This is a common dilemma faced by folks who are beginning their careers. What should young data scientists focus on — understanding the nuances of algorithms or faster application of them using the tools? Some of the veterans see this as an “analytics vs technology” question. However, this article agrees to disagree with this concept. We will soon discover the truth as we progress through the article. How should you build a career in data science?
Analytics evolved from a shy goose, a decade back, to an assertive elephant. The tools of the past are irrelevant now. Some of the tools lost market share, their demise worthy of case studies in B-schools. However, if we are to predict its future or build a career in this field, there are some significant lessons it offers.
The Journey of Analytics
A decade back, analytics primarily was relegated to generating risk scorecards and designing campaigns. Analyticalcompanies were built around these services.
Their teams would typically work on SAS, use statistical models, and the output will be some sort of score -risk, propensity, churn etc. Its primary role was to support business functions. Banks used various models to understand customer risk, churn etc. Retailers were active in their campaigns in the early days of adoption patterns.
And then “Business Intelligence” happened. What we saw was a plethora of BI tools addressing various needs of the business. The focus was primarily in various ways of efficient visualizations. Cognos, Business Objects, etc. were the rulers of the day.
But the real change to the nature of Analytics happened with the advent of Big Data. So, what changed with Big data? Was the data not collected at this scale, earlier? What is so “big” about big data? The answer lies more in the underlying hardware and software that allows us to make sense of big data. While data (structured and unstructured) existed for some time before this, the tools to comb through the big data weren’t ready.
Now, in its new role, analytics is no more just about algorithmic complexity. It needs the ability to address the scale. Businesses wanted to understand the “marketed value” of this newfound big data. This is where analytics started courting programming. One might have the best models, but they are of no use unless you trim and extract clean data out of zillions of GBs of data.
This also coincided with the advent of SaaS (Software as a service) and PaaS (Platform as a service). This made computing power more and more affordable.
By now, there is an abundance of data clubbed with economical and viable computing resources to process that data. The natural question was – What can be done with this huge data? Can we perform real-time analytics? Can the algorithmic learning be automated? Can we build models to imitate human logic? That’s where Machine Learning and Artificial Intelligence started becoming more relevant.
What then is machine learning? Well, to each his own. In its more restrictive definition, it limits itself to situations where there is some level of feedback-based learning. But again, the consensus here is to include most forms of analytical techniques into it.
While the traditional analytics need a basic level of expertise in statistics, you can perform most of your advanced NLP, Computer vision etc. without any knowledge of their details. This is made possible by the ML APIs of Amazon/Google. For example, a 10th grader can run facial recognition on a few images, with little or no knowledge of Analytics. Some of the veteran’s question if this is real analytics. Whether you agree with them or not, it is here to stay.
The Need for Programming
Imagine a scenario where your statistical model output needs to be integrated with ERP systems, to enable the line manager to consume the output, or even better, to interact with it. Or a scenario where the inputs given to your optimization model change in real-time, and model reruns. As we see more and more business scenarios, it is becoming increasingly evident that embedded analytical solutions are the way forward. the way analytical solutions interact with the larger ecosystem is getting the spotlight. This is where the programming comes into the picture.
Imagine yourself as Stephen Strange a.k.a as Doctor Strange for a minute. Suppose it to be your alternate ego, different from your primary Mobile App entrepreneur personality. Imagine being given the power to know what is going to happen even before it does. The power to know the bad things – when your users would abandon the app, what would drive them to leave your mobile app for some other app and the power to know the opportunity that is waiting to be explored – which device and operating system version will they visit your app from and even how many times in a day would they visit your app. Sounds like a modern-day mobile app business-centric scene from Marvel Franchise, doesn’t it? But what if we tell you that you have the ability to estimate what is going to happen next in your app and how your users will react, all before it does? Believe it or not, estimating your app users’ moves before they make them is possible. Imagine what this knowledge would get you – Lower Churn Rate, Skyrocketing User Engagement, and a Revenue Scale flying off the roof. The superpower that will get you all these and so many other benefits – the one we are going to looking into in much detail today – is Predictive Analytics.
Being one of the four most insight offering Analytics forms – Descriptive Analytics, Diagnostic Analytics, Predictive Analytics, and Prescriptive Analysis – Predictive Analytics is one that gets you the information on how users are going to act within the app.
The ultimate aim of incorporating Predictive Analytics in a mobile app is simple: Know what is going to happen and prevent/boost the action.
Let us look at what Predictive Analytics is before we move on to the mobile app development stages in which it can be incorporated, the benefits it would bring \+-
hs,ingto the mobile app-centered businesses, and some use cases on how the analytics can be added to different industries.
Predictive Analytics Definition
The Predictive Analytics Definition goes somewhere like this – The Analytics form tells what is going to happen. The estimation form analyzes data and statistics to create a pattern which then helps in doing the guesswork on what is going to happen next.
With predictive analytics definition now attended to, it is now time to look at the impact of the insightful analytics technique in the two phases of Mobile App Journey – Mobile App Development and Post Mobile App Launch.
How Does Predictive Analytics Expedite Mobile Application Development
Mobile app developers generate a huge amount of data specific to mobile app testing and quality check, running of a build, and a number of other daily tasks; these data mainly dictates short and long-term project success. Mainly, mobile app developers who have integrated Predictive Analytics in their development process gather data and then create a predictive analytics framework to find out patterns that are hidden in the many unstructured and structured data sets.
The end result: The mobile app developers get an algorithm using which they can forecast problems that the development cycle might face.
While this is the high level explanation of how Predictive Analytics in Mobile App Development Process works, let us now give you a practical insight by showing how we use Predictive Analytics in our mobile app development cycle to make the whole process a lot faster and quality ensured.
How Anteelo Uses Predictive Analytics For Mobile App Development
1. Predictive Planning
Mobile app developers and project managers very often underestimate the time, resources, and money it would require to deliver code. They might run into same delivery issues time after time, especially when they work on similar projects.
We use predictive analytics to identify the repetitive mistakes that result in buggy codes. We also factor the number of code lines delivered by the developers and time that it took them to write them earlier. It gives us the information to predict whether or not we would be able to meet the scheduled delivery date.
2. Predictive Analytics DevOps
The merger of mobile app development and operations – DevOps is known to expedite the mobile app delivery time. When the production environment data flows back to the developers, predictive analytics can help identify which coding approach is causing bad user experience in the market.
We analyze the data specific to the usage and failure pattern of the mobile app to then predict which features or user movement are going to make the app crash, then we fix the issue in future releases.
