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Home ownership is everyone’s dream. We spend a lot of time finding the best property for us. Consequently, it is one of the crucial decisions of our lives.

Real estate clients have enormous needs for as many insights as possible. In other words, they want to acknowledge the surrounding, transportation, to compare it with other options, find out the nearest mall, kindergarten or school.

Home key | New home| A man giving a new home key to a owner| real estate buyer

Real estate challenges

Real estate businesses are developing globally. Real estate industry, like other markets, faces a lot of real estate challenges. Interestingly, here are some of the substantial real estate challenges; to decrease the risk of buying a wrong property, to cut time of finding the right home, to develop a practical business approach, and to come to a buying decision easier. Most importantly, for investors, it’s important to make sure, that they invest in the right property, which represents the real value of its advertisement.

World leading real estate companies has already accepted the challenge of using new and innovative technologies, which is, in fact, the need of your customer in a contemporary market.

Most discussed tech challenges for real estate are A/B testing, machine learning, business intelligence (BI), and cloud computing. Big Data and analyses are changing all the imagination about real estate challenges.


What does big data mean for real estate data visualization?

Big data is a large volume of structured and unstructured data. Big data is usually collected from a large amount of information. The information often comes from the recent analysis.

The other interpretation of Big Data is Business Intelligence, which uses statistics to measure items and find out new trends.

Data visualization can help real estate companies track the actions, assess risks, optimize the process, analyze, and get the visualization of GEO analytics.

Why visualize real estate data?

Visualized Data has a lot of benefits for real estates.  Real estate data analyzing is an essential factor when buying or renting the most desired property.

BI helps us get visualized context which is more straightforward for an eye for obtaining the information. It shows infographics, charts, pies, as well as maps.

This is a de facto standard for modern business intelligence (BI)

It Cuts Your time

A watch in a hand. Drinking a cup of coffee. ofee

WIth data, you reduce your agents and brokers time, and the money You need to pay them for finding the best home for customers. Sometimes the process takes a long time. For example, the price for a car to accompany your home buyers for the first view. You can have all AI data of the view from the house, the malls, the kindergartens. With data visualization, the buyers will get all the answers regarding the property. If a buyer doesn’t have any interest in it, you will not visit the house. If you’re a realtor, you’ll evaluate, that this is a time-consuming process, and cutting down this, undoubtedly, is beneficial. You will make sure that you don’t wast your time for the wrong options.

Personalized experience

Losing one real estate buyer will cost too much for your company than paying a company to create Mapbox for You. If you don’t give personalized experience, there is a lot of chances to lose the potential client.

Now it’s easy for you to create software that can help your customers reveal their expectations and housing aspirations, as well as to uncover the types of ideal buyers. As a result, this will help you improve your targeted marketing.

It makes Your realtors’ life easier

Salesman | Sales manager | A businessman sitting at the office with notebook|

Does the house have a garden? Whats the facade like? Is it close to a bus station or a subway? We know how it feels, and sometimes you are becoming tired of these questions. It’s better to show the property than to speak about something intangible. Your customers and brokers will appreciate your care. 


Be an innovative real estate agency

Innovation | Innovative lamps| Innovative real estate

In the contemporary market place, businesses are dying without taking the chance to innovate their approach. Real estate numbers are increasingly growing, and the ones who are innovative will survive, unlike the others who are doing dinosaur marketing.

It reduces financial risks

Data visualization helps real estates to track investment details. For example, it’s easy to find out whether the home repaired recently or not, to uncover the status of loans and investment.

FIlters and comparisons

Sometimes it’s hard for customers to remember all the aspects of the home they have seen. Their lousy experience of recognizing the ideal house for them will turn into disappointment. Data visualization will help you always compare each aspect and find the best set of variables for the client. For example, if John wants to buy a house he can view the prices of properties which are located on street X, he can compare it with the features in Y location. He can see the pros and cons of these houses. Also, the homes in X location are cheaper, but they are way more far from the station, consequently, but this one has a beautiful balcony in comparison with the houses of Y location.


The importance of data visualization tools?

Data Visualization | BI-business intelligence| Data on A notebook screen

Digital Map market revenue is projected to grow in a short period. The reason for this is that smartphones, and other mobile devices or navigation tools have their specific role in each aspect of our life.

Data visualization tools are essential for getting analyses, data-driven insights for the workers through organizations. Data visualization software also plays a vital role in big data and advanced analytics projects.

Visualization software is based on customers behavior and needs. It helps them find their desired home. They can discover how many bedrooms it has, what the neighborhood looks like, does it feel like their home?


What is the best data visualization technology?

The most common Data Visualization software which is excellent for maps is the combination of Mapbox and

What is Mapbox

Mapbox is a solid provider of the best online maps for websites and applications. First of all, It helps developers to create smooth, fast, on the other hand, its a real-time map. It includes more than 130 multi validated sources for a world map. Map APIs get 5 billion requests per day. A dozen templates are available for you to choose.

