Big Data Projects
Big Data projects: Everything you need to know
Businesses encounter massive amounts of data on a daily basis. Much of it is disorganised and unstructured, but in order to identify and utilise data of value, businesses need some kind of processing system. In this article, we´ll explain the benefits and difficulties of Big Data Projects, exemplifying with different kinds of Big Data Analytics and real time Big Data. You can understand Closer’s Data Science Approach by consulting our website.
Big Data projects have become increasingly essential for business digitisation, permeating all sectors including finance, health, commerce, logistics and education. By implementing approaches to increase their own capacity and performance, businesses can, among other things, provide insight into consumer needs, support decision-making, improve security of digital systems and increase a company’s bottom line.
However, Big Data only is short in the business context. To create value from Big Data you have to change business practices and technologies and, to do this, you have to start a big data project.
One of the business components that has benefited first and most from Big Data projects is the area of marketing and sales. The information generated by data science implementations allows for the design of solutions that align with customer expectations and preferences. In this way, relevant information can be obtained from the target audience to maximise sales opportunities. The synthesis of independent data becomes market intelligence, which directs highly focused communication actions.
Through market intelligence research, Big Data projects can detect unfulfilled consumer needs. The results of this process may prompt the organisation to choose to develop new products and services to take advantage of this business opportunity. The information gathered and analysed helps companies adapt their offerings and generate alternative solutions. Machine learning algorithms and artificial intelligence are two key techniques in this context to create value from data. In business, they facilitate the analysis of the market and the strengthening of a company's capabilities.
Big Data projects have created a paradigm shift in the acquisition and transformation of information and knowledge. This can have many benefits for those willing to learn and implement analytics and other Big Data projects to benefit from the data. The development of sophisticated algorithms and the substantial improvement in computing capabilities now allow for a better relationship with and understanding of the data. Like the adoption of any new system, this implementation also poses practical and technological challenges. These can be easily overcome with knowledge and strategic focus.
What is a Big Data project?
Big Data projects refers to the initiatives of processing large datasets (of structured and unstructured data) collected by organisations that can be transformed using analytics, predictive modelling and other machine learning algorithms into useful and actionable information. The large volume of data may initially be structured or unstructured and can come from customer databases, transaction records, system logs, emails, medical records, social media and mobile apps. With the implementation of Big Data projects, these large datasets can become a crucial part of a company’s insights.
Big Data projects can improve day-to-day operations, allow for personalised marketing campaigns and create better customer service among other benefits, ultimately increasing company revenue. Historical and real-time data projects can turn large datasets into a gold mine through the benefit of insights. Big Data projects provide a window into the evolving needs and preferences of customers, allow companies to identify risks and opportunities and inspire innovation using a proven track record of data that can lead to future success.
The following are some examples of tasks that Big Data projects cover in various functional areas of business:
- Customer management and loyalty
- Promotional activities and online advertising
- Cross-selling and upselling
- Control and optimisation of sales resources
- Increase in expected customer value
- Sales forecast
- Risk analysis and credit rating
- Stock market patterns
- Fraud detection
- Forecast of revenues and expenses
- Supply chain management
- Distribution route planning
- Inventory management
- Production systems and operations
- Detection of abnormal behaviour for crime prevention purposes
- Facial, fingerprint recognition
- Identification of environmental risk patterns
- Background checks
- E-commerce recommendation systems
- Mail sorting
- SaaS Development
- Human Resources
- Recruitment and selection of personnel
- Human resources management
- Validation of leaves of absence and holidays
- Health services
- Administration of health centres
- Patient diagnosis and monitoring
- Testing and development of medical trials
In short, it is possible to apply Big Data projects to nearly any aspect of business. Technological applications as well as statistical and mathematical functions make it possible to optimise processes and convert data into competitive advantages.
Big Data projects and the importance of Big Data analytics
Big Data analytics is the key to deciphering useful information that can be applied for improved decision-making. Instead of leaving data in a data warehouse to incrementally grow without benefit, analytics can filter and organise the data in a way that is useful and profitable. It gives meaningful interpretation to bits of data that may alone be useless, thereby creating new knowledge or predicting future actions. Data analytics employs Big Data methodologies, applying statistical, logical or mathematical functions to transform data and make it easier to examine.
Big Data analytics employs several methodologies, including deep learning which allows machines to mine the data, create predictive modelling, streamline analytics and statistically analyse the data. By using machine learning models, even unstructured data can be transformed into useful information. Parallel processing is possible, allowing large volumes of data to be processed simultaneously with an almost linear scalability. This way, the difficult task of parsing, profiling and validating datasets can be left to machine processes, reducing the likelihood of error and increasing productivity.
Customer data is one of the primary sources of Big Data that can revolutionise a company’s marketing and operations activities. For instance, analytics can compare customer behaviour to determine the success of a marketing campaign. This can, in turn, be used to improve marketing campaigns. Social media can also be used to ascertain overall sentiment about a product or service. This may help uncover problems with a product or aid in identifying the ideal target audience.
