Data is critical for any organization to build and operationalize a comprehensive analytics strategy. For example, each transaction in the BFSI (Banking, Finance, Services, and Insurance) sector produces data. In Manufacturing, sensor data can be vast and heterogeneous. Most organizations maintain many different systems, and each organization has unique rules and processes for handling the data contained within those systems.
Google Cloud provides end-to-end data cloud solutions to store, manage, process, and activate data starting with BigQuery. BigQuery is a fully managed data warehouse that is designed for running analytical processing (OLAP) at any scale. BigQuery has built-in features like machine learning, geospatial analysis, data sharing, log analytics, and business intelligence. MongoDB is a document-based database that handles the real-time operational application with thousands of concurrent sessions with millisecond response times. Often, curated subsets of data from MongoDB are replicated to BigQuery for aggregation and complex analytics and to further enrich the operational data and end-customer experience. As you can see, MongoDB Atlas and Google Cloud BigQuery are complementary technologies.
Introduction to Google Cloud Dataflow
Dataflow is a truly unified stream and batch data processing system that’s serverless, fast, and cost-effective. Dataflow allows teams to focus on programming instead of managing server clusters as Dataflow’s serverless approach removes operational overhead from data engineering workloads. Dataflow is very efficient at implementing streaming transformations, which makes it a great fit for moving data from one platform to another with any changes in the data model required. As part of Data Movement with Dataflow, you can also implement additional use cases such as identifying fraudulent transactions, real-time recommendations, etc.
Announcing new Dataflow Templates for MongoDB Atlas and BigQuery
Customers have been using Dataflow widely to move and transform data from Atlas to BigQuery and vice versa. For this, they have been writing custom code using Apache Beamlibraries and deploying it on the Dataflow runtime.
To make moving and transforming data between Atlas and BigQuery easier, the MongoDB and Google teams worked together to build templates for the same and make them available as part of the Dataflow page in the Google Cloud console. Dataflow templates allow you to package a Dataflow pipeline for deployment. Templates have several advantages over directly deploying a pipeline to Dataflow. The Dataflow templates and the Dataflow page make it easier to define the source, target, transformations, and other logic to apply to the data. You can key in all the connection parameters through the Dataflow page, and with a click, the Dataflow job is triggered to move the data.
To start with, we have built three templates. Two of these templates are batch templates to read and write from MongoDB to BigQuery and vice versa. And the third is to read the change stream data pushed on Pub/Sub and write to BigQuery. Below are the templates for interacting with MongoDB and Google Cloud native services currently available:
1. MongoDB to BigQuery template: The MongoDB to BigQuery template is a batch pipeline that reads documents from MongoDB and writes them to BigQuery
3. MongoDB to BigQuery CDC template: The MongoDB to BigQuery CDC (Change Data Capture) template is a streaming pipeline that works together with MongoDB change streams. The pipeline reads the JSON records pushed to Pub/Sub via a MongoDB change stream and writes them to BigQuery
The Dataflow page in the Google Cloud console can help accelerate job creation. This eliminates the requirement to set up a java environment and other additional dependencies. Users can instantly create a job by passing parameters including URI, database name, collection name, and BigQuery table name through the UI.
Below you can see these new MongoDB templates currently available in the Dataflow page:
Below is the parameter configuration screen for the MongoDB to BigQuery (Batch) template. The required parameters vary based on the template you select.
Automated outbound calls can save you a lot of time and money as an organization, by automating the frequently repeated calling processes. For instance, having your phone system automatically ask a user for their basic information can be much more efficient than having your agents do the same. And that’s especially when you have hundreds or thousands of users to get data from. This is why a growing number of businesses are leveraging AI technology to automate their outbound calls.
Though automated outbound calling holds a lot of benefits like low time expenditure and low costs, is it really effective? Are there more valuable ways to use AI to grow your business that offer a higher ROI? These are some lessons that we learn today in this guide. For instance, we’ll dive deep into what automated outbound calls are, how AI helps them work, as well as their top benefits and drawbacks. With that said, let’s dive in.
What Are Automated Outbound Calls?
The calls your phone system program makes to get certain information from a user are termed automated outbound calls. These calls are mostly pre-recorded and the program only runs the right recording at the right time. This helps save the calling agents from repeating themselves over and over.
This is one of the ways that companies can use AI to save money. AI helps them use their human resources more efficiently, which increases productivity at the same expense.
An example of automated outbound calls is when a provider lets you know the package you ordered has been shipped through a pre-recorded message. Similarly, when a company walks you through its setup process through a pre-recorded call, it’s using the automated outbound call feature that is managed through an AI interface.
How Do Automated Outbound Calls Work?
The automated outbound calls are directed by an AI program that automatically dials a list of given phone numbers as instructed and runs the given pre-recorded message. The AI takes a variety of variables into account before processing the call.
This way, automated calling allows businesses to deliver a certain message to as many users as they want, within the click of a button. Imagine sending a product update of a brand to thousands of customers manually. Automated calling can save several hours of your employees’ time and do the same with much less hassle.
Benefits of Using AI to Handle Automated Calling
Here are the main advantages of using AI to handle automated outbound calls.
Automated calling can take the productivity of your support staff to the next level, by letting them avoid repeating themselves over and over. Instead, this feature lets them focus on solving problems that require human support, like guiding a user through a complicated procedure. This way, your calling department will be able to handle lots of users efficiently, without requiring a lot of staff. This is one of the many ways that AI increases productivity.
Low Call Center Costs
Having an automated outbound calling feature for your company can bring your call center costs down to nothing. It’s because it only takes a pre-recorded message and an AI application to make automated outbound calls. Instead of paying a lot of callers for running a phone advertising campaign, you can set up automated outbound calls and have a program do the same.
The automated outbound calling feature is widely used for marketing purposes. If you’re a business, calling your potential customers and introducing them to your services or products can be effective. Similarly, phone calls can also help you introduce a new service or product you just launched or an upcoming event to your existing customers. Either way, automated outbound calls can help you market your business a lot more effectively.
