Let’s say that you released an excellent mobile app for iOS and Android and believe that it can be used by many.
The problem is that both Apple Store and Google Play Store are filled with various applications in all the available categories.
Expecting to just get lucky is hardly a winning strategy. No, you need to create a strategy to help your app go viral.
Some Key Steps to Take Before the Promotion Campaign
Before you begin, there are a few important things to make sure of. For one, the app should be optimized for different devices.
That includes performance and storage consumption. According to the Backlight blog, it can be a bit tricky to manage a smartphone’s storage, and apps that consume more space than they realistically should are the opposite of user-friendly.
Another thing is to optimize the app for a store search. People who are interested in downloading and installing the app should be able to find it without problems.
Finally, get a monetization model in place that clearly states whether the app is free, freemium, or paid. The pricing model should be clear and not mislead the consumers.
Run Ad Campaigns on Social Media
Let’s start with social media. It is often the go-to platform for marketing. Mobile device users browse social media often, so it makes sense to strike where your potential users are.
You can launch a paid ad campaign and set specific demographics that you want to target in relation to what your app offers.
Post on Forums
Traditional forums are not as prominent as they were back in the day, but you can still find plenty of them. You can encourage influencers to add a link to Instagram bio, helping them direct their followers to download your app and learn more about it.
Creating a thread or replying to a forum user in a way that you can mention your app naturally is a good approach. For instance, if someone creates a thread asking how to save more money and you have an app that helps you manage finances, it makes sense to advertise the app as a potential solution, right?
A similar approach can be made not just on traditional forums. You can find relevant threads on Reddit and Quora, as well as various posts on social media groups.
Collaborate With Influencers
Get in touch with influencers and offer them something in exchange for promoting your application.
The influencers should have demographics that are relevant to your application. For instance, if the app is all about promoting a healthy lifestyle, then Instagram influencers who have a brand built around a healthy lifestyle are a good fit.
You do not have to limit yourself just to social media. Twitch TV is excellent for those who want to promote a gaming app.
Even though most Twitch streamers play on computers and consoles rather than mobile devices, there are some who do. Not to mention that some streamers would be fine switching to a mobile game for a bit if the offer is good enough to persuade them.
Reach Out to Podcasts
Podcasts are worth a shout as well. If you listen to one, you are likely to hear how a service or a product is the sponsor of the episode.
It is common for podcast hosts to monetize their content through various advertising deals or by collaborating with top podcast ad agencies. A short break in the middle of a podcast or an introduction is when you hear the promotional part.
Ideally, you should come on as a guest and talk about it from a developer’s point of view and why people should consider using your app. However, if such an opportunity does not present itself, a simple promotional bit on a podcast is still a good marketing method.
Pitch to Tech Writers
Many tech writers are looking for new things to cover on their websites. Some are eager to get exclusives because the tech industry is competitive.
You could reach out to various bloggers and let them know about your application. Give them early access and inform them that they are part of an exclusive group that gets to try the app before everyone else.
Advertise in Apps
In-app advertisements are quite common, especially when an app is free but has a paid version. It encourages users to eliminate the apps by getting the paid version.
One of the downsides of this method is that you might find it difficult to strike a deal with other app developers to let you promote your app on theirs. Nevertheless, the idea is worth a try.
Closing Thoughts
Launching a new app is a stressful feat, especially if you put a lot of effort into it and received positive feedback from your circle.
The next step is about getting the word out there and letting people know about your application. The methods mentioned in the article should be enough to create a solid marketing campaign but do not be afraid to explore them even further.
With an estimated compound annual growth rate (CAGR) of 19.8%, the Global Fintech market will hit $332.5 billion by 2028. And there are no signs of slowing down in sight.
Deloitte says the second wave of fintech is right around the corner. And it involves partnering with technology companies to use data to gain access to new markets, understand their customers, and learn the “secret sauce” that powers innovation.
In a fast-paced industry that requires quick thinking to stay at the top, fintech companies are turning to geospatial data to help improve their offerings.
By using this information, businesses can understand their customers’ behaviors and use that knowledge to create products and services that meet their needs.
In this article, we’ll explore how geospatial data will revolutionize how you bank and participate in any other kind of financial transaction, for that matter.
Let’s dive in.
What is geospatial data in fintech?
Geospatial data or location data is inherently dependent on location or organized so that it can be easily mapped to various locations.
For example, geospatial data helps track where people use mobile banking services or how often they visit their bank in person. Now banks can access powerful data that’s perfect for making data-backed decisions about which locations should receive new branches or ATMs based on customer demand and behavior patterns.
With the growing demand for geospatial data, financial institutions are rapidly investing in data center infrastructure. Fintech companies need the right technology to store and manage new data sources. Without it, they can’t keep up with a competitive industry that wants to serve its customers.
Let’s review some ways geospatial data in fintech is revolutionizing the banking industry.
Increase location-based services
Location-based services are one of the most popular uses of geospatial data. These services use your phone’s GPS (or other location-based technology) to pinpoint your location and then deliver relevant information based on that location.
Take Starbucks, for example. As you drive or walk closer to a Starbucks brick-and-mortar location, your Starbucks card that’s stored in your Apple wallet suddenly appears on your smartphone’s lock screen. If you weren’t daydreaming of a pumpkin spice latte, you are now.
Banks can learn a thing or two from Starbucks. Starbucks holds more than $1 billion in deposits from gift card sales, both in the physical form and on their mobile app.
Not only do they hold more cash than many banks, but they also put geospatial data to excellent use. The simple notification that you are near Starbucks plants a seed in the consumer’s mind. Now, they can’t pass up that pumpkin spice latte on a crisp fall afternoon.
According to a recent study by Capco, customers are highly interested in text alerts and notifications about opportunities to transact more efficiently.
Banks can use Starbucks’ location-based strategy to send a message to your phone when you are near an ATM. This alert is especially helpful if it’s been a while since your last visit, and you might need to restock your wallet with cash.
Or, if you enjoy the buy now, pay later option, you might receive alerts for all the stores that accept this payment method during your next shopping spree.
Improve customer experience with personalized services
The future of financial services is here and more personalized than ever.
In the past, you might have had a relationship with your bank that was mainly transactional: you’d go in, deposit checks, withdraw cash from an ATM, maybe get a loan, or apply for credit. That’s all well and good — but it doesn’t give you the kind of personalized experience that we’re seeing more and more in fintech these days.
Nowadays, banks are looking to improve their customer experience by offering tailored services based on where you live, what kind of lifestyle you lead, and your spending habits.
With this information, they can offer different products or services based on what would be most helpful to each client individually — and ultimately build a foundation of loyal customers.
Consumers want an easy, streamlined experience with their bank. When customers receive offers for products that are contextually relevant, they’re more likely to engage with them. Regarding finding and shopping for financial products today, 65% of banking customers believe institutions should make it easier.
Geospatial data will become an integral part of customers’ experiences in the future as they interact with their financial institutions through mobile apps.
Fintech companies can use data about people and their assets to make better decisions about product development and deliver personalized marketing strategies.
Enhance fraud detection and prevention
With great power comes great responsibility. Fintech companies have access to a lot of data, and they’re using it in new ways to detect and prevent fraud.