3. Predictive Testing
Instead of testing every combination of the user actions and interfaces with other systems, we use predictive analytics to find the path that users commonly take and identify the stage where the app is crashing. We also, at times, use algorithms to measure commonalities between all user execution flow and identify and focus on overlap which indicates common execution paths.
Now that we have looked at how Predictive Analytics in Mobile App Development works, it is time to look at the benefits that the analytic framework has to offer to the mobile app centered businesses and entrepreneurs.
How to Use Predictive Analytics for Bettering Your Mobile App Experience
There are a number of ways businesses can leverage predictive analytics for bettering the overall experience their mobile app leaves.
From giving them better insights on the research front, in terms of which geographical region should they promote their app more in to identifying the devices they should get the apps designed according to, there are a number of ways Predictive Analytics come in handy for the future centered mobile app businesses.
1. For Greater User Retention
Predictive Analytics helps in bettering the user retention number to a huge extent. By giving the app admin a clear statistical based picture of the problem areas of the mobile app, giving them the time to get it corrected before it becomes a persistent issue making app users abandon the app.
By giving the businesses an exact picture of how users are interacting with their app and the ways they wish to interact with the app, Predictive Analytics help entrepreneurs correct issues and amplify the features that are attracting the users.
2. For Personalized Marketing
Personalized Marketing is the biggest sign of how companies use analytics to lure customers to use their app.
Ever wonder how Spotify gives you recommended song playlist or how Amazon shows you Customer who bought this also bought list? It is all a result of predictive analytics. By implementing it in your mobile app, you will be able to give your users a more personalized listing and messages, thus making the whole experience a lot more customized for the end users.
3. For Identifying which Screen’s Content Need to be Changed
Predictive Analytics help identify which element of the app is turning down the users or which screen are they using before leaving the app, this information helps mobile app entrepreneurs immensely as they get face to face with the problem area. And now, instead of changing the whole application, they are only concentrated on improving a particular segment/ section.
4. For Identifying the Time to Make Device Switch
When employed right, Predictive Analytics in mobile apps gives entrepreneurs insight into which device and in fact which operating system their users are getting active on to use the app. This information is a goldmine for the tech team as they can then get the app designed according to the specificity of that specific application.
5. For Making Their Notification Game Better
Predictive Analytics helps businesses identify which notification message is causing what reaction and if there is a difference between varying time and content. This information helps marketers plan their notification push in a way that it gets a maximum positive outcome.
By categorizing the mobile app users in segments like those who are interacting most with the app, those who are most likely to abandon the app, and those who have simply made your mobile app the case of install and forget, Predictive Analytics help mobile app marketers with a platform where they know how to segregate their push notifications and between what people.
With this, we have now looked at the contributing role that Predictive Analytics plays in the mobile app development industry, both from the end of the mobile app development agency and the mobile app centered business, it is now time to look at some use cases with respect to how you can add the analytics form in your mobile app, across industries.
Predictive Analytics Use Cases in the Real World
While there are a number of Predictive Analytics examples around us, let us look at those areas that are more prone to give instant high returns when incorporated with Predictive Analytics.
1. Predictive Analytics in Healthcare
The reason this is one of the prominent Healthcare Trends in 2019 in beyond is that it has expanded itself from its once prominent role of being a personalized healthcare enabler.
Earlier used only to help send a personalized recommendation to the patients in terms of health and care considerations that they would have to make, it is now being incorporated in the healthcare industry for three crucial requirements – For risk estimation, Geo-mapping, and for planning out the what-if scenarios in terms of both surgery and patient inflow in the hospital.
The prospect that Predictive Analytics in Healthcare comes with is one that promises mass transformation of a complete industry.
2. Predictive Analytics in eCommerce
When we talk about Predictive Analytics examples, it is important to have a discussion without the mention of the eCommerce industry. The analytics not just help users by giving them listings related to ‘Customers who bought this also bought’ but also in showing them ads of offers that have arrived on the products that they were looking to buy earlier or have in their shopping cart.
The benefit of keeping the users hooked to the website by giving them offers and discounts on the products that they actually wish to purchase and at the same time helping them decide what to buy next are the two factors that have drawn eCommerce giants like Amazon, eBay etc. integrate Predictive Analytics in their website and mobile apps.
3. Predictive Analytics in On-demand
In the on-demand economy specific to transport and commutation, predictive analytics come in very handy in terms of estimating the areas that are going to ask for maximum fleet demand, the price that users are most likely to pay for a tip, the stage at which they are cancelling the ride, etc.
Apart from this, it also helps in estimating the accident scenario in terms of drivers who are most likely to rash drives, the geographical area that is most prone to accident, etc.
The on-demand fleet economy has a lot to take advantage of from the predictive analytics algorithms. The industry-wide realization has led to brands like Uber and Didi Chuxing apply Predictive Analytics and Machine Learning in the business model.
4. Predictive Analytics in Enterprises
The what would happen next information that Predictive Analytics offers to the company’s business team comes in as a golden opportunity for enterprises who are struggling in their CRM domain and also in the HR area.
Predictive Analytics can give insight into the stage at which a customer is most likely to take their business elsewhere and the performance-based analysis of employees, giving the HRs an insight into whether or not the employee should be kept associated.
By researching on the skills that are most demanded by the industry, predictive analytics and enterprise mobility can together up the employees’ skills to a huge extent.
Now that we have seen all – Impact of Predictive Analytics in Mobile App economy (an impact that both mobile app development company and the mobile app businesses face) along with the real world use cases, it is now time to bring the guide to an end by giving you an insight into the Predictive Analytics tools that offer the most calculated inferences.
Predictive Analytics Tools
While a quick search on the internet will get you a great list of predictive analytics tools, here are the ones that we rely on to help our partnered entrepreneurs get a better hang on where their app business is headed –
This brings to an official end to our 10 Minutes guide to Predictive Analytics in Mobile Apps. If you need more information on how to integrate predictive analytics into your mobile app and reap the benefits of low churn rate and minimize the app abandonment instances, get in touch with our team of Predictive Analysis Experts, today!
AI has become the pillar of growth for companies when it comes to maintaining relevance as well as an edge over the competition. What’s more, AI based models have become the new revenue drivers for companies looking to capitalize on data as a competitive advantage. The rise in algorithmically driven successes can be attributed primarily to enhancements on the hardware side. Big data tools, and an infrastructure based on both on-premise and cloud services, have paved the way for this fully evolved AI ML ecosystem.