Drawbacks of Using AI to Manage Automated Calling
AI has its limitations like anything else. Below are the main cons of automated outbound calls:
May Irritate Customers
Humans love talking to humans, and not to pre-programmed machines – especially when they need some serious help. This is one way automated outbound calls can irritate your customers. When a customer picks up an automated call, they may find it annoying to not get the chance to ask a question or two, as in a regular call.
Low Conversion Rate
Another key disadvantage of automated calls is their low conversion rate. First and foremost, automated calls and robocalls often end up blocked by the user’s phone system. It’s because many users use phone systems that block callers who don’t dial a given digit, to validate if it’s a human being on the other end.
Apart from that, automated calls won’t give you the choice to target the unique pain points of each customer, which helps tremendously improve your conversion rate.
May Affect Overall Brand Image
Though automated calls are cost-effective and efficient, their user experience is nothing close to a professional humanoid call. For instance, the users will get the chance to interact and ask questions from a support agent but they can’t do the same when they receive an automated call. So, solely relying on automated calls can damage your brand image in the long run.
AI Technology Helps Companies Boost Productivity by Automating Outbound Calls
AI technology has provided a number of opportunities for companies to boost productivity. One of the benefits is by making outbound calls possible. This guide thoroughly explains what automated outbound calls are and how they work. We also looked into the top advantages and disadvantages of automated calls to help you determine if they’re a good option for your business.
To conclude, automated calls are definitely a productive feature but they shouldn’t come in the way of providing your customers with a good calling experience. Until next time, cheers!
Many data-driven marketers are taking advantage of Microsoft Outlook. They recognize that they can get a greater benefit from this email provider if they know how to work with the right data files. Some details on these processes are listed below.
Data-Driven Organizations Must Work with Data Files to Take Advantage of Outlook
Outlook has two types of data files (PST and OST) to store the data from the email servers. The PST file is mostly used to store data such as email or past account, personal archives and legacy protocols of messaging. If you are configuring an Exchange Server account with MAPI or Office 365 account on your Outlook and want to view the data which is locally cached without connecting to the Exchange Server on-premise or Online, Outlook will store such information in OST file format.
What’s the difference between PST and OST?
PST file is used by legacy messaging protocols – POP, IMAP, or older mail servers. In Outlook 2013 and earlier versions, IMAP accounts also used .pst file. In Outlook 2016 and Outlook for Microsoft 365, IMAP accounts use .ost file. The PST file is used for personal archives or to backup emails which can be easily exported. The personal archives are commonly mistaken with the Exchange Archive mailbox. The Exchange Server Archive mailbox is stored on the server itself. If the computer loses the files or gets formatted, the archive is re-synced. For such things, the Outlooks Data File (.pst) is used.
When setting up an Outlook account from local Exchange Server or Exchange Online (Office 365), an Outlook Data File (.ost) is created on your computer. This file is a copy of the actual mailbox which resides on the server. There is no need to back up the file. If something happens and the computer is reset, nothing is lost.
Outlook PST File and its Uses
PST file, i.e. Personal Folders file, is a format which is used by Outlook to store data. The file is fully portable and can be copied to another computer and opened in Outlook with no issues. PST file is commonly used with older email messaging protocols, like POP and IMAP.
POP is the internet protocol, which is used for email transfer, where the data is kept on the server until the computer or device synchronizes with the server. Once this is done, the emails downloaded on the client will be purged on the server. If something happens to the device, the data will be lost. Also, if you have multiple devices with the same account, emails would be downloaded only on one device.
This is somehow resolved with IMAP where there is synchronization between the server and the device. The emails are kept in a local PST file with only headers until it is opened and then it will be downloaded to the local server. This is not very secure as it uses PST which can be easily ported to another computer.
A PST file cannot be larger than 5 GB as large files are prone to complications and corruption. Also, opening and accessing PST files from a network share or sharing via OneDrive is highly discouraged as this may corrupt the files.
Here is how to create a new PST file:
Open Outlook and click on File.Click on the dropdown on the New Email.Click on More Items.Click on Outlook Data File.
To open an existing PST file, click on File, Open & Export, and then on Open Outlook Data File.
Once opened or created, the PST file will be shown in the left pane.
To move or copy emails to a PST or number of PST files, you can simply drag and drop the items/files.
Outlook OST File and its Uses
Outlook Data File (.OST) is automatically created when setting up an account with the local Exchange Server or Exchange Online. The OST file is a local cache of the mailbox data on the server. If you receive, send, or delete an email, this is synchronized between all the connected devices.
The OST file also acts as a buffer between the actual mailbox and the device. First an email is saved locally on the OST file and then it’s synchronized to the server once the connection is up. You can create calendar entries, tasks, contacts, and send emails while offline. When online, these will synchronize to the server.
To view the location of the OST file, go to File > Info > Account Settings > AccountSettings.
Then, click on the Data Files tab.
An OST file cannot be moved to another machine or opened from another computer and application. If there is an issue with the account or machine, you will need to re-setup the account. But any changes done while offline will be lost. Once the OST has been set, if the mail profile is re-done, you cannot reuse the OST file to recover the data. If something happens to the Exchange Server and the data is lost, you will not be able to recover any data from the local OST file in case Outlook is not opening.
In order to recover data from the OST file, you need OST to PST converter application. Stellar Converter for OST is one such application that can easily open an OST file from any version of Outlook and convert it to PST file. You will be able to view the whole hierarchy of the file. You can also export the OST data to other file formats. In addition, you can easily export OST data directly to Office 365 and any live database of Exchange Server.
Data Driven Companies Must Take Advantage of Outlook the Right Way
There are a number of reasons data-driven companies are using email marketing. Some of the best features are available in Microsoft Outlook. However, marketers need to know how to work with data files properly. Fortunately, the guidelines listed above can help.
AI has become one of the most important gamechangers for businesses and customers relying on mobile technology. This is one of the reasons companies are spending over $328 billion on AI technology. One of the many reasons that AI is changing the landscape of mobile technology is that it helps develop and distribute apps more easily than ever.
