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AI Technology is Essential for Online Fraud Prevention

AI Technology is Essential for Online Fraud Prevention

Online fraud is growing at a frightening pace. Many cybercriminals believe they can con eCommerce stores out of their cash and never be caught because they are operating over the internet. One particular scam called fraudulent Buy Online Return In-Store (BORIS) is thought to have cost retailers a staggering $1.6 billion last year.

However, new advances in AI are changing this situation. Here’s how AI is being used to prevent online fraud from happening in the first place.

The Emergence of Fraud Teams

One of the most significant recent changes in fraud prevention is the rise of fraud teams. These are groups of specialists who understand how to use AI to track down and prevent potential security breaches. This dedicated squad operates entirely in the online world, building algorithms that make online purchases safe and limited the losses that can come through fraud. This is one of the reasons that global companies are projected to spend $46.3 billion on AI for cybersecurity in 2027.

It’s not just about preventing losses, though. Investing in AI is a way to radically increase the profits of a retailer by building trust and loyalty from customers. Around a third of all purchases are now made online and this number is only growing. Retailers are putting less money into preventing physical thefts and more into building AI to act as a shield around online purchases.

Making Use of Big Data

Every time a customer makes a purchase – and even long before they do – fraud teams are collecting data. They’re building up a picture of their customer base to see where the threats lie. This data is then analyzed and used to create better and better AI for detecting and stopping fraud before it can happen. In addition to stopping fraud against the company itself, AI will stop identity fraud as well.

As soon as a person visits a website, the data collected on them can determine the likelihood that they might be acting maliciously. Without any human intervention needed, AI defense technology can then block this person from making a purchase. The tech isn’t perfect yet but it signals a huge step forward in the fight against online fraud.

Upgrading Payment Gateways

Fraud is most commonly committed against online stores and eCommerce businesses. This is where people are regularly putting in their payment information, meaning that there is plenty of money passing through these platforms. That’s money that hackers would gladly like to take a share of. However, this is becoming increasingly difficult.

Fraud teams using big data analysis are now able to consistently upgrade payment gateways. Every time they do, hackers have to relearn how the system works. You can even now get a free eCommerce website with integrated payment gateway that will have all the security sorted for you. This has made fraud detection easier than ever before.

Advances in AI are changing the way we shop online. These algorithms are becoming better than humans at spotting and preventing fraudulent activity before it ever takes place. This will save retailers millions of dollars each year, which are savings that can be passed onto the customers in the form of cheaper products. That makes it a win for everyone involved and a good reason to keep supporting big data and AI.

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Search Engine Marketers Without Data Analytics Knowledge Are Obsolete

Search Engine Marketers Without Data Analytics Knowledge Are Obsolete

Data analytics has led to a huge shift in the marketing profession. A large part of this is due to advances in digital marketing. Digital marketers have an easier time compiling data on customer engagements, because most behavior and variables can be easily tracked.

This is particularly true for search engine marketers. Earlier this year, VentureBeat published an article titled How data science can boost SEO strategy.

Search engine marketers must be data analytics experts if they hope to create value for their customers. You have to vet a search engine marketing firm carefully before hiring them to make sure they understand data analytics and can use it to their advantage.

Data Analytics is a Gamechanger for Search Engine Marketing

A team of Greek researchers led by Ioannis C. Drivas of the University of West Attica published a paper on the benefits of data analytics in the search engine marketing profession. The authors made some startling discoveries about the benefits of leveraging data analytics for search engine marketing.

They analyzed 171 cultural heritage websites with data analytics tools. They were able to get a lot of useful insights that could be repurposed for search engine marketing strategies.

Modern search engine marketers must stay up to date with these changes. The changing reality of search engine marketing is in equal parts intimidating and fascinating, as more experts must turn to data analytics to make meaningful SEO insights. For someone with an online business, staying on top of hundreds of Google algorithm updates and implementing data-driven SEM practices is the key to place digital content on top of search engine results and enhance visibility.  

Moreover, 75% of people in a survey said that paid ads make it easier to find the information they are looking for. They won’t be able to optimize their paid ads strategy without collecting data on user engagement. But does every business have the search engine marketing expertise, resources and knowledge of data analytics to manage paid search?

The simple answer is no. And it is for this reason that most businesses outsource their SEM needs to a search engine marketing firm with a background in analytics.

Key Takeaways

Before you can appreciate the need to hire an expert with a background in data analytics, you need to understand the basics of search engine marketing. Here are some essential principles and definitions:

Search engine marketing means promoting a business using paid advertisements. SEM, along with SEO, is essential for every business to enhance visibility.  A search engine marketing firm helps with market analysis, designing, running, and managing campaigns, along with reporting results. Search engine marketing helps drive conversions, increases brand awareness, and brings in qualified leads.

These strategies can be very effective when used in conjunction with a stellar data analytics platform.

What is the Backbone of a Search Engine Marketing Strategy Based on Data Analytics?

If you search for ‘ways to increase online visibility’, you will surely come across data-driven search engine marketing as a technique. This is because SEM is among the most effective ways to grow your brand in the competitive marketplace. Precisely, search engine marketing uses paid advertisements that appear on search engine page results (SERPs). When a user searches for a keyword, search engine marketing enables your brand to appear as a result of that search query.

There are a number of ways that data analytics can round out your search engine marketing strategy:

You should always used Google Analytics to see how much search engine traffic your site is receiving and which landing pages are getting visits from Google. Google no longer shares data on the exact keywords driving traffic through organic search results, but you can at least see which topics are driving traffic and make some guesses.You can use data mining tools to find new keywords to target.Data mining tools can also help you discover new linkbuilding opportunities. You can use data analytics features in tools like Ahref and SEMRush to see which sites are linking to your competitors to reverse engineer their linkbuilding strategies and improve on them.

Data analytics is a crucial element of any successful search engine marketing strategy.

