=Paper=
{{Paper
|id=Vol-2830/paper23
|storemode=property
|title=Methods of Analysis, Visualization, Forecast of Financial, Economic and Marketing Data by Means of Integration of Google Technologies and GitHub
|pdfUrl=https://ceur-ws.org/Vol-2830/paper23.pdf
|volume=Vol-2830
|authors=Vasily Yavorskiy
}}
==Methods of Analysis, Visualization, Forecast of Financial, Economic and Marketing Data by Means of Integration of Google Technologies and GitHub==
https://ceur-ws.org/Vol-2830/paper23.pdf
Methods of Analysis, Visualization, Forecast of Financial,
Economic and Marketing Data by Means of Integration
of Google Technologies and GitHub
Vasily M. Yavorskiy [0000-0002-3040-0668]
1
Russian Presidential Academy of National Economy and Public Administration,
Lipetsk branch, 3 Internatsionalnaya str., Lipetsk, 398050, Russia
lip@ranepa.ru
Abstract. In this article we discuss problems of usage of the integration of
Google technologies, Android and GitHub in analysis, visualization and fore-
casting of financial and marketing data.
Google Sheets tool, which allows you to work in online mode using any brows-
er, is efficient for collaboration and analytics as well. We can highlight some of
the advantages, for example, there is an ability to connect to dynamic data on
international web resources, data parsing, automation of processes in Google
Sheets and other Google applications, creation and modeling of systems which
are processing numerical data. It is also possible to connect to Google Data Stu-
dio to create dashboards for visualization of the calculated data, generate eco-
nomic reports, connect to neural network models via Google Colaboratory and
GitHub.
Regarding marketing features, one can check the popularity of a website (i.e.,
with data analytics) using Google Analytics tool, a free web and mobile track-
ing service for measuring digital marketing KPIs. After creating Google Analyt-
ics account and posting a short script code on a website, one can start collecting
the website or application data. Google web browser for Android [1] allows you
to work on data processing using a desktop computer and a mobile device at the
same time.
We have identified the features of information flows, we explained and pro-
posed the main directions of modeling systems for the analysis, visualization
and fore-casting of economic data in the context of the integration of Google
Android and GitHub technologies. We determined methods for modeling sys-
tems for the analysis, visualization and forecasting of economic data using
Google technologies.
Keywords: Google, Analytics, Android, GitHub, Colaboratory, Drive, Internet
browser.
1 Introduction
Internet collaboration, electronic manufacturing and electronic services introduced
over the past decades have provided the foundations for the design, development and
Proceedings of the 10th International Scientific and Practical Conference named after A. I. Kitov
"Information Technologies and Mathematical Methods in Economics and Management
(IT&MM-2020)", October 15-16, 2020, Moscow, Russia
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
management of next generation manufacturing systems, providing computer support,
communication and cyber-enhanced collaboration, production activities [2]. There-
fore, different principles of working in the network are required while conducting
analysis and data processing. This circumstance determined the topic and direction of
the research.
New computing power of desktop computers and mobile devices, various resources,
modern engineering technologies make it possible to process not only individual in-
formation, but also use recommendation systems, transforming expert knowledge into
data processing tools.
The main tools for visualizing the results of the research are Web 2 Technologies,
since they are widely used in knowledge management systems [3, 4, 5], these tech-
nologies allow you to see the research results interactively online.
For the convenient viewing and study of the proposed research, we use the visualiza-
tion of Internet resources used in my work using QR code technologies. Also, indi-
vidual links to research resources are indicated in a short version. The research algo-
rithm and results are shown on the site http://bit.do/research-results [6], QR code in
Fig. 1
Fig. 1.
The research objective is to determine the directions and methods of systems model-
ing for the analysis, visualization and forecasting of economic and marketing data in
the context of integration of Google, Android and GitHub technologies.
