How to implement Big Data in a company

What is Big Data and how does it work?

Big Data refers to the handling of data sets that are so large, fast or complex that they cannot be managed using traditional data processing methods. This data comes from multiple sources, such as social networks, online transactions, IoT sensors and others, and is characterised by its size, speed and variety.

Companies use specialised technologies to handle these features, such as Apache Hadoop and Spark. These platforms allow for data processing to be distributed across hundreds or even thousands of servers at the same time, making it easy to handle large volumes of information efficiently and in real time.

How can Big Data be useful for companies?

Big Data can transform every aspect of a company. By analysing large volumes of data, companies can discover patterns and relationships that are not obvious to the naked eye or through traditional methods. As the amount of data available has grown, so has the complexity of finding these relationships, leading to the development of new technologies and methods to harness this vast resource effectively.

• Improving decision-making: Historically, business decisions were largely made on the basis of intuition and experience. Over time, companies began to adopt a more analytical approach, using historical data to guide their decisions. For example, they analysed product sales throughout the year to plan future purchases. However, with today's volume of data, traditional technologies are not enough. Big Data enables predictive and prescriptive analytics, helping to forecast trends and behaviours more accurately. This optimises strategies based on real data rather than assumptions.
• Customising the offer to the customer: Customisation is crucial in today's market, where consumers expect experiences tailored to their needs and preferences. Companies such as Netflix, Amazon and YouTube use Big Data to analyse the behaviour of their users. Netflix, for example, analyses its users' viewing history to recommend series and films they are likely to enjoy. Amazon uses data from previous purchases and searches to suggest relevant products, improving the customer experience and increasing sales. Another example of the use of Big Data is to help optimise the layout of products in a shop. For example, if it is seen that customers who buy bread also tend to buy butter, these products can be placed near to each other, even if the relationship is not obvious to the naked eye.
• Optimising operations: Big Data enables companies to identify bottlenecks, predict equipment failures, and manage resources more efficiently. For example, in the manufacturing industry, the use of IoT sensors on machinery can generate data that, when analysed, can predict when a machine is likely to fail. This allows for preventive maintenance, thus reducing downtime and operating costs. In the supply chain, the use of Big Data can improve logistics efficiency, from inventory management to product distribution. Companies can now analyse real-time data to optimise delivery routes and reduce waiting times. In addition, the business and distribution environment is more complex than ever, and Big Data can help discover how to improve operational processes. For example, a supermarket chain could use Big Data to analyse the buying patterns of its customers and adjust its inventory and distribution accordingly, something that would be impossible to do manually or with traditional techniques due to the sheer amount of data involved.

Steps to implement Big Data

Step 1: Definition of objectives and strategies

The first step for a successful Big Data implementation is to define the objectives you want to achieve. These can range from improving operational efficiency to increasing customer satisfaction. Establishing a clear vision will help guide all future decisions related to technology selection and system design. In addition, it is crucial to change the company's philosophy to a data-oriented approach. This means that decisions should be based on hard data and rigorous analysis rather than on intuition or personal experience.

Step 2: Data collection

Once the objectives have been defined, it is crucial to determine which data is needed to meet them and how it will be obtained. This includes deciding between internal data, such as transaction records, or external data, such as social media data. Protocols must also be established to ensure the quality and integrity of the data collected. For example, e-commerce stores can collect data on shopping behaviour and clicks on their website to better understand their customers' preferences. It is important to foster an internal culture that promotes data collection in all areas of the company, ensuring that each department contributes to a central repository of information.

Step 3: Data storage

Choosing the right storage solution is vital. Options include cloud-based systems or on-premises infrastructures. Aspects such as scalability, security and cost must be considered.

The choice between cloud storage and local storage has its own advantages and disadvantages:

• Local storage: This provides full control over data, which is crucial for high privacy and compliance data. Businesses can directly manage data security and access, ensuring that all privacy requirements are met. However, setting up and maintaining a local infrastructure can be costly and complex, especially when the company must scale rapidly.
• Cloud storage: This offers a solution that is quick to implement and easy to scale. Providers such as Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure allow companies to quickly adjust their storage capacities according to current needs. However, this option may create a dependency on the cloud service provider, which may result in cost overruns if platform conditions change or if the company decides to switch providers in the future.

Step 4: Data analysis

The heart of Big Data is analytics. To do so, it is essential to create a data analytics department, which is responsible for cleaning the data and applying statistical and machine learning techniques to extract valuable information from the stored data. This department should be made up of professionals trained in data science, statistics and data analysis.

The company must cultivate an analytical mindset, where staff are trained and motivated to use data in their daily decision-making. It is important that data analysts also work closely with other departments to ensure that the insights generated are relevant and applicable to the needs of the business.

Step 5: Visualisation and interpreting

Data should be presentable and understandable. Visualisation tools transform large data sets into interactive graphs and visualisations that facilitate interpreting and data-driven decision-making. Tools such as Tableau or PowerBI can help transform complex data into understandable graphs and reports. For example, a company can use data visualisation dashboards for the real-time monitoring of the performance of its operations, sales, marketing, or any other critical area of the business. Fostering an internal philosophy that values transparency and accessibility of information is key to ensuring that insights derived from data analysis are used effectively.

How is it possible to process so much data in Big Data?

Technologies such as Hadoop use a distributed processing model that allows for large volumes of data to be analysed and processed at the same time. This is complemented by NoSQL database systems such as MongoDB, which are able to handle dynamically changing varieties of data and structures. In addition, graphics cards (GPUs) have played a crucial role in Big Data processing. GPUs are extremely efficient at performing parallel computations, making them ideal for machine learning and big data analytics. For example, LinkedIn uses these technologies to process interaction data from its users, enabling them to continuously improve their algorithms for recommending professional connections.

Conclusion

Implementing Big Data in a company is a complex yet fundamental process in today's digital age. By defining clear objectives, collecting and storing relevant data, and analysing it appropriately, companies can transform their operations, improve decision-making and personalise customer experiences. The key to success in Big Data lies in a consistent integration of storage and processing technologies with an organisational culture that values and uses data as a strategic resource. This not only provides a competitive advantage, but also facilitates a more agile response to changing market demands and continuous optimisation of operations. In short, Big Data offers companies the ability to uncover new opportunities, improve efficiency and deliver differentiating value to their customers.

To lead this change, strong data science skills are needed. The Bachelor's Degree in Data Science from Universitat Carlemany, part of Planeta Formación y Universidades, prepares you to face these challenges, teaching you to handle large volumes of data and extract valuable information to optimise business strategies. With advanced tools and a hands-on approach, this programme equips you to become a key professional in any organisation looking to harness the power of Big Data.