Contrary to traditional relational databases, Big Data processing platforms combine multiple functions for fast analytics reporting from datasets of any size. Using large-scale analytics engines, such as Apache Spark, allows for the integration of transactional and analytical processing in the same platform. These are designed to automatically handle software failures within the framework to prevent data loss.
Big Data projects and the importance of real time
The ability of Big Data projects to incorporate real-time information brings flexibility and speed to organisations. Real-time analytics can analyse data from the moment it is created using the company’s established Big Data infrastructure. This can help businesses detect cyber security threats, track web performance and measure critical applications for which downtime is simply too costly. This allows for the timely facilitation of business decision-making because up-to-date information is available instantly.
It’s no longer viable to simply review event logs after the fact to detect issues. The reality of today’s fast-paced technological world may induce serious revenue losses. Server downtime can affect your company’s search-engine placement, and a cyberattack can compromise your customers’ information. If these issues aren’t handled quickly, customers may lose faith in your organisation which will be reflected in your revenue. Thus, real-time Big Data analytics is a necessity as it enables the detection and subsequent mitigation of the effects of system problems the second they occur so they can be resolved as soon as possible.
Real-time analysis will not only lead to customer-facing improvements but also ease the burden for employees. Big Data analysis reduces the time employees must spend carrying out the tedious creation of reports. It streamlines internal processes so that the knowledge is accessible immediately, thus providing employees with the most up-to-date information to make changes that can improve operations.
Benefits of Big Data projects
Undoubtedly, Big Data projects offer a wealth of benefits to an organisation. They allow for the weighing of the costs, risks and constraints and for the calculation of returns. Big Data projects in analytics are valuable resources that drive change management. Since transformation and evolution are intrinsic characteristics of the process, they provide currently accurate data that streamline the decision-making process, making Big Data an integral part of the entire operation.
Big Data projects can generate better knowledge of customers and more insightful market intelligence, allowing companies to develop a closer and more profitable relationship with the customers. Through analytics, companies can glean essential information about consumer habits, wants and needs that allows for better targeted marketing. This, in turn, can improve operations by eliminating costly and time-consuming marketing tasks that have not been shown to improve the relationship with the consumer or generate sales.
In addition to marketing, Big Data projects can handle arduous operations tasks such as real-time supply chain management to help visualise and track supply and demand at every stage of the process. This up-to-the-minute monitoring can help adjust resources quickly to prevent lapses in business operations before they occur.
A foundation for business growth can also be formed by means of Big Data projects. Through smarter machine learning algorithms and their recommendations, companies can implement data-driven innovation that can lead to higher profits. These changes can streamline internal processes, customer service, sales, and risk assessment.
Big Data project challenges
It is said that information is power, but there is little point in having large datasets if these are not properly processed.
Bellow, we focus in detail on the main challenges that Big Data faces nowadays.
One of the biggest challenges with Big Data projects is change in the technological platforms and the integration of Big Data and real-time in-depth interpretation, requiring machine-to-machine interaction without human intervention. Proper implementation requires creating meticulous processing and analytic guidelines that accelerate and improve decision-making. In other words, algorithms must be accurate at the point of implementation for the project to run as intended. When implemented properly, organisations can study different variables and predict potential scenarios by evaluating and tracking Big Data to facilitate decision-making.
- Data volume and growth is another challenge to overcome with Big Data projects. Since datasets grow exponentially over time, organisations may encounter issues with data storage and handling. Sifting through the information can be especially challenging when it comes to unstructured documents, videos, audio and other files that may take up a lot of storage space and be difficult to find in a database. Using real-time analysis, these large datasets can be automatically organised at the point of creation in order to avoid missing, incomplete or inaccessible chunks of data.
- Another challenge with Big Data projects, aside from the volume and speed of information, is the difficulty of transforming data from different sources and establishing relationships between them. This requires the use of a reliable Big Data processing platform that is equipped to process large volumes of data with linear scalability. By combining transactional and analytical processing, data from various sources can be examined, enabling organisations to identify useful trends in the data.
These trends can serve as target guidelines and may have predictive, prescriptive or analytical purposes. Proficient data professionals, including data scientists, data analysts and engineers who can create and maintain the tools that continually process incoming data, are needed for the handling of Big Data platforms. Since technology often evolves faster than professionals can, it is important to invest in an IT workforce and maintain ongoing training to bridge the gap.
Critical aspects for starting a Big Data project
The goal of any Big Data project is to get the most out of the data collected. For this purpose, different data warehouses, including those housing unstructured data, are mined to obtain meaningful data. Big data projects turn raw data into valuable decision-making and evaluation criteria.
Multiple disciplines converge in a Big Data project in order to provide a holistic overview of the problem, highlighting the most pressing issues and important factors. Artificial intelligence resources, such as machine learning and deep learning algorithms, are applied. Cloud computing resources are also leveraged for the storage and management of documents and other data formats. The success of a Big Data project, thus, depends on the correct identification of the variables and the selection of the suitable technological tools.