Importance of a Search Engine Marketing Firm

Someone rightly said, “stopping advertising to save your money is like stopping your watch to save time.”

SEM is indeed important for every business. Here’s what an SEM firm that understands data analytics can do for it. Take a look!

Understand the Market with Data Analytics Tools

Market analysis is a significant part of the services provided by a search engine marketing firm. It helps you get the top-ranking keywords for improving the ranking of your business.

An SEM firm helps you identify and bid on the keywords being used by competitors that would leave them behind. There are a number of powerful data analytics tools that can help them do this.

Campaign Management

A campaign’s success depends on choosing the right combination of ads and managing it over its life cycle. SEM experts monitor your ad campaigns closely and make required adjustments so that your business gets optimal ROI.

Campaign Analysis

Analyzing the campaigns is essential to ensure that it provides desirable results. Most search engine marketing firms integrate in-depth analytics as a part of the strategic plan.

These analytics are used to study several parameters, such as user search history, keyword searches, hardware, and geographical location.

Did You Know?

Quality score is one of the most crucial factors for successful search engine marketing. It is Google’s rating of the quality and relevance of your keywords and PPC ads.

Designing Ad Campaigns

A search engine marketing firm also handles the designing part. It crafts and runs targeted ads on different social media platforms such as LinkedIn, Facebook, Twitter, and Google.

Reporting Results

The duty of a search engine marketing firm goes beyond designing and running ads. It also delivers measurable results that the clients can easily track. Most reputable search engine marketing firms have advanced tools that help gauge the efficacy of paid campaigns.

Now, unless you can handle all these tasks, we suggest getting in touch with a reliable search engine marketing firm. But wait, let’s first see why SEM is beneficial in the first place.

SEM Drives Conversions

Search engine marketing is a significant conversion driver for diverse types of marketing campaigns. Whether you want new subscribers, contest entries or newsletter signups, SEM can help you with everything.

This is because paid ads are linked to conversion-focused landing pages.

SEM Increases Brand Awareness

Although considered a bottom-funnel marketing channel, SEM can increase brand awareness. But what if your ad does not even get clicked? Doesn’t really matter! Your brand name is still visible on top, and searchers will recognize the product and brand name.

SEM Leads to Consistent Traffic and Qualified Leads

Search ads can help generate consistent traffic for specific keywords and control the amount of traffic coming to your website.

What’s more? The sole aim of SEM is not just to bring leads but qualified leads. SEM allows you to create a custom audience that will be more likely to see and engage with your ads.

So, Should You Hire a Data-Driven Search Engine Marketing Firm?

Of course! Brands need to focus on SEM to see the immediate and long-term results and convert visitors into leads. Search engine marketing has the power to do this and much more. Big data technology has made it even more effective.

If you are also looking to increase brand awareness and target the right audience, get in touch with a reputed SEM firm like AdLift. The paid media marketing agency has delivered exceptional results for various brands, such as Vega, Nicobar, and Andamen. You can be the next and witness a boost in leads.

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Quality Control Tips for Data Collection with Drone Surveying

Quality Control Tips for Data Collection with Drone Surveying

Here at Smart Data Collective, we never cease to be amazed about the advances in data analytics. We have been publishing content on data analytics since 2008, but surprising new discoveries in big data are still made every year.

One of the biggest trends shaping the future of data analytics is drone surveying. As more surveying companies harness the power of drones, they are compiling more data that can be used for energy procurement construction, oil drilling, defense applications and many other purposes.

However, despite the benefits they offer, Colin Snow points out that drones create some challenges with data analytics. You must have quality control systems in place to get reliable data with drones.

Drones Surveyors Are Pioneers in the Data Analytics Field

It may seem like all one has to do to use a drone for surveying is learn how to use the drone properly. While this is definitely true, there are a few best practices that users need to learn to use it properly.

Drone surveyors must also know how to gather and use data properly. They will need to be aware of the potential that data can bring to entities using drones. Indiana Lee discussed these benefits in an article for Drone Blog.

“Demand for big data for commercial uses, technological advancements, and increased venture capital funding will continue to drive rapid growth in drone use. Agriculture, real estate, construction, and highway safety are some of the industries analyzing this data. While drones are valued for the images and video footage they collect, they are increasingly able to store other types of information, including radio signals, soil moisture, factory emissions, and geodetic data, which includes precision measurements for land surveys.”

The following are some practices in drone surveying that could make a difference. You will also want to know how to harvest the data that you get.

Do an Overcast Survey to Ensure You Get Reliable Data

We have talked extensively about the importance of both data quality and data quantity. You have to ensure that you have quality assurance protocols baked into your data collection methodologies. Otherwise. your data might be tainted and lead to poor data-driven deductions down the road.

When you are collecting data with drone surveillance, you will want to make sure that you conduct your surveying during the rights time of day. This requires you to make sure that light levels are optimal.

When you’re surveying, you might think the best time to do so is when the sun is out. Sunlight is used to take some spectacular photographs, so it should work for surveying, right? Well, that’s incorrect. Too much sunlight could cause some issues. and lead to poor quality through your surveying efforts

The model quality may suffer because too much sunlight may add a lot of reflections to the images the drone is capturing, which in turn reduces data quality. That’s not a good thing for anyone. Drone users will have to schedule surveying on overcast days. It doesn’t have to be heavy overcast, just a day that isn’t too sunny for best results.

Use Compatible Drones for Higher Quality Data

Sometimes, people think that all drones are pretty much the same, but there not. Some drones are specialized, and this is something you need to be aware of. Choosing a compatible drone should help ensure your drone surveying goes well and data quality is maximized. For example, if you need a drone to help improve security within a warehouse or something similar, you’ll need indoor drones for security.

An indoor safety drone has all sorts of features that make it ideal for indoor use, like safety guards and motion detectors. When one is shopping for a drone, the objective has to be clear. Once that objective is clear, it’ll be a lot easier to find the right drone. Take your time. Finding the right drone isn’t simple, but it’s worth it. Talk to a drone specialist and describe the functions you need.

