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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. 

Categories
Cloud Computing Technologies and Resources Designs & Invention

Can Big Data be the new BFF for Digital Marketers?

Can you imagine your life without gadgets? Imagine you are asked to stay without your cell phones and laptops for a week. Unimaginable right? Well, technology nowadays has become the heart and soul of our lives. We wake up with technology and go back to sleep with technology.

In the race of mobile apps, CRM solution, data security and networking commonly used software related to, AI, machine learning, big data IoT, customer management software, etc. are creating their own impression in IT markets. To stay ahead of the competition, most of the companies in India are opting for CRM software services. In the past few decades technology has evolved drastically. For us as consumers, it is extremely important to understand the technology evolution and its impact.

The rise of every technology has some motive behind it. To understand the technology, understanding the motive behind it is very much crucial. Let’s take an example of CRM. Customer Relationship Management software solutions have recently originated term, yet explored and tapped by almost all the internet and software companies in India. Internet nowadays has brought the world even closer. Also, it has changed the dynamics of consumer behavior and purchase decision making. Almost everyone nowadays goes online to read the product/ service reviews before making any purchase. CRM services help companies understand their exact target audience, traffic sources and approaches to convert their prospects.

An AI-powered (artificial intelligence) smart city was made possible with power of big data collection and machine learning.

What is Big Data?

These days, one of the buzzes is big data. It generates a massive amount of data that can be gathered from every search made on the web to every activity done on social media and pinned into your CRM system. NGOs, government bodies, private institutions, IT firms, CRM services companies, name it and all of them are already making use of it.

There are some mind-boggling stats related to big data. Over 50 billion smart connected devices will be available in just 5 years from now to gather, analyze and process the data. The only challenge with big data is producing a vast amount of information in an unstructured way. This can be addressed through customer management software. CRM is a customer relationship management software that helps with lead nurturing.

SMEs and enterprise level companies usually have a vast amount of information about their leads, which needs to be organized. According to Statista, CRM services let companies achieve data hygiene and management freedom. 

Revenue from big data and business analytics worldwide from 2015 to 2022 in Billion US dollars
Here’s a projected revenue from big data and business analytics worldwide from 2015 to 2022 in Billion US dollars just to tell you how great the future looks.

Big data operates on three major principles – volume, velocity, and variety. Big data produces a large amount of data that is gathered from multiples sources like business directories, social media channels, marketing campaigns, and multiple other possible sources. After data acquisition, it is streamed at pretty high bandwidth to be accessed in a timely manner. This is done in order to generate faster real-time results.

Talking about variety, big data comes in different structured and unstructured forms. It could be anything and everything from videos, social media posts, text files, emails, audio clips, podcasts, eBooks, etc.

But, is that all? Is that enough?

Well, most of the digital B2B marketers will complain that they face major issues when it comes to lead nurturing. Big data with CRM software in place can help you generate higher ROI.

Digital Era

We are living in a world of digitization. Smartphones, smartwatches, tablets, and laptops have converted strangers into a community. Social media groups, Google searches and ecommerce product reviews have completely transformed the way consumers are making purchases these days. Recent customer relationship management software trends say that 94% of consumers nowadays check for online reviews or products/ services before shopping, according to data from Marketing Charts below.

When it comes to making purchase online, what online media do you regularly use to find inspiration for your purchases?
When it comes to making purchase online, what online media do you regularly use to find inspiration for your purchases?

Therefore, digital marketing is the most prioritized activity for every brand. Understanding where your consumers are, what are their usual behaviors and what instigates their decision making helps brands connect with their potential clients in a personalized manner. This, of course, saves on time, yet increases productivity.

But, to gauge all this, it is important to have detailed information. A lot of CRM software companies in India helps you achieve that. Do you know what your user persona is like? Which social media channels they often use and at what times? Who are your competitors? Are your potential customers visiting your website, but are not motivated enough to make a purchase or drop an inquiry?

A customer relationship management software will help digital marketers address these pain points in order to optimize their brand identity, draw in more traffic and help the sales team achieve more closures.

Big Data Empowering Digital Marketers

Additionally, imagine something like ‘big data’. Some tool that can provide all this information about hundreds and thousands of your potential customers with just a single click. Yes, that’s exactly what I am talking about.

A vast amount of data combined with a customer management software describing the source of your website visitors, various mediums of social media channels, their behavioral analysis, their motivation factors, their interests, etc. all brought to you right in front of your computer screens. This will help digital marketers define their target audience, create user personas, draw better digital marketing strategies and deliver the right content at the right times.