According to a study, AI is the next digital frontier and organizations that leverage models have a 7.5% profit margin advantage over their peers. With AI models becoming the key pillar for building valuable IP and revenue, Anteelo shows the way with a new approach to model management.
With more research being plowed into tweaking neural networks, businesses face a bunch of tricky questions-how profitable it is to go full ML? Is the available compute infrastructure sufficient enough to take the leap? Can the deployed model adjust to the changing grounds and business requirements?
From training personnel to acquiring tools, business leaders are also grappling with critical questions related to model management — model validity in the face of changing business realities. Models lose validity over time as market realities change, new contingencies emerge, and new variables come into the picture. Hence all models need to be revamped and refreshed regularly to ensure they remain relevant. However, the refresh process is often manual and possesses a lot of scope for improvement.
Anteelo employs machine learning algorithms to develop analytics solutions for its customers. Our solutions range from providing prediction frameworks for online retailers in the US to cutting costs for manufacturers of thermal insulation materials. We are embracing a factory approach to building AI models.
Need For A Move to a Factory Approach
There are multiple reasons models needs to move to a factory approach. Setting up models for the first time is a highly ad hoc process which is over-dependent on the skill of the data scientist building the model. The process is also highly susceptible to human biases and is very labor intensive. Model refreshes, on the other hand, are reactive and end up following a blind process, and remain labor intensive.
The term ML model refers to the model artefact that is created by the training process. The training data must contain the correct answer, which is known as a target or target attribute.
The learning algorithm finds patterns in the training data that maps the input data attributes to the target (the answer to be predicted), and it outputs an ML model that captures these patterns. A model can have many dependencies and to store all the components to make sure all features available both offline and online for deployment, all the information is stored in a central repository.
The new set up for a model factory approach should start with a strong clarity about the business requirements and environment. When building the model for the first time, the bounds for the model should be clearly defined, and the best model identified. If necessary, an ensemble of multiple models should be used. A good model can be identified basis multiple criteria, such as quality metrics, cumulative gains, heat maps, bootstrapping methods and other techniques.
The model refresh process should go through the following steps:
Define frequency of refresh, as well as exception conditions under which an out-of-turn refresh must be done
Define when the refresh will occur – is it when the current scenarios repeat, or when new scenarios emerge
Automate the refresh process, with clear bounds of the process defined. Data collection, splitting the dataset into training and validation samples, running the models, and validating and analyzing them for accuracy, are all steps than can be automated.
Importance of the AI-human Interface
The ultimate goal of any AI research is to derive insights about the business. Highly accurate AI models are usually harder for a human (especially a non-data scientist) to interpret, so the right model which balances accuracy vs. interpretability should be deployed. Since the eventual value of a model lies in its usage by business teams to meet targets or achieve goals, human review and understanding of models is essential. The model factory is intended to save human time in refreshing models through automation. This human time can in turn be used to analyze and derive the right insights from the mode results.
Future Direction
Traditional data storage and analytic tools can no longer provide the agility and flexibility required to deliver relevant business insights. An AIML based factory model approach augmented within human intelligence can help organizations overcome maintain competitiveness and relevance. Organizations seeking transition to an AIML based model factory setup can get an idea of how to scale by looking at Anteelo’ s approach.
Recently I had to stand up a Next Generation Firewall (NGF) in an Azure Subscription as part of a Minimum Viable Product (MVP). This was a Palo Alto NGF with a number of templates that can help with the implementation.
I had to alter the template so the Application Gateway was not deployed. The client had decided on a standard External Load Balancer (ELB) so the additional features of an Application Gateway were not required. I then updated the parameters in the JSON file and deployed via an AzureDevOps Pipeline, and with a few run-throughs in my test subscription, everything was successfully deployed.
That’s fine, but after going through the configuration I realized the public IPs (PIPs) deployed as part of the template were “Basic” rather than “Standard.” When you deploy an Azure Load Balancer, there needs to be parity with any device PIPs you are balancing against. So, the PIPs were deleted and recreated as “Standard.” Likewise, the Internal Load Balancer (ILB) needed this too.
I had a PowerShell script from when I had stood up load balancers in the past and I modified this to keep everything repeatable. There would be two NGFs in two regions – 4 NGFs in total and two external load-balancers and two internal load-balancers.
A diagram from one region is shown below:
With all the load balancers in place, we should be able to pass traffic, right? Actually, no. Traffic didn’t seem to be passing. An investigation revealed several gotchas.
Gotcha 1. This wasn’t really a gotcha because I knew some Route Tables with User Defined Routing (UDR) would need to be set up. An example UDR on an internal subnet might be:
0.0.0.0/0 to Virtual Appliance pointing at the Private ILB IP Address. Also on the DMZ In subnet – where the Palo Alto Untrusted NIC sits, a UDR might be 0.0.0.0/0 to “Internet.” You should also have routes coming back the other way to the vNets. And, internally you can continue to allow Route Propagation if Express Route is in the mix, but on the Firewall Subnets, this should be disabled. Keep things tight and secure on those subnets.
But still no traffic after the Route Tables were configured.
Gotcha 2. The Palo Alto firewalls have a GUI ping utility in the user interface. Unfortunately, in the most current version of the Palo Alto Firewall OS (9 at the time of writing) the ping doesn’t work properly. This is because the firewall Interfaces are set to Dynamic Host Configuration Protocol (DHCP). I believe, as Azure controls and passes out the IPs to the Interfaces Static, DHCP is not required.
The way I decided to test things with this MVP, which is using a hub-and-spoke architecture, was to stand up a VM on a Non-Production Internal Spoke vNet.
Gotcha 3. With all my UDRs set up with the load balancers and an internal VM trying to browse the internet, things are still not working. I now call a Palo Alto architect for input and learn the configuration on the firewalls is fine but there’s something not right with the load balancers.
At this point I was tempted to go down the Outbound Rules configuration route at the Azure CLI. I had used this before when splitting UDP and TCP Traffic to different PIPs on a Standard Load Balancer.
But I decided to take a step back and to start going through the load balancer configuration. I noticed that on my Health Probe I had set it to HTTP 80 as I had used this previously.
I changed it from HTTP 80 to TCP 80 in the Protocol box to see if it made a difference. I did this on both internal and external load balancers.
Hey, presto. Web Traffic started passing. The Health Probe hadn’t liked HTTP as the protocol as it was looking for a file and path.
Ok, well and good. I revisited the Azure Architecture Guide from Palo Alto and also discussed with a Palo Alto architect.