2 Literature Review
2.1 Convergence of devices in data processing
Convergence is an important trend in information technology. There are several types
of convergence. For example, convergence of services, where various services are
combined into one converged service. As per network convergence, individual net-
works are included into the converged network, this has developed to the point that
the distinction between wired and wireless networks is almost non-existent.
As marketing and business are getting closer to each other, many value chains or
business strategies change to newer and more convergent chains or strategies. With
interface and terminal convergence, also called device convergence, many kinds of
existing devices and terminals used for different media are incorporated into a new
converged device that allows consumers to use converged services and connect to the
converged network.
Convergence itself is an important trend not only in IT, but in twenty-first century
society in general. Over time, given the rapid advances in information technology and
changing consumer needs, there is a trend towards overlapping and unification of
applications and blurring difference between previously distinct industries will be-
come increasingly prevalent (Blackman, 1998) [7].
Kim Y., Lee J.D., Koh D. noted the possible direction of convergence of devices
based on consumer preferences in relation to the main attributes of the mobile termi-
nal [8]
2.2 Mobile platforms usage in data analytics
Methods and software for mobile data visualization platforms are being improved.
Existing applications can help to create, edit and analyze data using spreadsheets (in
particular, Google Sheets), it allows pre-submissions, documents editing, messaging
and other user actions. Users can store data files associated with the usage of these
applications to improve performance across a variety of distributed or cloud environ-
ments. These storage and processing systems are designed to make data files available
wherever a suitable network connection is available. Thus, a user gets a flexible and
portable set of applications which are designed to improve productivity of data analy-
sis too.
A very promising direction of the technological development is the open source digi-
tal platform for developing applications for Android [9]. On this platform, a lot of
attention is paid to the development of Google applications for Android, which makes
it possible to use the computing power of mobile devices in data analytics.
The GitHub and Google Play platforms are crowdsourced and provide separate pub-
licly available data that can be used in various development and research projects
through the source code. Social factors and peculiarities of crowdsourcing based on
open source technologies are very important and it explains the popularity of Google
Play in comparison with other applications.
K. Crowston, B. Scozzi [10] relate this popularity to open source projects, which at-
tract the attention of the end user. Most of these studies [11, 12, 13] examined how
the technical aspects of applications are related to the popularity of projects.
With increasing competition in sales of mobile apps, application developers have to
use advanced machine learning algorithms to control and determine various aspects of
users’ behavior and their decision making, including downloading and installing ap-
plications. This can be done using PRADO technology [14].
The PRADO programming pattern allows users to manage events, it helps developers
to focus better on business logic, rather than being distracted by tedious and repetitive
processing of low-level code.
PRADO has many features that can significantly help to reduce development time.
Specifically, it provides a rich set of pluggable web controls, full database support
including both active writing and complex object mapping, seamless AJAX support, a
theme and a wrapper, internationalization and localization, various caching solutions,
security measures, and many other features that are rarely found in other program-
ming environments [15].
In the Google Play ecosystem, which is focused on applications storing, application
developers need to optimize their release. Shen S. suggests to optimize Google Play
app release strategies to maximize the likelihood of success for those apps. The stud-
ies were conducted on the basis of checking the difference in users’ feedback regard-
ing software updates for mobile devices in accordance with different update intervals,
building an update rating, identifying factors affecting the consequences of the up-
dates. [16].
2.3 Google Cloud digital platform and useful repositories
Google Cloud digital platform definitely deserves specific attention. The platform
includes a variety of solutions that allow users to form, process and visualize numeri-
cal data, using comprehensively the advanced features of Google technologies on one
digital platform.
Google Cloud platform has (as of September 23, 2020) 787 repositories and 513 peo-
ple are working on them. Google Cloud has its own repositories on GitHub [17]. This
is where the content uploaded to the Google Cloud community is stored, files that
were added to tutorials will appear on page [18].
Let's take a look at the individual Google Cloud repositories stored on Github [19]:
Magic Modules is a tool used for automatic generation of support in various open
source DevOps tools for Google Cloud Platform.
