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Big data implementation: roadmap and examples

Jul 05, 2022

11 minutes read

Big data is a variety of data that comes at an ever-increasing rate and volume. The three main properties of big data are diversity, high speed of arrival, and large volume. Big data is a larger and more complex dataset, especially from non-standard sources. These datasets are so large that traditional processing programs cannot handle them. But these huge amounts of data can be used to solve business problems that previously seemed too complex. Let’s see how big data implementation can help your business.

What is big data for business?

Over the years, big data has developed two additional characteristics: value and validity. Data has intrinsic value. However, for them to be helpful, this value must be unlocked. Just as necessary, how reliable is your data—and how trustworthy is it?

Today, big data has become a form of capital. Think of the biggest technology companies. The value of their offerings relies heavily on the data they’re constantly analyzing to improve efficiency and develop new products.

The latest advances in technology have made it possible to significantly reduce the cost of storage and computing, which makes it possible to store and process ever-growing amounts of data. Modern technologies allow you to store and process more data at a lower cost, making more accurate and informed business decisions.

Extracting value from big data implementation is not limited to their analysis (this is their distinct advantage). It is an end-to-end exploratory process involving deep analysts, enterprise users, and executives who will ask the right questions, identify patterns, make informed guesses, and predict behavior.

By 2005, businesses began to realize how much data users create when using Facebook, YouTube, and other internet services. The same year, the open-source Hadoop platform appeared, created specifically for storing and analyzing big data sets. At the same time, the NoSQL methodology began to gain popularity.

The advent of open-source platforms such as Hadoop and, more recently, Spark has played a significant role in the spread of big data, as these tools make it easier to process big data and reduce the cost of storage. Over the years, the volume of big data has increased by orders of magnitude. Users still produce huge amounts of data, but now it’s not just them who produce it.

With the advent of the Internet of Things (IoT), an increasing number of devices are connected to the Internet, which allows them to collect data about user behavior patterns and product performance. And when machine learning technologies appeared, the amount of data grew even more.

Big data has a long development history, but its potential is still far from being unlocked. Cloud computing has pushed the boundaries of big data implementation. Cloud computing provides flexible scaling options, allowing developers to deploy clusters to test sample data on demand. Graphical databases are also becoming increasingly important because they display huge amounts of data to provide fast and comprehensive analytics.

Benefits of big data

  • Big data allows you to get more complete answers, providing more information.
  • More detailed answers mean you can be confident in the validity of the data, providing an entirely new approach to problem-solving.

Related: Challenges and benefits of Big Data in healthcare

Step-by-step big data implementation strategy

Big data allows you to extract valuable insights that open up new opportunities and business models. There are three steps to getting started with big data implementation.

Step #1 Integration

Big data allows you to combine data from disparate sources and applications. Traditional data integration mechanisms, such as extract, transform, and load (ETL) tools, are not up to the task. Analyzing terabyte or even petabyte datasets requires new strategies and technologies.

During the integration phase, data is added, processed, and formatted so it is convenient for corporate analysts to work with them.

Step #2 Storage

Big data management requires storage. The storage solution can be hosted on-premise, in the cloud, or both. You can store data in your preferred format and apply your desired processing requirements (and required processing engines) to datasets as needed. Most companies choose a storage solution based on where they are currently stored. Cloud storage is growing in popularity as it supports up-to-date computing requirements and allows you to use resources as needed.

Step #3 Analytics

Your investment in big data implementation will pay off when you start to analyze the data and start acting on the insights. Gain a new level of transparency with visual analysis of diverse datasets. Use deep data analysis to make new discoveries. Share your discoveries with others. Build data models with machine learning and artificial intelligence. Put your data to work.

We have prepared a list of tips to help you master the new technology. Below are our recommendations for building a solid foundation for working with big data.

Recommendation for building a solid foundation for big data analytics 

Align data exploration goals with business objectives

Larger datasets allow discoveries to be made. Therefore, it is essential to plan investments in people, organizations, and infrastructure based on clearly defined business objectives to ensure constant attraction of investments and funding. To see if you’re on the right track, ask yourself how big data supports business and IT priorities and drives critical goals. For example, it could be filtering web logs to understand e-commerce trends, analyzing customer feedback on social media and interacting with the helpdesk, studying statistical correlation methods, and comparing them with the customer, product, production, and design data.

Use standards and guidelines to compensate for lack of qualifications.

The lack of skills is one of the most significant barriers to capitalizing on big data implementation. This risk can be mitigated by incorporating big data technologies, plans, and solutions into the IT management program. Standardization of the approach will allow more efficient management of costs and resources. When implementing solutions and strategies related to big data, it is necessary to assess the required level of competence in advance and take measures to eliminate gaps in skills. This may include training or retraining existing staff, hiring new specialists, or contacting consulting firms.

Optimize knowledge transfer with centers of excellence

Use centers of excellence to share knowledge and oversee and manage project communication. Whether you’re starting with big data or continuing, hardware and software costs should be spread across the company. This structured and systematized approach helps to empower big data and raise the level of maturity of information architecture as a whole.

Aligning structured and unstructured data brings the best results

Big data analytics is valuable in and of itself. However, you can extract even more insights by collating and integrating low density big data with structured data already in use.