For example, a bank uses geospatial data from its mobile app to determine the location of its customers. This data provides insight into how often customers use the app in certain areas, helping to indicate potential fraudulent activity.
So if someone is logging into their account from Moscow, Russia, instead of Los Angeles, California, for example, that could indicate a red flag — and adjustments are made to freeze the account until the transactions are approved or denied by the account holder.
Other fintech companies are turning to the dynamic duo, geospatial data and artificial intelligence, to create algorithms that detect money laundering based on behavioral patterns found within financial transactions.
Geospatial data helps them identify specific areas where this activity occurs (think casinos), allowing them to focus their attention on one area instead of wasting resources elsewhere.
Add in the power of machine learning, and a computer can learn to identify fraudulent schemes from previously identified patterns and then decide whether to approve an ongoing transaction.
Geospatial data has a lot of potential for fintech, but one of the most precise ways it’ll revolutionize the way you bank is by enhancing fraud detection and prevention.
Wrapping up
The wants and needs of consumers are constantly evolving and, frankly, only becoming more demanding. We all want transparency, simplicity, and convenience in every aspect of our lives, including how we manage our money.
Geospatial data will improve banking services and fraud detection by showing how consumers spend their money in real-time. Add in the power of artificial intelligence and machine learning, and you won’t even recognize the banking industry anymore.
It pays to be a mover and a shaker.
Banks that quickly shift to investing in geographic information systems will set themselves apart from the crowd by creating a competitive advantage that’ll drive long-term business growth.
Footfall data is something that has been around for a while now. But what do we mean by footfall?
This kind of dataset has changed depending on the use case and industry.
In fact, footfall has moved beyond simply measuring the number of people that enter a location.
We’ll take you on a deep dive into footfall data. We’ll show you what it is with detailed examples, as well as what it can be used for across many industries.
What is footfall?
Before we look at footfall data, we need to explain what footfall is.
For us, we have always defined footfall as:
The way that a group behave and move in the real world.
This explains the who, what, when and why of how this group of people visit a location.
This could be different for each business.
But mainly footfall can tell you:
Trends around behaviour
Changes in demographics
Visits to real-world locations
Anonymised data trends
Essentially footfall means understanding how people move and behave in the real world.
So what is footfall data, and what does it look like?
Footfall data is sometimes referred to as foot traffic data. It’s a data set that will usually contain a number of entries.
The dataset as a whole will signify a number of visits to a real-world location.
These are aggregated and delivered in a few different ways.
Aggregated visits to a location
This will be a data set in which the number of visits to a location is aggregated. This is usually done by some kind of time window, such as hourly, daily, weekly or monthly.
Individual visits to a location
Similar to the above, but this time each row will signify a visit to a location. This will usually come with a timestamp and will be up to the person receiving the data to aggregate the data as they wish.
Characteristics of visitors at a location
In this dataset, the visits to a location are overlayed with demographics data to understand the calibre of person visiting the chosen location.
Comparisons of visitors to a location
This dataset will contain a comparison between two locations based on the desired metric. This could be demographic or an hourly number of visits.
Where is footfall/foot traffic data generated from?
These datasets can come from a myriad of sources. It’s important that you understand where your footfall data is generated from, as this can affect its accuracy. The most common sources are as follows:
Geospatial/Location data
Data is usually generated from a mobile device. This is collected and aggregated to protect user privacy. A good amount of versatility as a single data set can be used to measure visits to numerous locations. A good balance of scale and accuracy.
Sensory data
These are usually physical sensors that are placed in entrances to stores. Very accurate but limited mainly to retail and requires stores to install physical tech, so not very scalable.
Purchase data
This kind of footfall data involves taking payment data to understand changing traffic in stores. It can be scalable but is not very accurate. This is mainly due to the fact that you are measuring purchases as opposed to visits.
How can footfall data help my business?
Traffic and movement trends
One of the main use cases for footfall data is for understanding changing traffic and movement trends.
These kinds of insights are valuable for businesses that are interested in physical locations.
Examples of this use case are:
A retail location understands the changing number of visitors to its location. This could be a store or a real estate planner.
A city planner understands macro visits changes to plan infrastructure.
Financial companies looking to identify trends in behaviour for investment purposes.
OOH media owners measure how many people have seen their ads.
Visitor demographics
As mentioned, with overlapping datasets, it’s possible to show the demographic of visitors to locations. These demographics are features such as age, gender, interests.
This use case traditionally sits more on the side of marketing and advertising.
Example use cases are:
Marketers target consumers who have visited a real-world location.
Building lookalike audiences in advertising platforms.
Competitive analysis
This is similar to our first use case, but the target location will typically be a competitor.
Examples of this use case are:
A store measures competitors’ traffic to target them with advertising.
A new site planner understands competitive performance to decide where to open a new site or venue.
Training ML
Footfall data can also be used to train emerging ML models. These models are being used to power new tools that can help solve problems in the real world.
Examples of this use case are:
Predictive insights into footfall
Complex financial predictions
Example of footfall data
Get started with best-in-class footfall data today.
Event organisers are always looking for new and interesting ways to engage with and keep their attendees engaged during and after their event. From the increasing variety of vendors, dedicated areas to network, and recognizable speakers, to modern event attendee tracking technology and digital rewards. In this article explore the tools and strategies used to attract and engage event attendees so they return year after year and are motivated to get their peers involved.
The Importance of Event Engagement
Without engagement, events are doomed to fail. It may not be straight away but over time the audience dwindles, tickets fail to sell, and organisers can no longer fund the event. Many events have gone this way over the years as organisers fail to prioritise their attendees. Attendees expect more from today’s events. They are over the excitement of returning to in-person events after several years of hybrid and virtual events and are experiencing increased costs for travel and accommodation. This is why attendees and their employers need assurance that the event they attend will be providing value for their time.
How Technology Improves Event Engagement
Technology is woven into events. Organisers use tools and technology to streamline the administration behind the event, from selling tickets to onboarding vendors, to analysing results. Some of this technology is already being utilised to engage event attendees. Ticket platforms provide information, agendas, maps, and more for attendees to familiarise themselves before turning up to the event. Visual technology like screens, projectors, and speakers are used around the venue to show attendees where to go, what’s going on, or what sessions are coming up. Aside from the common technology used in events, organisers also benefit from the use of QR codes, footfall data, digital awards, and live translations.
QR Codes
QR codes are a fantastic technology that almost slipped by the world without much attention. Fortunately in the last few years they have drastically grown in popularity and many organisations are using QR codes in different ways. Restaurants use them to replace and supplement physical menus, advertisers use them on billboards, and estate agents feature them on ‘For Sale’ signs. Users scan the QR code with their smartphone to access information, register interest, watch videos, and more.
Event organisers can utilise QR codes in several ways; featured on the event ticket linking to exclusive attendee offers, at entry points to streamline checkline, and on vendor booths to measure interest. The use of QR codes also helps to reduce physical touch points which is in keeping with the latest health guidelines and reduces the risk of illnesses spreading. QR codes are cheap to create and maintain, easy to use, and can be designed with imagery and brand colours. With an efficient QR code generator, event organisers can quickly produce custom codes tailored to their needs. They are an ideal technology for event organisers to explore as a way of engaging their attendees.