They mentioned SSH – Port 22 for health probes. I changed that accordingly to see if things still worked – and they did.
Finding the culprit
So, the health probe was the culprit — as was I for re-using PowerShell from a previous configuration. Even then, I’m not sure my eye would have picked up HTTP 80 vs TCP 80 the first time round. The health probe couldn’t access HTTP 80 Path / so it basically stopped all traffic, whereas TCP 80 doesn’t look for a path. Now we are ready to switch the Route Table UDRs to point Production Spoke vNets to the NGF.
To sum up the three gotchas:
Configure your Route Tables and UDRs.
Don’t use Ping to test with Azure Load Balancers
Don’t use HTTP 80 for your Health Probe to NGFs.
Hopefully this will help circumvent some problems configuring load balancers with your NGFs when you are standing up an MVP – whatever flavour of NGF is used.
We often marvel at the fact about the unbelievable progress our world has made in terms of technology. We are literally living with all the gadgets and apps that only existed in sci-fi movies like it’s no big deal. But the truth is, it has taken years to research and development to reach a point where advanced technology has become the normalcy of our lives. From Amazon’s Alexa to Google Assistant, AI software are doing our daily chores for us. In the end, all these software are mobile applications developed by a mobile application developer. This shows us that mobile app development has reached the peak of its evolution and that there are endless innovation opportunities in the mobile app development field. Also, it tells us that for the success of any mobile application, it requires the developer to be outstanding. This is because although there are over 5 million applications in the major app stores, less than 1% of them make it to the 1 million downloads mark. Whether as an app development agency you are looking for app developers or you are a mobile app developer, looking forward to making a breakthrough mobile application, either as a freelancer or in association with an enterprise, there are a few mobile application development skills that you need to learn in order to make your app revolutionary.
Let us take a look at what special skills you can gather to become a successful Android and/or iOS app developer.
Skills required for Mobile App Developers
Cross-Platform App Development – Learning to be the best Android or iOS developer is a long-forgotten agenda in the mobile app development industry. Today, every app developer knows better than choosing just one platform. The skill-set required for a mobile application developer consists of the knowledge of all the platforms available for app development. In fact, Google’s play store has, unarguably, a higher number of active users but the real app engagement comes from the App Store users. That is why it is understandable that being the master of only one of the platforms may not help you capture all of your audience. To be a successful app development company, you should know how to find mobile app developers that are best for your team. Therefore, you need to look for developers who are eminent in Cross-platform mobile app development skills. And as a developer, having knowledge of cross-platform app development opens up a larger market for you along with benefits like:– Less usage of resources in terms of time, efforts and money because the development of apps for different platforms separately is not required as Cross-platform app development allows the reuse of code across multiple platforms. The best mobile app developers are the ones who don’t settle for either Android or iOS but go for both. And Cross-platform development is the best way to do it because it gives the app uniformity across different platforms which results in higher user engagement.
UI/UX Skills – The ardent purpose of an experienced mobile application developer is to make a mobile application that will attract and engage as many users as possible. And user engagement is a skill that can be acquired by having knowledge about how to make an app likable for users. And that can only be done by learning UI/UX skillsets. Although, the mobile app development team in a company consists of UI/UX designers who are solely responsible for the designing part of the mobile application as a developer, having the basic knowledge of front-end design and development is important. And as a mobile app development company, you should be hiring developers with UI/UX design skills.
Knowledge of popular programming languages (Preferably multiple) – As we all know that there are several programming languages in the in the app development world and each and every one of the languages has some unique characteristics which can be applied in specific situations. As an experienced mobile application developer, having the knowledge of multiple programming languages gives you an edge over the developer who is a specialist. Languages such as Java, Python, C#, Javascript, PHP for Android and Swift and Objective-C for iOS are some of the most widely used languages. It does not matter, which language you choose to go forward with, it is important to know at least two of the languages. And while some professions give you the liberty to learn things on your own based on experience. And the answer to how to become a mobile application developer is that it requires you to be updated with the prevalent trends in the app development industry.
Experience in Agile Methodologies – Mobile app development is a highly organized sector to work in. For the ease of the processes, there is a planning methodology defines for developers that make things simple and more systematic to follow, which is Agile Methodology. Agile methodology is a set of different software development methods based on iterative processes and standalone routines that facilitate the overall development process. A successful mobile app developer should be well versed and experienced in mobile application development skills and methodologies such as XP (Extreme programming), Scrum, DSDM, etc.
Cybersecurity Guidelines – As a mobile app developer, you develop a specialty in app development, but still, every project is unique in itself. Apart from that, there are clients reaching out to you from different geographies, with diverse government and cyber security guidelines. Therefore, integrating app security in mobile app development is crucial. The growing news about malware attacks all around the world has peaked the need of cybersecurity professionals. And if you’re one the app developers who can make sure that along with being aesthetically and operationally outstanding, your app is also categorically safe from malware, you can be in great demand. Apps like FinTech or apps which need the banking information of users require plenty of safety precautions as it deals with the most sensitive user information. Therefore, there are many opportunities for developers who are experienced in data encryption and mobile app development skills and safety.
Mobile app development is a highly dynamic profession that is constantly evolving and upgrading, thus it becomes indispensable for mobile app developers’ qualifications to be up-to-date in the latest practices and trends of the industry.
Apart from the above-stated skills, developers should also possess data processing skills. And As a part of a mobile app development company, you also need to have an aptitude for working in teams and delivering your best. Even being a client means that to hire a mobile app developer, you need to know which skill-set to look for.
A wildlife videographer typically returns from a shoot with hundreds of gigabytes of raw video files on 512GB memory cards. It takes about 40 minutes to import the files into a desktop device, including various prompts from the computer for saving, copying or replacing files. Then the videographer must create a new project in a video-editing tool, move the files into the correct project and begin editing. Once the project is complete, the video files must be moved to an external hard drive and copied to a cloud storage service.
All of this activity can be classified as toil — manual, repetitive tasks that are devoid of enduring value and scale up as demands grow. Toil impacts productivity every day across industries, including systems hosted on cloud infrastructure. The good news is that much of it can be alleviated through automation, leveraging multiple existing cloud provider tools. However, developers and operators must configure cloud-based systems correctly, and in many cases these systems are not fully optimised and require manual intervention from time to time.
Identifying toil
Toil is everywhere. Let’s take Amazon EC2 as an example. EC2 provides Amazon Elastic Block Store (EBS) compute and storage capacity to build servers in the cloud. The storage units associated with EC2 are disks which contain operating system and application data that grows over time, and ultimately the disk and the file system must be expanded, requiring many steps to complete.
