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Extend your Dataflow template with UDFs

Extend your Dataflow template with UDFs

Google provides a set of Dataflow templates that customers commonly use for frequent data tasks, but also as reference data pipelines that developers can extend. But what if you want to customize a Dataflow template without modifying or maintaining the Dataflow template code itself? With user-defined functions (UDFs), customers can extend certain Dataflow templates with their custom logic to transform records on the fly:

Record transformation with Dataflow UDF

A UDF is a JavaScript snippet that implements a simple element processing logic, and is provided as an input parameter to the Dataflow pipeline. This is especially helpful for users who want to customize the pipeline’s output format without having to re-compile or to maintain the template code itself. Example use cases include enriching records with additional fields, redacting some sensitive fields, or filtering out undesired records – we’ll dive into each of those. That means you do not have to be an Apache Beam developer or even have a developer environment setup in order to tweak the output of these Dataflow templates!

At the time of writing, the following Google-provided Dataflow templates support UDF:

Pub/Sub to BigQuery

Pub/Sub to Datastore

Pub/Sub to Splunk

Pub/Sub to MongoDB

Datastore to GCS Text

Datastore to Pub/Sub

Cloud Spanner to Cloud Storage Text

GCS Text to Datastore

GCS Text to BigQuery (Batch and Stream)

Apache Kafka to BigQuery

Datastore Bulk Delete

Note: While the UDF concepts described here apply to any Dataflow template that supports UDF, the utility UDF samples below are from real-world use cases using the Pub/Sub to Splunk Dataflow template, but you can re-use those as starting point for this or other Dataflow templates. 

How UDF works with templates

When a UDF is provided, the UDF JavaScript code runs on Nashorn JavaScript engine included in the Dataflow worker’s Java runtime (applicable for Java pipelines such as Google-provided Dataflow templates). The code is invoked locally by a Dataflow worker for each element separately. Element payloads are serialized and passed as JSON strings back and forth.

Here’s the format of a Dataflow UDF function called process which you can reuse and insert your own custom transformation logic into:

Using JavaScript’s standard built-in JSON object, the UDF first parses the stringified element inJson into a variable obj, and, at the end, it must return a stringified version outJson of the modified element obj. Where highlighted, you add your custom element transformation logic depending on your use case. In the next section, we provide you with utility UDF samples from real-world use cases. 

Note: The variable includePubsubMessage is required if the UDF is applied to Pub/Sub to Splunk Dataflow template since it supports two possible element formats: that specific template can be configured to process the full Pub/Sub message payload or only the underlying Pub/Sub message data payload (default behavior). The statement setting data variable is needed to normalize the UDF input payload in order to simplify your subsequent transformation logic in the UDF, consistent with the examples below. For more context, see includePubsubMessage parameter in Pub/Sub to Splunk template documentation.

Common UDF patterns

The following code snippets are example transformation logic to be inserted in the above UDF process function. They are grouped below by common patterns.

Pattern 1: Enrich events

Follow this pattern to enrich events with new fields for more contextual information.

Example 1.1:

Add a new field as metadata to track pipeline’s input Pub/Sub subscription

Example 1.2*:

Set Splunk HEC metadata source field to track pipeline’s input Pub/Sub subscription

Example 1.3:

Add new fields based on a user-defined local function e.g. callerToAppIdLookup() acting as a static mapping or lookup table

Pattern 2: Transform events

Follow this pattern to transform the entire event format depending on what your destination expects.

Example 2.1:

Revert logs from Cloud Logging log payload (LogEntry) to original raw log string. You may use this pattern with VM application or system logs (e.g. syslog or Windows Event Logs) to send source raw logs (instead of JSON payloads):

Example 2.2*:

Transform logs from Cloud Logging log payload (LogEntry) to original raw log string by setting Splunk HEC event metadata. Use this pattern with application or VM logs (e.g. syslog or Windows Event Logs) to index original raw logs (instead of JSON payload) for compatibility with downstream analytics. This example also enriches logs by setting HEC fields metadata to incoming resource labels metadata:

Pattern 3: Redact events

Follow this pattern to redact or remove a part of the event.

Example 3.1:

Delete or redact sensitive SQL query field from BigQuery AuditData data access logs:

Pattern 4: Route events

Follow this pattern to programmatically route events to separate destinations.