Footfall Data
Foot traffic data or footfall data are the metrics tracked from real-world footfall; the movement and behaviour of individuals and groups. Metrics can be tracked in several ways, by aggregate visits to a location, individual visits to a location, characteristics of visitors at a location, and comparisons of visitors to a location. The data comes from different sources depending on the application. For example, geospatial/location data is gathered from mobile devices that can be set-up in multiple locations. Sensory data is the data commonly collected by retail stores using physical sensors that are installed at the entrance. Purchase data are the metrics gathered from payments and purchases.
Event organisers can use geospatial/location data collected from devices placed around an event venue to understand what their attendees are interested in. Whether certain vendors were more popular, how long attendees spent at different vendors or stands, and how groups moved through the event. This provides actionable insights for attendee engagement. Based on the data, organisers can understand what sort of vendors best resonate with their audiences and whether stands need to be moved to improve attendee flow.
Digital Awards
Digital awards such as digital credentials, digital certificates, and digital badges are a popular method of rewarding and engaging learners, members, candidates, and attendees. They are extremely versatile and are used in a variety of environments including higher education, associations, professional certification, and events. Organisations issue digital awards to individuals to recognize effort and illustrate growth. Recipients then share their digital awards in celebration to social media platforms, add them to LinkedIn profiles and professional resumes, and embed them online and in email signatures.
Event organisers can use digital awards to recognize attendance, reward volunteers, and credit speakers. Attendees are issued digital badges that are fully portable meaning they can be uploaded to smartphone devices where they are used to streamline check-in. They share their attendance badges to social media increasing visibility for the event and creating discussion. The increased visibility helps to drive referrals, increasing future attendance, and keeping the event at the top of conversation for longer.
Live Translation
English is the most common language spoken at events, conferences, and tradeshows in the UK and the US, but international attendees also frequent events in these locations. Especially large, industry-leading events where there are plenty of opportunities to learn and network. Live translation are tools that enable real-time language interpreting for live streams and in-person presentations. The tools became readily available during the pandemic following the rise of virtual events and should be a must-have for all future events. In addition to live translation tools, using VPNs can enhance accessibility for remote attendees by bypassing geographic restrictions. For instance, sports fans who face location-based blackout policies can use VPNs to bypass NHL blackouts on ESPN, ensuring uninterrupted access to live broadcasts.
In Summary
Embracing technology in events to engage attendees is a must for organisers that want their event to succeed and stand out amongst competitors. This is especially important for organisers that intend to bring their event back year after year. They need to be prepared to understand, research, and explore the demands and expectations of their audience and ensure their needs are met and surpassed to keep them interested and returning. Through research, strategizing, and innovation, organisers can ensure that the technology used delivers a great user experience for themselves, the sponsors, the speakers, and most importantly, the attendees.
Having spent the last several years in an industry that’s been in constant change, Tamoco has always tried to remain agile, forward-thinking, and adaptable to change. This has enabled us to build a brand synonymous with large-scale geospatial data capability.
Today, we are thrilled to announce the next step in this journey, one that promises to enhance who we are as a business and how we deliver value to our customers.
Some of Tamoco’s operational assets have been acquired by pass_by in a commitment to redefining Geospatial standards through AI-driven intelligence and ground truth verification.
This isn’t just an asset acquisition; it’s the coming together of two leaders in the geospatial landscape. Combining large scale geospatial engineering expertise with sophisticated AI and ML thinking.
The synergy?
A new type of geospatial data business. By integrating our strengths, we aim to not only provide granular, accurate data in all shapes and sizes, but also predictive insights that empower businesses to make informed, strategic decisions. This combination enhances our capability to deliver comprehensive solutions, marking a new day in our shared mission to equip businesses with state-of-the-art geospatial tools and insights.
GIS analysis uses geographic data to solve issues and make choices. It analyzes patterns and correlations using geography, computer science, and statistics. Geospatial data has affected environmental research, urban planning, public health, and transportation. However, the discourse community, the collection of individuals who share a language and purpose, can also influence geographical data discovery and growth.
Finding Opportunities
Funding and grants may help the discourse community shape geospatial analytic research. Varied organizations have different interests and aims, which might affect financing. With that being said, reading discourse community essay examples for college can be effective in understanding this matter. These articles are useful and informative for this niche enthusiasts. A public health discourse community may emphasize research on disease outbreak spatial distribution, whereas transportation discourse communities may favor transportation network optimization. These objectives affect research and methodologies.
The Role Of PGIS
Traditional PGIS uses paper maps, interviews and questionnaires to maintain spatial properties. Data is gathered to be searched and analyzed by computer GIS software and disseminated through computer-generated maps. Specialized knowledge and class- and gender-segregated local wisdom are used. It gathers various sets of participants from the community and non-governmental institutions. It is built on elevated levels of stakeholder engagement in the stages of spatial learning, judgment, and action. Technological agencies and politicians might debate concerns and exchange ideas here.
Influence On Certain Data
Geospatial analysis data and tools are affected by discourse organization. Data availability and accessibility vary per discourse community, as do data sources and formats. Geospatial analytic software and techniques may also vary by discourse community. Environmental research groups may choose open-source software, whereas urban planning teams may prefer software with a better visualization. And these are great examples of how certain data can influence discourse units.
Shaping The Guidelines
Discourse unity can play a role in shaping the standards and guidelines that are used in geospatial analysis. And that’s important for every student to remember. Varying communities have different data quality, accuracy, and precision expectations. And that might affect standards and recommendations. The discourse collective may also shape geographic data and analysis of best practices and ethics. Personal data utilization in spatial thinking may vary by discourse community.
Education And Practice Of Geospatial Analysis
Finally, professional organizations and certification programs may help the GIS community educate geospatial analytic experts. Professional groups may help members network and improve professionally, as well as set industry standards. Certification programs can assist geospatial analysts to show their expertise and create industry standards. They are based on prolific data that can bring positive results. People might work together alongside these courses. In the end, education is the core of success. And geospatial analysis is not an exception.
Sharing The Ideas
The discourse community sets research, communication, and cooperation standards that affect geospatial analysis. It may provide criteria for gathering and analyzing geographical data, standards for making maps and other visualizations, and methods for sharing and utilizing spatial information. It promotes geospatial analysis through sharing ideas, information, and skills. Discourse community members may exchange research results, debate new methods and technologies, and cooperate on projects via conferences, journal papers, and other means.
Setting Research Priorities And Providing Support
The discourse community can influence the direction of research in geospatial analysis by identifying and prioritizing important questions and issues that need to be addressed. This can help to ensure that research efforts are focused on areas of greatest importance and relevance. The discourse community can provide resources and support to professionals working in the field, such as access to data, software, and other tools. This can help to foster the development of new techniques and technologies and support the advancement of geospatial analysis.
Conclusion
The discourse community helps geospatial analysis evolve. It affects research paper topics, information transmission, and professional training. Geospatial analysis practitioners and scientists must interact with the discourse collective to remain abreast of field advancements and best practices. It’s all in them putting in enough effort.
Welcome to the wild and wonderful world of geospatial data visualization! If you’re anything like us, you find that maps just have a certain je ne sais quoi that makes them endlessly fascinating. But let’s be real, maps aren’t just for daydreaming about far-off places. They’re powerful tools for understanding and communicating information about the world around us. And that’s where geospatial data comes in.