The Camera Settings

Camera settings are also going to affect the quality of data you get through drone surveillance. Many are tempted to change their camera settings, thinking they might get better results, but that isn’t always the case. If you want to stay with the basics, stay safe, you’ll want to make sure the format is set to jpeg. In addition to that, you’ll need to keep the aspect ratio at 4:3. Doing this should help you capture images well without any distortions.

You might also want to turn the vivid color mode on to ensure the images look alive. If you don’t like the vivid color mode because the colors look too vibrant, you could also increase the sharpness of the pictures. You’ll want to try to increase the sharpness only a little. Evaluate the results and adjust as needed. You want clarity when surveying to get the highest quality data, so be sure to pay close attention to that.

The Flight Speed

Speed is a big deal when surveying, yet it’s something folks don’t always pay much attention to. Generally speaking, you want your drone to fly between two to five miles per hour. Those who need the survey for context can speed things up a bit but stick to four or five miles per hour.

Your pictures don’t have to be that detailed if that’s all you want to accomplish, but if you want details, then you’ll need to slow down a bit. You want to keep your drone flying at two or three miles per hour, nothing more. The drone is going to take a picture every two to four seconds. That’s a lot of images. Evaluate the pictures taken and adjust the speed if necessary. Sometimes, depending on the size of the drone and the quality, you might need to change speeds.

The Vegetation Issue

Yes, there’s a vegetation issue that needs to be addressed. The fact is that surveying land with twigs, thin fences, or dormant trees could be a problem. For some reason, drones have a hard time capturing these sorts of images and being aware of this should help you find ways around this small issue.

The first thing you want to do is make sure you slow down when surveying. You already know how to do that. A slow survey is one thing you need to do, but there are others, like making sure you take a few pictures manually for greater overlap.

It may also be a good idea to take a few pictures at an angle. This is a helpful trick because that thin vegetation can’t hide because drones can easily capture vertical structures. If you want high-quality results, be sure to avoid windy days. These days usually cause errors when taking pictures because those thin twigs and trees move from image to image causing distortions.

These are some of the best practices to keep in mind when drone surveying. It’s going to take some time to get good at surveying, but with patience, you’ll get there.

Implement Data Quality Control with Drone Surveying

Drone surveillance is changing the future of data analytics. However, it is important to have a solid data quality control system in place. The aforementioned tips can do a lot to help.

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How to Bring Presentation Data to Life with Powered Template

How to Bring Presentation Data to Life with Powered Template

We have talked in the past about the importance of data visualization in business. One study by Robert Horn at Stanford found that 64% of participants made a decision immediately after watching a presentation with an overview map.

However, many companies are struggling to figure out how to use data visualization effectively. One of the ways to accomplish this is with presentation templates that can use data modeling. Keep reading to learn more.

Taking Advantage of Data Visualization with Presentation Templates

It’s essential to make sure that information is communicated clearly in an age when everyone is working from home. As many businesses swap meeting rooms for Zoom calls, the task of sharing data in an engaging way has become more challenging. While body language and face-to-face interaction has been lost in WFH environments, screen sharing and cloud storage means that our presentations can be more accessible and more detailed than ever before. 

Sadly, the task of making a great presentation can be a daunting one for many individuals. It is even more complicated when you need to implement data visualization. For the many of us who aren’t natural graphic designers, we need to know how we can create a visually effective presentation from scratch that will help to clearly communicate the key points on our agendas while effectively explaining things without overwhelming our audience.

This is where platforms like Powered Template can enter the fray and help companies make the most of data visualization technology. Through Powered Template, users have access to more than 90,000 pre-designed slides that are available to download and edit on-demand. They can utilize data visualization tools to create even more effective presentations.

Powered Template’s extensive repertoire of pre-prepared templates allows you to build presentations with ease and to create stunning data-driven visualisations. Let’s take a deeper look into how:

Creating a Presentation to Suit your Goals

Powered Template showcases pre-prepared templates for Microsoft PowerPoint, Google Slides, and Apple Keynote presentation platforms. It also boasts a highly intuitive menu system to allow users to pinpoint exactly what they’re looking for in terms of creating the presentation that suits their goals. It is one of the most remarkable solutions in data visualization for anyone trying to make a great presentation.

Significantly, Powered Template’s menu system updates data in real-time and allows users to combine criteria to gain access to more intuitive results. 

For instance, let’s take a deeper look at what appears if we search for 3D data driven diagrams and charts. Upon running the search, Powered Templates has returned a total of 2,500 options that suit the descriptions that are being queried. 

For ease of reference, each template comes with an average star rating provided by users and a figure that shows how often the template has been downloaded. Images of crowns on red backgrounds signify premium content which require either a subscription or a signup. 

As an example of how Powered Template can help bring presentation data to life, let’s take a deeper look at this template for a hexagonal organisation chart. Let’s imagine that the colour coordination and space for eight central points within the chart is perfect for the message that I’m looking to get across to my audience and that I want to use the template for myself. 

To access the chart, I can either be a premium account holder, or appropriately attribute the presentation to Powered Template when it comes to using the template to create data visualization. Upon selecting the option that suits my needs best, I’m ready to load the template in my chosen presentation software. 

Now my template is ready to become a fully-fledged actionable part of my presentation. As we can see, there is plenty of placeholder text which signifies where I can begin adding my own content to begin sharing data. 

As we can see from the image above, all aspects of the template are free to edit, and the content arrives readily attributed for free users. To seamlessly include your visualisations as part of a larger, more comprehensive presentation, you can simply build your slide before copying and pasting into a pre-prepared presentation – or by combining it with another template that’s formatted in a suitable manner. 

Data Visualization Templates on Demand

One of the best perks of Powered Template is the fact that the platform’s pricing structure is highly adaptable to suit the needs of all users and organizations. It is one of the many reasons companies can use this data visualization solution.