Example 4.1*:

Route event to the correct Splunk index per used-defined local function e.g. splunkIndexLookup() acting as a static mapping or lookup table:

Example 4.2:

Route unrecognized or unsupported events to Pub/Sub deadletter topic (if configured) in order to avoid invalid data or unnecessary consumption of downstream sinks such as BigQuery or Splunk:

Pattern 5: Filter events

Follow this pattern to filter out undesired or unrecognized events.

Example 5.1:

Drop events from a particular resource type or log type, e.g. filter out verbose Dataflow operational logs such as worker & system logs:

Example 5.2:

Drop events from a particular log type, e.g. Cloud Run application stdout:

* Example applicable to Pub/Sub to Splunk Dataflow template only

Testing UDFs

Besides ensuring functional correctness, you must verify your UDF code is syntactically correct JavaScript on Oracle Nashorn JavaScript engine which is shipped as part of JDK (8 through 14) pre-installed in Dataflow workers. That’s where your UDF ultimately runs. Before pipeline deployment, it is highly recommended to test your UDF on Nashorn engine: any JavaScript syntax error will throw an exception, potentially on every message. This will cause a pipeline outage as the UDF is unable to process those messages in-flight.

At the time of this writing, Google-provided Dataflow templates run on JDK 11 environment with the corresponding Nashorn engine v11 release. By default, Nashorn engine is only ECMAScript 5.1 (ES5) compliant so a lot of newer ES6 JavaScript keywords like let or const will cause syntax errors. In addition, it’s important to note that Nashorn engine is a slightly different JavaScript implementation than Node.js. A common pitfall is using console.log() or Number.isNaN() for example, neither of which are defined in the Nashorn engine. For more details, see this introduction to using Oracle Nashorn. That said, using the utility UDFs provided above without major code changes should be sufficient for most use cases.

An easy way to test your UDF on Nashorn engine is by launching Cloud Shell where JDK 11 is pre-installed, including jjs command-line tool to invoke Nashorn engine.

Let’s assume your UDF is saved in dataflow_udf_transform.js JavaScript file and that you’re using UDF example 1.1 above which appends new inputSubscription field.

In Cloud Shell, you can launch Nashorn in interactive mode as follows:

In Nashorn interactive shell, first load your UDF JavaScript file which will load the UDF ‘process’ function in global scope:

To test your UDF, define an arbitrary input JSON object depending on your pipeline’s expected in-flight messages. In this example, we’re using a snippet of a Dataflow job log message to be processed by our pipeline:

You can now invoke your UDF function to process that input object as follows:

Notice how the input object is serialized first before being passed to UDF which expects an input string as noted in the previous section.

Print the UDF output to view the transformed log with the appended inputSubscription field as expected:

Finally exit the interactive shell:

Deploying UDFs

You deploy a UDF when you run a Dataflow job by referencing a GCS file containing the UDF JavaScript file. Here’s an example using gcloud CLI to run a job using the Pub/Sub to Splunk Dataflow template:

The relevant parameters to configure:

gcs-location: GCS location path to the Dataflow template

javascriptTextTransformGcsPath: GCS location path to JavaScript file with your UDF code

javascriptTextTransformFunctionName: Name of JavaScript function to call as your UDF

As a Dataflow user or operator, you simply reference a pre-existing template URL (Google-hosted), and your custom UDF (Customer-hosted) without the requirement to have a Beam developer environment setup or to maintain the template code itself.

Note: The Dataflow worker service account used must have access to the GCS object (JavaScript file) containing your UDF function. Refer to Dataflow user docs to learn more about Dataflow worker service account.

What’s Next?

We hope this helps you get started with customizing some of the off-the-shelf Google-provided Dataflow templates using one of the above utility UDFs or writing your own UDF function. As a technical artifact of your pipeline deployment, the UDF is a component of your infrastructure, and so we recommend you follow Infrastructure-as-Code (IaC) best practices including version-controlling your UDF. If you have questions or suggestions for other utility UDFs, we’d like to hear from you: create an issue directly in GitHub repo, or ask away in our Stack Overflow forum.

In a follow-up blog post, we’ll dive deeper into testing UDFs (unit tests and end-to-end pipeline tests) as well as setting up a CI/CD pipeline (for your pipelines!) including triggering new deployment every time you update your UDFs – all without maintaining any Apache Beam code.

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How Big Data and AI are Revolutionizing Payments

How Big Data and AI are Revolutionizing Payments

Data has become an essential asset for companies everywhere. The financial sector has been one of the most affected industries.