At Tamoco,we’re used to map vizualizations with our cutting-edge data collection and analysis techniques. We’re providing high quality data that goes into creating those mesmerizing maps we all love so much.
But whether it’s our data or data from another source, what do you do with it? That’s where visualization comes in, and that’s exactly what we’re here to talk about today.
We’ve scoured the depths of the internet and consulted with experts in the field to bring you a comprehensive list of 20 different methods for visualizing geospatial data on a map. Whether you’re a data scientist, a GIS specialist, or just someone who appreciates a good map, we’ve got something for you.
Method 1: Heat Maps
Alright, a heat map is like a choropleth map’s cooler and more sophisticated cousin. Both use colours or shades to represent different values or value ranges, but where a choropleth map uses discrete cells constrained by geographical or political boundaries, a heat map presents them as a smooth and seamless spectrum.
This makes heat maps perfect for uncovering hot spots and low concentrations of a variable with more precision. But, just like anything worth doing, this precision comes with a price. Heat maps often require converting discrete data points into a continuous spectrum via algorithms, which can compromise on accuracy.
Use case: Smart Cities
Let’s say I’m looking to understand where to build a new cycle lane in a city. By generating commuting data and using it to build a heat map, I can identify hotspots where cyclists will cause a lot of disruption. Using this data, I can see the areas which need alleviating.
Example of heat maps
This is a great map of the distribution of restaurants across the US.
These maps use different shades of colour to represent different values or value ranges within geographical or political boundaries. So, in a nutshell, it’s like a colouring book for data nerds where each country, state, or region gets its own colour.
Creating a choropleth map is a piece of cake. You start with a base map, and then you use different shades of colour to represent different values or value ranges within geographical or political boundaries.
But before you get too excited, it’s important to remember that choropleth maps also have their limitations. They don’t give you any information about the magnitude of the variable, and they can be misleading if the geographical or political boundaries aren’t well-defined. But when used correctly, choropleth maps can be a powerful tool for understanding the distribution of a particular variable within geographical or political boundaries.
Use case: marketing
Let’s say you’re a marketer, and you want to see which states in the US have the highest sales of your product. You could use Tamoco’s data to create a choropleth map that shows the visits to your stores by state. The states with the highest sales would be coloured differently than the states with the lowest sales.
Example of a Choropleth map
The classic example which can be seen below is a population density map.
Up next on the geospatial data visualization train is the Proportional Symbol map. These maps use symbols, such as circles or squares, to represent different values or value ranges, and the size of the symbols is proportional to the value of the variable.
Use case: analysis
For example, let’s say you’re a scientist, and you want to see the distribution of a certain species of birds across a region.
You could use a dataset that shows this distribution to create a proportional symbol map that shows the number of sightings of the species by location. The locations with more sightings would have bigger symbols than the locations with fewer sightings.
Proportional symbol maps also have their limitations. They can be misleading if the symbols overlap and they don’t give you any information about the geographical distribution of the variable.
Example of a Proportional Symbol Map
Again it’s a political example where the breakdown of votes in each state are shown by using a pie chart as a symbol on this map of the US.
Let’s have a little look at the next visualization type in this post: the Dot Density Map. These maps are used to represent a variable within a certain area. The more dots within an area indicate that the variable is more abundant.
Use case: health
If you work in public health, then you might need to understand the distribution of a disease across a geographical region. You could use Tamoco’s data to create a dot density map that shows the number of cases of the disease by location. The locations with more cases would have more dots than the locations with fewer cases. In this example, The dots can be colour-coded to represent different types of cases, for example, severe or mild cases.
The limitations of this kind of map are usually that they can be misleading if you don’t have enough detail in the map. There is also no magnitude of the variable in a lot of cases.
These maps are game changers. They take a little bit of everything and make it into a scrumptious feast for the eyes. It blends the beauty of contour lines with the detail of a choropleth map to give you an explosion of information. The lines represent equal values, and as they get closer, the values get higher. Think of it as a topographical map but more impressive.
Use case: meteorologist
Well, let us imagine you’re a meteorologist, and you want to study the precipitation patterns in your city. With an isarithmic map, you can show the rainfall distribution across the city in an easy-to-understand manner. The closer the lines, the higher the rainfall. It’s a visual representation of the data that brings it to life.
Not only do isarithmic maps make data more digestible, but they also add a touch of artistic flair to your presentations. No longer do you have to stare at boring bar graphs or pie charts. With an isarithmic map, you can show the world’s location or geospatial data in all its splendour.
Let’s take a look at a real-world example of flow maps in action. Say you’re the CEO of a large logistics company and you want to visualize the shipping patterns of your fleet of trucks. With a flow map, you can plot the origin and destination of each shipment, creating a web of lines that show the routes taken by your trucks. The thicker the line, the more shipments moved along that route. This allows you to easily see the busiest routes, where bottlenecks might be, and where your fleet is most efficiently moving goods.
Use case: logistics
Let’s take a look at a real-world example of flow maps in action. Say you’re the CEO of a large logistics company and you want to visualize the shipping patterns of your fleet of trucks. With a flow map, you can plot the origin and destination of each shipment, creating a web of lines that show the routes taken by your trucks. The thicker the line, the more shipments moved along that route. This allows you to easily see the busiest routes, where bottlenecks might be, and where your fleet is most efficiently moving goods.
Example of a flow map:
Method 7: Density-Equalizing Maps
Have you ever seen a map that looks like a distorted mess, leaving you feeling discombobulated? That’s where Density-Equalizing Maps come in to save the day! These maps make sure that regions with higher density are represented as larger in area, as opposed to just appearing larger because they’re closer to the center.
Use case: city planning
One classic use case of Density-Equalizing Maps is in the field of population demographics. By accurately visualizing areas with higher population density, policy makers and urban planners can make informed decisions about urban development, resource allocation, and emergency preparedness.
Dot maps are quite beautiful when you think about it. A great representation of multiple data points – that use dots to represent individual parts.
The density of dots in a single location represents the concentration of data points in that area. Each dot represents a single instance of data, so it’s a very, very effective way to visualize the distribution of data.
These maps are even more useful when you have huge amounts of data to understand and want to avoid excess clutter on your map. They are great for visualizing data over a large area, such as a city or country, as they allow you to see patterns and relationships that may not be immediately obvious with other mapping methods. They are also a fantastic way to visualize changes over time, as you can create a series of maps that show the evolution of the distribution of your data.
Use case: store planning (retail)
Let’s say you want to understand every Starbucks in your city. A dot map would be a great way of plotting this on a map. A single dot equals a single Starbucks. The more dots you see in an area, the higher the distribution of Starbucks in the area.
Cartograms are a phenomenal way to visually represent geographical data. Instead of using traditional maps, cartograms distort the size of geographic regions to reflect the magnitude of the data being displayed. Think of it as a funhouse mirror for geospatial/location data, where the size of each region is adjusted to reflect its importance in the data set.
Cartograms provide a unique and fun way to represent geographical data, while still being a powerful tool for visualizing and understanding complex data sets. Whether you’re a data analyst, geographer, or just someone who loves maps, cartograms are sure to add a new dimension to your understanding of the world around you.