Creating content, social media posts, remarketing strategies, eBooks, case studies, PPC campaigns, video marketing plans all will become easier and more sensible when you know who to sell, where to sell, when to sell and how to sell.

Can Big Data be the new BFF for Digital Marketers?

Big Data Benefits

  • Cost-efficient: Instead of searching your potential audience at different places manually, get a tool that can do the tedious job for you, while you relax and work on ways to reach your target audience through a CRM software in India.
  • Time-saving: Ever wondered how much time and efforts you will have to put into research about at least half a billion people with a proper CRM system in place? Possibly, your entire career span, and that is a lot of time. But, what if you have a big data and CRM software/ tool that can do this for you?
  • Online reputation management: Big data is capable of analyzing human emotions. Based on that data, you can easily address grievances if any, moderate pointless communications on social media and turn negative publicity into a loyal customer base through personalized customer relationship management software.
  • Explore newer markets: When you have a sufficient amount of data about your target audience and their behavior, you can easily understand the red ocean and blue ocean segments. This will help you explore more viable opportunities to enhance your business potential and create personalized campaigns within customer management software.

Conclusion

Big data helps digital marketers get a massive amount of data in a structured and unstructured manner through various sources like social media, website, emails, audio-video clips and text files to understand and process this data in real time for increased productivity. This data can be analyzed, segmented and acted upon through CRM- customer relationship management software services in India. To better justify your digital marketing efforts, explore big data tools along with CRM services to gather, analyze and strategize your business potential.

Create a scalable and seamless product/ service range for your customers and build long-lasting relations. Build a strong consumer base that can help you earn referrals and earn a credibility factor, thereby giving you an upper hand over your competitors.

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Cloud Computing Technologies and Resources Data Theft Disruption IoT (Internet of Things) Security Software

Understanding Big Data Database Testing vs. Testing of Traditional Database

Big data is now full-fledged as a modern-day technology which sets the base to many of the enterprise resource planning applications and platforms. In the current world of massive digitization, we are getting overloaded with various kinds of data, which needs to be captured, stored, and analyzed for various references. Mishandling of vital data may lead to many issues and disgruntled operations, hence the need for proper big data database testing.

Big Data acts as a solution to all such problems. It facilitates effective management of huge datasets which would other be difficult to handle with the traditional database management systems. Big data is all about storing, managing, retrieving, and analyzing data which is a bit in terms of volume. The approach is to use various kinds of big data tools to perform these operations at lowered cost and in a minimal timeframe.

Big Data analytics
How to properly manage big data information: Mishandling of vital big data content may lead to many issues and disgruntled operations, hence the need for proper big data database testing for effective processes.

After Big Data, there had been a radical change in the concept of database structuring and maintenance. Now, one may think of data warehousing in connection to big data. Similar to how we use its industrial terms, data warehousing is all about storing huge amount of data. Unlike relational database management models, Big Data’s idea is to manage all formats of data in the structured and unstructured form securely and consistently.

Unlike kilobytes and gigabytes, nowadays we are referring to data in terms of petabytes and terabytes which needed to be handled effectively. The global giants like Facebook, Amazon, LinkedIn, etc. are producing data in high standards daily and the major challenge in database management is to handle data from various sources in various formats and ensure consistency in the database.

Big data takes up the enterprise database scenario as an all-inclusive solution for the problem of maintaining solid consistency. The advancement of big data rightly supplemented the organizational needs to handle huge volumes of data, live streaming, or it and handling various data formats in terms of symbolic, numerical, video, audio, mail data, etc. irrespective of the exponential growth in data volume.

The latest situation is like there is a vast range of services which are out there at the disposal of the users over the web. One can access any website for gathering any information from anywhere in the world. So, the data provided by the web users has to be stored in a way to make it accessible anytime from anywhere in the desired mode of easy interpretation.

Why Big Data Database Testing?

Information technology enables big data database testing and sorting
How do you analyse, sort and properly use big data content without getting entangled in the moment?
Advancements in information technology enables big data database testing against the traditional testing process.

Big Data database testing aims at ensuring the quality of data and also to assess the efficacy of the data mining process. Even though big data offers solutions for many of the enterprise problems related to data management, it is a big challenge to deal with big data effectively. This is primarily because of the sheer volume of data to be handled, along with its velocity of creation and the varying data structures to accommodate.