The high-level steps involved in expanding a disk are time consuming. They include:
Get an alert on your favourite monitoring tool
Identify the AWS account
Log in to the AWS Console
Locate the instance
Locate the EBS volume
Expand the disk (EBS)
Wait for disk expansion to complete
Expand the disk partition
Expand the file system
One way to eliminate these tasks is by allocating a large amount of disk space, but that wouldn’t be economical. Unused space drives up EBS costs, but too little space results in system failure. Thus, optimising disk usage is essential.
This example qualifies as toil because it has some of these key features:
The disk expansion process is managed manually. Plus, these manual steps have no enduring value and grow linearly with user traffic.
The process will need to be repeated on other servers as well in the future.
The process can be automated, as we will soon learn.
The move to NoOps
Traditionally, this work is performed by IT operations, known as the Ops team. Ops teams come in variety of forms but their primary objective remains the same – to ensure that systems are operating smoothly. When they are not, the Ops team responds to the event and resolves the problem.
NoOps is a concept in which operational tasks are automated, and there is no need for a dedicated team to manage the systems. NoOps does not mean operators would slowly disappear from the organisation, but they would now focus on identifying toil, finding ways to automate the task and, finally, eliminating it. Some of the tasks driven by NoOps require additional tools to achieve automation. The choice of tool is not important as long as it eliminates toil.
Figure 1 – NoOps approach in responding to an alert in the system
In our disk expansion example, the Ops team typically would receive an alert that the system is running out of space. A monitoring tool would raise a ticket in the IT Service Management (ITSM) tool, and that would be end of the cycle.
Under NoOps, the monitoring tool would send a webhook callback to the API gateway with the details of the alert, including the disk and the server identifier. The API gateway then forwards this information and triggers Simple Systems Manager (SSM) automation commands, which would increase the disk size. Finally, a member of the Ops team is automatically notified that the problem has been addressed.
AWS Systems Manager automation
The monitoring tool and the API gateway play an important role in detecting and forwarding the alert, but the brains of NoOps is AWS Systems Manager automation.
This service builds automation workflows for the nine manual steps needed for disk expansion through an SSM document, a system-readable instruction written by an operator. Some tasks may even involve invoking other systems, such as AWS Lambda and AWS Services, but the orchestration of the workflow is achieved by SSM automation, as shown in this table:
Step #
Task Name
SSM Automation Action
Comments
1
Get trigger details and expand volume
aws:invokeLambdaFunction
Using Lambda, the system must determine the exact volume and expand it based on a pre-defined percentage or value.
2
Wait for the disk expansion
aws:waitUntilVolumeIsOkOnAws
Disk expansion would fail if it goes to the next steps without waiting for time to complete.
3
Get OS information
aws:executeAwsApi
Windows and Linux distros have different commands to expand partition and file systems.
4
Branch the workflow depending on the OS
aws:branch
The automation task would now be branched based on the OS.
5
Expand the disk
aws:runCommand
The branched workflow would run commands on the OS that would expand the disk gracefully.
6
Send notification to the ITSM tool
aws:invokeLambdaFunction
Send a report on the success or failure of the NoOps task for documentation.
Applying NoOps across IT operations
This example shows the potential for improving operator productivity through automation, a key benefit of AWS cloud services. This level of NoOps can also be achieved through tools and services from other cloud providers to efficiently operate and secure hybrid environments at scale. For AWS deployments, Amazon EventBridge and AWS Systems Manager OpsCenter can assist in building event-driven application architectures, resolving issues quickly and, ultimately, and eliminating toil.
Other NoOps use cases include:
Automatically determine the cause of system failures by extracting the appropriate sections of the logs and appending these into the alerting workflow.
Perform disruptive tasks in bulk, such as scripted restart of EC2 instances with approval on multiple AWS accounts.
Automatically amend the IPs in the allowlist/denylist of a security group when a security alert is triggered on the monitoring tool.
Automatically restore data/databases using service requests.
Identify high CPU/memory process and kill/restart if required automatically.
Automatically clear temporary files when disk utilization is high.
Automatically execute EC2 rescue when an EC2 instance is dead.
Automatically take snapshots/Amazon Machine Images (AMIs) before any scheduled or planned change.
In the case of the wildlife videographer, NoOps principles could be applied to eliminate repetitive work. A script can automate the processes of copying, loading, creating projects and archiving files, saving countless hours of work and allowing the videographer to focus on core aspects of production.
For cloud architectures, NoOps should be seen as the next logical iteration of the Ops team. Eliminating toil is essential to help operators focus on site reliability and improving services.
When Facebook bought Instagram in 2012, it sent a clear signal that this was a platform with real potential. Since then, Instagram has gone from strength to strength and its increasing usage has made it a place where businesses can have a real marketing impact. In this post, we’ll look at the platform and show you eight tips to help boost your Instagram sales.
A growing platform
Instagram has grown massively in recent years, expanding its monthly user base to over one billion. That’s three times as many users as Twitter. This has made it a very appealing place for businesses to advertise their products and to run social media campaigns. Indeed, half of all businesses now use Instagram as a marketing tool and in 2017, they spent almost £2 billion on advertising. With this amount of investment, it is obvious that these businesses are seeing great returns.
Advantages of Instagram for online retailers
Instagram is a media that focuses on high-quality images and video, making it ideal for posting highly visual and creative product photographs and marketing that can link directly to your online store. In this sense, Instagram becomes an extension of that store – people stumble upon a product they like and can click through to buy it. Nothing could be easier.
And with such a large and growing audience, it can massively expand your company’s reach, enhancing user engagement while helping to improve your brand’s positioning. Add to this the option to link Instagram and Facebook accounts, so that posts which appear on Instagram also appear on Facebook, and the potential for spreading the word is even higher.
Tips on boosting Instagram sales
Set up an Instagram business profile
Instagram now lets you set up a business profile, so you won’t need to rely on using a personal account to do your marketing. One great feature of the pro-style, business profile is that you can import all your Facebook contacts. It also gives you analytics data to help you see how well your posts are doing.
Take advantage of the selling tools
There are many tools now available that help you to sell products on Instagram. Essentially, these use a variety of techniques to let users click on your photo and go buy what they see. These clickable shopfront tools include ‘available to buy’ icons, item prices or ‘shop now’ buttons. All a user has to do is click on an icon or button and they are taken directly to the store or to your Instagram bio URL.