Use case: government
An example of a use case for cartograms can be seen in the representation of population data. A population cartogram would adjust the size of each region to reflect the size of its population, with larger regions representing areas with higher populations. This type of representation can quickly bring attention to the areas of the world with the largest populations and help to identify the potential locations for targeted campaigns or resource allocation.
Now we arrive at the world of Hexbin Maps! Picture a world where data isn’t simply scattered like confetti on a map, but is instead grouped into beautiful, hexagonal shapes. That is the wonder of hexbin mapping.
Hexbin maps, as their name suggests, involve aggregating data into hexagonal bins. This is particularly useful when you have a large volume of data points to represent in a small space, and want to avoid clutter. The size of the hexagons represents the density of data points in a given area.
In a nutshell, hexbin maps are a way to take data overload and turn it into a chic and digestible form. They’re perfect for situations where you have large datasets and want to make quick and easy comparisons between areas
Use case: Tamoco
So, let’s take Tamoco for instance. We collect location data from millions of devices every day. Now, imagine if we had to display the data points for each device on a map. It would be a bit of a mess mess! But, with hexbin maps, we can aggregate the data into hexagons, effectively summarizing the data in a visually appealing and meaningful way.
Oh, now we’re getting into some serious geospatial magic. 3D maps take mapping to a whole new dimension (literally!) and can bring a level of realism to your data visualization that’s simply unmatched.
Think about it, with traditional 2D maps, you’re stuck with a flat representation of the world. But with 3D maps, you can now see buildings, terrain, and other features in their actual, three-dimensional form. This allows you to better understand the relationships between various elements and how they interact in the real world.
Use case: real estate
Let’s say you’re a real estate developer and you want to showcase your latest project to potential buyers. You could create a 3D map that allows people to explore the virtual city, walk down the streets, and see the buildings from all angles. This not only gives potential buyers a better sense of the project, but it also provides an immersive, interactive experience that’s simply not possible with 2D maps.
These maps allow you to explore and interact with data in a way that’s both intuitive and engaging. With interactive maps, you can drill down into the details, play with filters, and uncover hidden patterns and insights. It’s like having your very own personal geospatial detective ready and waiting to solve any mapping mystery.
This is exactly what we provide to many of our clients here at Tamoco. We take complex geospatial data and provide our customers with a detailed interactive map where they can filter and change the view depending on their current needs.
Use case: real estate
For example, imagine you’re a real estate agent with a portfolio of properties. You can use an interactive map to showcase your listings, highlight the best neighborhoods, and provide a wealth of information to potential buyers. With an interactive map, you can easily filter properties by price, location, square footage, and more. This helps you to tailor your pitch and demonstrate why your properties are the best investment. Your clients will love the ability to explore the data for themselves, and you’ll love how it streamlines the sales process.
This method uses graduated symbols to represent the dataset – it assigns different sizes of symbols to different values in the data. This size can be related to a metric of your choice in the datset itself.
These maps are a great way to visualize data that has a large range of values, such as population or income. By using different symbol sizes, you can effectively convey the information without overwhelming the viewer with too much detail.
Use case: government
An example of a use case for graduated symbol maps is to visualize population density in cities. By using graduated symbols, you can see which areas have the highest population density and which areas have the lowest population density. For example, in a city with a high population density, the symbols would be large, while in a city with a low population density, the symbols would be small.
It’s time to get a little bit fancy with our maps. Have you ever stumbled upon a choropleth map and thought to yourself, “Well this is nice, but it doesn’t quite capture the real deal”? Enter dasymetric mapping, the map lover’s answer to the choropleth’s limitations.
Dasymetric mapping, also known as “value-by-alpha mapping”, takes the idea of choropleth mapping and adds a little extra oomph by incorporating detailed boundary information to create more accurate and nuanced maps. This method allows you to control the boundaries of your map areas and assign data to specific areas within those boundaries.
At Tamoco, we’ve used dasymetric mapping to help companies better understand the distribution of their customer base. By mapping out population density and overlaying customer data, we’ve been able to identify areas with a high concentration of customers and make more informed decisions about where to open new locations.
Use case: population density
A prime example of where dasymetric mapping can come in handy is when mapping population density. In a typical choropleth map, population density may be portrayed on a large scale, with a single color representing the entire area of a city or even a whole country. But with dasymetric mapping, you can get down to the nitty-gritty by mapping population density at the block or even the building level!
The concept behind these maps is simple – these maps depict changes in elevation with contour lines, much like those you’d see on a topographic map. But why settle for 2D when you can have the whole shabang? With Contour Maps, you can visualize the changes in elevation as a 3D representation of the terrain.
Use case: agriculture
One stellar example of where Contour Maps can be a real game changer is in the world of agriculture. Picture this: you’re a farmer, and you want to maximize the yield of your crops. But, you don’t want to leave anything to chance, you want to know exactly how the elevation of your land affects the growth of your crops. Enter Contour Maps. These bad boys can help you determine the slope of your land, which in turn can help you determine the best irrigation and drainage strategies for your crops. You can even take it a step further and integrate satellite imagery with your Contour Map to get an even more accurate representation of your land.
In a nutshell, bubble maps are maps that use bubbles (or circles) to represent data points. The size of the bubble is proportional to the magnitude of the data being represented, while the color and position of the bubble provide additional metadata or information.
Use case:
Bubble maps are an excellent way to showcase data that has both a geographical and a numerical component. For instance, let’s say you’re trying to visualize the distribution of billionaires across the world. You can use bubble maps to show not only where the billionaires are located, but also how many there are in each location. The bigger the bubble, the more billionaires in that area.
Well, that was fun, wasn’t it? A lot of maps for you to dig into, each with its own quirky strengths and charming quirks. But with so many options, how do you choose the right one for your geolocation data?
So let’s start by asking what you’re trying to accomplish. Are you simply looking to display raw location data, or do you want to do something related to footfall to visits? Do you want to highlight patterns and correlations, or are you more interested in conveying information through symbols and shapes?
Once you’ve got a handle on your end goal, consider the data you’re working with. Is it dense or spread out? Does it have many dimensions, or just a few? Do you need to represent change over time or just a snapshot in time?
And finally, think about your audience. Will they be looking at your map on a screen or holding a printout in their hands? Are they data experts, or will they need a little extra guidance to understand your message?
No matter what method you choose, the key is to ensure your map is visually appealing and easy to understand. After all, the most beautiful map in the world is useless if no one can figure out what it’s trying to say.
At Tamoco, we understand the importance of maps and the role they play in visualizing and communicating data. That’s why we offer a wide range of mapping options for you to choose from, each with its own unique style and capabilities.
So why not give Tamoco a try and see what amazing maps you can create with our data today?
Mastering the Art of Protecting Sensitive Information in the Digital Era
In an age where digital connectivity is ubiquitous, this article delves into the critical aspects of data privacy and security. It offers businesses actionable insights and strategies to safeguard sensitive information, with a special focus on complying with GDPR and other privacy regulations. The article also highlights how tools like our paystub generator adhere to stringent data security measures to ensure the protection of financial data.