One major challenge in handling big data challenge is the unstructured format of data in big data applications. The traditional mode of data testing in RDBMS may not work ideally in big data database testing, but it has to be a well-defined and matured process to ensure accuracy.

A study conducted by RemoteDBA.com had shown that the poor quality of database applications ends up in wasting almost 14% of the enterprise revenue. Another study among the big data platform developers had shown that nearly about 20% of them point out data quality as the biggest problem which may adversely affect consistency and performance.

Unlike the structured data format, the unstructured data loads will not have a definite model to define. Such data is now so common among the social media applications like Facebook and Twitter and also data inflow from chat and email applications, audio and video files, call records, etc. These are raw human-generated data, which cannot be brought under a well-defined, structured format. So, the need is for massive and fast-growing data volumes.

Big data database testing is, in fact, the process of data testing for the integrity of processing so that the enterprises can verify their data and make use of it for analysis. Big data puts for bigger computing challenges with the massive volume datasets in a wide range of formats. Organizations now largely trust on business intelligence based on big data, so data testing also becomes crucial.

Testing of unstructured data

Testing of both structured as well as unstructured data share the same objectives as to:

  • Validate the data quality, and
  • Validate the efficacy of data processes.

Even though some of the testers may use ETL principles to described unstructured data testing, testing tools for this are totally different. Unstructured data may not be a part of the conventional relational databases. So, automation of unstructured data becomes a vital requirement. The tools used for testing unstructured big data sets are also complex, and the process is too complicated.

Steps in big data database testing

Big Data Database Testing
Big data database testing helps you to put everything in right perspective. It enables the tester the room to dismantle, sort, and rearrange all the data with the help of big data testing tools.

Step #1: Validating the Staging

Validating the data staging primarily starts with bigger data cluster, i.e., Hadoop (cloud or on-premise). Testers pull the unstructured data into test from its source and use the tools to compare the staged data to the source data.

Step #2: Validation of testing rules

In a typical Hadoop environment (on-premise or cloud), this process validates MapReduce transformations for the unstructured data sets. This approach to big data database testing proves whether the set business rules which are used to aggregate or segregate the data are properly working. The big data test is run node-by-node to check the efficacy of the business logic of each tested node.

Step #3: Validation of output

This step is used to validate the tested data and the process involved. It can verify that the step #2 testing is successful in terms of applying the business rules and also that the tested workload can retrain the integrity of data and there is no data corruption due to business logic.

On completion of these big data database testing steps, the big data tester can then move the tested and verified data into the storage systems or delete it from testing cluster. The whole process requires significant automation to handle massive volumes of data. Moreover, big data database testing is an expert task even when we use all advanced automated toolsets.

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Cloud Computing Technologies and Resources News

Use of Cloud is Growing With Cloud Security Concerns

As companies moved to the cloud to simplify their IT management, including improving security, they’re learning that it’s not as simple as “shift applications to the cloud and watch the magic happen.”

It’s not that most organizations see it that way, but that is how it is when the companies feel it one way to better security. It’s why it’s been a rough couple of years for cloud security. And while recent vulnerabilities disclosed such as Spectre and Meltdown have cast a long shadow over the ability of enterprises to defend themselves enterprises need to be vigilant for potential exploitations.Network Data Connection - Use of Cloud is Growing With Cloud Security Concerns

One thing that security concerns are not expected to cast any shadow on cloud sales. A recent market research data released by QYResearch forecasted the global cloud computing industry to grow by roughly 26% from 2017 to 2022.

This current bevy of hardware flaws won’t slow that demand down any time soon. According to a report from Synergy Research, and reported in CRN, the overall cloud market grew 24% last year.

“Over the past four quarters leading up to September of 2017- essentially Q4 of 2016 to Q3 of 2017 – cloud sales, including on all services and all infrastructure products used for building clouds, achieved an overall market of $180 billion,” Joseph Tsidulko wrote in his story Cloud Computing: Now A $180 Billion Market.

According to the story, cloud infrastructure and platform services grew 47 percent, with hosted private cloud growing 30 percent. That’s quite a clip.

The unfortunate reality is, however, that many organizations don’t need a low-level hardware flaw in the infrastructure of their cloud providers to make themselves vulnerable. They can do that all on their own. Perhaps it was the rush to the cloud that is part of the reason why there have been so many cloud-related breaches, such as when a poorly configured AWS S3 bucket exposed thousands of military and intelligence personnel records to unauthorized public access. According to the story, thousands of military contractor files allegedly left online, unsecure, published in The Hill, those files included personal contact information.