The Google Secret Manager is a provider for the Private Information CSI driver.
Container Definitions is a repository that contains Bazel targets for generic Google-
maintained container definitions and their dependencies. Each folder in this repository
is a separate Bazel project that creates deterministic and reproducible containers for
every commit.
COVID-19 Open Data, this repository contains daily time series data related to
COVID-19 for over 50 countries around the world. The data has state / provincial
spatial resolution for most regions, and district / municipal resolution for Argentina,
Brazil, Chile, Colombia, Czech Republic, Mexico, Netherlands, Peru, United King-
dom, and United States.
OSS PROW, this is where the OSS prow configuration is for Google-owned OSS
projects. This file can only be obtained under the Apache License, version 2.0.
Anthos environment sample package. Sample Anthos Environment Package is for
creating GKE clusters on any Anthos platform and managing the lifecycle of Anthos
components in those clusters. It relies on the Anthos admin service cluster to provide
functionalities.
Python Samples for Google Cloud Platform. This repository contains samples used in
the Python documentation at cloud.google.com.
Google Play works with Android, not only introducing Android apps, but also offer-
ing mobile apps for developers.
2.4 Overview of GitHub repositories which are useful for data analytics
Simple Stock Analysis in Python is a stock analysis tutorial, which is based on Python
programming language. This guide is particularly useful for a beginner in stock mar-
ket analysis [20].
Stock Analysis is a repository that contains Python scripts that are developed to ana-
lyze, visualize stock prices and other data. License belongs to Massachusetts Institute
of Technology, and this is a free software [21].
Surpriver – “Find High Moving Stocks before they Move” - This repository allows a
user to determine the movement of high-yield stocks before they start moving, using
anomaly detection and machine learning techniques [22].
Stocksight is a stock market analyzer and stock prediction tool, which uses
Elasticsearch, Twitter, news headlines, Python natural language processing and sen-
timent analysis [23].
Quant_stock is a model for analyzing and forecasting stock prices using machine
learning, taking into account the influence of various factors not related to the market
(weather, etc.) [24].
Trendet – “Trend detection on stock time series data” is a Python package for identi-
fying and analyzing trends on the market [25].
StockRecommendSystem is designed to collect and store warehouse-related data in
MongoDB or CSV mode [26]. In our research, we used CSV files.
StockInsider is a Python based tool to calculate trading indicators, to analyze and
visualize stock prices and indicator charts [27].
3 Materials and methods
3.1 Possibility of using Google Sheets for data analysis, and visualization of
the data using Google Data Studio
We have investigated the possibilities of using Internet and Google tables for data
analysis based on works of multiple research scientists [28, 29, 30, 31, 32], we con-
ducted the work on connecting international resources to dynamic data, parsing data,
automating information processes in Google Sheets and other Google Applications,
creating models of systems for processing numerical data and connecting to
https://datastudio.google.com/ to create dashboards for visualizing calculated data,
generating economic reports.
Below we describe the research algorithm.
We will obtain relevant links with dynamic, regularly updated data from International
Monetary Fund website [33]. In order to do so, we go through the Data tabs, and then
proceed to Principal Global Indicators http://bit.do/principalglobalindicators and next
to the tab with National Data Sources http://bit.do/National-Data-Sources.
After that we choose the country which statistics we are going to study. We have
selected the data of the National Summary Data Page (NSDP). The page of the na-
tional summary statistics of the Russian Federation is located here
http://bit.do/Russia-Economic-financial-data [34]. Further, we parse the links from
this website to connect to the dynamic data.
As a next step, we create a Google table with NSDP - http://bit.do/NSDP. For the
quick analysis of data on Internet web sources, we use the ImportXML function in
Google Sheets.
Next, we implement ImportXML (Url; XPath) function to start data analysis.