Introduction:
The digital revolution has transformed the way businesses operate, leading to an era of unprecedented connectivity. While this hyper-connected world offers immense opportunities, it also presents significant challenges in terms of data privacy and security. Recent high-profile data breaches have brought these issues to the forefront, emphasizing the need for robust security measures. This article aims to provide a comprehensive guide for businesses seeking to navigate the complex landscape of data privacy and security. It will explore the implications of data breaches, the intricacies of GDPR and other privacy regulations, and present practical strategies for maintaining data integrity. Additionally, the article will illustrate how our paystub generator tool exemplifies a commitment to the highest standards of data security and privacy.
Understanding the Landscape of Data Breaches and Their Implications
In this hyper-connected digital age, data breaches have become a common headline, causing significant disruptions to businesses and consumers alike. Understanding the landscape of these breaches is crucial for companies to develop effective strategies to safeguard their sensitive data, including financial records managed by tools like paystubs generators.
1.1 The Rise of High-Profile Data Breaches:
Recent years have witnessed an alarming increase in high-profile data breaches. These incidents have affected organizations across various sectors, from retail giants to financial institutions, and even government agencies. For instance, a major credit bureau experienced a breach compromising the personal information of millions of consumers. These breaches not only result in the loss of sensitive data but also lead to financial losses and regulatory penalties.
1.2 Common Vulnerabilities Exploited:
Several factors contribute to the vulnerability of systems to data breaches. These include outdated security protocols, lack of employee training in cybersecurity, and weak points in third-party vendor systems. Often, breaches occur due to simple oversights, such as weak passwords or unpatched software vulnerabilities. Understanding these weak points is crucial for businesses to strengthen their defense mechanisms.
1.3 Consequences of Data Breaches:
The implications of data breaches extend far beyond the immediate financial losses. They significantly impact a company’s reputation, eroding customer trust and loyalty. The long-term damage to brand reputation can be far more costly than the initial financial loss. For instance, when customers entrust their personal and financial data to a service, such as a paystub generator, they expect the highest level of security. A breach in such services can lead to a substantial loss of trust, making it challenging for businesses to regain their clientele’s confidence.
1.4 Addressing the Challenge:
In light of these risks, businesses must prioritize data security. This involves not only implementing advanced technological defenses but also fostering a culture of security awareness among employees. Regular training and updates on security best practices are essential. Additionally, companies providing services like paystubs generators must ensure their tools are equipped with state-of-the-art security features to protect user data effectively.
In summary, understanding the landscape of data breaches is the first step in developing a comprehensive data protection strategy. Recognizing common vulnerabilities and the far-reaching consequences of breaches can guide businesses in strengthening their data privacy and security measures. As we proceed to the next section, we will delve deeper into the complexities of GDPR and global privacy regulations, and how businesses can navigate these to ensure compliance and safeguard their data.
GDPR and Global Privacy Regulations: Navigating Compliance
In the current digital landscape, compliance with data protection regulations such as the General Data Protection Regulation (GDPR) has become a cornerstone for businesses, especially those handling sensitive financial data like paystubs generators. This section explores the intricacies of GDPR and other global privacy laws, and outlines strategies for ensuring compliance.
2.1 Understanding GDPR:
The GDPR, implemented in 2018 by the European Union, represents one of the most stringent privacy and security laws in the world. It imposes obligations onto organizations anywhere, so long as they target or collect data related to people in the EU. The regulation is designed to give individuals more control over their personal data and to unify data protection laws across Europe. Non-compliance can result in hefty fines, making it imperative for businesses, including those operating paystubs generators, to adhere strictly to its guidelines.
2.2 Global Privacy Regulations:
Apart from the GDPR, numerous countries and regions have introduced their own data protection laws. For example, the California Consumer Privacy Act (CCPA) in the United States offers similar rights to consumers as the GDPR. Businesses must stay informed about these laws, especially if they operate internationally or handle data from overseas customers.
2.3 Strategies for Compliance:
Compliance with these regulations requires a proactive approach. Key strategies include:
– Conducting regular data protection impact assessments to identify and mitigate risks.
– Implementing privacy-by-design principles, ensuring that data protection is an integral part of system design and operation.
– Regularly updating policies and procedures in line with evolving regulations.
2.4 Role of Paystub Generators in Compliance:
Paystub generators, which handle sensitive financial information, must demonstrate a high level of compliance with these regulations. This includes incorporating features like data encryption, access controls, and regular audits to ensure data integrity and security. By doing so, they not only comply with legal requirements but also build trust with their users.
Navigating GDPR and global privacy regulations is a complex but essential task for modern businesses. By understanding these laws and implementing robust compliance strategies, businesses can protect themselves from legal repercussions and build a foundation of trust with their customers. In the following section, we will delve into the best practices for data privacy and security, offering practical advice on how businesses can safeguard their data in this challenging environment.
Section 3: Best Practices for Data Privacy and Security in Businesses
In a world where data breaches are increasingly common, it is essential for businesses, particularly those handling sensitive information like paystubs generators, to adopt best practices for data privacy and security. This section outlines key strategies and approaches that can help businesses protect their data and maintain customer trust.
3.1 Establishing Robust Data Security Protocols:
The foundation of any data security strategy is the establishment of robust protocols. This includes:
– Implementing strong encryption methods to protect data both in transit and at rest.
– Regularly updating and patching software to protect against known vulnerabilities.
– Utilizing secure network architectures, including firewalls and intrusion detection systems.
3.2 Employee Training and Awareness:
Human error remains one of the leading causes of data breaches. To mitigate this risk, businesses should:
– Conduct regular training sessions to educate employees about the importance of data security.
– Foster a culture of security awareness, where employees are encouraged to report suspicious activities.
– Implement clear policies for handling sensitive data, including guidelines for using paystubs generators and other financial tools.
3.3 Technological Solutions for Data Protection:
Advancements in technology offer additional layers of security. Businesses should consider:
– Utilizing cloud services with robust security measures for data storage and processing.
– Implementing two-factor authentication to add an extra layer of security to access controls.
– Using advanced threat detection tools to identify and respond to security incidents promptly.
3.4 Regular Audits and Compliance Checks:
To ensure ongoing protection, businesses should:
– Conduct regular security audits to identify and rectify potential vulnerabilities.
– Perform compliance checks to ensure adherence to data protection regulations.
– Continuously evaluate and update security practices in response to new threats and regulatory changes.
For businesses using or providing paystubs generators, adhering to these best practices is not just about compliance; it’s about building and maintaining a reputation as a trustworthy entity in the financial sector. By implementing these strategies, businesses can significantly reduce their risk of data breaches and ensure the security of sensitive information.
In the next section, we will explore the specific role of tools like paystub generators in maintaining data integrity, discussing the security features that are integral to these tools and how they contribute to overall data protection efforts.
Section 4: The Role of Tools like Paystub Generators in Maintaining Data Integrity
In the realm of financial data management, tools like paystub generators play a pivotal role in maintaining data integrity and security. This section highlights how these tools are designed to uphold the highest standards of data protection, providing peace of mind for both businesses and their employees.
4.1 Prioritizing Data Security in Paystub Generators:
Paystub generators handle sensitive financial information, making security a top priority. These tools are typically equipped with advanced security features such as:
– End-to-end encryption to safeguard data from unauthorized access.
– Secure login protocols and multi-factor authentication to ensure that only authorized users can access the data.
– Regular security updates and patches to protect against emerging threats and vulnerabilities.