When it comes to subscription services in order to access premium quality content, users have the option of paying on a monthly basis for short term access, buying an annual subscription at a far more effective monthly cost – or even to purchase an on-demand subscription which allows users to access 10, 20, 50, or 150 template downloads as and when they want.

This highly adaptable pricing structure means that users can find a balance that suits their needs, even if they’re unlikely to seek out many templates on a recurring basis. 

Another great aspect of Powered Templates is the fact that the website supports a useful live chat function which puts visitors in touch with an agent at the touch of a button. This can be highly useful for individuals who may not be experienced in downloading presentation templates, or those who need a little help understanding how to access a download within their chosen presentation software. 

Powered Templates’ dedication towards serving all users – whether they’re causal individual users or Fortune 500 companies is likely a key reason behind why the company continues to thrive despite operating since 2004. 

Powered Template is a Fantastic Presentation Tool for Using Data Visualizations Effectively

Packed with a vibrant community of creators and new templates being uploaded to the platform on a daily basis, we can expect Powered Template to continue to aid its ever-growing user base for many more years to come. It is a pioneer in presentation creation for companies that want to make the most of data visualization.

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Google Cloud Cortex Framework extends offering in latest release and beyond

Google Cloud Cortex Framework extends offering in latest release and beyond

Last year we announced an exciting new initiative, Google Cloud Cortex Framework, to accelerate business outcomes with less risk, complexity, and cost. By providing endorsed solution content, including reference architectures, deployment accelerators and integration services for common business scenarios, Google Cloud Cortex Framework helps you kickstart time-to-value to get up and running quickly with Google Cloud.

At the same time, we also announced our first content release, our Data Foundation release — providing a rich set of building blocks and templates for SAP enterprises looking to modernize with our Data Cloud. This includes predefined operational data marts, data processing scripts, machine learning templates, and plug-and-play reporting samples for common business use cases leveraging BigQuery — our serverless, highly scalable, and cost-effective cloud data warehouse with proven value for SAP enterprises.   

Today, we’re expanding our Data Foundation release for SAP enterprises in new and exciting ways. 

New analytics content for SAP

We’re extending our available content to include predefined BigQuery data marts and Looker reporting templates focused on enabling faster time to insight for common order-to-cash and financial analytics including: Order Fulfillment, Order Status, Sales Performance, and Billing & Pricing to enable better understanding of sales, delivery and billing performance, as well as Accounts Receivables, Overdue Receivables, Days Sales Outstanding and Doubtful Receivables to help enterprises better understand their financial position. Customers have told us that by having the various lines of business data available within BigQuery it opens up new opportunities to extend with other data sets to gain new insights and better optimize core business processes.

Easier SAP and Non-SAP data integration   

We’re also making it easier to combine SAP data with other data sets for new insights and  extended business value.

Integrating SAP and Other Data: We’re helping accelerate data integration with expanded solutions across our partners including: Fivetran/HVR, Informatica, Palantir and Qlik in addition to those available from Google Cloud. By leveraging solutions that can integrate both SAP and non-SAP data all sorts of new opportunities become possible. For example, by combining enterprise data from SAP and Salesforce with Google Trends, businesses can start to better understand broader insights for sales and marketing and better understand the demand for specific products and services. Google Cloud also provides easy access to a curated catalog of external datasets to help enhance analytics and AI initiatives. Customers can also take advantage of BigQuery’s Data Transfer Service to combine data from Google Ads, Google Play, YouTube and more.  Through partners, BigQuery’s Data Transfer Service also supports 150+ data sources such as Adobe Analytics, DoubleClick, Facebook Ad Insights, Shopify, etc.

Cloud Native Application Layer: We’re also introducing a first wave of recipes for application modernization and integration, including a microservices-based application layer that will allow external applications to consume results through scalable, production-ready APIs and Pub/Sub messages. This new capability helps facilitate integration with external applications and platform capabilities, such as SAP Business Technology Platform (BTP), to consume results from Cortex Data Foundation and enhance functionality for applications through comprehensive interfaces and solution templates. For example, you can better plan inventory with trending sales items, or automate late payments alerts for overdue receivables in downstream systems.

What customers are saying and what’s next

We launched Google Cloud Cortex Framework and our Data Foundation content so that our customers can accelerate innovation, and we’re pleased that some of our largest enterprise customers have already shared their interest, feedback, and results.  

“Operating in 18 countries with one of the top 10 ecommerce sites in the world, our transaction volume is extremely high and continuously growing. Google Cloud technologies such as the BigQuery Connector for SAP and the solution content provided by Google Cloud Cortex Framework are allowing us to quickly integrate our massive volumes of SAP data with other data sets on a scalable cloud foundation leveraging BigQuery. We look forward to deepening our use of these key capabilities and solution accelerators for both business efficiencies and improved customer experiences.” —Alejandro Bonsignore, Finance & People Systems Senior Manager, Mercado Libre

As excited as we are to bring you these new capabilities, we’re only at the beginning of our Google Cloud Cortex Framework story. The path forward is clear: to accelerate business outcomes for our customers; reduce risk, complexity, and cost; and create a launchpad for innovation. Look for an expansion of our Data Foundation and use case-specific content for other data sets, and business scenarios across the enterprise. You can find our Cortex Framework Data Foundation content now available on GitHub, and our application layer specific solution content available on Google Cloud Marketplace. Give them a try!

To learn more about Google Cloud Cortex Framework visit our solution page, and tune in to our on demand session at our Data Cloud Summit for more about what’s possible.

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Pulse Surveys Must be Part of Every Company’s Data Strategy

Pulse Surveys Must be Part of Every Company’s Data Strategy

More companies than ever are investing in big data. However, many feel that their data strategies are not proving to be effective. According to a report by VentureBeat, only 13% of companies feel that their data strategies are providing the results they are looking for.