Whether you’re collecting data about customers, products, equipment, or the environment, the goal is to add more relevant information to your benchmarks and insights and provide more accurate insights. For example, it is important to distinguish the attitude of all customers from the attitude of the most valuable of them. That is why many companies see big data as an integral part of their existing business intelligence tools, data storage platforms, and information architecture.

Top 7 examples of big data implementation

Big data can be used in various areas, from customer interaction to analytics. Here are just a few examples of big data implementation.

Product development

Companies like Netflix and Procter & Gamble use big data to predict consumer demand. They create predictive models for new products and services by classifying key attributes of previous or existing products and modeling the relationship between these attributes and the commercial success of the offerings. In addition, P&G uses data and statistics from focus groups, social media, market tests, and sales trials to launch new products.

Predictive maintenance management

Factors that predict mechanical failures can be buried in structured data such as equipment year, make, and model, or unstructured data such as log entries, sensor data, error messages, and engine temperature information. By analyzing indicators of likely problems before they occur, companies can improve the cost-effectiveness of maintenance and maximize the life of parts and equipment.

Customer service quality

The fight for customers is in full swing. Getting accurate customer experience data is easier than ever today. Big data will allow you to extract useful information from social networks, information about website visits, and other sources, thus improving the quality of interaction with customers and making your offers as helpful as possible. Provide a personalized experience, reduce customer churn, and prevent problems.

Related: How big data help to solve problems in healthcare?

Intrusion detection and compliance

Regarding security, it’s not just a couple of hackers: you’ve got teams of experienced people up against you. Regulatory requirements and safety standards are constantly changing. Big data allows you to identify fraud patterns and collect significant data to speed up regulatory reporting.

Machine learning

Today, machine learning is one of the most popular topics for discussion. And data, especially big data, is one of the reasons for this popularity. Today we can teach machines instead of programming them. It is the availability of big data that has made this possible.

Operational efficiency

Operational efficiency is rarely a topic of discussion, but it is in this area that big data plays the most significant role. Big data allows you to access and analyze the production, customer sentiment, revenue, and more to reduce downtime and predict future demand. Big data also allows you to make smarter decisions in line with market demand.

Implementation of innovations

Big data allows you to identify interdependencies between users, institutions, and companies, process these interdependencies and determine new ways to use the information obtained. Use data insights to improve financial decisions and planning. Study trends and customer desires to launch new products and services. Implement dynamic pricing. The possibilities are truly endless.

Big data implementation in healthcare

Operational activities of medical institutions

It becomes possible to study the effectiveness of treatment by processing all available information about the treatment practice. Based on the analysis of all known case histories and diagnostics, the widespread use of decision support systems will enter physicians’ practice, allowing the clinician to gain unprecedented access to the experience of thousands of colleagues across the country. Methods of personal and preventive medicine based on remote patient monitoring will significantly reduce costs and improve the quality of life. The spread of various human body activity sensors connected to wearable gadgets reduces the need for laboratory tests, prevents unexpected complications, and automatic reminders of the need for independent medical and preventive manipulations will improve the quality of the prescribed treatment.

System of pricing and payment

Analysis of invoices and receipts using automatic procedures based on machine learning and neural networks will reduce the number of errors and thefts in payments. The formation of pricing plans that consider the population’s real possibilities and the need for services also increases the overall income from patients. Only systems that work with “big data” make it possible to move towards payment based on the quality of care provided and to jointly regulate the costs of medicines and the work of medical staff.

Research and development

The most significant effect here should be expected from the new possibilities of predictive modeling in drug development. Statistical algorithms and big data tools equally impact clinical trial planning and patient recruitment. Processing the results of such tests is another important application of big data. Innovations in personalized medicine now occupy a special place in research and development in healthcare. Based on the processing of gigantic amounts of genetic information, which are becoming more and more accessible to humans, doctors will be able to prescribe completely unique drugs and treatments. Finally, developments in the identification of disease patterns will make it possible to obtain reasonable prognostic estimates for the development of various types of diseases, to identify risk profiles, and not only to carry out preventive measures but also to predict the need to develop treatment methods that are effective for future types of diseases.

Related: Big data analytics: types and tools

New business models

Based on digital health data, these models can complement existing ones or even compete with some. These data aggregators deliver analyzed and assembled blocks of data that meet specified conditions to third parties. For example, all medical histories of patients who used a particular pharmacological drug are important for pharmaceutical companies, who are ready to buy such data. Other potential new business models are online platforms for patients and physicians, medical researchers and pharmacologists.

Mass screening and prevention and detection of epidemics

This direction is based on big data. The development of technologies allows us to build both geographical and social models of public health and predictive models for the development of epidemic outbreaks.

How can VITech help you integrate big data into your business?

The use of big data analytics enables healthcare providers to shorten hospital stays, contain rising costs, and reduce readmissions.

Medical analytical tasks that can be solved using big data analysis can be of various types depending on the level of maturity:

  • descriptive analytics (answering the question “What happened?”);
  • diagnostic analytics (“Why did this happen?”);
  • predictive analytics (“What will happen in the future?”);
  • prescriptive analytics (“What needs to be done to prevent this from happening?”).

As the complexity of tasks grows, so does the complexity of the analytical system and algorithms, as well as the number of data sources needed: from simple information from medical records and biometric monitoring data to genomic and family data and even to information from social networks. Contact us to learn more about big data implementation in healthcare.

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