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The Evolution of Mobile Data Collection Solutions on Business Growth [Infographic]

Discover how your organization can use mobile technology to boost the effectiveness of your data collection methods in 2020.

As we enter a new year and a new decade, now is the ideal time to upgrade your organization’s data collection methods. Due to the evolution of mobile data collection technologies, there has never been a better range of solutions to choose from.

Evolution of Mobile Data Collection -Infographic
This post detailed how to use mobile tech data collection solutions to solve and boost the effectiveness of your organization’s data collection methods.

Switching from the Old to the New

Traditional data collection methods are still widely used. This method involves gathering information in person with a clipboard, pen, and paper and then manually entering data into a spreadsheet. The main drawbacks of the traditional method are that it tends to be quite time-consuming, resource-heavy and labor-intensive. As such, it’s not surprising that so many organizations are now making the switch to newer technologies such as mobile data gathering.

Mobile Data Collection Explained

It is a method of information gathering that involves the use of mobile devices such as smartphones or tablets. It can be used to gather, compile and analyze both qualitative and quantitative data. As all the information is stored in a single centralized location, there is no need for post-information-gathering data entry. Another major advantage of mobile data collection tools is that they also often include extra functionalities that allow researchers to fulfill additional tasks such as asking research questions, testing hypotheses, evaluating results and much more.

Business Applications of Mobile Data Collection

Mobile data collection can be put to use across a diverse range of business activities. It can be a useful tool for many departments such as accounting or marketing. Some examples of business use cases include executing execute audits, scheduling jobs, market research, and lead capture.

Streamline Processes with Mobile Data Collection

By using mobile data collection solutions, organizations can streamline business processes. This boosts efficiency and can save a lot of time and resources. The automation of certain tasks not only ensures that the work is completed more quickly, but it also frees up employees to focus on more important jobs.

Improve the Accuracy of your Data

Mobile data collection software can help to significantly to improve the quality of data. Firstly, it does this by reducing the likelihood of error at the point of collection. What’s more, the real-time collection of data prevents memory bias. Another major advantage is that it can swiftly collect diversified information and produce data that is ready to share and analyze.

Find Out More

If you are keen to learn more about making the switch to mobile data collection solutions, then you should check out the infographic below. This insightful infographic features an easy-to-understand overview of mobile technology data collection and lays out some of the reasons why your organization should adopt this mobile tech in 2020. It also includes some sample use cases and offers advice on how to select the best data collection solution for your needs.

The Savvy Researcher’s Guide to Mobile Data Collection [Infographic]

The Evolution of Mobile Data Collection Solutions on Business Growth [Infographic]
The post elaborated on the beginning of this type of information gathering and roadmap to achieve the maximum result.
Scroll down to the below infographic to find out more today from ERS IT Solutions.

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Mobile App Web Development & Design Ideas

Data Science – 6 Pointers on How Web Developers Can Leverage Data Analytics to Solve Problems

In its simplest definition, data science is a method used to solve complex problems. This often involves a multidisciplinary blend of information extraction, data inference, program development, and application of technology.

In its technical description, data science is the practice of learning, mining, storing, moving and using data that comes in different forms. This includes troves of hard information, stored data, unstructured and semi-structured data such as text files, financial logs, multimedia forms, instruments ad sensors. Since this includes a diversity of data in huge volumes, simple BI tools are incapable of processing it. With data science, developers are able to use more complex procedures with analytical tools and advanced algorithms, to analyze this data and generate meaningful insights out of it. When used in creative ways, data science can build turn-key solutions and generate great business value for the user.

In this article, we will share 6 ways developers can leverage from data science in their programming and product building operations.

6 Ways How Developers Can Leverage from Data Science
What are different ways website and mobile app developers can leverage data science to create solutions for real-world problems?

Understanding User Behaviors

Developers use data science to discover important information on user behaviors. This includes, how users are interacting with a particular website? What are their most commonly visited pages? Which products do they buy the most? What improvements do they want on the website?

This allows developers to effectively build patterns in consumer behaviors and integrate self-learning programs in websites so customers get exactly what they want when they interact with it. This up-gradation process can be automated on regular intervals so the demands of customers are met without the developer needing anything to do.

Data Visualization

Another way developers use data science to their advantage is by producing visual data from raw data. With data science, developers are able to visualize data using different methods and data visualization tools such as d3.js, ggplot, Tableau, and Matplottlib. This allows them to easily convert complex results from different projects in a structured format that is easy to understand and present. As a result, organizations are able to manage data head-on and grasp all the insights that are necessary for acting on a particular business opportunity. Its gives a broader range of knowledge and builds the organization a competitive edge in the market.

Machine Learning

Learning data science helps developers attain a deeper understanding of machine learning and develop better algorithms for programs. Data science gives exposure to a range of learning such as neural networks, adversarial and reinforcement learning, decision trees, supervised machine learning and logistic regression to solve problems that are the core of algorithmic architectures. Machine learning uses large amounts of data sets which data science can help translate and transform. Developers that are skilled in data science make great resources for AI development in organizations.

Machine learning algorithm
Machine learning algorithm in action showing how it is possible for an artificial intelligence system to gather data for storing, sorting, measuring, and proper usage.