We use ImportXML for parsing this way:
In one cell we write = IMPORTXML (“Our URL”; “//a/@ href”), and in the next cell
to the right put = IMPORTXML (“Our URL”; “//a”)
The complete functions for Google Sheets in our case look like this:
In cell A1 you can find links -
= IMPORTXML
("https://www.minfin.ru/en/key/macroeconomics/national_summary/"; "//a/@href")
In cell B1 there is a list of keywords, to describe links -
= IMPORTXML
("https://www.minfin.ru/en/key/macroeconomics/national_summary/"; "//a")
Check the Key Economic Indicators link http://www.eeg.ru/pages/123.
We use the following function to get the dynamic data from this page:
= IMPORTHTML("http://www.eeg.ru/pages/123"; "table"; 3).
For Google Sheets function we change numbering “table”; 3 and we get the necessary
values from the third to the sixth, thus we obtain we dynamically updated data for the
necessary calculations.
We have formed tables of dynamically updated data, which we will use in further
analysis and visualization. For the convenience in understanding of labels of the data,
we will duplicate the data using Russian language in this sheet of google tables using
the technical translation and the function = GOOGLETRANSLATE (text; [origi-
nal_language]; [translation_language]). The obtained dynamic data will be deter-
mined by a specific date.
The resulting tables http://bit.do/Key-Economic-Indicators/ can be used in further
calculations, since later, when the data on the original resource changes, the data in
the table will change as well.
We can also design a data visualization dashboard using the service
https://datastudio.google.com. First, we create a new report called Key Economic
Indicators. Next, we add a data source through the resource tab, and select Google
Sheets, then synchronize and link to Key Economic Indicators table and insert it into
the report. Then for completing the setup, select the Key Economic Indicators data
source. We will get the dashboard in the report http://bit.do/Dashboard-Tablecy.
3.2 Analysis and prediction of data for the future using machine learning and
neural networks
The use of artificial intelligence in the study of economic data is an interesting topic.
Machine learning and neural networks allow not only obtain and analyze data, but
also make predictions for the future.
Usage of Pandas and Matplotlib packages in stock price analysis.
Based data from the studied repositories [20 - 27], we carried out the analysis of eco-
nomic data using Interactive visualizations in Python in the Google Colaboratory
environment http://bit.do/Google-Colaboratory. An example is shown on the link
coded in Fig. 2.
Fig. 2.
Pandas is a Python library for working with a variety of data structures, the library is
good for working with structured datasets common for statistics, finance, social sci-
ences, and many other fields. The library provides integrated, intuitive procedures for
performing data manipulation and analysis of such datasets.
Matplotlib is integrated with Python in Google Colab to create graphs with, for exam-
ple, metadata. In order to start working with the library, one needs an installed devel-
opment environment, for example, Anaconda. In general, Python has many packages
that create interactive visualizations in Python, while embedding Javascript code di-
rectly into a notebook / page in a browser.
We initialized the model, trained it with all default parameters, and also predicted the
data for a year in advance.
We obtained forecast charts with the extracted trend component and the confidence
interval of changes in values, as well as charts of changes for different seasons. The
implemented code is copied and saved in GitHub repository http://bit.do/Analysis-in-
Python, see QR code on Fig. 3.
Fig. 3.
3.3 Possibility of integrating digital platforms for data analysis based on
crowdsourcing
The Google Analytics plugin for Unity (beta) was hosted on GitHub in 2014.
With the help of Google technology, you can integrate customer relationship man-
agement (CRM), tracking of sales and lead data, develop lead generation strategies,
and visualize the data. Based on such analytics, one can more efficiently develop
tactics to optimize channels of sales and use the data to make decisions and to attract
leads. CRM integration strategies can play the important role in your marketing deci-
sions.
Web presence nowadays is essential for all organizations and businesses. The Internet
provides a variety of multimedia functions that allow you to determine the effective-
ness of the interaction of organizations with their customers, suppliers, competitors
and employees using Google Analytics [9].