One of the reasons that data strategies often turn out to be ineffective is that companies define them too narrowly. They often end up focusing entirely on using big data to optimize their financial and marketing strategies. However, they don’t use data enough to improve their internal culture.

One option data-driven companies should take is to invest in pulse surveys. As a manager, executive, supervisor, or human resources professional, part of your role is to develop and help talent grow. This comes in the form of developing talent, retaining employees, giving/receiving feedback, and interacting with employees in a meaningful way. Understanding your employees, helping them achieve work-life balance, and soliciting feedback from them is beneficial to your business. Pulse surveys can be helpful, especially when used in conjunction with other data analytics tools.

Surveys are an ideal delivery system for these efforts. Surveys give employees voice while identifying opportunities and exposing potential issues that can be resolved. They are a valuable way to gather opportunity with the extra benefits of holding leadership accountable and improving employee morale. Surveys serve a dual function at most organizations. On one hand, they help human resources departments establish a direction to focus their efforts. On the other hand, they help employees feel heard while seeking to develop workplace culture and a more robust operation. Here is how to use them at your organization.

Pulse Survey Defined

Pulse surveys are short, direct surveys given out to staff frequently. Other, longer surveys—such as baseline engagement surveys and job satisfaction surveys—do exist for gathering other types of information. A pulse survey should be short and sweet, handed out roughly every other quarter. Their frequency means it’s easier to obtain meaningful data that actually matters. Its immediacy is punctuated by the fact that pulse surveys are not designed to measure specific items, instead existing as a vehicle for rapid, high quality feedback.

Keep It Short and Simple

Anyone who’s ever worked in data science knows that people don’t always enjoy taking surveys. They especially abhor lengthy surveys with no clear direction or compensation. This idea should extend to designing a pulse survey for an organization. Remember that your employee’s time is valuable and you should design your survey with brevity in mind. Believe it or not, plenty of excellent quality data can be obtained from a few simple questions.

The optimal length for a pulse survey should be between five and fifteen simple questions, but ten is the best length. Keeping it short and simple will get you better results and make people more willing to fill out your survey when it’s administered.

Don’t Ask Intrusive or Inappropriate Questions

Surveys sometimes run into an issue where they tend to ask intrusive or invasive questions. Never ask questions that might offend, intrude, or possibly be illegal. It’s also wide to avoid leading questions in your survey design. These questions prompt the respondent to answer any particular way, ultimately leading to bias and spoiled survey results. If the researcher wants the respondent to reply with a particular response, leading questions are used to do that. Avoid it at all costs in your survey design! Assumptive questions can be a problem in survey design as well. Double barreled questions—where you’re asking two questions at once—can confuse respondents. They also mar the data a bit because of the lack of precision such questions can generate. Avoid nonsensical questions and always make sure to offer quality scale choices. A likert scale isn’t going to provide quality data if the respondent doesn’t understand the options! The goal of creating a survey is to be as straightforward as possible, in order to obtain the best possible data.

Use Artificial Intelligence to Administer Surveys

Technology is a powerful tool for any aspect of business. In data analysis, artificial intelligence can be quite useful. It makes it easier to do complicated tasks quickly and with higher efficiency. This is especially prevalent in administering employee surveys. In the past administering surveys was a much more lengthy process. Sending them out through emails, using an employee work platform, or standing by with the old paperwork in the break room method weren’t necessarily the best ways to administer surveys. Using artificial intelligence to check and double check survey rollout, reception, and results assessment is incredibly useful for running your pulse surveys. Remember that AI is not a replacement for human workers; instead it is a valuable tool to aid in analyzing data and drawing conclusive insights from pulse surveys. A survey platform that incorporates both AI and machine learning.

Find A Platform Rooted In Data Science/Analysis

Selecting a sample size, defining variables, and gathering data are only the starting points for your survey. It’s imperative to find a platform for the surveys that is rooted in data science. That way, you have the tools you need to disseminate and parse data at your disposal. Simply trying to do a survey in house without any kind of platform isn’t very efficient. That’s why you need a data-driven pulse survey platform. These type of platforms are meticulously designed to have the data science aspects baked in already. That means you can get important information and insights from your surveys, while largely automating them. Using AI to assess results, and having ready reporting that includes benchmarks and context means that giving out these surveys—and getting the results you need—is no longer the headache it was before the days of high technology applications.

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AI Technology Leads to Impressive Benefits with Algorithmic Trading

AI Technology Leads to Impressive Benefits with Algorithmic Trading

Artificial intelligence has revolutionized the world of business. You can take advantage of a number of AI tools to find new ways to jumpstart your career or start a new business. One of the ways to make money through the use of AI technology is with algorithmic trading. This is a huge market driven by AI technology that is expected to be worth $19 billion by 2024.

What is algorithmic trading?

An entrepreneurial mindset and a knowledge of AI can help you unlock multitudes of ways to make money. Our highly connected world and digital economy make it much easier to accomplish your entrepreneurial goals. One such avenue for making money is algorithmic trading.

You probably already know all about trading, which is the activity of generating profits by making use of price movements of financial instruments. Automated trading (also known as “algorithmic trading”) works by using AI algorithms to perform trades. Algorithms are sets of programmed instructions to perform some tasks. As AI technology has improved, algorithmic trading has become more effective and given traders the opportunity to realize higher ROIs.

In algorithmic trading, rule-based trading instructions are automatically executed when certain conditions are met. You can also use machine learning technology to adjust your rules to better align with your investing goals. There is no need for a human trader to perform the trades, since artificial intelligence is capable to handling these processes on its own. But humans are still needed to set the rules according to which the algorithm works, even if AI can help adjust these rules over time.

Most large financial institutions employ AI to execute algorithmic trading. In fact, the best hedge fund in the world specializes in algorithmic trading and is a pioneer in the field. Renaissance Technologies is a New York-based hedge fund that exclusively engages in algorithmic trading. Towards Data Science published an article titled How Renaissance beat the markets with Machine Learning, which emphasized the major benefits of AI in their trading. Medallion Fund, the flagship fund of Renaissance, gave an annualized return of 66 percent over the 30 years starting from 1988. This figure appears to have improved as AI helped boost their ROI.