4.2 Compliance with Regulations:
Paystub generators are not just tools for convenience; they are also bound by legal and regulatory standards pertaining to data protection. Compliance features include:
– Adherence to GDPR, CCPA, and other relevant data protection regulations.
– Regular audits and compliance checks to ensure ongoing adherence to legal standards.
– Features that allow users to control their data, in line with privacy laws.
4.3 Building Trust with Advanced Security Features:
To build and maintain trust with users, paystub generators often incorporate several advanced security measures, such as:
– Data anonymization techniques to protect personal information.
– Role-based access controls to ensure that only individuals with the necessary permissions can view or modify sensitive data.
– Continuous monitoring systems to detect and respond to any unusual activity or potential breaches.
4.4 Case Studies and Testimonials:
The effectiveness of these security measures is often reflected in positive user feedback and case studies. Businesses using these tools can provide testimonials about how the security features of the paystub generator have enhanced their data protection practices, contributing to an overall stronger security posture.
In conclusion, tools like paystub generators are more than just a convenience; they are a critical component in the data security framework of modern businesses. By integrating robust security measures and ensuring compliance with data protection regulations, these tools play a crucial role in safeguarding sensitive financial information.
This comprehensive exploration of data privacy and security in a hyper-connected world underscores the importance of adopting effective strategies and utilizing secure tools like paystub generators. By doing so, businesses can navigate the challenges of this digital era with confidence, ensuring the protection of their data and the trust of their stakeholders.
Conclusion
In conclusion, the journey through the landscape of data privacy and security in a hyper-connected world is both challenging and essential. As this article has highlighted, understanding the nature and implications of data breaches, navigating complex global privacy regulations like GDPR, implementing best practices for data security, and recognizing the critical role of tools like paystub generators in maintaining data integrity are key steps in this journey.
Data breaches have shown us the vulnerabilities in modern digital systems and the profound impact they can have on businesses and individuals. Regulations like GDPR have set a new standard for data protection, demanding diligence and compliance from organizations worldwide. Best practices in data security, from establishing robust protocols to ensuring employee training and awareness, are no longer optional but a necessity. Furthermore, tools like paystub generators exemplify the commitment to data security in the handling of sensitive financial information, playing a vital role in upholding data integrity.
This comprehensive approach to data privacy and security is not just about avoiding penalties or preventing financial loss; it is about fostering trust and reliability in an era where data is a valuable asset. Businesses that embrace these practices and tools will not only safeguard their own interests and those of their customers, but they will also position themselves as responsible and forward-thinking entities in the digital landscape.
As we continue to navigate through this ever-evolving domain, the principles and strategies discussed in this article will serve as a guide, helping businesses and individuals alike to remain vigilant, compliant, and secure in a world where data is both an opportunity and a responsibility.
Marketers have long since stopped catering to the lowest common denominator. Instead, they’re now leveraging the copious amounts of data we create online to find just the right audience for the products they’re promoting.
Geospatial data is indispensable for modern customer segmentation and profiling. What exactly is geospatial data? How does it benefit marketers’ efforts? Why is taking pains to protect such data crucial? This article explains everything you need to know.
What Is Geospatial Data?
Any data that allows one to determine and track the location of a person or object is geospatial. GPS images of a local road network or a Google Maps overview come to mind first. However, different entities can also collect geospatial data on individuals. This can include their place of work and residence but also go beyond.
Some intrusive apps may collect real-time location data on users. The reasons might be innocuous, like showing you the best restaurants or museums in an area. Others could collect more sensitive information like which places you take your dates to or how much you work out and where.
How does marketing benefit from geospatial data?
Location-based customer information is a boon to marketers for several reasons.
They may use it to improve customer segmentation based on habits and activity levels. Such data is also useful in scoping out the competition and tailoring campaigns to account for local preferences better. Given enough time and quantity, geospatial data can help marketers predict and plan for future demand and launch timely, more successful campaigns.
How Can Marketers Safeguard Geospatial Data?
While it comes with a wealth of understanding for marketers to draw on, geospatial data is also rife for exploitation by bad actors. Inadequate protection measures can lead to data breaches and theft, exposing potentially millions of customers and prospects. Even if the data you collect is anonymized, it doesn’t take much to infer more about an individual with their location and movement habits.
Here are the steps any responsible marketing team should take to reduce the risks.
Prioritizing data quality
Geospatial marketing depends on collecting large amounts of data. However, that’s just the prerequisite. Analysis of this data is what yields the insights marketing strategies depend on, so that’s what you should focus on storing.
Even if the strongest cybersecurity methods somehow fail, you can still protect people’s anonymity by not storing any information that could compromise it in the first place. The correct approach is to focus on bulk data, which is useful for uncovering and taking advantage of local patterns and opportunities.
Even so, some marketing efforts are most successful when interacting with people on a personal level. In that case, being transparent and upfront about the data you collect is crucial. Asking for consent and highlighting how their contribution builds trust while also making your efforts compliant with data protection regulations.
Secure access controls
Not everyone in your organization needs or should have access to the geospatial data you do end up storing. Setting up access controls is a way of minimizing threats from malicious insiders. It also ensures that only trusted actors who have business handling the data can interact with it.
Creating a hierarchy with different user classes and corresponding privileges is best. Very few trusted people have privileged access, while everyone else should only be able to view or alter data within the scope of their responsibilities and current projects.
A comprehensive password policy
Role-based access works only if a unique and complex password secures each account. That’s rarely the case if you leave it up to employees since most will reuse passwords or go with variations on familiar favorites that take little effort to guess, for the convenience’s sake.
Implementing an enterprise-level password manager is an elegant and cost-effective solution to this. Its greatest strength is the ability to create and securely store strong passwords not just for geospatial data access but for any account all employees could need.
Besides that, multifactor authentication is an extra security measure industry-leading managers let you set up for each password. It further enhances account security by requiring a separate code alongside the password whenever someone tries to log in from an unknown device.
Data backup
A single copy of something as relevant as geospatial data is a considerable risk. Ransomware attacks are on the rise. A single successful one is enough to make the data inaccessible unless you comply. There’s also the matter of power outages and various hazards that could render the hardware you store the data on useless or inaccessible.
Keeping at least two more up-to-date copies of geospatial data is advisable. One can be on a disconnected physical drive, which will protect it from cyberattacks. Using cloud storage for the other offers access control while mitigating physical risks.
Encryption at rest and in transit
Securely storing geospatial data is important, but not enough. Encryption adds another layer of protection that makes it impossible to make sense of the contents. Even if someone were to steal the encrypted data, it would be useless without a decryption key.
Local encryption takes care of data at rest but is also vulnerable when shared. A marketing team can have members working from home or somewhere else in the world. They all need a secure means of accessing geospatial data and sharing their work, which business VPNs deal with expertly.
Connecting through a VPN ensures encrypted data sharing and complete anonymity. That way, no one can track interactions between team members and wouldn’t be able to benefit even if they could. This one solution is a viable alternative to the safeguards companies set on their main network while protecting anyone using a VPN’s encrypted tunnel wherever they’re connecting from.
Needless to say, market is brimming with a lot of options when it comes to VPNs. Yet, businesses need a thorough comparison before they commit to a provider. In this case, sources like VPN comparison table comes in handy to make decision processes effective and easier.