Here we consider an example of using Google Analytics technology and Google Data
studio for data visualization. For example, you can check the popularity of nology.
We have created a dashboard http://bit.do/youtube-and-site, check the Fig. 4 which
will lead you to the tryour website with the data analytics using Google Analytics
techaining on Youtube channel and to the analytics of the research website.
Fig. 4.
4 Results
In our research we determine the main directions of modeling systems used for the
analysis, visualization and forecasting of economic data in the context of the integra-
tion of Google Android and GitHub technologies:
• use of Google Sheets technology for data analysis of various web pages;
•creating dashboards for data visualization using the service
https://datastudio.google.com, and by connecting Google Sheets to this service;
•use of Google Analytics and Google Data Studio technologies to visualize marketing
data. Website popularity check for data analysis is done using Google Analytics tech-
nology;
•Google technologies for Android allow you to work on data processing from a desk-
top computer and a mobile device at the same time. Fast transfer and efficient syn-
chronization of economic calculations on stationary and mobile devices simultaneous-
ly and online improves the conditions for online economic research;
•work with economic data through interactive visualizations using Python in Google
Colaboratory, the use of neural networks in the analysis and forecasting of economic
data;
•using the capabilities of Google integration of Android and GitHub technologies in
analysis, visualization, and forecasting of financial, economic and marketing data
using device convergence and data synchronization.
In this work we have identified methods for modeling systems for the analysis, visual-
ization and forecasting of economic data using Google technologies.
We automated the processes of obtaining information with the help of syntactic anal-
ysis in Google Sheets. This is how we parsed actual links through the ImportXML
(Url; XPath) function implementation - http://bit.do/NSDP.
We have demonstrated a selection of Google spreadsheet functions used for work
with Internet resources in order to obtain the necessary data, as well as functions that
allow you to interact with sources of dynamic economic data. We have conducted
work related to connection of Google Sheets http://bit.do/tabl-
Key_Economic_Indicators to the dynamic data of international analytical resources.
We obtained dynamic data of key economic indicators, which were used by students
to study the balance of payments of the Russian Federation for the period from Sep-
tember 2019 to August 2020. When this data is updated on the website
http://www.eeg.ru/pages/123, the data in our table will change, the information panel
of the visualized data will change accordingly, and the system we constructed will
allow the use of Google Sheets data in new studies.
The technology of dynamic data visualization is illustrated through methods of creat-
ing models of systems for processing numerical data based on connecting Google
Sheets to Data Studio https://datastudio.google.com/ to create dashboards for visualiz-
ing calculated data http://bit.do/Dashboard-Tablecy. These methods are recommended
by us for creating economic and financial reports in economics and finance.
During the research, we demonstrated the capabilities of Google Analytics and
Google Data Studio technologies for visualizing marketing data. We tested the popu-
larity of the website for data analysis using Google Analytics technology by creating a
dashboard http://bit.do/youtube-and-site, which shows analytics using Google Analyt-
ics technology on the Youtube channel and analytics of a research website. These
methods are applicable to marketing research of various Internet resources. Using
these methods, it is possible to implement real time marketing research: analysis of
the audience, effectiveness of methods used for attracting customers, behavior of
customers on selling pages, conversion from views to purchases.
We used the general capabilities of Google's Android technologies for processing,
viewing and transmission of the obtained data. These methods were used by students
for the analysis of data, both in the computer classroom and outside of it, see Fig. 5.
Fig. 5 indicates the following elements:
1 - Google technologies, resources and services;
2 - Android technologies, resources and services;
3 - GitHub technologies, resources and services;
4 - users, developers;
5 - desktop computers;
6 - mobile devices.
Fig. 5.
We have carried out work with economic data through interactive visualizations using
Python in Google Colaboratory http://bit.do/Google-Colaboratory, we applied neural
networks to analyze and forecast economic data, this method is efficient since stock
price forecasts are carried out automatically using neural networks dynamically.