Renaissance started algorithmic trading access to cutting-edge AI-driven computing tools was limited to a few. But today the most advanced tools are available to everyone at a fraction of the cost. This makes anyone able to emulate the algorithmic trading strategies performed by Renaissance technologies.

Advantages

Algorithmic trading is performed based, AI-driven approach to trading based on a predetermined set of rules. This gives the strategy a distinct set of benefits.

Eliminates the challenges of human psychology

Human psychology is the largest deterrent to consistent trading success. Even with a sound strategy, a human trader can bungle up trades due to the heuristics that exist solely in their mind. Algorithmic trading avoids that as trades are executed on predetermined rules.

The psychology of the trader is taken out of the equation of trading success by dehumanizing financial markets.

High-frequency trading

Human traders can perform only a limited number of trades. This is limited by the number of clicks and inputs she can provide in a brief time. But AI-based trading automatically executes trades. Algorithms can execute thousands of profitable trades every second. This is high-frequency trading which can only be done with algorithmic trading.

Accuracy

Fat-finger trades are trades that are caused by errors. This is common when human traders are actively involved in trading. AI-based trading eliminates any such possibilities and accurately performs trades every time. Algorithmic trading helps to avoid such mistakes.

Scanning

There are thousands of financial instruments that are actively traded across the world. A human trader cannot scan the complete universe of financial instruments. But this can be done with algorithms, and human traders are alerted only when favorable signals are identified.

Backtesting

Algorithms help to backtest trading strategies. This helps the trader to determine the soundness of the strategy before trades are executed. AI algorithms can run the strategy across vast amounts of past data to determine the efficacy of the strategy.

Reduced cost

The high speed enabled by algorithmic trading increases the number of transactions in a given time frame. This helps to reduce costs needed to achieve the same profit. Since AI algorithms automatically scan for trades and also execute trades, opportunity costs also can be offset.

How to make money?

Algorithmic trading is a lucrative field to make money. It is a broad field that enables profit generation in a wide variety of ways. You can choose to actively trade or provide support to trade. Some of the major avenues to make money with algorithmic trading are covered in the following sections.

Algorithmic trading

The most obvious approach is to use algorithmic trading. Thousands of profitable trades can be executed every second with sophisticated AI algorithms.

Actively engaging in algorithmic trading is monetarily rewarding. MetaTrader 5 is a popular platform that is accessible to everyone. You can easily set up an account, execute algorithms to perform strategies, and generate profit. All this can be done in a short time.

Even if you are a novice, MQL5.community provides adequate support to perform algorithmic trading. The marketplace of MQL5.com lets you buy trading signals and trading bots. You can just implement it to execute algorithmic trading. The ecosystem is helpful for newcomers to algorithmic trading trying to get started and generate profits.

But there are some barriers to entry for algorithmic trading. You need to have substantial capital to get started on lucrative trades. This could be a deterrent to many who are just getting started. Though algorithmic strategy is accurate, ‌profits depend on a sound strategy. A faulty strategy can result in losses, and the trader will have to bear those losses. One has to be aware of the risks involved in algorithmic trading before executing those. One should also consider that there are many risk-free avenues to make money with an AI-driven approach trading.

Supporting algorithmic trading

Another avenue for earning money is by supporting algorithmic trading. This requires no capital to be invested and therefore, no downside risks. Individuals and firms engaging in algorithmic trading require various support services that provide access to powerful AI tools. You are essentially selling services to traders who have the financial ability but do not have the time or knowledge. Fulfilling the needs of such traders in itself is a lucrative business.

Selling signals

A predetermined set of rules can be used to know which assets to trade and how to trade them. You can identify successful signals and sell them on the marketplace to customers. You can ensure success by communicating the success rate and backtests of the signals. You can employ many business models to sell those signals. You can sell indicators for free and upsell them on value adds. You can also sell for a fixed fee upfront. If you are an active trader, you can generate additional revenue by selling profitable signals.

Trading bots

Trading bots execute trades automatically for you. They are capable of executing a wide range of strategies. You can create a trading bot, place it in the marketplace, and set a price. You will get the revenue every time someone buys your trading bot. The advantage is that you need to create the bot only once. After that, you will have recurring revenue without doing any effort. This is an easy way to earn passive income supporting algorithmic trading.

Cloud network

When you use your computer for general use, it does not consume the complete processing power available. Most of the time, the CPU is idle. You can sell this space power to interested users with the MQL5 Cloud Network. Others on the network can use your spare CPU power to develop mathematical models, perform backtests, and optimizations. You will get paid for the CPU power used. You can also use AI to make sure your computing resources are used more efficiently.

Custom orders

Users of MQL5.com have custom requirements for their algorithmic trading. You can fulfill those orders to make money. For this, you will need to know the basics of programming and markets. If you are knowledgeable and capable, you can earn a good living by fulfilling the custom orders of clients.

Algorithmic trading: A thousand ways to make money with AI

AI technology has revolutionized the field of financial trading. Algorithmic trading has a stronghold in modern financial markets. Today, anyone can access powerful techniques to make stellar revenue. The field is so wide and diverse that there are many avenues to make money. You can actively engage in algorithmic trading or support the activities. The beauty is that there are plenty of opportunities for experts and beginners. All you have to do is get started.

The post AI Technology Leads to Impressive Benefits with Algorithmic Trading appeared first on SmartData Collective.

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What Data Methods Can Businesses Invest In to Get Better Consumer Results?

What Data Methods Can Businesses Invest In to Get Better Consumer Results?

There is no doubt that big data is important in many organizations. Over 65% of large companies invested over $50 million in big data in 2020. That figure is growing faster in recent years.