Post photos that attract attention
With millions of photographs added every day on a platform that aims to promote great photography, you need to post images which stand out. The better the visual experience you provide for users, the more your brand will get noticed. Experiment with different techniques of taking photographs and use filters and editing tools to create an identifiable brand style of your own.
Promote your website in your pictures
A creative way to get people to visit your website is to show your URL in the photos you post. Some do this with added text or through watermarking, however, there are more ingenious ways to do this – have someone wear it on a t-shirt or have it graffitied on a wall in the background, for example. Subtlety like this is intriguing and will develop curiosity without users feeling over-marketed to.
Make the most of your captions
Aside from the image, you can also add a textual caption to your photo. With up to 2,200 characters available, including the use of emojis, captions are a valuable opportunity to develop your brand’s identity, engage your audience and slip in those important calls to action. You can also add numerous hashtags, too, helping your post turn up in relevant searches.
Use hashtags wisely
Just as on Twitter, hashtags are widely used on Instagram, enabling people to search for them. For this reason, all your marketing images need to have relevant keyword-style hashtags added to their captions. Doing this helps your products get seen by a wider audience and ensures that searchers have a better chance of finding them.
Attract Instagram influencers
Influencers are a big deal on social media. If they like or share your marketing material, it can have a massive and instant effect on your sales. This is why many of the famous vloggers and bloggers now have lucrative sponsorship deals with major brands. However, if you can grab their attention they may like your products without you having to pay them huge sums of money. To do this, mention them in your captions and give some positive responses to the things they post in order to try to establish a relationship. While getting a positive response back is never guaranteed (these people have millions of followers) the potential results can be worth the work.
Use Instagram ads
Finally, you should consider paying for advertising on Instagram in the same way you would on Facebook. Instagram ads are more direct than a social media campaign and can have a quicker impact, helping businesses get established on the platform sooner.
One reason for advertising on Instagram is that, statistically, its users are sixty times more likely to engage with your ad than users on Facebook, leading to a much higher ROI.
Conclusion
As you can see, Instagram is a highly useful platform on which to market your products. Is it ideal for every business? No, you’ll need to research whether your target audience is part of the Instagram diaspora. If they are, however, following the tips given above should help you boost your online sales.
Businesses are juggling an increasingly complex data and IT environment, in which they are faced with overwhelming amounts of data and vast numbers of applications.
Many current on-premises storage environments, with infrastructure that typically has reached the end of its service life, are not sustainable. The result: inefficiencies, the inability to leverage innovative capabilities, poor performance, escalating maintenance costs, underutilized storage capacity and complicated security arrangements. Compounding the problem is the fact that companies are typically spending 70 cents of every dollar on managing their traditional IT environments, leaving little room for investment in next-generation capabilities.
Using a consumption-based private cloud for storage, also known as Storage-as-a-Service (STaaS), opens doors to cost savings, secure and compliant infrastructure, the ability to leverage the latest technologies – such as automation, artificial intelligence and bionics – and a reduction in waste. STaaS can transform your storage environment in five important ways:
Eliminate unused capacity. Typically, companies have tended to overprovision storage capacity because of uncertainty about how much would be needed. While most companies think they use as much as 75% or 80% of capacity, detailed analysis has found that as much as 60% of storage capacity is not used. Although 100% utilization is an unrealistic target, companies should be aiming for 85%. The question you have to ask yourself is, are you willing to pay a slightly higher premium per gigabyte of storage for better utilization? Adopting an agile storage model will show in a somewhat higher per unit cost but opens the way to a comprehensive and flexible service that can result in overall cost reduction opportunities of up to 50%.
Effectively balance performance and capacity. A simple way to think about storage is as capacity vs performance. In the past, your data was distributed on a spinning disk that involved a trade-off between capacity and performance. To get acceptable performance, companies typically compromised with too much capacity, leading to inefficiencies. Today, with the advent of solid-state drives, you can dial between your capacity and performance based on compression and deduplication technologies that expand the amount of data you can store. This allows for greater flexibility between capacity and performance storage, which is hugely significant to the chief information officer, who no longer has to guess in advance just how much capacity versus storage will be needed. The end result is economic efficiency because you aren’t overprovisioned in any particular aspect of your storage kit, and you can respond to growth or decline, seasonal variations or fluctuations in demand.
Scale capacity. There’s a good reason why companies typically massively overprovision for storage, and that’s because they know that it takes months to get the storage they require. If they aren’t sure, they will buy more than they need rather than take the risk of running short. But STaaS provides the elasticity you need to quickly scale up capacity without being held hostage to the limits of a particular storage array. By paying a flat rate per GB based on volume, your price per GB goes down as your volume increases — and you aren’t held back by the limitations involved with adding a new storage array.
Increase manageability. STaaS has some distinct advantages when it comes to managing storage, including the ability to remotely manage and monitor the performance and health of your storage for capacity, failures and other issues. Furthermore, several levels of data protection ensure against unwanted replication of the data or loss of data due to disaster. You’re also better able to manage cost and seasonal requirements because you are only paying for what’s on the storage array. So, for instance, a retail customer who has greater analysis requirements in October and November in preparation for the holiday season can dial up that additional storage capacity, pay for it on an as-needed basis and get rid of it in January.
Use bionics and analytics to increase availability. The true benefits of bionics and analytics in STaaS lie in providing a better, faster and cheaper service. The service provider can use the technologies to automate alerts and auto discover any issues, so the storage environment runs more smoothly. You might not notice that capabilities are being scaled up faster or that processes are more repeatable and scalable, but you will notice cost reductions and greater reliability of reporting.
Changing the storage story
The pressure continues to mount on CIOs and other business leaders to embrace digital transformation and the capabilities enabled by AI and other advanced technologies. The challenge, however, is where do you find the resources for this transformation if you’re investing 70% of every dollar on managing your traditional IT environment? By reducing your spend by up to 50% through STaaS, you can reinvest in digital technologies and in the resources and skills you need to meet the technology challenges of the future.
Healthcare Industry is evolving at a lightning speed. What was once a traditional industry that worked around several rounds of doctor and patient interaction, which almost always led to a very low positive sentiment to now when telemedicine has made the doctor patient relation real-time and without geographical limitations and brought mobile technology trends in healthcare.
We have covered many topics related to healthcare and its trends. From turning healthcare consumerism challenge into an opportunity to a guide to Healthcare Compliances to the future of healthcare apps, and many more we have provided immense knowledge on the topic and now as we have entered a new year, it is only apt that we look into where the Healthcare industry is headed in terms of technology.
Without further delay, let us look at healthcare mobile apps trends 2021 and beyond.