We would like to highlight some stages of our research in this direction. At the begin-
ning of this research, we have installed the required development environment Ana-
conda, as well as the necessary modules. Then we figured out how we can work with
financial data using the Pandas package and Matplotlib. Next, we imported the neces-
sary libraries. The Datetime library allowed us to work easily with dates, matplotlib to
draw plots, pandas to process data, and pandas_datareader, the newest input/output
library for pandas, to write the results. After that it was a right moment for initializa-
tion. We have defined the style for the graphs. Next, we recorded the start and end
dates of the interval for which we were going to load the data, converted the data into
a DataFrame object. A DataFrame can be represented as a table from a database
stored in computer memory, it has an index and column names. The neural network
uses pandas_datareader to download information on Tesla (TSLA) stocks from Yahoo
servers for any date stored in the start and end variables. Thus, our DataFrame con-
tains the following information: Date, High, Low, Open, Close, Volume, Adj, Close.
We downloaded Tesla (TSLA) stock information from Yahoo's servers. We have
carried out data forecasting using Facebook Prophet. This package allows you to fore-
cast, search for anomalies without diving into the algorithm itself. First, we installed
the dependency, then we initialized the data analysis model, trained it with all default
parameters and predicted the data for the year ahead. We have constructed the fore-
cast graphs with the extracted trend component and the confidence interval of changes
in values, as well as graphs of data changes for different seasons. Neural networks
made it possible to predict the TSLA stock prices based on current data, but this
method can be used to study changes when the updated data will ne available in fu-
ture.
We have shown the possibilities of integration between Google Android and GitHub
technologies for the analysis, visualization and forecasting of financial, economic and
marketing data using device convergence and data synchronization. The data used in
the research can be downloaded from the Google Colaboratory platform -
http://bit.do/Google-Colaboratory to GitHub platform http://bit.do/Analysis-in-
Python, and this process is tracked through the GitHub Android app, see Fig. 5..
5 Discussion
The main result of our research is the definition of information flows of data, within
the framework of which analysis, visualization, forecasting of financial, economic and
marketing data are carried out by means of integration of Google technologies, An-
droid and GitHub. Also the definition of methods for the formation of information
flows of economic, financial and marketing research based on the convergence of
devices and data synchronization of the integration of Google Android and GitHub
technologies. These technologies can form the basis for the functioning of various
systems for processing numerical data.
Let us consider some of the features of the formation of information flows according
to the diagram on Figure 5. This diagram shows the capabilities of analysis, visualiza-
tion, forecasting of financial, economic and marketing data by means of Google tech-
nologies integration, Android and GitHub.
A special attention deserves the integration inside Google of technologies - 1, An-
droid - 2 and GitHub - 3 (see Figure 5). Some of the features of this integration are
described above. Here we define the features of the work of a user, a developer - 4
with mobile devices - 6 and desktop computers at the same time - 5. Users and devel-
opers - 4 are using the capabilities of Google technologies – 1; Android - 2 and
GitHub - 3 can synchronize information flows of devices - 5, 6 (see figure 5). Syn-
chronization of data through the convergence of devices while working with the anal-
ysis, visualization, forecasting of financial, economic and marketing data by means of
Google integration with Android and GitHub technologies (see Figure 5) allows users
and developers - 4 to use both desktop computers - 5 and various mobile devices - 6.
Analyzing and processing information received from Internet resources on a desktop
computer - 5 (see Figure 5), saving results on Google Drive, users and developers can
synchronize the data of various resources - 1, 2, 3, visualize data using Google tech-
nologies - 4 (Google Analytics, Google Data Studio), share versions of your devel-
opments automatically on GitHub - 3. Having Google Drive on a desktop computer,
users and developers - 4 can work with neural networks in Google Colaboratory (see
the link http: //bit.do/Google-Colaboratory), save automatically obtained research
results and data on the GitHub repository (see. link http://bit.do/Analysis-in-Python).