“Chris Vickery who works at UPGUARD as researcher said he discovered some set of unsecured resumes on a public-facing AWS cloud server in July that was left unprotected by any form of login. Typically, this happens as a result of misconfigured security settings.

It’s certainly not the only example last year. Verizon exposed millions of customer records due to another unprotected S3 bucket. Dow Jones experienced a similar gaffe. In fact, throughout 2016 and 2017 we witnessed many poorly configured cloud-based databases being breached.

Fix it with a minimal effort

As if organizations weren’t having a challenging enough time keeping their environments secure, along came to the vulnerabilities Meltdown and Spectre just recently made public by Google. These hardware flaws affect almost every current processor and, through exploitations against “speculative” code execution, attackers can capture passwords and private encryption keys. Filip Truta provides an overview in his post, Meltdown and Spectre: decades-old CPU design flaws put businesses at risk. And fixing some of these precarious hardware glitches can have a significant impact on the cloud systems performance

“Not only did we see significant slowdowns for many applications, but we also noticed inconsistent performance, since the speed of one application could be impacted by the behavior of other applications running on the same core. Rolling out these mitigations would have negatively impacted many customers,” wrote the Google Cloud team in this blog post.

Most organizations do not have just one, or two, or three cloud service providers to worry about. According to RightScale’s 2017 State of the Cloud Report, 85%t of survey respondents are using multi-cloud, with most running production applications in four clouds.

“Companies now run 79% of workloads on the cloud, with 41%of workloads in the public cloud and 38% in private cloud. It is essential to note that the workloads running in the private cloud may consist of workloads running in present virtualized environments or bare-metal environments that have been “cloudified,” the report states.

Turns out the move to the cloud weren’t the security nirvana many (incorrectly) expected. And effective cloud management requires all of the things that traditional and on-premises systems require – good asset management, classification of data and resources, having the right security controls in place and monitored; as well as good incident response and business continuity plans.

So as enterprises clouds scale, we have seen threats and other risks to cloud apps and data. This is why cloud security strategy has to scale as cloud adoption grows more complex. One good place to begin, or make sure your company’s cloud security strategy is on point is, the Cloud Security Alliance’s most recent Guidance for Critical Areas of Focus in Cloud Computing 4.0 is a great place to start.

The guidance 4.0, as detailed by the Cloud Security Alliance, functions as a practical roadmap for those on the lookout for safety and to securely adopt the cloud model.

According to the Cloud Security Alliance, about 80% of the guidance was revised to better embody the current and future state of cloud computing security and reflects real-world safety measures for cloud practices.

While we are still a long way away from cloud security Nirvana, there are many enterprises and other organizations can do to bring more serenity to their current environments.

Study the Infographic below from Synopsis titled 10 Critical Cloud Security Threats for 2018 and Beyond.

Study the Infograph below from Synopsis titled 10 Critical Cloud Security Threats for 2018 and Beyond.

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CyberSecurity Data Theft

7 Killer Data Visualization Tools for Meetings

You could robotically spout off data and facts to your audience during a presentation, but this may not have quite the effect you desire. Even though you may have carefully selected and analyzed the right statistics that support your claim or proposal, raw facts and data can be difficult to understand and to follow along with. However, when your audience gets visualized data, they can more quickly absorb the information and more easily see the trends or analytical points that you are focused on. Visualization has improved people’s lives so dramatically—from reducing anxiety to resolving personal conflicts, to help people achieve their life goals—it is a very powerful tool that can change lives.

You may think that it would take a substantial amount of time and effort to analyze data and to create an attractive, accurate visual display of it. In fact, there are exceptional visualization tools you can use to create attention-grabbing graphics to present to your audience:

Top Data Visualization Tools for Meetings; Professionals Don’t Joke With These

In this article, here’s an in-depth guide about data visualization tools, and how we can use it to improve our lives. It’s right here:

7 Kick-ass Data Visualization Tools for Professional Meetings/Presentations

1. Datawrapper

DataWrapper - Data Visualization Tool for Meeting professionals
Datawrapper makes it easy for small business and startups to create beautiful charts. It enriches your stories with charts and interactive media, in seconds.