By interpreting and analyzing the data, organizations can understand and predict trends, improve security and make data-driven decisions. Big data and the artificial intelligence technologies used to leverage it can go beyond market predictions, and you can use data to improve working processes and optimize your return on investment (ROI). In this post, we’ll explore how organizations can leverage big data and AI instruments to improve their ROI.

How Big Data is changing the finance and retail scene

Let’s start with a use case. Typically, finance and retail sectors face challenges in optimizing their ROI. In retail, in particular, although it is always possible to reach the customer, doing it with the minimum spending of time and money is a challenge. Leveraging big data helps by aggregating information about customer behavior and making predictions about it, which helps target promotions.

The finance sector, specifically banks, is using big data analytics to understand transactions and payments and help customers. Banks are transitioning into data-driven organizations, using big data solutions to expand their offers to digital wallets. Big data is helping banks to tie their offers beyond the typical bank card, transforming digitally and making payments more secure and simple for their users.

The benefits of big data analytics for business are not only for financial and retail. Data analytics improve efficiency, performance, and productivity for every organization, regardless of size. One-way big data technologies are helping companies is by simplifying payment processing.

Data analytics simplifies and personalizes payment methods

Two technologies are spreading due their convenience and security: virtual cards and e-wallets.

What are Virtual cards?

A virtual card consists of a randomized credit card number that is used for payment and purchases. Companies use this unique 16 digit number for B2B payments and employee expenses. A virtual card program offers a secure payment product that can be redeemed instantly. Virtual cards are also a savvy way for data-savvy companies to manage corporate expenses.

Companies like meshpaymens.com offer a way to simplify corporate payments without corporate cards. Processes typically time-consuming, like credit card payment reconciliation, are automated and simplified. In addition, virtual cards integrate seamlessly with ERP and internal accounting systems via new data-driven capabilities.

E-wallets

An e-wallet is an application that uses complex data algorithms to enable you to make online payments with an email address and a password. You can link the e-wallet to one or more accounts or cards and then spend money online without sharing sensitive information. Examples of e-wallets are Paypal, Google, and Apple Pay. In some cases, you can use the e-wallet to pay for in-store purchases if the application is installed on your phone.

E-wallets are convenient since they can store money, loyalty cards, credit cards, driving license and other details.  You can use them online and for in-store payments. The latter is still not universally adopted so it is sort of a downside. They are not really useful for business payments because they don’t integrate well with internal accounting and ERP systems.

How virtual card numbers impact B2B payments

Companies are leveraging data to improve processes, streamline workflows and reduce costs. One area that can be significantly improved with virtual cards is payments processing. Processing payments, expenses, and invoice reconciliation are some of the biggest time-consuming activities. 

Organizations across industries need to reconcile an increasing number of expense payments and purchases. More data involves more time employees need to spend matching records, more errors, and more overhead costs. When using credit cards or cheques reconciling transactions requires a lot of manual intervention and it is a pain point for many organizations 

One of the benefits of using virtual card numbers is the automation of the B2B payments process. Usually, virtual car numbers are single-use. That means, the identifier is unique and linked to a specific transaction, supplier, and amount. VCNs provide security at a granular level that is not available for traditional credit card transactions. You can set company-specific information like cost and project code, amount of the transaction, and timeframe. 

Benefits

Some of the advantages of virtual credit cards that rely on big data include:

Safety: since there are no physical cards, transactions are more secure than credit cards. This reduces the risk of payment fraud and prevents sharing cards among employees by using the best big data capabilities.Better cash flows: it is a faster payment method, therefore, giving more insights and control over the company’s cash flow. Virtual payments optimize the working capital of your company by processing payments immediately. This prevents accounts payable teams to hold on to funds for longer than needed.Budget management: virtual cards enable organizations to manage their expenses budget. You can allocate spending in different virtual cards, so you can deal with multiple payment accounts.

Considerations

Virtual cards can have a bit of a downside since vendors need to accept this type of payment to work. Additionally, it depends on the type of big data technology vendors use.

Big Data has made virtual cards and e-wallets highly effective payment management options

Big data has significantly changed our approach to payment management. Virtual cards are one of the most effective ways to optimize payment management for organizations. A virtual cards platform enables the automatic generation of virtual card numbers, integrating it with internal accounting and resource management systems. Ultimately, implementing virtual cards helps better manage and control corporate spending, improving the ROI.