Trends in Healthcare Mobile App Development
1. Artificial Intelligence
AI is changing our outlook on modern-day healthcare delivery. The potential that the technology has in changing the industry has placed it in the list as one of the prominent technology trends in healthcare 2021-2022.
It represents a set of several technologies that enable machines to comprehend, sense, learns, and act like humans to an extent that is prepared to perform a number of clinical and administrative healthcare functions. The global AI in the healthcare industry market size is expected to grow and reach USD 45.2 billion by 2026 from USD 4.9 billion in 2020.
When we talk about the application of AI in healthcare, Accenture, through a survey, has identified 10 areas that will be impacted by AI adoption in the healthcare sector, along with the revenue they will be able to generate for the Healthcare economy –
The AI inclusion in medical mobile apps is going to grow the AI healthcare market to over $6.6 Billion by 2021. The fact that AI is going to bring innovative healthcare apps in the market is a direct sign of how businesses in the Health IT domain should get prepared for the intelligence inclusion.
2. Big Data
Researchers have found that the adoption of Big Data in the Healthcare industry will grow up to $34.27 Billion by the time we reach 2022, placing it as an important impact of technology on the healthcare sector.
While already used to a huge extent in the EHR domain, there are a number of healthcare domains that will benefit immensely from Big Data in 2021 and beyond, making Big Data tech in Healthcare the most looked out for technology.
To list it down, here are the Healthcare domains that will get benefited from the inclusion of Big Data in the industry –
Remove Medication Mistakes
Lowered Wait Time and Hospital Cost
Improved Quality of Service
Greater Security
Preventive Care over Reactive Care
Greater Personalized Treatment and Medicine
3. Internet of Things
IoT Healthcare Market is going to be $158.07 Billion by 2022. A forecast that has increased the number of IoT based device installations to a huge extent, on a worldwide scale, making IoT an important part of healthcare mobile apps trends 2021-2022.
There are a number of reasons why mHealth technologies needs IoT advancement in the industry. Reasons that range from Turning data into action, Improvement of Patient health, Promotion of Preventive Care, Enhancement of Patient Engagement and Satisfaction, and Advancement of Care Management.
IoT application has already been seeing a lot of attention from the Healthcare domain. There are a number of viable examples of how Healthcare is getting better with IoT, here are a few of them –
Closed Loop Insulin Delivery
Activity Trackers
Connected Inhalers
Ingestible Sensors
Connected Lens
4. Telemedicine
While telemedicine has already revolutionized the healthcare industry to a huge extent by covering the gap between the patient and their doctors/caregivers, the adoption will only increase than its current levels. Thus making it a major part of emerging trends in healthcare 2021-2022 and a crucial technology impacting home healthcare.
As per the telehealth trends, the facility which is known to reduce the overall cost incurred by the hospital structure will see a number of new entrants in the economy. The implementation of this tech in Healthcare will all aim at providing real-time patient and doctor access in a way that prevention is taken before a medical emergency occurs.
5. AR/VR
Technology and healthcare have come far, and one of the biggest contributing roles in healthcare mobile apps trends has been played by Augmented Reality and Virtual Reality development services. Healthcare based AR and VR industry is poised to become a $5.1 Billion market by the time we reach 2025. While the time is still too far off, the impact of AR/VR in the Healthcare domain is already seen in multiple arenas.
The impact that AR/VR has shown in Healthcare and the potential that it carries together makes Healthcare the biggest use case of the AR/VR industry.
While on one hand, the technology help doctors make more precise surgeries, the patients on the other hand are known to benefit from the virtual reality scenes that help them overcome trauma with greater ease while being in a safe environment. Both the reasons together make AR/VR one of the most important trends in Healthcare App Development.
AR/VR are known to help hit upon all the four main healthcare domains – Diagnostic, Training, Treatment, and Rehabilitation.
6. Voice
Voice technology has a place in a number of healthcare domains. From helping the end user find the right doctor in their proximity to helping the doctors in surgery by giving them step by step checklist of the whole procedure, voice technology-based devices like Alexa and Google Homes have found a place in a number of hospitals and clinics.
While voice technology is changing how patients and doctors both respond to medicine, on one hand, on the other, the voice based search is changing how individual doctors and hospitals are marketing their services in the world.
The rise of voice technology has made a number of skilled custom healthcare software development company develop mHealth apps around the in-trend tech in healthcare.
7. Gaming
While the mobile app industry is not new to the concept of games based mHealth app, the audience has majorly been children who play basic level surgery games for entertainment.
But now, the situation is changing.
Healthcare industry is now opening up to the onset of gamification in the different areas of Health and care. Games are now finding their place in Medical Training, Rehabilitation Games, Fitness Games, Participatory Health Games, and Emotional/Cognitive Games.
8. Cloud
Healthcare cloud computing market is going to grow to an approximate value of $44.93 Billion by the time we hit 2023. Cloud computing, while had moved beyond Enterprise world ages ago, the impact that it is bound to have on the Healthcare Industry is still fairly new and yet huge.
There are a number of ways through which Cloud technology is known to benefit Healthcare. Here is how Cloud is poised to change the Healthcare economy:
As Software as a Service – Cloud can provide the healthcare organization a range of on-demand hosted services that would give them a fast access to the business applications.
As Platform as a Service – Cloud can provide a security enhanced environment for the web based service and cloud application deployment.
As Infrastructure as a Service – The cloud solutions can provide on-demand computing and storage for medical facility.
9. Predictive Analysis
There was a time when the usage of predictive analysis only surrounded personalized healthcare. But in the present day scenario, the most invested upon and researched technology has found a number of other applications as well in the medicine ecosystem and mobile healthcare apps.
In 2021, hospitals will be utilizing the power of Predictive Analysis in one of the three ways:
For risk estimation
For geo-mapping
For simulations/ what-if situations
Under these three ways, there are a number of situations that come up which can be bettered with Predictive Analysis, ones that range from avoiding 30 days hospital readmission, being one step ahead of patient deterioration, forestalling no show in appointments, preventing patient self-harm, etc.
10. Chatbot
Chatbots have come a far way from once dominating the e-commerce and website domains. Specific to the healthcare industry, there are a number of use cases which promise a more intuitive, instant, and personalized transformation of the healthcare industry. While the healthcare chatbot market is estimated to reach a level of +20% CAGR by 2030, the impact on mHealth apps would be seen from the next year itself.