AI Technology Leads to the Inception of New App Marketplaces
The number of mobile apps has skyrocketed over the last few years. In 2021, the number of mobile applications hits around 3 million on Google Play alone, while the App Store has around 3.5 million applications available for download.
With the competition so tough, app publishers are looking for other places where they can publish their solutions. And although the App Store is the main hub for mobile app development, it’s not the only place where developers can sell their creations.
There are a variety of ways that AI technology is helping app marketplaces. In order to appreciate the benefits of using AI to create a new app marketplace, it is important to first address the challenges. We will discuss these issues before identifying some of the best app stores that have been developed as a result of AI technology.
Apple’s App Store has become one of the most important platforms in mobile technology and entertainment. It’s a platform where hundreds of thousands of apps are available to users worldwide, including well-known brands like Netflix and Spotify. However, despite its success and popularity, the Apple App Store still faces several challenges that can make it difficult for developers to succeed on the platform.
The first challenge is that Apple’s app approval process is rigorous. The company maintains a list of guidelines that all developers must follow or their apps will be rejected from the iOS store. These guidelines include using only one language for all text dialogues in an app, having no duplicated content on any page, and not including third-party ads in games or apps.
Another challenge facing developers is that they must comply with Apple’s 30 percent commission fee on each sale made through the iOS app store. This fee goes directly to Apple, so if you want your app to be successful on this platform, you’ll need to find ways to offset these costs by charging higher prices or adding extras like in-app purchases or subscriptions
How AI Technology Helps Solve Challenges for App Marketplaces
There are a number of ways that AI technology can help new app marketplaces. Some of them are listed below:
Mitigate app security risks. One of the biggest challenges that App Store struggled with was dealing with apps filled with malware. AI technology has made it easier to identify apps designed with malicious purposes.Help with marketing. AI technology also helps companies implement more cost-effective marketing strategies. They can find creative ways to reach new customers more easily than ever.Improve maintenance. AI technology has also made it a lot easier to maintain websites. Companies use machine learning to identify technical issues with their sites and automate maintenance. Since app marketplaces depend on stellar user experiences, they depend on AI to meet these customer expectations.
AI is clearly of crucial importance for app marketplaces trying to compete with the App Store.
Where to publish your apps in 2022?
The App Store is the most popular destination for mobile applications. The store received more than 1 billion unique visitors in 2021 and continues to be the dominant destination for posting applications. This growth has resulted in more competition, resulting in a number of alternative stores like Google Play Store, Amazon Appstore, Steam, and others.
Google Play Store
Both the App Store and Google Play Store account for the largest shares of the market. Google Play Store offers plenty of apps and games, along with Google’s own suite of apps like Gmail and Google Maps. It also includes an app store within the Play Store itself — something Apple doesn’t do on iOS devices.
The Google Play Store is the most popular app store, and it’s a great choice for users looking for apps that don’t exist in Apple’s App Store. It’s also worth noting that there are some exclusive features in the Google Play Store that aren’t available on the iOS version, such as Material Design, which is a design standard used throughout Google products.
The Google Play Store is a bit different from Apple’s App Store. The latter requires developers to pay upfront fees and fees during the process of submitting their apps to Apple, whereas Google offers free distribution for all Android devices. However, if you want to make money from your app, you’ll need to pay for advertising, in-app purchases, and subscriptions.
One of the e-commerce giants, Amazon also offers its own app store for Android devices called Amazon Appstore. The platform has been around since 2011 and offers access to more than 250,000 apps that have been downloaded over 1 billion times. As with other app stores, it has its own revenue share model — developers can receive 70 percent of all revenue generated through in-app purchases or subscriptions (the remaining 30 percent goes to Amazon).
The app marketplace also helps generate revenue via advertising. Moreover, app publishers can include virtual currency (Amazon Coins), extra lives for a game, or subscriptions for purchase.
GetJar is an alternative to the App Store that’s popular in Europe and other parts of the world where Apple doesn’t have as much dominance. The GetJar store offers more than 2 million apps, games, music, and movies (with movies available in various languages).
As for monetization, developers can benefit from its freemium model which includes adverts and in-app currency. Also, GetJar Gold is one of the most popular virtual currencies in use and is available to millions of users.
Samsung Galaxy Store
The Samsung Galaxy app store is a great alternative to the App Store. It’s got a lot of great games and other apps, but it’s not as wide-reaching and convenient as the App Store.
The Galaxy app store doesn’t have as many apps as Apple’s App Store. However, low competition can be beneficial for fledgling applications and app publishers. The best part is that the app store is completely free, so users won’t have to worry about paying any fees when downloading apps from this store.
However, this app marketplace does charge for some premium services like music streaming and video streaming. Also, Samsung Galaxy Store doesn’t work with non-Samsung phones.
The F-droid app store is a community-driven repository of open source apps, where you can easily search for, download, and install apps on your Android device. F-droid is a great alternative to the Google Play Store for Android users who want to avoid using Google’s proprietary software and its restrictions on what apps you can install and how they can be packaged. F-droid offers a much smaller range of applications than Google Play, but it’s still worth using if you’re looking for something a little different.
Choosing the best alternative app market may seem to be a tough task. While Google Play and the App Store have got a foothold in the market, less-known alternatives such as F-droid and GetJar can help you beat the competition. However, none of these app marketplaces can topple the Google Play Store and the App Store in terms of exposure and number of visitors. Therefore, we recommend combining distribution channels to get additional traction for your application.
The market for cloud technology is growing considerably. Ninety-one percent of companies are on the cloud. As this figure grows even further, the demand for cloud solutions will rise.
Therefore, this is one of the best times to create a cloud business. However, there are a lot of challenges that you have to consider as well. One of the biggest is finding ways to fund your cloud startup.
An enterprise cannot just become successful based on the ideas or business plans of its creator. Before your enterprise can become successful, you will need to fund it. Unfortunately, the amount of money needed to finance an enterprise can sometimes be larger than what you can bear. This is especially true for cloud startups.