Given the complexity of Big Data projects, it is essential to pay attention to the methodological aspects that can affect the results. Some of these aspects are detailed below.
Defining the problem
It is necessary to first understand the problem fully in order to define the objectives of a Big Data project. The expected scope of the solution should also be specified. It may seem obvious, but such projects often stall or fail because of an inadequate approach, lack of transparency or communication failures. Data scientists and stakeholders should ensure that there is a consensus regarding the understanding of the aim of the project. In addition to clearly defining the problem, it is essential to specify the expected results. This will ensure the commitment of all involved, the fluidity of the process and the correct orientation of the Big Data project.
Decision-makers and operational stakeholders must be involved in this phase of the process; data scientists alone should not oversee delimiting the object of study. Technical knowledge must be combined with business intelligence for more effective results. Otherwise, there is a risk of generating Big Data analyses that do not meet the objectives.
We have already mentioned the importance of engaging competent operational professionals for a Big Data project. However, this point should be emphasised, as the lack of infrastructure or its inadequate management will affect the results. The available resources, including professional support, are critical for the project, particularly in its beginning stages. Inadequate resources will only limit the potential of the project.
A Big Data project must be planned with large-scale execution in mind. Sometimes the scalability factor is overlooked, and big data projects fail. To prevent this, organisations should establish an infrastructure framework that can handle the expected volumes of data, accounting for anticipated growth over time.
The quality of the data feeding the system will determine the reliability and efficiency of the results. If biased or inaccurate data is used in a Big Data project, the results can lead to erroneous analyses.
The correct modelling of the proposals requires a preliminary step of data cleaning and debugging. This includes tasks such as:
- Allocation of missing values and elimination of meaningless or duplicate data.
- Normalization of numerical variables, determination of hierarchies, and the creation of new attributes.
- Reduction of data size by coding into categories.
Machine-learning technology identifies patterns in the data from which it generates the models needed. Inaccurate or low-quality inputs in a Big Data project will result in unreliable models.
On the other hand, the continuous and incremental nature of the process must be considered. Therefore, it is necessary to add new relevant data sources regularly and ensure that changes are incorporated into the system.
Competencies for the development of Big Data projects
The following are some basic recommendations that can contribute to ensuring the effective development of Big Data solutions and products. Although these are not strict rules, they are based on experience in big data projects.
Employing the right talent
It is becoming increasingly common for organisations to employ data specialists as part of their core staff. However, Big Data projects are complex undertakings that require a very broad skill set. A Big Data project requires the business skills of an analyst and the technical knowledge of a data engineer. A data scientist can be the bridge between the two functions.
If there is one trait that should characterise Big Data project teams overall, it is their adaptability and flexibility. Evolving technology requires continuous learning and adaptation. Roles may be interchanged throughout the course of the project. It is therefore not surprising that some responsibilities are shared in such a multifaceted and collaborative discipline with a promising future.
It is important to be clear about the value of a Big Data project before implementing it. Depending on business needs, Big Data may be most beneficial when it comes to predictive analysis, segmentation and customisation, innovation and product development or data security, to name a few.
A common feature of any Big Data project is its ability to challenge existing assumptions. Herein lies their real power as the review of processes generates improved solutions, regardless of which aspect of the business these may address. These are iterative and continuous optimisation techniques that continuously generate value for the organisation.
If resources are limited, it’s crucial to implement Big Data projects that will yield the most value. As more processes within a business are streamlined, more resources may be freed up to tackle other Big Data projects in the future. In the short term, the projects that will create the most value indicate the best immediate investment.
The genesis of a Big Data project is strategy. In some organisations, a reverse process occurs, and data are collected based on commercial or operational assumptions. The information collected in this manner may obscure the true needs of the consumer or organisation, and the infrastructure created based on it may be flawed and insufficient.
Incorporating data specialists and carefully thought-out processes from the outset ensures a better use of resources as well as more accurate and reliable results. This can guarantee the quality of the data and its relevance to the project so the aim of the project can be achieved.
From less to more
Some Big Data projects are aimed at reducing risks, but a poorly conceived plan can put the entire organisation at risk. While it is important to keep an eye on the future, it is also necessary to plan for progressive and controlled development. It is a risky decision to embark on a new Big Data project that will challenge existing methodologies.
Gradual implementation allows processes to be fine-tuned along the way so results can be optimised. At the same time, gradual implementation will provide knowledge and give your team confidence in the project. The value of early successes lies in their use as a learning tool and the validation of the project.
In conclusion, when it comes to providing insight, supporting decisions, and improving security, Big Data is invaluable. Anticipating changes in the market paves the way to make efficient and logical operations decisions and implement risk prevention or control mechanisms. This makes Big Data projects an essential tool for innovation that is guided by informed decisions.
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