With the help of Google Drive application, one can store, synchronize data and do the
processing [35].
Individual functions of mobile applications complement the capabilities of stationary
devices. For example, using a mobile application, you can create, synchronize with
various resources on other devices, edit Google Documents, Google Sheets on Google
Drive, also scan various documents and images using the scanner function on Google
Drive and convert the result into pdf format.
Data synchronization allows user and developers to receive information promptly, it
provides the opportunity to control versions of the development, to participate in col-
laborative work with other developers online.
In the section of the article "Possibility of integrating digital platforms for data analy-
sis based on crowdsourcing" we provide an example of interaction between Google
Play and Android through the creation of mobile GitHub applications for Android.
For example, the GitHub mobile app for Android - 3 installed on a device - 6 (see
Figure 5) will help you do a variety of actions without requiring a complex develop-
ment environment, i.e, sharing design reviews or looking at a few lines of a code.
GitHub for Android gives you an opportunity to collaborate with your team from a
mobile device [36].
The development of new computational methods in parallel with other sciences such
as statistics, operational research, and computing has revolutionized the world of fi-
nancial analysis. An example of this are the expert systems, they represent knowledge
in a symbolic way, explicitly programmed in the system. Neural networks is another
method of computation that has recently been applied to many problems in the real
world [37].
Tölö E. in the Journal of Financial Stability considered the possibility of predicting
systemic financial crises on one to five years’ window using repetitive neural net-
works. Forecasting efficiency is assessed using the Jorda-Schularick-Taylor dataset,
which includes crisis dates and corresponding macroeconomic time series for 17
countries over the period 1870-2016. Study of the previous research has shown that
simple neural network architectures are useful in predicting systemic financial crises.
Researchers showed that such predictions can be significantly improved through the
use of repetitive neural network architectures, especially efficient for work with time
series. They note that the results remain reliable after sensitivity analysis. [38].
6 Conclusion
During our research, we have identified the main directions and methods for modeling
information flows for analysis, visualization and forecasting of economic data using
Google technologies. The capabilities of Google technologies are growing, the inte-
gration of Google technologies with other technologies expands the range of data that
can be explored, and it also contributes to the improvement of scientific research
methods.
Obtaining and analysis of big data from the Google friendly resources allows you to
process this data without buying powerful private local servers of large capacities,
which is relevant for projects that do not have a commercial component.
Device convergence has become a new driving force for IT industries suffering from
market saturation, as it creates new needs, radically changes market structures, re-
quires new standards and regulations, inspires companies to conduct research and
development or improve business strategies, that impacts the whole society in general.
The ability to use the computing power of mobile devices allows you to process data
permanently online.
The technologies, which we have considered for the construction of information
flows, can be used by small businesses and developers of crowdsourcing platforms to
solve various problems of interaction both within the company and outside of it.
For small businesses this is an opportunity to improve work with electronic document
management, increase the efficiency of CRM, ERP systems. For developers this is the
ability to implement different projects online using the synchronization of data ob-
tained from different sources, and using different technologies on different devices
(see Figure 5). A dynamic business environment requires partners to share knowledge
and capabilities while conducting their activities. Beyond internal operations, infor-
mation and communications technology (ICT) accelerated data flow empowers organ-
izations to distribute data to their partners, contractors, and customers. IT integration
has made a significant contribution to the development of new directions in all areas
of organizational development, including the work with systems for processing nu-
merical data using the convergence of stationary and mobile computing devices.
The competitive status of any organization is based on the use of its capabilities. The
introduction of the proposed technologies can bring competitive advantages to organ-
izations, for example, by reducing the cost of purchasing additional resources, compu-
ting power, software and equipment for solving problems of analysis, visualization,
forecasting of financial and marketing data.
The proposed methods of analysis, visualization, and forecasting of financial, eco-
nomic and marketing data contribute to broader interaction of the users and develop-
ers with other developers and with potential customers.
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