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Using AI to Optimize Cybersecurity Apps in the Remote Working Era

Using AI to Optimize Cybersecurity Apps in the Remote Working Era

Artificial intelligence has led to a number of developments in many industries. A growing number of companies are using AI technology to transform many aspects of their workplace.

One of the biggest benefits of AI is that it has helped streamline many workplace functions. Many companies are using AI technology to make it easier for employees to work from home. Countless services like Zoom use AI to offer more robust features to their users, which helps companies offering work from home opportunities do so more efficiently.

AI is also helping solve some of the challenges that have come with working from home. We stated that AI is essential to fighting cybercrime during the pandemic and this will hold true after the pandemic ends. Cybercriminals have started scaling their cyberattacks to target people working from home, since they tend to have less reliable digital security. AI has led to some important advances that will shore up defenses against these criminals.

AI is Going to be Essential in the Fight Against Cybercrime as More People Work from Home

According to the analysis of Cybersecurity Ventures, the yearly cost of cybercrime is expected to reach $10.5 trillion, and ransomware damage costs will reach $20 billion by 2025. It indicates that businesses should do everything they can to protect their critical data. AI is going to be more important than ever.

This article will help you to understand how remote working has caused cybercrime, its consequences, and proactive measures focusing on AI-driven cybersecurity apps to handle this critical issue.

Remote working – Pre and Post Pandemic

Remote working is not a wholly new concept; before the coronavirus pandemic, some companies had arrangements to work from home once or twice a week. However, COVID-19 has made this the rule rather than the exception.

As the consequences of a global pandemic, cybersecurity statistics show a significant increase in data breaching and hacking incidents from sources that employees increasingly use to complete their tasks, such as mobile and IoT devices. So, the value of a free VPN service that ensures cybersecurity has tremendously increased.

Remote Working and Use of Technology

According to Statista, 44% percent of U.S. employees are working from after the pandemic. In addition, most company transactions were reliant on the internet and devices such as Laptops, Desktops, Androids, iPhones, iPods, Macs, and more.

Communication is the heart of every business, and these technologies make it possible for employees to communicate while working from anywhere. The reality is that without these devices, little or nothing can be accomplished.

Cybercrime and IoT devices

Businesses have a large number of employees working remotely. So, these remote employees are more likely to be attacked by cybercriminals. Sometimes employees also rely on public WIFI, which is notoriously unreliable. Cybercriminals are fast to exploit these flaws. That’s why cyberattacks are continuously increasing.

ZecOps, a mobile security forensics firm located in San Francisco, uncovered a problem in the Mail app for iPhones and iPads, which is a vulnerability that allows hackers to remotely take data from iPhones even if they are running the latest versions of iOS. As a result, it has increased the security concerns for businesses.

Optimizing AI-Driven Cybersecurity Apps

AI has been incredibly important in the evolution of cybersecurity. While precautions such as VPNs and a zero-trust strategy are still important in preventing cyberattacks, however, you can consider incorporating the following AI apps into your security network to improve threat detection, response, and reporting.

1.      Intruder

The recent estimates uncover over 8,000 new vulnerabilities in mainstream software and hardware platforms every year on average. That’s over 20 every single day. There is an intruder to fix this issue.

It uses sophisticated AI algorithms to scan and detect vulnerabilities and cybersecurity flaws in your digital infrastructure, helping you avoid costly data breaches. It is one of the most effective cybersecurity apps that use AI to thwart hackers.

2.      WiFi Proxy and Switcherry VPN

Switcherry VPN is stated to provide a one-touch connection. Its free mode will keep you private and anonymous on all of your devices. It will also use complex AI features to safeguard your data and privacy on public WiFi and prevent your connection from any tracking. It is compatible with Android, iPhone, Windows, iPad, and Google extensions.

3.      Syxsense secure

Synsense secure is a cybersecurity app that uses some of the most advanced machine learning tools to protect against cybercrime. This AI-powered software combines vulnerability detection, patch management, and endpoint security in a single cloud console, making Syxsense Secure the world’s first IT management and security solution. It also works with Windows, Mac OS X, and Linux.

With a drag-and-drop interface, the software streamlines complex IT and security operations. In addition, the pre-built templates can keep your organization secure in the absence of huge teams. You can also read out 10 ways to ensure data security

Device-Based Cyberthreats

Businesses usually face phishing and ransomware kinds of cyber-attack. The details of each are presented below.