Data. The word gets used so often that it’s become vague. You are talking about data, sure, but what kind of data? Finding the right data sets and knowing how to use them is key to any data implementation strategy.

In this article, we take a look at three different sets of goals your business might have—branding, marketing, expansion. We will then take a direct look at what sort of data sets you can use to improve these efforts. Read on to learn more about how you can revolutionize your data implementation strategy.

Creating a Data Strategy for Branding

Modern consumers are almost as concerned with what a company believes in as they are in what it sells. Branding is an important part of thriving in the modern business world, but it can also be a difficult art to master.

Without the right intel, it involves a lot of guesswork. And of course, mistakes come at the cost of public opinion and sales. To find out what sort of branding initiatives a business should adopt, they should begin with several different large data sets.

How do people currently view your brand?: Getting an answer to this question is important for several reasons. Naturally, it’s good to know where you are at. If there is a serious problem with our public image, or if it simply does not reflect what you are hoping it will be, this is how you are going to find out. This data point will also give you a good idea of what direction to take your future rebranding efforts. Unless there is a very compelling reason to do so, it’s usually better not to go sharply against the grain of public opinion. Doing so runs the risk of coming across as disingenuous. Instead, emphasize branding initiatives that improve public precepts that already exist.Who is your existing demographic?: This data point will tell you a lot about what sort of points to emphasize. For example, if you find out that the majority of people who use your business are millennials, you can assume that certain, activist or social justice-oriented adjustments to your public perception may be met with appreciation. For example, millennials are very concerned with the environment. Being the business that recycles, or avoids carbon emissions can be a big boon.

With all branding efforts, it’s important to practice what you preach. If you are the company that cares about the environment, you can’t also be the company that dumps chemicals in the river. In other words, be less like Leanardo DiCaprio talking about climate change from his private plane, and more like George Clooney getting arrested at a mass demonstration.

Taking Stock of Marketing Initiatives with Big Data

Marketing when done right is a critical business asset. Big data is helping improve the marketing profession dramatically. When it’s done wrong, however, enormous amounts of effort and money are wasted. The old marketing practice of throwing things on the wall and seeing what sticks is no longer needed in the age of Big Data.

With Big Data you can learn:

Who you dealing with: If your customers are stuffy business people, they probably aren’t going to appreciate a marketing campaign with say, a talking cactus. If they are Generation Z, they might be more receptive to something light and humorous.Where they are: We mean online, of course. Social media is a huge marketing opportunity but it’s important to target your markets carefully. Use data sets to make predictable, accurate assumptions about where to reach your audience. For example, if you find that most of your customers use Twitter, you needn’t waste much time dealing with Facebook.When to deal with them: Timing is everything. Say you’ve found out that your base spends most of their online time on Twitter. Great intel, right? The only problem is that Twitter runs around the clock. Your ads might not. Using social media analytics, you can learn not only who is engaging with your ads, but also when they are doing it. You might learn that get most of your engagements in the afternoon. Knowing this, you can funnel the majority of your marketing efforts towards a time slot that is most likely to produce great results.

Not only does data implementation in marketing have the potential to produce big returns, but it also helps you to avoid wasting money. With data, you can make sure that no more cash goes down the trains on bad wasted marketing efforts.

Opportunity

Let’s say you are a retail franchise that wants to open up a new location. What has to happen to make this go well?

You need to know:

Market size: How many people live in the area you are considering for a location?Location: Is the spot you are looking for in a place that will naturally get a lot of traffic?Demographic: What percent of the people living in the area are likely to shop at your store?

These data points take the guesswork out of expansion. Naturally, if you are a boutique fashion chain, you probably aren’t going to want to set up shop in a low-income area. If, on the other hand, you are a discount grocery franchise, you may actively target areas that have a very low median income. You might also consider the accessibility of your location. Is it close enough that people who rely on public transportation will be able to access it?
Expansion efforts create a lot of questions. Data can answer them.

Conclusion

Knowing how to use data is key to finding success and staying competitive. Unfortunately, data comprehension and implementation are not always intuitive. It’s a skill that needs to be learned. Businesses can improve their data fluency by hiring data analysts and scientists to tame and interpret their information.

Small business owners who cannot afford to staff data experts can still get in on the game by furthering their education. From administrative to analytic-specific advanced degrees, there are a wide range of ways to improve your business skills.

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Extending the power of Chronicle with BigQuery and Looker

Extending the power of Chronicle with BigQuery and Looker

Chronicle, Google Cloud’s security analytics platform, is built on Google’s infrastructure to help security teams run security operations at unprecedented speed and scale. Today, we’re excited to announce that we’re bringing more industry-leading Google technology to security teams by integrating Chronicle with Looker and BigQuery. Backed by this powerful toolset, security analysts can create brand new visual workflows that increase efficiency and improve outcomes in the Security Operations Center (SOC). 

New Looker visualizations in Chronicle

Chronicle’s new visualizations – powered by Looker, Google Cloud’s business intelligence (BI) and analytics platform – enables a multitude of new security use cases such as dashboarding, reporting, compliance, and data exploration. Out of the box, security teams can access brand new, Looker-driven embedded dashboards in five content categories at no additional cost to the Chronicle license:

Chronicle security overview – a set of overview visualizations that surface high level insights such as statistics and trends on ingested events, number of alerts, and a global threat map

Data ingestion and health – an overview of all security telemetry ingested into Chronicle, including data types and volume

IOC matches – a granular view into IOC matches detected in Chronicle, with views into IOC matches across IPs, domains, and assets

Rule detections – detailed insight into the top 10 triggered detection rules, the top users, IPs, and assets associated with rules, and more

User sign-in data – insights into sign-in data across the organization including sign-in status over time as well as top sign-ins by application and user

Example Looker-based dashboard displaying visualizations related to IOC matches in the Chronicle environment

Chronicle’s dashboards are easy-to-use and fully customizable so that you can access and display the security information that’s most important to your organization. In addition to out-of-the-box visualizations, it’s simple and straightforward to create your own dashboards from scratch based on a number of parameters. This flexible dashboarding framework powered by Looker allows all default and custom dashboards to be edited, saved, and shared for on-demand analysis and reporting.

