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Big data is the next business challenge

Big data is the next business challenge
DEAC | 20.12.2019

Why is big data important to business, what is so interesting and useful in it? The answer is very simple - huge amounts of data collected by machines, sensors and other equipment from various sources give an idea of real events, behavior or any changes, and can reveal unbelievable patterns.

Big data makes it possible to respond much faster to various aspects of activity to make decisions, thereby increasing business efficiency. It is not easy to work with huge data arrays, but after processing the big data, you can see and take into account new risks, discover hidden patterns, compare various indicators and results hundreds of times faster than a team of analysts.

Companies have been working with big data for many decades, but the term "big data” itself has been developed only 20 years ago. In some areas, big data allows you to get significant advantages, as, for example, in retail - understanding of your consumer, which, of course, is an important aspect.

Let’s take a closer look at several industries that successfully use big data.


Big Data in Agriculture DEAC

Big data is widely used in agriculture - in addition to IoT devices (Internet of Things), systems analyze microclimatic changes during the cultivation of plants and crops, collects the obtained data online and then analyze it to give a quick response. Machine learning and algorithms can be configured to take into account any amount of information or tasks, to simulate various forecasts for planning or decision making, with one important goal - to understand how the environment and other conditions affect business.

Media and Marketing

From now on, marketing and media companies cannot imagine their daily routine without consumers’ and their behavior data; therefore, their activities directly depend on the use of technologies and big data solutions.

Retail. Marketers and PR managers in retail companies have successfully used big data for a long time to increase sales. Data received from various devices, shopping centers, for example - customer flows, data from loyalty cards or payments can help to adapt advertising campaigns, promotions to different requirements.

An excellent example of data adaptation for a targeted advertising campaign is the media giant Netflix, with more than 10 million subscribers. Company uses search history and video views to determine the interest and sends recommendations with the next movies to watch.


Big Data in Healthcare DEACBig data analysis is often used in pharmacy, hospitals or laboratories. Scientists and specialists still argue about the accuracy and "truthfulness" of the data and ability of artificial intelligence to contribute to medical staff and doctors. For now, in the growing amount of medical data, the system helps to monitor the patient’s condition and, accordingly, helps the medical staff to quickly obtain the necessary information about the patient’s history, make a quick diagnosis or compare the dynamics of the latest tests.

Image processing. Healthcare produces a lot of images, photographs and x-rays. Until now, doctors have manually examined each patient’s image, but experts suggest that algorithms can be used, for example, for primary processing of x-ray images and determining the diagnosis.

Prediction of patients flow. If two or more hospitals synchronize their systems, they could, for example, predict emergency peaks, as well as avoid repeated or unreasonable situations.

Medicine records in the hospital. DEAC has developed and implemented for its customer - regional hospital - a solution, which records and controls all medicine flow in the hospital. The same system analyzes the use of drugs in the hospital, and automatically sends orders to restock them in time, thus avoiding situations if someone forgets to order them.


Big data analysis can be a breakthrough for manufacture and production, as it allows you to more effectively manage and control processes. Machine data not only notifies of equipment malfunctions, but also calculates and monitors the remaining service life of systems and components, maintaining their performance in accordance with the manufacturer's specifications.

Each detail is important. Imagine a pharmaceutical factory: if some part has lost some of its functionality, but still works, then the machine can inject more or less of the active ingredient into the pill.

Downtime. How much can cost downtime due to a technical failure? If notifications are delivered on time and before a potential incident occurs, the company can avoid unexpected losses.

Big Data in Manufacturing DEAC

The financial sector and banks

Banks operate with a large amount of data, which is also successfully used to develop and promote a business. Big data is often obtained to make reasonable investments that are likely to be profitable.

What big data can improve in banking sector?

  1. Customer experience
  2. Employee performance
  3. Optimization of operations

Big data in government

The data accumulated every day by the government institutions (and there is a lot of data) are a valuable asset for managing and controlling workflow and databases, demographics of the country, energy, geographic resources, transport, medicine, climate observations, economic indicators etc.

Internal information threats. Using fluctuations and abnormalities in behavior, security service can identify and prevent various threats, for example, data leakage, cyberattacks, malware etc. Use of CCTV cameras. A classic example of using a large data stream. Face recognition and search systems, fixing non-standard behavior, traffic analysis or traffic jams.

Big Data in Government DEAC

How to start using big data?

Step 1

First of all, for such volume of data your company will require an impressive system for data storage and processing. Big data has a tendency to grow faster than the company manages to process, structure and obtain, therefore, to work with big data, you need a scalable storage platform which can quickly increase resources. To store data, we recommend choosing a cloud provider, the data processing can be managed on your own resources.

For example, a cloud platform with a NoSQL Cassandra or InfluxDB database (both open source) is able to receive data from IoT sensors in seconds. NoSQL databases provide a mechanism for storing and retrieving data and is perfect for scalable applications.

Step 2

Next, frequently used big data analytics systems like Hadoop, Apache Spark, Apache Storm, Apache Spark or Disco allow you to simultaneously analyze huge data arrays.

Systems can be hosted on the cloud or physical resources, for example, on a server cluster. What is very important, choosing a powerful clustering solution, the selected systems can distribute big data to different nodes within the cluster, thereby balancing the load and providing near 100% availability. Indeed, the main task of the cluster is to exclude system downtime caused by any internal or external incident.

Step 3

After the data is processed, additional solutions for interactive visualization and business analytics, for example, PowerBI, can be used, allowing you to adopt the data for your needs. You can use the common Elasticsearch search program to select and search data that the system stores on the SQL server, which makes easier to look for documents and control the workflow.

It certainly makes sense to use big data technologies in small businesses only in case if business is ready to analyze and use such data. CRM-systems and other databases already contain many customers’ and products’ data, which can help improve sales, technical support and other business processes. No man is able to analyze large amounts of data manually, as a result, valuable information often “lies” unused.