Data visualization example for real estate. What is data visualization? Real estate mapbox.

What is

This is a data visualization tool. Many web applications, like Uber, are based on it. Above all, is created with React JavaScript library. data visualizatiom

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The article is written by Gayaene Melkumyan



The term Big Data refers to the entirety of the process that information goes through for real-world application. This means that it encompasses data gathering, data analysis, and data implementation.
This post is Marketing Insider Group’s insight on the trends we should be aware of today.

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1. Fast growing IoT networks
People can now control their humble home appliances through smartphones, all thanks to the concept of IoT. No one knows yet if gadgets like the Amazon Echo will be a mainstay in homes, but the involvement (and investment) of big companies means that businesses and consumers will continue to use internet-connected devices.

With more organizations launching IoT solutions, the growing IoT craze will help create more data and touchpoints for businesses to collect information. Many will also need new technologies and systems to manage and analyze the flood of big data coming in as a result.

For 2018 and beyond, responsive devices and smarter networks are what the market will be focusing on. With all the new devices coming online, there is also an expected increase in the growth of data collected. Expected total business expenditure towards IoT will be at $6 trillion by 2021, with a $15 trillion contribution to global GDP by 2030.

2. Artificial intelligence is becoming more accessible
Companies, both large and small, are now utilizing AI functionalities like chatbots to automate specific processes. Since there are prebuilt AI applications now due to high demand, SMEs can easily get their hands on this technology, which levels the playing field for all. It’s now up to their Sales and Marketing teams on how to better utilize this technology.

3. Predictive analytics is on the rise
Analytics is now a substantial strategy for businesses to achieve their targets. Companies look at big data to see what happened and use their analytical tools to find out why those things happened. Predictive analysis uses big data to predict what might happen in the future.

No doubt that it will be useful for data-driven marketing, as it can help analyze data to predict consumer behavior, allowing Sales and Marketing teams to know a customer’s next action before they even take it. Analytics is also trying to provide more context to data to help understand the why behind the what.

Today, only 29% of organizations use predictive analytics, according to a survey from PwC. This number is expected to grow this year moving forward, as many vendors have recently come out with predictive analytics tools.

4. Dark data in the cloud
Dark data, or information that is yet to become available in digital format, is an untapped reservoir for now. In 2018, these analog databases will hopefully be digitized and put in the cloud so they can potentially increase the range of trends and cycles that businesses can predict.

5. CDOs are stepping up
With data becoming an integral business strategy, Chief Data Officers (CDOs) are becoming a more critical role in an organization.

In a Forbes survey, more than 50% of CDOs will likely report directly to the CEO in 2018. They’re bound to take on more active roles in shaping their businesses’ initiatives. This is good news for data marketers in a more personal sense, as this means there is room for career growth.

big data agency marketing

6. Quantum computing
Imagine being able to crunch billions of numbers at once, in a matter of minutes. Such immensity and speed cut big data processing time by more than half, allowing companies to take action in a more timely manner for improved results.

Something this massive is only possible through quantum computing, which will likely be utilized soon as companies like Google, Intel, and the Turing Institute start actively developing and battery testing quantum computers.

7. Better, more intelligent security
Last year’s scandals are enough to make any enterprise paranoid about hacking and breaches, so in 2018 companies are focusing on fortifying data confidentiality. IoT is also a cause for concern with possibilities of cybersecurity issues. Big Data companies are now helping providers market products that use data analytics as a tool to detect and predict threats.

Big Data can be used as a security strategy. A security log data can provide information about past threats, which companies can use to prevent and mitigate future attempts. There are also those that can choose to integrate their security information and event management software with Big Data platforms.

8. Open source

It’s likely that public data solutions like Apache Hadoop and Spark will continue to dominate. In 2017, enterprises expanded their use of Hadoop and NoSQL and looked for ways to speed up data processing. In 2018, technologies that allow access and response to data in real time will be in high demand.

Open source applications are no doubt cheaper and will cut costs for your business, but as with any other good thing, there are certain drawbacks that you should be aware of.

9. Edge computing

With edge computing, the big data analysis happens not in the data center or cloud, but close to the IoT devices and sensors. For companies, this means better performance since there’s less data flowing in the network and fewer cloud computing costs.

Storage and infrastructure costs also decrease because the company can choose to delete irrelevant IoT data. Edge computing can also speed up data analysis, allowing for faster action times from decision-makers.

10. Chatbots will get smarter

In 2016, Facebook allowed developers to integrate chatbots in its Messenger service. Since then, many companies have deployed bots to take queries from customers, giving them a more personalized method of interaction without the need for human customer service personnel.

Big Data is the foundation of this customer experience, as bots process volumes of data to produce the most relevant answer based on keywords in the query. They’re also able to pick up and analyze information about customers based on a conversation.

The improvement of bot technology will help marketers collect more/better data to develop both their customer service strategy and targeting of online ads. It also helps businesses cut costs on support resources.

hings to remember

The advancement you enjoy today in virtually every industry can all be traced to Big Data. It has helped create smarter cities, better academics, medical breakthroughs, and more efficient enterprise resource planning.