Conclusion
Geospatial data has become an indispensable wellspring of information for marketers. Knowing how to collect and protect such data guarantees its continued relevance and usefulness for many more upcoming campaigns.
Modern enterprises in various industries are essentially data-driven organizations. They gather vast volumes of business and consumer data for demand forecasting, advanced decision-making, strategic planning, supply chain management, service personalization, advertising campaigns, and whatnot.
Fintech companies and banks are no exceptions to this across-the-board drive, utilizing data engineering and analytics in their basic workflows, such as fraud prevention, risk assessment, customer segmentation and retention, algorithmic trading, operational efficiency optimization, and more.
As the competition in the niche becomes ever fiercer, forward-looking financial actors go beyond analyzing traditional data and tap alternative sources of information that can provide them with a deeper understanding of their target audience and additional insights into current market trends. Location intelligence is one of the alt data practices that allows financial organizations to hone their competitive edge.
Traditional vs. alternative data in the financial sector: The difference made plain
What information do financial firms conventionally employ in their pipeline routine? As a rule, these are records obtained from financial statements, earnings reports, press releases, SEC filings, macroeconomic summaries, industry digests, and other publicly available channels. While reliable and trustworthy, such traditional data can’t provide a comprehensive view of any shop floor area or entity.
Financial enterprises increasingly involve alternative data from social media, product reviews, email receipts, web traffic, IoT devices and sensors, satellite images, jet tracking, credit card transactions, and more to complete the picture.
After proper analysis and processing, this largely unstructured data allows financiers to fill in the gaps existing in traditional data, pinpoint risks, and identify growth opportunities that remained otherwise concealed. Currently, the alt-data market manifests a meteoritic spike, increasing from slightly above$1 billion three years ago to the expected $17.4 billion by 2027.
Geospatial information is one of the crucial growth drivers in this field, predicted to account for a $1 billion-worth market within three years – an astounding surge from $154 million in 2021. Such an impressive rise explains the ubiquitous advent of location intelligence into financial business practices.
Meet location intelligence
Location intelligence uncovers patterns and derives insights by analyzing spatial and geographical data. In other words, it studies what happens on the surface of the earth to arrive at a comprehensive understanding of spatial dynamics and figure out the business implications of these developments.
The most important data types location intelligence draws upon include:
Demographic data – age, gender, ethnicity, educational level, marital status, address, and other population characteristics.
Wealth data – information about income and economic status of people or households, pivotal for reaching out to affluent strata.
Transaction data – everything related to consumer transactions employed for dissecting customer preferences, spending behavior, and sales performances.
Retail outlets data – geographical locations of stores and other commercial establishments, instrumental in analyzing niche competition and market saturation.
Points of interest data – positions of hospitals, schools, parks, and sports facilities that are the staple of urban planning and real estate assessment.
Footfall data – information on the quantities of people passing through a certain place that can be instrumental in business site selection and choosing marketing initiatives.
Administrative boundaries – geographic delineations of countries, states, provinces, cities, and districts, pivotal for market analysis and regulatory compliance.
Where can you obtain this data? Satellite imagery, public statistics, and enterprises’ business records (like clientele addresses or store locations) are the typical sources of relevant location intelligence that can be accessed free of charge or bought from its owners. Besides, you can leverage cutting-edge technological tools (Geographic Information Systems (GIS), Internet of Things sensors and gadgets, cloud computing, etc.) to collect, process, and analyze geographic data.
Yet, the most widespread and valuable source of location intelligence is GPS data pinged by people’s phones across cellular networks. With the advent of 5G and the increasing availability of mobile devices, this source of alternative data is likely to become the foundation of location intelligence for digitally driven enterprises.
Given the multitude of channels and huge volumes of data to be searched, retrieved, and processed, location intelligence can’t be effective without the employment of state-of-the-art know-how, such as:
Big Data. This technology excels at collecting and handling limitless datasets containing both historical and real-time data points.
Artificial intelligence. Whatever insights are derived from geospatial data, AI can swiftly and accurately sift them through to forecast trends, detect anomalies, and automate complex workflow processes.
Machine learning. Its algorithms enable high-precision data analysis and service personalization by learning from training data and enhancing software performance over time.
Financial organizations can enjoy the boons location intelligence relying on geospatial data ushers in by applying it across multiple shop floor processes. Investment management is one of such areas where the perks of location intelligence are particularly evident.
How to improve investment decisions with location intelligence
The investment domain is a high-risk financial sphere where an imprudent step or wrong decision can cost you thousands and sometimes millions of wasted dollars. Modern fintech solutions (for instance, aninvestment application or an investment feature of a personal finance app) benefit greatly from harnessing the power of location intelligence, allowing individuals and organizations to maneuver their portfolios. What are the investment sectors where geospatial data can unlock hidden business value?
Real estate
Before investing in this kind of property, you should study geospatial data displaying its land topography, proximity to commercial and recreational amenities, public transportation, infrastructure, school districts, natural landscapes, historical landmarks, etc.
Location intelligence can also supply such risk-related information as earthquake fault lines, flood areas, and wildfire zones. When you amplify it with other critical data (population’s income level, crime rates, demand for properties, and more), you will have a clear understanding of whether purchasing property in some areas is a sensible and safe investment.
Agriculture
Here, you should look for climate and weather-related insights (for instance, extreme temperatures, altitude, soil fertility characteristics, precipitation volume, probability of natural disasters, etc.) to determine the economic sensibility of investing in agricultural business initiatives in a specific location.
Energy generation
Weather conditions also play a mission-critical role for investment decisions in this field. Cold climates and long night hours condition more demand for energy, so investing in a power station in such places is worthwhile.
Another investment-inviting development in this realm is geospatial data that registers many people moving to an area, which is indicative of the potential surge in economic activity and, consequently, a future increase in the local demand for energy. And when you discover that the number of sunny or windy days in an area is quite significant, you can consider financing the construction of a solar power plant or a wind farm there.
Retail
To predict whether a site for a retail outlet or an existing store is likely to bring profit (and therefore is worth investing), you should analyze such geospatial data as its proximity to residential areas, competitors’ presence, availability of public transportation routes, vehicle traffic intensity, foot traffic data (with typical movement patterns), visit attribution, and even parking lot fill rate.
As a result, you will arrive at a ballpark figure of consumers who will patronize the mall in question and see whether the money they are likely to spend there is a satisfying ROI for you.
Telecommunications
By analyzing population density, foot traffic, and hotel booking dynamics, you can gauge the demand for internet consumption.
If it proves sufficiently high, you can invest in telecommunications organizations that will set up Wi-Fi hotspots and develop digital infrastructure in such potentially income-generating neighborhoods.
Key takeaways
For any future-oriented business with big-time aspirations, analytics, and decision-making based on traditional data can’t provide a sharp competitive edge. To thrive and expand, they should tap alternative data sources, where location intelligence reigns supreme. The geospatial data it furnishes is highly instrumental in many verticals, the financial sector including.
By taking any information from the physical world and mapping it onto your investment strategy in real estate, agriculture, retail, telecommunications, and other domains, you can revolutionize your investment decision-making and reposition your portfolio to let it bring maximum profit.
Some select assets of tamoco have been acquired by pass_by, a leader in the geospatial world, in a commitment to redefining standards through AI-driven intelligence and ground truth verification.