The average cloud hosting company is lower than many other cloud startups. Some experts have shown that you can start a cloud hosting business for as little as $1 a month per customer. Unfortunately, starting a different type of cloud-based business might be a lot more expensive.
In this case, you will need a good investor. The good news is that many investors recognize the merits of cloud computing and are happy to get behind promising cloud startups.
Find the Best Investors for Your Cloud Startups
Investors are people who help to provide the finance an enterprise needs, for a share of the profit. In other words, they help to give you the money you need for your enterprise, for a small cut. Due to how simple this seems; you might not know other ways that an investor can benefit your cloud startup. You may even want to find a venture capitalist for your cloud business. If you don’t know, no need to worry, as some of the ways are stated below.
This is the basic way by which an investor can help your enterprise. Investors can help to provide finances for enterprises they think will have great profits. If you do not have the finance your business needs, an investor can help with that. All you need to do is to find a worthy investor and convince them that your enterprise is worth investing in. A good investor can help to make a difference in your enterprise. Why? because most businesses usually require a large amount of money that a single person cannot provide. An investor is usually someone who has a lot of businesses and money to spare. However, be sure you think about the profit that will be lost before you seal a deal with an investor.
Aside from the fact that an investor gives you money that you need, they also help to monitor the enterprise. This is because their own money is also at stake if the enterprise crashes. Due to this, investors are also good guardians. They help to mentor you and the enterprise effectively. Before you make any decision, you are not sure of, you can check with the investor. Investors are usually people with a lot of experience, so you can trust what they tell you.
Investors are usually people that have experience in various forms of businesses. This experience usually comes with a lot of various connections with important people. Aside from the finance that an investor can give you, they can also provide you with connections. You can, therefore, make use of these connections in ways that will benefit your enterprise.
For example, as a cloud-based business, you might need to hire programmers or network administrators. You will want to try seeing if they can help you. You can also try seeing if they know any Python programmers as well.
The cloud computing industry is very different from many other sectors. You need to find a clever monetization strategy. Investors are usually experts with a lot of different strategies for the enterprise. If you can get a good investor, he or she might be able to give you some good strategies for your cloud startup. You can be able to trust their word, as their money is also on the line. A good strategy can go a long way in helping your enterprise become successful. The best way you can get a good strategy is by getting it from someone who has already made use of it.
5. Getting resources
Aside from the finances, there are also some vital resources that every cloud startup needs to become successful. Investors usually know where you can get the best resources, such as materials, workers, and customers. This is because your investor might also have other businesses. If you have a good investor, you don’t have to worry about where to get these resources.
Find the Right Investors to Help Finance Your Cloud Startup
Investors are very useful for any cloud startup. Aside from giving finances, investors also help to do things that can boost the progress of any enterprise. Finally, data collection companies can help check where to beam your searchlight for good investors. Moreover, data collection Hello Pareto will be helpful in this regard.
Last decade made a pretty bold promise to digital advertising, which more than other industries suffers from insufficient transparency and a fraudulent environment.
The IAB Tech Lab conferences, in particular, frequently gathered blockchain evangelists and ad tech experts who discussed how this technology would finally drive authentication to programmatic chains. Reduced budget waste, elimination of intermediaries and fraudulent traffic are the core challenges of ad tech that decentralized ledger promised to resolve.
As soon as the IAB Tech Lab blockchain working group started developing principles of decentralized networks, the advertising industry was believed to be standing on the threshold of massive technology introduction into a real business. Now that we’ve marched into 2022 it’s time to recollect those talks, inspect the current market and finally understand if blockchain really brings some changes into ad tech and martech scene.
The landscape of blockchain-driven solutions: from 2018 to 2022
Currently, buy-side is most enthusiastic about blockchain implementation in ad tech, because advertisers and media buyers need good quality traffic. Globally, ad fraud will most certainly cost advertisers $81 billion in 2022. Roughly speaking, ad fraud takes $1 from $5 invested in digital ads.
In 2018-2019, budding blockchain-based advertising projects provided the first opportunity to buy clean and secure traffic, enriched with genuine data about ad campaign performance. In 2019, this environment evolved, multiplying the number of blockchain marketing startups from 22 (2017) to 290 (2019), which is more than 13 times in a year. These commercial and non-commercial projects mainly function in the area of social marketing, data, commerce, content marketing, and digital advertising, where the number grew from 10 to 105.
Starting from 2019 society has shifted its attention toward NFT technology (which also uses a blockchain ledger for protecting the ownership of digital assets). With this, blockchain is featured among the top martech trends in 2022. Over 31% of industry experts think that VR/AR, Metaverse, NFT, and Blockchain technology will define the trajectory of martech and ad tech development.
Sure, right now crypto markets are not being in the best of times – the growing cryptotrading markets recently crippled in China. In January 2022, Spain and the UK also introduced new stifling regulations to eliminate crypto advertisements. However, the growth in the number of successful blockchain advertising startups signals that companies have finally started to understand the true value of technology without associating it with the token economy.
What blockchain solutions are here and how do they reshape advertising?
Let’s recall why the industry clung to blockchain technology in the first place – because it created a distributed database that could be trusted by demand and supply partners alike. When companies add a blockchain to digital advertising, they reach the next level of clarity and accountability.
Every action or impression is recorded on a distributed ledger: which user watched the ad, which advertiser bought the impression, what was the cost and the components (commissions, actual bid), and so on. This way, all data becomes auditable to every chain participant on an event-level basis. The parties themselves can detect fraud and initiate automation removal of a suspicious component from the chain.
The majority of martech-powered solutions are currently focusing on improving transparency and mitigating advertising fraud, for example:
AdEx – the platform that connects publishers and advertisers directly, reducing the middlemen and thus hidden commissions; Rebel AI – focuses on identity to eliminate the problem of domain spoofing;XCHNG – Fights with ad fraud thanks to the application of smart contracts;AdBank – secures transactions between advertisers and publishers while also delivering transparency over payments; Papyrus – ensures fairness and efficiency of media supply chains with the blockchain network.