Phishing: Email plays an important role in any business, which is why phishing is likely to be on the rise, as seen during the epidemic. Even though phishing assaults decreased in 2019, they still accounted for one out of every 4,200 emails in 2020.

Ransomware: This cybercrime is becoming more sophisticated, and the consequences for businesses are becoming more severe. According to Cybersecurity Ventures, the average cost of a ransomware attack on a business is 133,000 dollars. The prominent businesses suffered by ransomware are the following.

1.      Software AG

Software AG is Germany’s second-largest software corporation and Europe’s seventh-largest. In October 2020, the company was subjected to a cyber-attack. Clop ransomware was distributed by the hackers, who wanted a $23 million ransom in exchange for the company’s records and personal information.

2.      Sopra Steria

Sopra Steria, a French IT services provider, was also affected hard by Ryuk ransomware.

3.      Seyfarth Shaw

In October 2020, a ransomware attack targeted Seyfarth Shaw LLP, a renowned multinational law company in Chicago. Their entire email system was compromised as a result of the hack.

AI-Powered Apps Are the Secret to Stopping Cybercrime

As most businesses have adopted remote working, more devices are required to ensure that employees can communicate easily and the commercial transactions can flow freely. AI technology is helping fight cybercrime.

Cybersecurity apps are the most recent advancement in the fight against cybercrime. The above discussion helps you to know cybersecurity apps primarily intended to protect networks, websites, and wireless devices such as iPhones, iPads, Android phones, iPods, and Macs from malicious hackers.

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Embracing Big Data Technology Makes Traders’ Lives Much Easier

Embracing Big Data Technology Makes Traders’ Lives Much Easier

Big data technology has had an enormous impact on many sectors. The financial industry is absolutely no exception. Big data is completely changing the securities trading profession.

Modern advances in big data technology, the internet, and the arrival of the digital age have been the driving forces behind a true revolution in the ways we communicate that the world has experienced over recent years. The digital age is here to stay and big data has changed how business operate forever.

Big Data is Transforming the Trading Profession

While traders used to spend their time working in large office buildings and hectic trade floors in places like New York’s Wall Street and London’s city exchange, a lot of trading is now done across digital platforms with a few simple clicks of a button. In some cases, they don’t even need to perform some of the tasks that they need to accomplish. Big data technology has led to some more impressive advances in AI that can help automate many of these tasks.

Remote Virtual Private Servers (VPS) mean that traders don’t always necessarily need access to a home internet connection to be able to trade on the foreign exchange market (Forex) from absolutely anywhere in the world. Data transmissibility has improved so much that people can place trades in a matter of minutes.

Here we have come up with a guide to how traders today can embrace big data technology to make their job easier in 2021.

Advanced Technology Trading Terminals

A trading terminal or ‘an electronic trading platform’ is computer software that allows traders to place orders and is a gateway to the markets. Trading terminals which utilize highly advanced big data technologies such as trading robots can be very beneficial and provide traders with the improved speed and reliability that they crave. Download MetaTrader 5 if you want to make the most out of a trading platform which uses innovative trading ideas and cutting-edge modern technologies. This wouldn’t have been possible without new machine learning advances predicated on data technology. The MetaTrader 5 download will come with a multicurrency tester and is capable of doing 6 types of pending orders.  

Traders should make sure they stay ahead of the game and embrace the latest technologies in the 21st century. Modern technology can provide traders with the best quality up-to-date, in-depth breakdown and analysis of global markets and international currencies. Sophisticated data analytics capabilities can handle this task in a fraction of the time that it used to take.

Modern traders should embrace and welcome the developments in big data technology as a new tool they can use to their advantage on a daily basis.

Analysis and Forecasting Markets with Modern Technology

One of the hardest parts of trading is predicting what will happen in the future with markets. When will a stock or commodity go up or down in price? When will an international currency decrease significantly in value? Accurate and reliable forecasting is therefore as important as ever but thankfully nowadays traders have modern technologies to help them. There is now amazing predictive analytics software, trading robots, which utilize modern technologies to come up with market forecasts. These technologies are some of the most impressive developments brought on by advances in data analytics and AI.

However, robots and modern technology unfortunately could not have foreseen the extent of the chaos that the coronavirus caused to the global economy when everywhere started shutting down and imposing lockdowns in 2020.  Traders should nevertheless not shy away from using modern technologies for forecasts and crucial trade tips.