Datawrapper is an online tool that you can easily use to create stunning visuals for all of your presentations. It’s a user-friendly resource that allows you to quickly upload your data and select the right type of chart for your needs.

A few of the many options you can choose from including a line graph, a bar graph, a donut, a stacked bar graph, and others. You can also refine and annotate the graph as desired. In addition, through Datawrapper, you can even upload the graph to your own website in order to publish the graph for everyone to see.

Datawrapper is available in your choice of packages. There is a free package that offers the basic services, and also single flat, team, custom or an enterprise package depending on your needs and your budget.

2. Plotly

Plotly - Modern Visualization for the Data Era
Plotly is the modern data visualization tool for this big data era. It let’s you creates leading open source tools for composing, editing, and sharing of interactive data visualization through the internet.

Another excellent tool that you can use is Plotly. This is an online resource that is designed with functionality and ease-of-use in mind. Once you simply upload a file in CSV or SQL to the website, you can choose from numerous chart styles.

Plotly takes this a step further by allowing you to create a professional dashboard as well. Your creation can be shared with others or published to a slide, a website, or another location. As is the case with the previous visualization tool, Plotly is available in various plans so that you can control cost and features.

3. Sisense

Sisense - Data Visualization tool for experts
Sisense data visualization tool lets you simplify business analytics for a much more complex data. Sisense software makes it easy to instantly reveal business insights from any data source of any size.

Regardless of whether you need to make sense out of your gathered data for your own professional needs or you need to create an attractive graph or visual to present in a meeting, Sisense is a great resource to use.

This is a highly evolved resource that can analyze huge volumes of data quickly. Its intuitive AI features, an agile platform and numerous tools and features are all designed to take the time and effort out of generating incredible visuals. The visuals can be used on-site, stored in the cloud or even embedded in your website. Pricing is available upon request.

4. iDashboards

iDashboards - Bring clarity to your data
iDashboards – Bring clarity to your data. It empowers you to visually express the message in your data through interactive charts. Visually data expression has never been easier until iDashboards debuts into this industry.

While some visualization resources require you to have basic coding knowledge or to spend a considerable time crunching numbers to refine your data and visuals, iDashboards gives you an entirely different experience.

One aspect of this tool that stands apart from the rest is the usability. You can easily manipulate data and generate charts and graphs through a user-friendly dashboard. Everything from the type of the graph to fonts, color schemes, and more can be adjusted within seconds to meet your needs. There are also drill-down features to clarify your understanding of the complexity of your data. There is a free 30-day trial available for new customers.

5. Power BI

Power BI Data Visualization Mastery
Business intelligence like never before using Power BI from Microsoft. Easily switch from data to insights in minutes, and process any data, any way, anywhere. And all in one view. Welcome to the future of big data analytics.

If you are accustomed to using Microsoft products, Power BI may be the tool for you to use. This is actually an entire suite of products that can be integrated with Office 365 and allow you to upload your own data.

Once you have data selected, you can create amazing visuals with minimal time and effort required. These can be published on a website, sent to different mobile devices and more. You can also create and manage a customized dashboard for additional benefits. Pricing is free for a basic version of Power BI, and the highest-level package is priced based on your unique needs.

6. Looker

Looker - Analytics Evolved
It is high time everyone has access to fresh, undiluted and reliable data. Time to drill deeper in order to make the right call with flexibility in sharing the information gathered. Here comes Looker that actually look deeper!

If you are looking for a business intelligence and visualization program to work with SQL databases, Looker is a resource you should consider. Within minutes, you can upload a SQL database, and manipulate the data as needed inside the Looker platform.

Once uploaded, you can create dashboards or reports that have customized graphics representing your analyzed data. Looker is known for being user-friendly, and there are many resources to help you learn how to maximize its features and benefits. The company offers a free demo upon request, and can request customized pricing to determine if this is an affordable visualization tool for you to use.

7. Tableau Desktop

Tableau Desktop Visualization tool
It is time for action; get actionable insights fast, connect with more funnel to acquire data, present effectively by answering deeper questions and publish your output on variations of options using Tableau desktop data visualization application.

In order to create effective visuals from a large amount of data, you must understand the insight that your data provides. Tableau Desktop is one of the few tools that help you to understand your data and determine the best ways to organize it before creating charts and graphs. This can essentially make your visuals much more impactful and useful.

Tableau desktop also works with Google Analytics, SQL, Salesforce, Access, and other database formats that you may already be using. If your data is drilled down by region, you may love the fact that this program has easy-to-use mapping technology. After your free version expires, you can choose from several affordable plans.