Apart from fraud protection, blockchain solves many problems in different areas of advertising. Based on the events recorded on the ledger, document flow, and paper bookkeeping become a matter of one click automated by smart contracts.
Additionally, blockchain in martech helps to protect the interests of users, e.g. Brave browser users, actually earn revenues in the form of BAT (Basic Attention Token) in exchange for ad watching. Since global attention is also focused on privacy protection and digital ownership, most likely the next generation of blockchain startups in marketing will be developed exactly in these niches.
What about challenges?
In the absence of regulation, many blockchain pilot projects were at risk of ending up absolutely impractical. Nevertheless, the world is adapting to blockchain anyways. Luckily, market participants joined forces that contributed to faster technology adoption and regulation development. IAB Tech Lab’s initiatives support businesses in their desire to stay aware of the best practices of blockchain integration into ad stacks. A set of specific programs are constantly being developed in order to facilitate education, raise awareness and deliver a legal framework for such platform functioning.
To better understand market readiness and realize how blockchain works in the real world, the IAB Tech Lab started a blockchain pilot program that will deliver real-world algorithm for testing blockchain-based services and products.
The members that develop blockchain solutions are invited to showcase how their blockchain-based projects can work. Their advertising blockchain initiatives have deployed working mechanisms for addressing ad fraud, transparency, and efficiencies in the reconciliation of campaign data in their own way. Some have developed 2-layer protocols to accelerate transaction speed; some have automated purchases by smart contracts and others have combined IAB Tech Lab’s ads.txt, (registry of authorized sellers) with Ethereum to achieve increased inventory purchasing transparency.
The other obstacle of the past was speed. Now, blockchain transactions occur in minutes, while processes in digital marketing occur in milliseconds. This problem apparently found a partial resolution in 2019, when blockchain startups figured out how to cover the problem with multilayered protocols architecture that execute transactions outside of the main blockchain.
Finally, the third and probably the most important problem of blockchain in ad tech is a lack of education and the shortage of professionals well-versed in blockchain implementation in advertising. Despite obviously growing curiosity and awareness, actual comprehension of blockchain, NFT, and related technologies among mass audiences is very low – even in 2022 96% of Americans fail the common quizzes that include basic crypto concepts.
However, in order to improve the state of things in this department, we need to have more educational initiatives like those presented by IAB. We also need support and educational initiatives on the local and regional levels that would raise awareness of blockchain implementation in ad tech. This awareness could encourage broader blockchain solutions implementation and would persuade companies to invest resources in employees training and education.
Early experiments with blockchain in advertising were quite successful, which raised the bar for subsequent projects. Marketing and advertising tech built on blockchain, NFT, and similar technologies can become the new normal, although even in 2022 it is still rather a prerogative of niche enthusiasts and small startups. Regulatory uncertainty, poor awareness, and lack of professionals are still challenges that stand in the way of the mass deployment of such solutions. However, once these obstacles are passed marketing and advertising landscape will be redefined.
It will be possible to see how much money from advertisers’ budgets reach the publisher after passing the pipeline. In turn, impressions served by websites will be easily signed and tracked. Finally, the users will be able to receive rewards for their attention and protect their digital assets.
So, obviously, blockchain in advertising doesn’t have to be overhyped, nor does it have to be underestimated. For this, we shouldn’t take it as a universal hammer able to fix every problem, but rather see it as a tool of great capacity if the problem it needs to solve is right.
Did you know that around 2.5 quintillion bytes of data are generated each day? Businesses are having a difficult time managing this growing array of data, so they need new data management tools.
Data management is a growing field, and it’s essential for any business to have a data management solution in place. A data management solution helps your business run more efficiently by making sure that your data is reliable and secure. You can use information management software to improve your decision-making process and ensure that you’re compliant with the law.
A data management solution can help you make better business decisions by giving you access to the right information at the right time. Data engineering services can analyze large amounts of data and identify trends that would otherwise be missed. If you’re looking for ways to increase your profits and improve customer satisfaction, then you should consider investing in a data management solution.
In this blog post, we’ll explore some of the advantages of using a big data management solution for your business:
Big data can improve your business decision-making.
Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. Big data solutions are used to analyze the massive amounts of unstructured information collected from various sources, such as sensors, devices, and social media networks. This type of analysis helps you make better business decisions based on trends and patterns.
For example, if you want to know what products customers prefer when shopping at your store, you can use big data analytics software to track customer purchases. You can then optimize product placement on the shelves or product placement in advertisements based on customer preference.
Big data analytics can also help you identify trends in your industry and predict future sales. For example, if you’re a retailer and you notice that your competitor is selling more products than usual on a particular day, then you may want to increase your inventory so that you don’t miss out on any potential sales.
Big data management increases the reliability of your data.
Big data management has many benefits. One of the most important is that it helps to increase the reliability of your data. Data quality issues can arise from a variety of sources, including:
Duplicate recordsMissing recordsIncorrect data
The proper use of data management solutions can help you identify these problems and correct them quickly and easily. This reduces the risk that inaccurate information will be used in your organization’s decision-making, which could result in poor business outcomes.
Big data management solutions can also help you to ensure that your data is secure. This is especially important in the modern business environment, where cybercriminals are constantly seeking new ways to steal personal information. The right data management solution can help you to secure your data, preventing it from falling into the wrong hands. This not only helps to ensure that your customers’ personal information is safe but also protects your own organization’s sensitive information.
Data management helps you comply with the law.
Data management helps you comply with the law, including government regulations, industry regulations, and compliance standards. For example, if you have employees working in multiple countries or locations, data management would help ensure that everyone is provided equal access to the same benefits and compensation packages. In addition to meeting internal requirements for managing your company’s information responsibly, it can also help improve customer service by reducing confusion over pricing and other policies.
Data management also helps your business comply with laws that protect consumer and employee rights. For example, the Health Insurance Portability and Accountability Act (HIPAA) requires that all healthcare providers protect patient information by using security measures to prevent unauthorized access.