But for it to reach its full potential, you must thoroughly understand and use the right technology, skills, processes, planning, and industrial applications.

As it becomes more available and affordable to implement Big Data strategies, you can expect more changes and trends that will help your business grow and thrive.

At Great Department we have a huge expertise in working with data; managing, analyzing and visualizing. Check our services here.


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Over the last five years, big data has become one of the most valuable assets in business. Although the process of gathering and storing large quantities of digital information has been around since the nineties, it’s only in recent years that it’s been put to good use. Indeed, as Harvard Business Review’s Kristian Hammond noted in 2013, big data is the means to evidence-based decision-making. By analyzing large quantities of information from a variety of sources, companies can make better decisions and, in turn, grow in a more efficient way. Taking this concept, companies of all sizes and persuasions have developed technology to harness the power of big data. As per a 2016 report by Forrester and TechRadar, the industry is evolving rapidly. The report noted the trend trajectory of 22 technologies within the big data sector, many of which have flourished as predicted. Of those highlighted, Forbes Gil Press noted 10 of the most significant for businesses: Predictive analytics This is the process of using data mining, statistics and modelling to make predictions about future outcomes. In other words, historical data defines a set of parameters, which computers can then use to determine what user behavior/responses might be in the future. Search and knowledge discovery These tools support self-service extraction of information from large, unstructured databases. In other words, search and knowledge tools allow someone to input a query and pull in data from a variety of unconnected sources in order to cover a single topic/request. Stream analytics This technology can take information generated from a variety of connected devices and sensors and turn it into actionable insights in real-time. This technology is most concerned with IoT and using data from a variety of smart devices to make almost instant predictions. For example, stream analytics could be used in an IoT home system to help determine the optimal living environment (heat, lighting etc.) for the user based on masses of data from the thousands of homes. NoSQL databases The use of NoSQL databases makes the processing of some big data sets more efficient. Because these databases are structured using key-values, graphs or documents instead of tabular structures found in relational databases. Distributed file stores These systems store data on multiple nodes instead of a single point. The data is replicated on each node to allow for improved processing performance i.e. the information is more readily available. This type of data storage is similar to the decentralized structure of block-chains. In-memory data fabrics This technology groups independent sources of data into a grid. This grouping not only allows each source to operate independently but as part of a collective, through which information can be analyzed either in parts or as a whole unite. Data virtualization Drawing information from disparate sources, this technique allows users to gain an overview of large sets of data in real time. This is possible because the software doesn’t replicate the data from each source. Instead, it simply delivers a unified data service that can support multiple applications and users. Data preparation With big data insights increasing, it’s becoming increasingly difficult to process it all in an efficient way even with all the current software on the market. Data preparation involves collecting and editing data from multiple sources before it’s plugged into a system and analyzed. Data integration To improve the communication between unconnected data sources, integration software has become important. Through products such as Apache Pig and MongoDB, it’s now possible to link data in a meaningful way even if the sources are completely unconnected. Data quality Not all data is good data. With speed and efficiency crucial in today’s world, businesses are now using products that analyze and cleanse data before it’s stored/analyzed. Predictive Analytics starts to shine Of the innovations listed, predictive analytics is one that’s showing it has the most utility in the current business climate. Despite the fact it’s been around for more than a decade, machine learning (a part of the artificial intelligence realm) has made this technology more effective. Prior to machine learning giving computers the ability to adapt in real-time, predictive analytics struggled with scale. Because AI systems can operate without human intervention, they can process more information. As an example, Magnetic’s AI system can process 1 petabyte of consumer information to suggest potentially profitable actions. Combining this technology with predictive algorithms can result in models that consider more information and, in turn, generate more precise outputs. big data In simple terms, predictive models allow businesses to determine customer responses or potential purchases by using historical data. An example of this would be the way gaming operators draw data from in-house marketing campaigns and external comparison sites to define their next marketing campaign. For instance, after sending out a promotional email, the company has the ability to record the number of clickthrough responses. On top of this, affiliate data from comparison sites gives the company further insight into what’s hot and what’s not. Indeed, because a platform like Casinos Killer ranks sites using a myriad of data, including betting options, bonuses and overall quality, it’s easy to see which offers players are attracted to. Just like PriceGrabber and NexTag are hotbeds for user preferences, the same is true in the gaming sector. So, by tracking data from affiliates and combining it with its own insights, it becomes possible to use predictive analytics and AI to highlight trends and launch campaigns based on this analysis. Of all the trends in big data, the evolution of AI-powered technology is by far the most significant. As we’ve shown, the ability to process larger quantities of data in real-time can result in more accurate predictions. The upshot of this is that business can be more efficient in whatever task it is they’re interested in. Whether it’s security or marketing, the crossovers between big data technology and AI are playing a central role in the action. Article by Daniel Smyth