Benefits of Mobile Health Apps
Reduced Risk of Errors
Medical professionals need access to numerous sorts of clinical resources to make precise diagnostic decisions. With the help of mobile health apps, doctors and nurses can access medical and data, lab results, etc. within a minute. They likewise can utilize smartphones and apps for drug reference guides, clinical rules, and other decision support aids. Informed medical choices bring down the error rate and improve practice productivity and information. On the patient’s side, they can get early warning for the discovery of complications and planning their treatment appropriately.
Streamlining Clinical Processes
As per the research, the IoT healthcare market will reach $188.2 billion by 2025, it is very clear that medical specialists are putting resources into innovative healthcare apps and healthcare app ideas to bring a massive change in the industry. Moreover, the IoT combination with healthcare has given an approach to manage the resources. The medical staff can also keep a tab on their enormous stocks effectively and systematically maintain them.
A Healthy Lifestyle
As more individuals are becoming conscious about maintaining a sound and healthy lifestyle, innovative healthcare apps are the most famous mhealth applications. With the cutting edge and advanced digital accessories, for example, smartwatches, health apps, and fitness trackers are basically intended to empower individuals to remain fit, eat healthy, and improve their sleep cycle. These health apps permit users to follow their regime, weight, food intake, pulse, heart rate, calorie consumption, and other personal information. Users who utilize these applications can share these information to their fitness coaches or companions to get more advice and support.
Wrapping Up
So here were the ten trends that are going to define Healthcare market in 2021 and beyond. What we can hope to achieve at the back of these upcoming trends is a Health and care economy that is more preventive than reactive and more human focused than revenue focused.
Since it is an advanced digital era, thus, you can also opt for expert agencies, without thinking much about the distance. For example, if you reside in European region, then you can look out for healthcare app development company USA or healthcare mobile app development company in other states. No matter where you are, if you choose Anteelo then an expert team will help you with your query and requirements.
Banks are finding exciting new ways to turn their data into valuable insights. To succeed in this new data-driven world, banks of all sizes are turning to the cloud. Cloud-based solutions provide the optimal storage and tools needed to manage vast data requirements while making data and insights easily accessible for analytics and the business.
Enterprise data and the insights extracted from that data are the fuel for business transformation, and banks are increasingly looking for a platform with robust analytics capabilities, self-service tools, centralized data governance and the ability to meet regulatory requirements.
Banks are in an era where they must modernize or risk failure. Legacy environments have high support costs, siloed IT environments and face challenges in finding skilled resources to support them.
Often, disparate technology stacks prevent the enterprise from being truly connected through data and insights, underscoring the need for an enterprise-wide, integrated solution. To address these challenges, Anteelo and Google are focusing on ways to create an optimal data-centric ecosystem, combining Google Cloud’s native tools with managed services ; thus, simplifying the management of cloud resources and optimizing analytics programs.
Power of the cloud
Much of the power of the cloud comes from creating a single platform that combines management, monitoring and automation features with security and compliance capabilities at the core of banking operations. This foundation, along with access to analytics products that underpin some of the world’s most widely used services, gives banks the ability to extract valuable business insights from large data sets.
Actionable insights that can be gained as enterprise data, such as transaction information and customer behavior trends, is processed using Google Cloud’s automation capabilities — driving operational efficiency, new revenues and the ability to compete more effectively.
Google analytics tools such as Dataflow, BigQuery, Bigtable and Looker enable organizations to advise, implement and operationalize artificial intelligence (AI) to yield competitive business intelligence. Much of this can be automated and integrated into actively managed business processes that ultimately produce tangible, repeatable business outcomes.
Why move to the cloud?
Analytics from a cloud-based platform can deliver benefits on many different levels. For a chief marketing officer, for example, real-time information on marketing campaign ROI is a valuable metric that is not as readily available or actionable with today’s on-premises systems. Or a chief information security officer could get access to data that improves a bank’s governance capabilities, which delivers benefits in areas such as security and compliance.
Banks are also using open APIs enabled by Google Cloud to authenticate and secure financial communications to customers, enabling a transition from face-to-face transactions to digital channels.
Banks also can use a wide range of cloud-native services for data warehousing and data management. Plus, a wealth of AI and machine learning tools that are integrated effectively can help automate tasks and improve personalized experiences. For example, cloud computing’s elastic capabilities have been shown to cut the training time of data models by more than 50 percent over traditional on-premises systems. Data models converge faster, enabling more rapid delivery of services to markets and enhanced services to consumers.
Another key benefit of moving to the cloud and engaging managed services is that applications can be managed consistently across different environments. Operating in a more efficient development environment means banks can rapidly introduce new products and services to the market and update them more easily.
Banks also have access to high-performance computing (HPC) resources for large processing needs. The use of cloud computing provides banks with modern alternatives for HPC that are manifested in containers and special hardware devices that are purpose-built such as graphical processor units and tensor processing units.
Traditional and legacy on-premises data architectures simply cannot support the variety of data or real-time data streams necessary for advanced enterprise analytics and AI. A modern data architecture is needed. Plus, a cloud environment enables banks to dynamically scale infrastructure up or down to meet demand, which is essential for digital success.
Trust, transform and thrive
Progressing data into actionable insights can be visualized as a process following these three keywords: trust, transform and thrive (see figure). Underpinning that process is a cloud platform tuned to improve visibility, security, scalability, speed and agility.
Analytics: Progressing data to actionable insights
In this model, data from many different sources, including unstructured and siloed data, is collected and ingested. The built-in security features of Google Cloud Platform keep data secure and help build trust in the enterprise. In a Google Cloud Platform deployment managed by Anteelo, for example, security and regulatory compliance are given the highest priority. Google Cloud Platform’s capabilities in areas such as network monitoring and identity access management help banks maintain a high level of data security and compliance.
A trusted partner
Choosing the right partner is crucial when moving to a modern cloud banking platform. It is preferable to have a trusted advisor with extensive experience and IT expertise in banking, capital markets and financial services. Anteelo has been providing banking services at an enterprise level for more than 40 years. Through our unmatched industry expertise we offer a robust set of data services using simplified tools and automation for rapid data acquisition and insights. We help banks identify patterns that can be used to drive business insights in a way that fits their needs. The key is to then prepackage these patterns so organizations can reuse them.
The potential is endless for a cloud platform and the analytics capabilities it delivers to help banks build trust, transform and thrive. A managed cloud platform, purpose-built for banking, provides a consistent surface for accessing the most accurate, up-to-date version of your enterprise’s data. And the technology now exists for analytics to be integrated into daily workflows so banks can extract maximum value from that data.