Information management mitigates the risk of errors.
Errors can be costly and time-consuming, so the more you reduce the risk of errors, the better. Data quality is important to achieving this goal. Big data management solutions help you to achieve accurate and timely responses to issues that may arise due to data errors.
When it comes to managing your business’ information, you need an automated solution that provides actionable insights into where your data resides and how it’s being used. This will allow you to determine which assets are most critical for operations so that you can focus on those assets when making decisions about investments in new technology or resources like human capital.
The most important element in ensuring data quality is knowing what you want your data to do. Be clear about what you’re trying to accomplish and how the data will be used before you start collecting it. This will help ensure that your efforts are not wasted on irrelevant information or flawed definitions of terms.
A big data management solution helps your business run more efficiently.
Big data management solutions help you make better decisions.
When you know what data is available and how to find it, you can make more informed decisions about your business. You can also save time by having access to the right data at the right time, which means that you won’t have to spend hours digging through a pile of Excel sheets looking for useful information. Instead, all of your critical information will be in one place so that accessing it is quick and easy—even if someone else needs access as well!
A big data management solution also helps find the right people who can work with your company’s specific needs based on their skill sets as well as their personality traits (like whether or not they’re collaborative). In addition, these solutions can help identify appropriate tools for accessing data, such as cloud storage platforms or relational database management systems (DBMSs). And finally, they’ll help identify vendors who offer services like big data analysis or predictive analytics modeling so that those resources are accessible when needed most.
We hope this article has helped you understand the benefits of big data management. We believe that a data management solution can help your business run more efficiently, which means you will have a better chance of success.
While there are many factors to consider when making this decision, we hope that our list of advantages provides some direction as you consider what’s best for your company.
OCR is the latest new technology that data-driven companies are leveraging to extract data more effectively. There are a number of benefits of using it to your company’s advantage.
OCR and Other Data Extraction Tools Have Promising ROIs for Brands
Big data is changing the state of modern business. A growing number of companies have leveraged big data to cut costs, improve customer engagement, have better compliance rates and earn solid brand reputations.
Data strategies are becoming more dependent on new technology that is arising. One of the newest ways data-driven companies are collecting data is through the use of OCR.
What is OCR and How do Data-Driven Companies Use it?
Optical Character Recognition, or OCR, is a technology for reading documents and extracting data. OCR software completely changes the way you can process your documents: automated and much more reliable.
Every company deals with a certain number of documents on a daily basis: invoices, receipts, logistics, or HR documents… You have to keep these documents, extract the useful information for your business, and then integrate them manually into your database.
It’s long, redundant, and particularly frustrating. One mistake and everything has to be checked again.
The processing time for each document varies depending on the nature of the document and the information to be extracted. Even so, it takes time and can quickly become an obstacle to the smooth running of your business.
Find out in this article how your company can benefit from the use of OCR. How does it work, and what are the clear benefits? This article reveals all!
Some things to understand about OCR technology
OCR is a well-known technology developed for text recognition in any medium: photographic, handwritten, or digitized. Optical character recognition is able to convert any text present on a medium into computer-readable textual data.
Scan the document containing the information you need. To do this, use a device that can support OCR technology. Usually, the camera of your smartphone allows you to scan directly into the OCR application. The latter will detect the document that is submitted to it. Cropped or not, it will remove the background to work only on the document.Once received by the application, the data are extracted from the document, but they are not yet structured at this stage. The software extracts all the information in plain text in a TXT format.In the last step, the extracted data is structured so that it can be used for further processing. Each data point is linked to its reference. You get the structured information in a machine-readable format, such as JSON. You can now save it in your database.
These three steps are performed by OCR in about 3 to 5 seconds observing an ever higher accuracy thanks to machine learning and artificial intelligence than manual extraction.
Using OCR: the benefits for your business
Automated data capture improves your document management and processing. Gain speed and accuracy with OCR technology. Here are just a few of the benefits your company can enjoy by integrating OCR software. Accuracy, efficiency, time savings… A non-exhaustive list that will help you better understand the change that OCR implies!
Improved accuracy and speed
Delegating the management and processing of your documents to an OCR solution is to be sure to process them faster and more accurately. Thanks to the work of machine learning and artificial intelligence, OCR is constantly evolving. The software learns from the documents submitted to it. Automatic data extraction drastically reduces manual input errors.
Because OCR works continuously, you can process a larger volume of documents. You can extract data from documents faster than with manual data entry.
Optimize your time
With OCR and the automation of your document processing, you will be able to process a much larger number of documents more quickly.
The OCR software analyzes your information and extracts it in a few seconds (2 to 5 seconds). Manually, this operation can take several tens of minutes.
OCR in application: driver’s license, identity card… Use cases and explanations.
Discover in pictures several use cases of OCR on different media, so you can see that this software adapts to all activities.
Driver’s license verification for insurance purposes
Let’s say your company is an insurance company. In order to insure their vehicle, motorists must provide their driver’s license in order to issue an insurance certificate. Can you see yourself extracting data from all your customers? Every day, repeating this long, tedious task? No? Good, see how OCR automatically extracts driver’s license information!
Start by submitting the driver’s license to be processed to the OCR software. Send a PDF, a photograph, or a scan via your smartphone to the OCR app.
The OCR application extracts the information automatically in a few seconds. All data of the driving license is extracted but not yet structured. The result is in TXT format for the moment.
For the last step, the parser structures all the data that is extracted. At this stage, the result is provided in JSON format.
Extraction of credit cards data
For online registration of new customers, you may need to extract credit card data, and you as a bank may need to extract credit card information. Secure, fast, and reliable, here is how in just three steps the OCR software extracts the data you need.
To extract the information, start by submitting a document containing the credit card to be processed. By PDF, photograph, or by scanning the blue card via the app using your smartphone camera.
To continue your document analysis, the second step extracts all the data present on the blue card. However, at this stage, they are not structured.
Upon receipt by the OCR application, the image is optimized and converted into a plain text file. At this stage, the text on the card is extracted but not yet structured.