Conclusion

You could simply include many spreadsheets of data in your reports and presentations, but this can be very challenging both for you and your audience. Fortunately, analyzing data and creating professional visuals within a matter of minutes, has never been so easy. Depending on your needs and abilities, you can now choose from a number of excellent data visualization tools and make your meetings more meaningful.

Categories
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.

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News

Here Are the Top Marketing Trends of Successful Marketers in 2017

When it comes to getting the words out there about your products and services, you need good marketing strategies. No matter how good your products or services may be, not setting aside winning plans to take it to the market will ultimately result into lack of exposure, and no sale! It had been proven by experts that people tend to buy what they have seen over and over again. There lies the challenge to know how to get people to see your products repeatedly. This article set to expose you to some of the top marketing trends for 2017 from successful internet marketers that you can copy and paste into your business marketing plans.

Top Marketing Trends by the Numbers for 2017

E-Commerce Trends for 2017

Content marketing

In the last few years, content marketing has been among the top three trends. As an online business owner or brand marketer, you should focus on developing integrated content marketing strategies that are effective and innovative.

Research shows that the number of businesses employing a strategic approach has increased and this growth is expected to continue throughout 2017. Additionally, it is expected that more attention is going to be directed toward measuring ROI for content marketing as a result of the rising competition and cost of this form of marketing.

Actionable Steps to Win Big in Blog Outreach...
Winning Big With Blog Outreach: Effective content marketing strategy is one of the most effective marketing trends for improving traffic and creating awareness. Guest Posting is one of the main-driver of content marketing, where an author write for another websites as a guest or a contributor in exchange for link juice.

Big data

 

The ways in which Big Data can be applied to marketing include predictive analytics as well as market and consumer insight. Numerous businesses have seen data formats, real-time data and volume increase. If yours is among them, you need to tap into this value to boost sales by using predictive analysis for emails marketing and personalizing your website. Machine learning also comes into play as Big Data is used to determine the likelihood of conversion based on varying behaviors and characteristics of consumers. Big data is one of the top marketing trends of 2017 because of its huge impact on business development.

 

Big Data and the Cloud
Exactly how big is big data? Microblogging platform Twitter sees almost 340 million tweets on a daily basis. Every second, ten thousand people use their credit cards one way or another. Where does all this information go and how is it stored? All over the world, almost three exabytes are created every day. It sounds like a small number but if you think of a single exabyte as one billion gigabytes, you’re in for a treat. Big data is beyond what you are thinking.

Mobile marketing

This includes mobile apps, mobile optimization and mobile advertising. Over the years, you along with other companies may have adopted email templates and mobile responsiveness so the trend may not seem new.

Nonetheless, research has demonstrated that on smartphones, conversion rates for retail are markedly low, meaning you still need to put in additional efforts toward optimizing mobile conversion, even though there is a likelihood that they will always be lower than desktop conversion rates.

Furthermore, mobile has a strong influence on search marketing as Google implements mobile-first indexing. It is, however, important to keep in mind that there are still numerous internet users who use devices other than mobile – desktops, laptops and tablets.

There is a slight possibility that with mobile-responsive designs, there will be a decrease in conversion on screen with higher resolution if the site is optimized for mobile. For the best results, focus on adaptive mobile design approaches that serve content that is more contextual and relevant, as well as calls-to-action and reduced load times.

Mobile marketing is one of top marketing trends for 2017
Statistics shows that people spend more time on mobile phones than on PC. But sales on this medium isn’t as encouraging as expected. You still need to put in additional efforts toward optimizing mobile conversion.

 

Another aspect where you need to be careful about your interpretation of the implication of mobile first is the overall journey that consumers take, in which various devices may be used at different stages. The best approach to your mobile strategy should therefore be multichannel or multiplatform.

Rather than making radical changes in response to a perceived seismic shift in Google’s mobile-first policy, it is best to make progressive changes while keeping a close eye on how consumers respond to the shift.

Automation of marketing

Marketing automation includes personalization of websites, behavioral email marketing and CRM. It has, like content marketing, been among the top three marketing trends in recent years. A number of brands still have some way to go in their marketing automation efforts, according to market research.

To fill this gap, some of the questions SEO experts such as Green Meadows Media should answer with regard to your brand include where to begin and how to take your marketing automation to the next level.

