It is now impossible to imagine our society without big data analytics. For example, many companies and institutions have already developed big data applications, although they have achieved varying degrees of success. Various platforms and technologies such as social media, IoT sensors are always generating data, and it is very difficult to convert that data into information.
In addition to the data that companies have, smart meters, Internet-connected trucks, aircraft engines, smart watches, refrigerators and more generate data today. These real-time data are called IoT big data because they contain large amounts of raw data. This data needs to be processed in order to present an analysis and be evaluated. But Big Data is a large amount of data that makes it almost impossible for computer programs to analyze, not just the human brain. So what exactly is this big data? What's good for?
Big data is very large unstructured or structured data and is the most complex to analyze. For this you need advanced big data technology and big data tools that can work with large amounts of unstructured data. The amount of data collected is constantly increasing. This is due to the widespread use of the Internet and the use of social media. Big data, in short, is the result of more and more data being recorded and becoming available in more places and times. Its sources are varied, because each data obtained is part of big data, regardless of whether the data is structured or not.
Being able to connect all these different types of data can give you new insights into market opportunities, customer behavior, and developments in the industry. You can also learn about social, economic, financial, commercial or political trends. Because the result depends entirely on how you analyze the data. Big data is about extracting information from a large amount of data from a large number of different sources.
Big data is the sum of data obtained from many different sources and usually defined by the following five specifically. volume (inolume), value (inarea), variety (inariety), velocity (inelocity) and accuracy (ineracity).
· Volume: It is the volume of data that will decide whether you can really accept the data as big data. So 'Volume' is a feature that you should always consider when dealing with 'Big Data'.
· Variety: Diversity refers to heterogeneous sources and the nature of data, which can be structured or unstructured. Data is collected from many devices in different languages. Therefore, the formats of the data are different.
· Velocity: It is expressed as the speed of data generation. In order for a data to be considered Big Data, business processes, application logs, network and social media sites, sensors, and the processing speed of data from mobile devices and constantly growing must also increase.
· Veracity: Data security is one of the most important components when it comes to big data management.
· Value: The processed data is valuable as long as it serves a purpose. Therefore, once the data has been processed, it must provide an information or provide analysis. The correct construction of data analyses is also very important.
In addition, the property of variability can be considered:
Variability: the changing nature of the data that companies want to capture, manage and analyze — for example, in emotion or text analysis, with changes in the meaning of keywords or phrases
Big data is an umbrella term that refers to the enormous increase in the amount of data obtained through information and communication technology systems, smart devices (IoT or internet of things), and online activities such as websites, search engines, web stores and social media. Everything that is produced in the form of digital data is included in this umbrella term. If the data can be stored in table-like structures (such as SQL databases), it is structured data. All other data has its own characteristics and is often not easy to record in a standard table-like database. This is unstructured data. The characteristics of big data are:
· The amount of data is very large.
· Contains a lot of useful information.
· It consists of both structured and unstructured data.
· It should be relatively quickly accessible and available for analysis/conclusions.
Knowing trends, developments and certain key figures in a timely manner guides you to make decisions in practically all commercial, economic, social or political spheres. Allows you to predict changing behaviors or new situations in a timely manner. It gets in the way of concurrent research that should have been done in the past. Thanks to the large amount of data that has been released and can be stored in automated systems, these investigations are no longer necessary. Big data also contains the desired management information. Additional business analytics or business intelligence tools enable this information to be derived from existing data.
There is often a lot of information hidden in big data that can help your company or organization perform much better. You can access a lot of information that you can't discover with normal data analysis, and you can get new ideas by looking at the data from a different angle. In short, when you run a big data study, you encounter new information that can give you a competitive advantage or significantly improve the quality of your services.
Before using big data, businesses need to consider how it flows across multiple locations, resources, systems, owners, and users. There are five basic steps to taking charge of this “big data structure”, which includes traditional, structured data, as well as unstructured and semi-structured data:
Big data processing has many benefits for all companies, organizations, and even governments, no matter how big or small. These benefits can be listed as follows:
· Contains ideal data for improving products or developing new products.
· Access to social data from search engine data such as Google, Bing or Yandex from platforms such as Twitter, Instagram and Facebook allows organizations to improve their business strategies.
· The classic systems for customer feedback are replaced by new systems that take advantage of big data technologies. New systems automate processes and provide clearer analyses. So you can read and evaluate consumer reactions.
· It is possible to better predict financial risks. You can identify the risks of the product or service early.
· With new applications, you can use big data technologies for temporary storage of new data before we know what data we want to include in the data warehouse.
· Integration of business applications with big data technologies and data warehouse solutions can help load the 'real-time data' you store here.
· Allows fraud to be detected.
Big data applications in the public sector are numerous. This is due to the fact that the public space itself is quite large. Roughly everything that is located between your home, office, and other places is included in the public domain and data. However, no matter how large the space is, it is now very easy to create photos and video images automatically using a camera with a drone. For example, photos can be obtained that show whether trees are sick, whether gardens have been cleaned, and whether weeds are too high. Also, the photos show whether parking spaces are occupied by vehicles without a valid permit, or the state of repair of objects in the open area.
There are also countless examples of big data healthcare. In health care, it is becoming increasingly common to use big data analytics, for example, where specialists can discover diseases at an early stage. Shipbuilding is a labor-intensive and costly process. But good planning has a huge positive impact on costs. Digitizing all the steps in the production process, the shipyard can determine which processes can proceed and which processes cannot proceed in the event of a delay. Thanks to the application of big data, more efficient and disciplined work of staff allows you to better manage business processes and saves costs.
Sensors installed in devices in any company or factory are able to predict maintenance times and avoid breakdowns, cost loss. Big data provides many benefits depending on how you use it, and there are examples that vary within each industry. If you want to save money for your own company or organization, increase customer satisfaction, develop products, provide services with a good market research, you can take advantage of the services offered by Komtaş.
Structured data are datasets with strong and consistent organization. Structured data is managed with structured query language (SQL), where users can easily search and edit data.
Automated machine learning, called AutoML (Automated Machine Learning) in the field of artificial intelligence and machine learning, describes integrated software platforms for the creation, training and optimization of a machine learning model.
Pattern recognition occurs when an algorithm places iterations or regularities within large datasets or into different datasets.
We work with leading companies in the field of Turkey by developing more than 200 successful projects with more than 120 leading companies in the sector.
Take your place among our successful business partners.
Fill out the form so that our solution consultants can reach you as quickly as possible.
By combining our 30+ years of experience and experience with the centers of excellence in our ecosystem, we prepare your organization to lead the digital transformation!