Trading without using the machine learning and big data technologies of today would be trickier, and more long-winded and time consuming.  The technology is a blessing for traders. Forex traders today can trade currencies from everywhere they are in the world. In the past you would have had to be in a sweaty suit and tie in the office, but today thanks to modern technologies you can even trade whilst relaxing and laying back on the beach using your phone.

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8 Ways Machine Learning Can be Used to Make Cities Smarter

8 Ways Machine Learning Can be Used to Make Cities Smarter

It’s no secret that artificial intelligence and technology has been developing quickly in recent times, with applications such as CAPTCHA that prevent bots from accessing sites, thermostats that adapt to our daily schedules or even algorithms that could choose potential vacation destinations for us.

But what if machine learning could be used beyond niche or individual contexts? Taking artificial intelligence a step further and implementing it into our cities and infrastructures has the potential for improving operating efficiencies, aiding in sustainability efforts, urban planning and more. Below, we’ll be exploring a few of the ways that machine learning can be used for improving our cities and making them smarter overall.

Using AI to account for carbon footprints

Often times, we will hear from various forms of media that we should be aiming to reduce our individual and collective carbon footprints – however, how can cities and organizations accurately calculate their contributions to carbon emissions? Overall, a carbon footprint can be broken down into three categories – direct emissions from the organization or city’s operations (scope 1 emissions), emissions that are related to the generation of electricity required to run the city (scope 2 emissions) and emissions from consumption and production of city product (scope 3 emissions), which involve upstream suppliers and downstream consumers (e.g., city inhabitants)1.

While obtaining and processing data is a challenge, several start-up companies are developing tools that will not only quantify emissions but also help develop plans (based on data) on how to reduce emissions, such as through laying out more sustainable and informed decision making or through switching to viable renewable energy sources. Many companies use platforms like Spark 3.0 to help with data processing, but it still proves challenging.

One particular company, Watershed, hopes to be able to build a tool in which raw data can yield insight and concrete actions in which carbon emissions are reduced.

Drought Risk Assessment and Prediction

With climate change on the rise, more severe weather events such as drought are becoming more prevalent.  Overall, droughts have cost the world $1.5 million between 1988-2017 and the resulting food insecurity has caused hundreds of thousands of deaths, if not more.2 Through artificial intelligence-based prediction, there can be improvement in decision making regarding droughts and better methods and timing employed to ensure optimal water resource allocation and disseminating information ahead of drought events.

One such example of AI being used for prediction of high impact weather events is the Gradient Boosted Regression Trees (GBRT) algorithm, in which it was found that in 75% of cases, AI-based forecast was chosen over human intuition by professional forecasters.2

Wildlife Conservation

There is growing evidence big data and machine learning can help save the environment. Preserving habitats for various animals is just as important within cities as it is in tropical rainforests.

Often times, conservationists and ecologists will set camera traps in order to get a better idea of what animals are living in an area, what time they are active as well and to monitor human impact on wildlife. Unfortunately, going through footage manually takes a tremendous amount of time and can delay actions that would benefit local flora and fauna. That’s where AI algorithms such as the one created by RESOLVE come in – this AI algorithm can let conservationists know about the presence of animals real time as well as identify any detected animals almost immediately so that appropriate action can be taken as soon as possible. Additionally, algorithms such as this one can be used to detect illegal activity in real time, meaning poachers will have a more difficult time capturing animals.

Air Quality Monitoring and Prediction

Air pollution unfortunately is a large issue globally. The United States alone in 2020 produced around 68 million tons of pollution4. Such pollution contributes to higher incidences of asthma and other respiratory issues, especially in vulnerable populations such as young children and the elderly. To help the general public better prepare for days of poor air quality and to put into place effective countermeasures, air quality warnings systems based off of artificial intelligence may be implemented.  In particular, the AI system proposed by Mo et al., (2019) in their article ‘A Novel Air Quality Early-Warning System Based on Artificial Intelligence’ is based on an air pollution prediction model as well as an air quality evaluation model.5 Its through this system in which an early-warning system can be implemented in regards to air quality and in which data can be analyzed and used to create reasonable countermeasures in addition to predictions of air quality in the future.

AI based Parking Monitoring.