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Business Cloud Computing Technologies and Resources

Big Data and the Cloud in 2016: Changing Formations

Big Data and The Cloud Features:

Big Data and the Cloud

Big Data and The Cloud:- The Internet of Things

Experts estimate that 50 billion objects will be integrated into the internet by 2020. Smart phones, tablets have been integrated for some time, and now wearables such as watches, eyeglasses and even some clothing items are being integrated as well. This integration has made life a lot simpler in many ways, as well as less expensive. It has also allowed people to utilize their resources, including time, more effectively.

For example, it is now possible to purchase a monthly pass for public transportation online, have a code delivered to your smart phone, and board a bus or train with the mere flash of a screen. Time that would have been spent traveling to the nearest vendor to purchase the pass can now be spent on a good book to read during your commute. That book can also be purchased from, and delivered wirelessly to, your smartphone within a matter of seconds.

Big Data and The Cloud:- Individualization

While some are concerned with the potential for violations of personal privacy, the data collected through internet interactions has resulted in some very real consumer benefits. For those who still enjoy driving, many insurance companies now offer reduced rates based on the number of actual miles people drive and their individual driving records. Healthcare providers are able to develop individualized treatment plans for their patients much more quickly and efficiently, since consultations with specialists can take place online within hours or days, rather than weeks or months.

Consumers benefit when companies are better able to customize their services to accommodate the personal preferences of their customers. Companies benefit by having more specific information with which they can make business decisions. The ability to maintain an inventory that accurately reflects the buying habits of their customer base can determine whether a company is able to stay in business. More companies succeeding means greater competition and lower prices.

Big Data and The Cloud:- Technological Advancements

Big Data and the Cloud ComputingOne of the things that make these beneficial changes possible is the development of HTAP, an acronym for hybrid transactional/analytical processing. HTAP is a relatively new type of data platform that can process and analyze transactions online without duplicating data. The less duplication of data, the easier it becomes to maintain a high level of internet security and reduce the risk of a potentially costly breach. Customer databases and the personal information they contain have never been as valuable as they are today, making data management and recovery more important than ever.

The cost of flash memory is predicted to decrease due to higher consumer demand since more businesses are utilizing it as a way to protect trade secrets while encouraging greater innovation. Cloud servers are also repurposing hardware for use as containers, the use of which has increased three times more than that of virtual machines. However, virtual machines will continue to play an important role due to their ability to run multiple applications, while most containers run only one.

Microsoft’s Windows Server 2016 will feature a combination of virtual machines and containers. The new Nano Server, designed to be automated with PowerShell, will have the ability to run a greater number of virtual machines using the same hardware. It will also feature nested virtualization, which is the ability to run one virtual machine from within another. The system supports both containers that share files and memory and separate Hyper-V containers that have their own memory and provide a greater degree of security.

Big Data and The Cloud:- The Role of IT Professionals

The continuing increase in the use of containers is sparking the development of new applications that can be divided into units able to run separately. As the fluctuating cost of many cloud services begins to stabilize, more companies will take advantage of their vast potential. Although a greater number of companies will be utilizing more cloud services, including infrastructure as a service (IaaS), platform as a service (Paas) and software as a service (SaaS), IT departments are still expected to play an important role in integrating those services and maintaining company security.

Human beings will always be the most important component of the internet of things, and efficient data management gives us all more time to enjoy the human experience.

Credit: Busitelce | StackExchange

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Business Cloud Computing Technologies and Resources Featured

How Big Data Solutions Inform Big Business Decisions

Exactly how big is big data? Micro-blogging platform Twitter sees almost 340 million tweets on a daily basis. Every second, ten thousand people use their credit cards one way or another. Where does all this information go and how is it stored? All over the world, almost three exabytes are created every day. It sounds like a small number but if you think of a single exabyte as one billion gigabytes, you’re in for a treat.

As institutions, governments and businesses utilize more of technology in paperless filing and work management systems, data begins to accumulate exponentially. This creates problems in data storage, processing and transfer. To properly capture, analyze and manage the massive amount of data, big data solutions are crucial. Databases, spreadsheets and other software tools are simply not enough, especially if a company is about to undertake a major project or management move. What exactly can these solutions do for businesses and how can these be used to make better business decisions?