In the example below, Windows security logs or EDR logs can be used to create powerful visualizations for ransomware detections including top hosts impacted by ransomware, number of alerts over time, fake process creations, and lateral movement activity.

Example custom-built Looker dashboard for ransomware detections

Take security-driven data science to the next level with BigQuery

Chronicle also now integrates with BigQuery, making it easier than ever for analysts to leverage complex, massive security data sets to find problems faster and more easily. With this integration, Chronicle customers can export petabytes of security telemetry into BigQuery – Google Cloud’s serverless, highly scalable multi-cloud data warehouse – introducing endless possibilities for security-driven data science. For example, security teams can use BigQuery to join the security telemetry in Chronicle’s Unified Data Model (UDM) with a dataset of their choice or run custom analytics on top of UDM data, such as in Deloitte’s PACE analytics solution.

 Data from Chronicle’s Unified Data Model (UDM)  can be sent to BigQuery for deep analysis and to create visualizations.

Each Chronicle tenant now includes a private, managed BigQuery data lake that features data export at regular intervals and 180 days of data retention included at no additional cost. In addition to Looker, customers can use any BigQuery compatible tool – such as Google Data Studio, Grafana, Google Sheets, and Tableau – to create visualizations with Chronicle data. 

Chronicle customers can get started today using the BigQuery data lake to build security visualizations in a tool of their choice, with embedded Looker-driven dashboards in Chronicle available to all customers in Preview mode. To learn more about Chronicle, complete the Contact Sales form.

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Bridge data silos with Data Fusion

Bridge data silos with Data Fusion

🎧 Prefer to listen? Check out this episode on the Google Cloud Reader podcast

A huge challenge with data analytics is that the data is all over the place and is in different formats . As a result, you often need to complete numerous integration activities before you can start to gain insights from your data. Data Fusion offers a one-stop-shop for all enterprise data integration activities including ingestion, ETL, ELT and Streaming and with an execution engine optimized for SLAs and cost. It is designed to make lives easier for ETL developers, data analysts, and data engineers on Google Cloud, Hybrid Cloud or Multi Cloud environments. 

Click to enlarge

Data Fusion is Google’s cloud native, fully managed, scalable  enterprise data integration platform. It enables bringing transactional, social or machine data in various formats from databases, applications, messaging systems, mainframes, files, SaaS and IoT devices, , offers an easy to use visual interface , and provides deployment capabilities to execute data pipelines on ephemeral or dedicated Dataproc clusters in Spark. Cloud Data Fusion is  powered by open source CDAP which makes the pipelines portable across Google Cloud or Hybrid or multi cloud environments.. 

Data integration capabilities 

Data integration for optimized analytics and accelerated data transformations

Data Fusion supports a broad set of more than 200 connectors and formats, which enables you to extract and blend data You can develop data  pipelines in a  visual environment to improve productivity. . Data Fusion provides data wrangling capabilities to  prepare data and provides capabilities to operationalize the data wrangling to improve business IT collaboration You can leverage the extensive REST API to design, automate, orchestrate and manage the lifecycle of the pipelines .Data Fusion supports all data delivery modes including batch, streaming or  real-time making it a comprehensive platform to address both batch and streaming related use cases.It provides operational insights so that you can monitor data integration processes. Manage SLA’s and help optimize and fine tune integration jobs. Data Fusion provides capabilities to parse and enrich unstructured data using Cloud AI, for example, converting audio files to text, applying NLP to detect sentiment, or extracting features from images and documents or converting HL7 to FHIR formats

Data consistency

Data Fusion builds confidence in business decision-making with advanced data consistency features: 

Data Fusion minimizes the risk of mistakes by providing structured ways of specifying transformations, data quality checks with Wrangler, and predefined directives.Data Fusion helps identify quality issues by keeping track of profiles of the data being integrated and enabling you make decisions based on data observability.Data formats change over time, Data Fusion helps handle data drift with the ability to identify change and customize error handling.

Metadata and modeling

Data Fusion makes it easy to gain insights with metadata:

You can collect technical, business, and operational metadata for datasets and pipelines and easily discover metadata with a search.Data Fusion, provides end-to-end data view to understand the data model, and to profile data, flows, and relationships of datasets.It enables exchange of metadata between catalogs and integration with end-user workbenches using REST APIs.

The Data Fusion data lineage feature enables you to understand the flow of your data and how it is prepared for business decisions.

Click to enlarge

Open, hybrid, and multi-cloud

Data Fusion is cloud-native and powered by CDAP, a 100% open-source framework for building on-premises and cloud data analytics applications . This means you can  deploy and execute integration pipelines in different environments without any changes to suit business needs. 

Data protection

Data Fusion ensures data security in the following ways:

It provides secure access to on-premises data with private IP.It encrypts data at rest by default or with Customer Managed Encryption Keys (CMEK) to control across all user data in supported storage systems.It provides data exfiltration protection via VPC Service Controls,  a security perimeter around platform resources.You can store sensitive passwords, URLs, and JDBC strings in Cloud KMS, and integrate with external KMS systems.It integrates with Cloud DLP to mask, redact, and encrypt data in transit.

Conclusion

Chances are that in your enterprise there is data siloed in various platforms.  If it’s your job to bring it together, apply transformations, create data pipelines, and make all your data teams happier and more productive then Cloud Data Fusion has what you need. And if you already use Google Cloud data tools for curating a data lake with Cloud Storage and Dataproc, moving data into BigQuery for data warehousing, or transforming data for a relational store like Cloud Spanner, then Data Fusion integrations make development and iteration fast and easy. For a more in-depth look into Data Fusion check out the documentation.

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

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