The third and final step structures all the extracted text and delivers it to you in JSON format.
OCR in your company, what if it was now?
Find your OCR solution provider and ask them for their OCR pricing.
No more wasted time, employee frustration, or manual input errors: OCR is the solution you need to better process and manage your documents. Using this technology within your company will allow you to enjoy the benefits we have listed above.
Given the growing importance of big data and the rising reliance of businesses on big data analytics to carry out their day-to-day operations, it is safe to say that big data has irrevocably altered the online world for anyone running a digital enterprise or an e-business.
Big data’s invaluable insights are an essential factor in the success of enterprises. Predicting seasonal bestsellers and providing a foundation for improving the brand-consumer relationship are just two areas of business management made easier by the insights revealed by big data.
However, most business owners and security experts tend to ignore the ‘dark’ side of big data, despite the fact that many have integrated the insights generated by big data into the core of their operations and are harnessing its power to set themselves apart from competitors.
While “the dark side” of anything certainly has a sinister ring to it, the many risks and dangers linked with big data are often overlooked and treated as minor issues. The concerns related to big data surge to the top of the ‘security and privacy concerns’ hierarchy as the power wielded by these big-data insights continues to expand rapidly.
To help our readers understand the gravity of the many security risks associated with big data-generated insights, we’ve created an article that focuses on the most pressing privacy worries and offers strategies for addressing them.
1. Breaches That Result in Obstruction of Privacy
Many people worry about the state of their privacy, especially their online privacy, in today’s rapidly evolving digital ecosystem, where phenomena like the “filter bubble” and “tailored marketing” are on the rise. There seems to be a constant decline in online privacy.
Considering the current state of cybersecurity, our future may very well resemble something out of an Orwellian nightmare. When we consider the privacy breaches made possible by big data insights and analytics, the gravity of the situation becomes even clearer.
Security-focused businesses are many times ahead of the curve, therefore we should draw our cues from them. NordVPN, a supplier of VPNs, has a solid reputation in Canada, mostly due to the company’s commitment to keeping user information secure.
Contrary to popular belief that a VPN only helps with unblocking websites or changing your Netflix region to access a more diverse library of movies/TV shows, these tools help boost consumer anonymity and privacy by encrypting all traffic.
Realizing the relevance of privacy and the value that sensitive information carries, it is essential to the survival of a company that they establish measures that prevent the disruption of customer privacy because of the value that sensitive information holds.
2. Anonymity is Difficult to Acquire – It’s a Fugazi
The ability to remain anonymous online is still regarded as a superpower, used by people like undercover journalists and those who live in countries with tight restrictions on free speech, despite coming under intense scrutiny in recent years.
Big data analytics makes it hard for any firm to keep any data files completely anonymous. Due to the heterogeneous nature of the raw data sets upon which big data insights are built, there is a substantial risk that consumers’ identifying characteristics will be made public, destroying any remaining privacy they may have.
Even though data is intended to be fully “anonymized,” it is common practise for many security personnel to combine valuable files in order to quickly spot an user. Since almost every small or medium-sized internet business uses finance, invoicing, and accounting software hosted by third parties in the cloud — and since these providers often have different policies on user data — anonymization becomes even more difficult.
3. Failure of Data Masking in a Big Data Environment
Most businesses today employ a process called “Data masking” to shield sensitive information from prying eyes on the Internet. Data masking, also known as data obfuscation, is the practise of concealing sensitive information by means of a false front of seemingly innocuous text or numbers. Data masking is typically used to hide sensitive data from prying eyes.
Data masking could completely fail if not used correctly, jeopardizing the security and, consequently, anonymity of many people engaged in big data analytics. Companies often overlook the hazards associated with big data, which magnifies the threats to users’ privacy.
To solve the problem of how to make data masking work with big data, businesses must implement a strict policy that details the criteria for data masking and ensures that they are followed by all employees.
4. Big Data Insights Result in Discrimination
One would think that as humanity evolves and embraces the arrival of the digital age, racism and other forms of overt prejudice would be left in the dust, but alas, they remain pervasive problems with lasting consequences.
Despite the fact that prejudice occurs in virtually every industry, the incorporation of big data insights and analytics has made it possible for businesses to learn an individual’s race and use that knowledge to their advantage.
For instance, a bank may use predictive analytics to learn a loan applicant is a member of a particular racial group and then reject their application; this practice, known as “automated discrimination,” has gained widespread attention in recent years.
5. All Patents and Copyrights Become Null and Void
Another issue that makes the incorporation of big data into organizations pretty difficult is the fact that in an environment driven by big data, obtaining patents can be an extremely difficult task, and ultimately as a result, it gets completely ignored.
One of the main reasons why it’s so hard to get patents in today’s big data era is because it takes so long to validate the patent’s uniqueness among the accessible database of information, which is why it’s often shrugged off.
Furthermore, in a world where big data reigns supreme, copyrights are mostly irrelevant because of how easily data can be manipulated. Consequently, this leads to the disappearance of any royalties earned from the creation of something original.
We can only hope that by the end of the article, the reader understands the gravity of the “Big Data Situation” and the myriad privacy concerns that arise in a data-driven environment. We can only keep our fingers crossed that the aforementioned privacy problems are given the attention they deserve by institutions, and that measures are taken to address them.
In 2022, digital natives and traditional enterprises find themselves with a better understanding of data warehousing, protection, and governance. But machine learning and the ethical application of artificial intelligence and machine learning (AI/ML) remain open questions, promising to drive better results if only their power can be safely harnessed. Customers on the Google Cloud Platform (GCP) have access to industry-leading technology for example, with BigQuery, with access to in a serverless, zero-ETL environment – but it’s still hard to know how to start.
While Google Cloud provides customers with a multitude of built-in options for Data Analytics and AI/ML, Google relies on technology partners to provide customized solutions to meet fit-for-purpose customer use-cases.