How Big Data Solutions Inform Big Business Decisions

Big data services collect and make sense of data

There are different types and sources of data. Everything from banking, politics, science, sports and retail has its own data sets that, in their raw form, are basically useless unless they are made sense of and analyzed. Data comes in two forms: structured and unstructured. Spreadsheets may work for structured data but companies will benefit more from solutions that are developed to handle both forms.

The right tools for the job

Large volumes of data cannot be poked at with a toothpick. Data analysts need a full arsenal of tools to help them undertake more accurate analyses in less time, which can help companies make more informed decisions. Traditional data processing applications and database management tools just don’t cut it when dealing with thousands of exabytes coming in from multiple sources. Analyzing big data demands a lot of resources in order to be able to discover business trends and patterns of consumer behavior.

Big data solutions provide insights

The key to making good business decisions lies in knowing what the current situation is and seeing where the industry is headed.

What are the kinds of information businesses require?

Product usage trends, consumer preferences and behavior patterns and location data are just a few of the data sets that have within them a wealth of insights that, when tapped, can be the best resource any business can have.

ALSO READ: Big Data Management: Steps to Manage Big Data in the Cloud

Big data solutions are able to collect, analyze and interpret data to come up with an infinite source of knowledge that can be used in a company’s decision-making process. What products or services do consumers use most of the most during the Holidays? What do Twitter users rave about in the mornings? Knowing the answers to these questions may help businesses decide what next to do in their marketing plan and social media strategy. In addition, the insights garnered from big data analysis can be used to improve products and services.

Using big data provide companies with myriad opportunities for business development. In order to take advantage of what big data can offer, companies should see big data solutions as a wise and long-term investment. After all, it’s not how large the volume of data is but what companies do with it that really counts.

For discussion on this subject, Big Data Solutions and its benefits for your business, please make use of the comment box below.

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Business Cloud Computing Technologies and Resources Information and Communication Technology (ICT)

Big Data Management: Steps to Manage Big Data in the Cloud

As the Internet rapidly becomes the place for almost anybody to store and transfer data, this space gets crowded with chunks of information that require huge computing power to process them. The term ‘big data’ refers to large amounts of digital data that are difficult to process and, up until recently, only huge enterprises had to face the problems of managing them. Now, as more and more small businesses expand their online presence, more tools are needed for efficient big data management.

Cloud computing technology has made it possible for many organizations to cope with this problem. As the cloud matures, it increasingly focuses on big data and developing solutions that are able to deal with them.

Big Data Cloud: How to Manage Big Data in the Cloud

Managing Big Data in the Cloud

Big data growth phenomenon

It used to be a tendency to relate particular fields, such as pharmaceutical companies and financial analytics, with large digital databases. However, there are plenty of other industries where a thorough analysis of large datasets is necessary. Whereas most business documents get transferred to a digital form, traditional software solutions clearly lose their power over them. Even average-sized companies now need access to software tools that would enable them to process large amounts of digital data accurately and, possibly, at a reasonable price.

The development of cloud computing resulted in the overall decrease in prices of advanced software solutions streamed via the web. This is not only because cloud vendors are many, but also because of the general elasticity of the cloud. Cloud platform enables users to buy a necessary software program through pay-per-use models and thus access all the resources they need. Then, as the amounts of digital data grow, a company may at any point upgrade to premium accounts and get more cloud storage space for a slight increase in price.

Clearly, before this form of data storage had become widely adopted, smaller businesses didn’t need particularly powerful software solutions for managing their data. NASA was one of the companies that needed incredible processing power technology to be able to cope with unstructured data. Now large databases and unstructured data are almost a household item, which justifies an increased requirement for advanced cloud solutions. This is why many industries, including educational, healthcare and governmental institutions, decide to implement cloud technologies.

Future predictions for Big Data storage in the cloud

Big Data and cloud computing are concepts that are frequently discussed in relation to each other. As simplified big data analytics means that more companies are able to achieve a competitive advantage, it is reasonable to expect that the focus of cloud technology will remain exactly here. According to the last year’s report by Gartner, big data market is to drive $34 million of worldwide IT spending in 2013. Their experts predict that by 2020:

“the functionality and features of big data will not be varying from what is expected from traditional enterprises vendors, and part of the products they offer.

Apparently, the cloud has had a major impact on the direction of development of many businesses worldwide. Yet, there are so many ways in which it can contribute to further progress. One of them is definitively its power over big data, which is why these two trends are expected to grow in line with one another and shape the industries in the future.

Do you agree with the writer’s claim about big data and cloud technology forecasts for 2020? Please share your views