understand the connections between people, institutions, and processes. From this understanding, you can make informed financial decisions, create new products or services based on customer behavior, and determine dynamic pricing based on market demand.

Contact ACS Group

ACS Group (PT Autojaya Idetech and PT Solusi Periferal), established in 1992, is a trusted professional company that has provided tailored solutions to thousands of enterprises across Indonesia.

We offer a comprehensive suite of cutting-edge solutions, encompassing AIDC, IT Infrastructure, Enterprise Security Systems, and Enterprise Business Solutions. With four strategically located branches in Cikarang, Semarang, Surabaya, and Denpasar, we are committed to delivering close and personalized service to our valued customers.

Contact us today via our Official WhatsApp +62 811-1944-534, Email sales.admin@acsgroup.co.id or, visit our website www.acsgroup.co.id to explore how ACS Group can help you enhance your business efficiency and productivity with Big Data implementation.

Big Data: The Key to the Future of Business

In today’s digital age, the amount and availability of data is growing rapidly. This development is driven by advances in digital technology, such as connectivity, mobility, the Internet of Things (IoT), and artificial intelligence (AI). Big data is a collection of very large and diverse data, consisting of structured, unstructured, and semi-structured data. This data is constantly growing over time. Its enormous volume, speed, and variety make it difficult to process using traditional data management systems.

Raw big data in large quantities has no value if it is simply collected and presented as it is. The important information and business insights within the data will not be automatically revealed. Therefore, this data needs to be transformed into reports that are easy to understand by  the management. The right tools are needed to process data in detail and on time.

Reports generated from this process should present useful analysis to support decision-making. Big data is used in machine learning, predictive modeling, and other advanced analytics to solve business problems and make the right decisions.

Characteristics of Big Data

The definition of big data may vary slightly from one source to another. However, the term is always explained based on its characteristics, namely volume (large data size), velocity (data growth rate), variety (variety of data types), veracity (data accuracy), and value (value that can be generated from data).

Volume

As the name suggests, the most common characteristic associated with big data is its high volume. Enormous amount of data is continuously generated from various sources. If the data is too large to be processed, sampling techniques can be used to determine data samples that represent the business conditions to be processed.

Velocity

In big data, velocity refers to how quickly data is generated. Nowadays, data is often produced in real-time or near real-time. As a result, the data also needs to be processed, accessed, and analyzed at the same speed to have a meaningful impact. There are various levels of data acquisition, namely: Ad-Hoc (as needed), Batch , Near Real-Time, and Real-Time.

Variety

Data is heterogeneous, meaning it can come from various sources and can be structured, unstructured, or semi-structured data. This diversity of categories adds complexity to data processing.

Veracity

Data quality and accuracy are crucial. The higher the data veracity, the more reliable the data. Therefore, it is necessary to determine an acceptable data deviation standard. This standard will aid in the data sorting process, so that high-quality data can be separated from low-quality data.

Value

It is essential to determine the business value of the data you collect. Big data should contain the right data and then be effectively analyzed to generate insights that can help drive decision-making.

When is Big Data Needed?

Utilizing big data is like exploring an ocean of information. However, in its application, preparation is key. Implementing big data requires a solid foundation, both in terms of qualified human resources and adequate hardware infrastructure. Not to forget, significant funds are also required to apply this technology.

Therefore, companies need to carefully consider before diving into the world of big data. The question is, when is the right time for a company to start considering big data processing?

Slow Query

The larger the data, the slower the traditional data request process. if this hinders your business processes, then a big data solution with high computing capabilities is needed to process large data quickly.

Data Analytics

For companies that require advanced data analysis, big data can process and transform data into conclusions or a series of facts that are easy to understand. This can help companies make more accurate and strategic decisions.

AI Development

If a business wants to utilize AI technology, the data it owns must first be cleaned and organized. AI learns through a machine learning process that is only effective with structured and organized data. AI development cannot process unstructured data. Therefore, organized data resulting from the big data process is needed for AI to learn. After that, the data can gradually be handed over to AI to handle.

Big Data Preparation

What needs to be prepared in developing big data?

Data Security

Big data consist of a  very large and valuable information, so it needs to be protected from various potential threats. Utilize security facilities such as WAF (Web Application Firewall) and AntiDDoS (Anti-Distributed Denial-of-Service) offered by cloud service providers. Implementing strict access controls to limit data center access to authorized personnel can also improve data security.

Operational Cost

Big data development requires significant investment, both in terms of human resources and hardware. Companies must prepare funds to hire and train expert staff in this field. IT infrastructure also needs to be built to accommodate processing.

Scalability

Businesses need to be able to anticipate changes in data volume. This can be done by providing the right resources, which means not being excessive when data volume is low and not lacking when data volume is high.

Data Visualization

Companies need to prepare tools to visualize the results of data analysis. Data can be displayed in the form of easy-to-understand graphs within an interactive dashboard.

Platform for Orchestration

The development process of this technology will be more efficient if it is automated. Thus, the IT team does not need to spend a lot of time coding and configuring.

Curious to learn more about big data applications and examples? Find the answers in Big Data: Applications and Examples.

Contact ACS Group

ACS Group (PT Autojaya Idetech and PT Solusi Periferal), established in 1992, is a trusted professional company that has provided tailored solutions to thousands of enterprises across Indonesia.

We offer a comprehensive suite of cutting-edge solutions, encompassing AIDC, IT Infrastructure, Enterprise Security Systems, and Enterprise Business Solutions. With four strategically located branches in Cikarang, Semarang, Surabaya, and Denpasar, we are committed to delivering close and personalized service to our valued customers.

Contact us today via our Official WhatsApp +62 811-1944-534, Email sales.admin@acsgroup.co.id or, visit our website www.acsgroup.co.id to explore how ACS Group can help you enhance your business efficiency and productivity with Big Data implementation.