Faraday is a Google technology partner focused on helping brands of all sizes engage customers more effectively with the practical power of prediction. Since 2012, Faraday has been standardizing a set of patterns that any business can follow to look for signal in its consumer data – and activate on that signal with a wide variety of execution partners.
Crucial to Faraday’s success is how it uses Google BigQuery, one of the crown jewels of GCP’s data cloud. BigQuery is a serverless data warehouse that provides data-local compute for both analytic and machine learning workloads. One of BigQuery’s core abstractions is the use of SQL to declare business logic across all of its functions. This is a design choice with wide implications: if you can write SQL, then BigQuery will take care of parallelizing it across virtually unlimited resources. This presents a very clear path from a Data Engineer persona to the use of machine learning without the need for deep expertise in ML.
On BigQuery, Faraday can ingest virtually unlimited amounts of client data, protect and govern it with best-in-breed Google tools, transform it into a standard schema, calculate a wide variety of features that are relevant to consumer predictions, and run data-local machine learning modeling and prediction using BigQuery ML. BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by letting SQL practitioners build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
Below, Faraday gives some examples of this work and also the comparative advantage from being Built on BigQuery.
Real examples of BigQuery ML
Use case 1: increase conversion with personalization
Clients who provide known identity to Faraday in any form – email, email hash, or physical address – can have their customers segmented into a brand-specific set of personas. Then they can personalize outreach to these personas to increase conversion. This is facilitated by Faraday’s “batteries included” database of 260 million US adults, with more than 600 features covering demographic, psychographic, property, and life event data.
Once the client requests a “persona set” in the Faraday API or application, Faraday joins any available client “first party data” (provided by the client) with the national dataset (“third party data”) and declares the following BigQuery ML statement:
What’s unique about BigQuery ML is that Faraday is able to do all data prep in SQL, and from that point on, Google is in charge of data movement, scaling and computation. The resulting cluster model can be used to predictively segment the entire US population, so that the client can personalize outreach in any channel.
Use case 2: lead scoring
As long as the client is able to provide a form of known identity for their customers, leads, or prospects, Faraday can construct a rich training dataset from first and third party data. This dataset can be used to predict the likelihood of leads becoming customers, of customers becoming great (high-spending) customers, or of customers churning or otherwise becoming inactive.
Once the client requests an “outcome” in the Faraday API or application, Faraday again joins any first and third party data, computes relevant machine learning features including time-based differentials, and declares the following BigQuery ML statement:
There are a couple points to note. First, in this use case and the previous (personalization), Faraday applies other optimizations using BigQuery ML – but they are a simple expression of normal data science practice to enhance feature selection using different forms of regression. In all cases, the SQL is straightforward – and perhaps more accessible than data pipelines expressed in Python, Spark, Airflow or other technologies.
Second, Faraday is not asking the client to act as a data scientist. Thanks to the explainability of BigQuery ML boosted tree models, the output to the client is an extensive report on feature importances and possible biases, but the initial input from the client is to simply select a population they would like to see more of. For example, if they can define what a “high spending customer” is, they can simply ask Faraday to predict more of those.
Use case 3: spend forecasting and LTV
Say a client wants to know what particular customers or customer segments (personas) will spend with them in the next year or 36 months. By requesting a “forecast” in the Faraday API or application, Faraday will perform the aforementioned data joining and feature generation and then declare the following BigQuery ML statement:
Currently, Faraday implements spend forecasting and LTV (Loan-to-value ratio) as a regression model, but even better options may become available in the future as BigQuery is under active development. Faraday clients would see this as an improvement in the signal that Faraday provided to them.
Why GCP is the best data cloud for building predictive products
In the first half of 2022, Faraday ran more than 1 trillion predictions for US consumer businesses. This was only possible due to a number of factors that make GCP the best data cloud for building predictive products.
Factor 1: Zero ETL
Did you know that when you build a BigQuery ML model, you are actually creating a Vertex AI model? Probably not – and it doesn’t matter in most cases. Google’s industry leading data cloud architecture means that the client (and Faraday) is not responsible for data movement, RAM allocation, disk expansion, or sharding. You simply declare what you want in SQL, and Google ensures that it happens.
Factor 2: Serverless, data-local compute
“Data locality” is not just a buzzword – ever since Faraday came to BigQuery in 2018, bringing the compute to the data instead of the other way around has enabled Faraday to scale its predictive capability by two orders of magnitude compared to its previous machine learning solution. Previously, Faraday had to build highly complex data copying and retry logic; now, the retry logic has been deleted and scaling problems are solved by increasing slot reservations (or rethinking SQL).
Factor 3: Model diversity and active development
If you want to model something, there is probably an appropriate model type already available in BigQuery ML. But if there’s not, Google’s continuing investment means that data pipelines built on BigQuery will grow in value over time – without the cognitive dissonance that arises from needing to learn languages and frameworks outside of SQL just to accomplish a particular task.
Digital natives and traditional enterprises alike will benefit from predictions made about their customers and potential customers. Faraday can provide a ready-made solution to this problem, both to enable immediate activation and to inspire and benchmark clients on their own data science journey. Google BigQuery’s scale, convenience, and active investment make GCP the best data cloud for Faraday to build its product – and provide a compelling reason for clients to consider it for their own architecture.
The Built with BigQuery advantage for ISVs
Through Built with BigQuery, launched in April as part of Google Data Cloud Summit, Google is helping tech companies like SoundCommerce build innovative applications on Google’s data cloud with simplified access to technology, helpful and dedicated engineering support, and joint go-to-market programs. Participating companies can:
Get started fast with a Google-funded, pre-configured sandbox.
Accelerate product design and architecture through access to designated experts from the ISV Center of Excellence who can provide insight into key use cases, architectural patterns, and best practices.
Amplify success with joint marketing programs to drive awareness, generate demand, and increase adoption.
BigQuery gives ISVs the advantage of a powerful, highly scalable data warehouse that’s integrated with Google Cloud’s open, secure, sustainable platform. And with a huge partner ecosystem and support for multi-cloud, open source tools and APIs, Google provides technology companies the portability and extensibility they need to avoid data lock-in.