SQL Database/Coding

Since NoSQL and Hadoop are now a central component of data science, developers are able to code and solve complex SQL queries more effectively. With Structured Query Language (SQL) programmers can carry out complex operations for adding, extracting or deleting data from different databases. It also helps in executing analytical functions and transforming database structures more effectively. Learning data science gives developers the edge they need SQL, allowing them to access, write, present data in any database. SQL uses concise commands which saves the developers time by shortening the amount of coding for different queries.

Python Development

Data science shares a lot of benefits for Python coders and is very commonly practiced in their programming tasks. Due to its versatility, Python as a language has a close connection with data science and is used in several stages of data inference. Python developers use data science to understand and move different formats of data and import SQL tables straight into their coding. With data science, a Python developer can create datasets and search for almost any form of dataset required on Google.Python Development

Web App and Extension Development

Web app developers use data science for remembering user’s preferences and study them to understand their lifestyle. Once all this data is collected and translated, these apps become ideal private assistants, smart repositories and even trustworthy partners. Using datasets, web app developers can program these apps to give uses reminders on their daily activities, provide suggestions, and identify gaps to prevent harmful habits. With data science, the dawn of AI-based web apps has already begun as developers are using data-driven strategies and developing self-learning extension for websites. Many Magento 2 extensions are developed using data science for predictive analysis for the merchant like, frequently bought together products, frequently viewed together products, etc.

Conclusion

Data Science is not just about managing data, it’s about store it, moving it one place to another, and ultimately using it and produce clever things. With its innumerable benefits in development, we have shared 6 methods ways how developers can leverage from it and use it to derive scientific methods, improve their coding, design algorithms, refine data, and improve user experience through their web platforms. 

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Business

How Fintech Companies Can Leverage Big Data Analytics

The entire financial service industry has been taking advantage of big data for some years now. Technological advancement is really changing how people handle transactions. Fintech basically comprises of companies that maximize the latest technology to boost their financial systems. Top financial companies and brick-and-mortar banks are enjoying the wide range of solutions that it offers. Startups all over the world now provide financial technology that include wealth management and alternative lending. Unlike before, small lenders no longer find it difficult to secure loans.

Without mincing words, fintech companies have encountered different challenges in the past, especially funding to facilitate expansion. Things are changing as the sector is currently experiencing an exponential growth rate. It was recently reported that the global investment in the industry reached a staggering $23.2 billion in 2016 alone. This was made possible by the adoption of innovations in departments like financial literacy, retail banking and investment. The competition is getting stiffer due to the fact that different companies are now delving into it.

One of the primary benefits that big data offers to fintech enterprises is predictive analysis. It is a tool for setting very precise borrowing terms and can help to minimize the chances of dealing with risk borrowers. Another benefit is that it helps brands to carry out internal audits in order to meet the compliance standards of financial regulators. Fintech companies can integrate big data analytics in the following ways.

How Big Data Analytics Can Help Your Business

How Big Data Analytics Works
How big data analytics can help your fintech business move to the next phase of development.

Customer acquisition

Big data analytics is a tool for determining the type of extra services that a particular client would be interested in. Public and internal data can come in handy for creating robust customer profiles. Sectioning the target audience based on relevant parameters is a sure way to attract more customers and build customized offers. The cost of acquisition will be greatly reduced once digital channels are employed.

Customer service

Fintech enterprises should be poised to deliver quality services to their clients under any circumstances. Today’s customers are ready to share their data as long as it’s protected from any third party and used to improve the range of services provided. It’s no surprise that big data analytics and AI algorithm are being trained to generate lots of ideas. The outcome is useful for enhancing the overall customer experience. There is no need for agents anymore as AI driven robotic advisors that offer financial advice online are on the rise. Even though public data is easily accessible, it doesn’t make any difference until it is mined and analyzed by experts. Later on, customers’ behavior can be revealed. This goes a long way in releasing products or services based on consumer needs, complaints and preferences.

Data Analysis for fintech company

Customer retention

Wearables, IOT, mobile technology and cloud computing are effective means to garner data and retain customers. Social media data is also a reliable source of valuable insights. Personalized products, services, discounts and other incentives are widely used to increase engagement, leading to an improved ROI. Fintech stakeholders are busy maximizing strategies for retaining existing customers through marketing campaigns that include loyalty programs. As a result, it’s easier to achieve both long and short term goals.

Credit scoring

Credit scoring involves examining the business operations of a financial organization in order to assign an appropriate credit score. Before the adoption of big data analytics and AI, the process relied on basic financial transactions. The scores were then used for all credit activities. In this era, other factors such as customers’ ability and behavior are duly considered.

How to implement big data analytics and statistic for your fintech company
If you read one article about big data analytics right before now, read this one too. It offers some of the best ways to utilize big data analytics for your fintech companies.

Risk management

Customers’ data must be kept secure by small financial institution and the established fintech companies at large. These organizations are expected to take proactive measures for protecting themselves and clients from fraud. Predictive analysis is gradually becoming a viable tool for minimizing fraud risk. The process involves supplying the algorithm with unprocessed data and training it to detect irregular patterns. Predictive analysis that make use of biometrics and device identification is gaining popularity for risk management solution.

Some of the fintech trends that were experienced last year will still be relevant, but in a better way. It has been predicted that more financial institutions will start relying on advanced blockchain software for handling digital payment. Other trends to expect is the collaboration between traditional banks and fintech enterprises and the use of wearables for managing digital transactions. It’s possible for financial institutions to come up with more creative solutions when they integrate big data with AI. This will in turn simplify the whole process and minimize risks in the sector.