One problem common to many cities is parking. If you’ve ever been frustrated from circling around in a packed parking lot looking for a spot, this particular application of artificial intelligence will probably be of interest to you. Artificial intelligence can help through using monitors and sensors to assess real time occupancy in parking garages – if there happens to be no vacancy, then visitors will be alerted so they won’t have to waste time circling the lot.6 Additionally, AI algorithms in particularly large parking spaces can be used to guide visitors to areas of vacancy, also saving time.

Smart parking systems can also be used to gauge times of high activity based on parking occupancy so businesses can better prepare for peak hours as well as times of low parking occupation and thus low customer turnout.

Optimization of Electric Vehicle Charging

As public transport vehicles move from being powered by traditional fossil fuels to being electrically fueled, there are quite a number of things that need to be taken into consideration, such as battery storage, electric generator backup and creating or adapting a charging system for these vehicles. Additionally, there are several variables that go into the amount and cost of energy that a vehicle uses, such as weather and traffic conditions, in house vs on the go charging and peak demand constraints just to name a few.7 If cities were to adopt an AI-enabled energy optimization system, expenses could be kept to a minimum through calculating the amount of energy sources and facilities required upfront as well as integration of renewable power sources to charge the vehicles as appropriate.

Additionally, artificial intelligence integration could also help extend battery life of electric vehicles through accounting for manufacturer-based constraints and real time conditions at the same time to optimize charge level as well as minimize degradation levels.7 One way of doing so would be AI algorithms alerting the public transit company of lower than usual electricity prices but also the amount that the vehicles should be charged such that none of the batteries are overcharged.

Improving Power Grid Performance

Depending on where you live in the world, you may already be familiar with smart grids. A smart grid refers to a modern electricity system in which there are sensors, automation, communication, and computers to improve the efficiency, reliability, and safety of an electricity system. Smart grid systems can benefit a city in numerous ways including8:

Automatic re-routing when there are abnormalities in the system.More integration of renewable energy systems and customer-owned power generation systemsMore efficiency electricity transmissionReduced operation and management costs for utilities.Reduced peak demand rates.Improved grid security

Faster restoration of power after power disturbances (which is critical in severe weather events like snowstorms or heatwaves.)

Public Safety

When it’s impossible for human eyes to keep track of all the security feeds within a city, artificial intelligence can assist – for example, microphone input from street cameras can be interpreted by AI as gun shots or other sounds indicative of distress. In such situations, AI algorithms can alert emergency service operators with location data and any other required data to decide to dispatch emergency services or not. Digital signage can be updated real time to alert the public of situations requiring attention such as flooding or other emergent situations. Another way in which AI can be used to improve upon public safety is through the controlling of traffic lights in order to clear the way for first responders rather than rely on police forces to arrive.

References

[1] R. Toews, These Are The Startups Applying AI To Tackle Climate Change (2021), Forbes.

[2] C. Huntingford, E. S. Jeffers, M. B. Bonsall, H. M. Christensen, T. Lees, H. Yang, Machine learning and artificial intelligence to aid climate change research and preparedness (2019), IOPScience.

[3] Smart Parks, Artificial Intelligence in Wildlife Conservation (2019).

[4] United States Environmental Protection Agency, Air Quality – National Summary (2021).

[5] X. Mo, L. Zhang, H. Li, Z. Qu, A Novel Air Quality Early-Warning System Based on Artificial Intelligence (2019), International Journal of Environmental Research and Public Health.

[6] N. Joshi, AI-based parking systems can address parking woes. Here’s how. Allerin.

[7] Sustainable Bus, Artificial intelligence as a mean to optimize vehicles’ charging. An interview with BluWave-ai (2020).

[8] SmartGrid.Gov, The Smart Grid (2021).

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Smart SMBs Are Taking Advantage of Major Advances in Data Security

Smart SMBs Are Taking Advantage of Major Advances in Data Security

A surprisingly large number of SMBs think they’re too small to be targeted by hackers. The truth is, cybercriminals sniff out opportunities regardless of the company size and the complacency of the SMBs is what draws them. 2020 was an especially eventful year for small businesses and it has exposed fundamental flaws in the way they handle data. Therefore, small businesses need to take stringent precautions to prevent cyberattacks.

Here are some numbers that every small and midsize business owner need to focus on:

28% of all data breaches in 2021 occurred in small businesses – VerizonData breaches from team members can cost businesses up to 20% of their annual revenue – Cyber Security Magazine37% of SMBs have lost a significant number of customers due to data leaks – PRWeb

The numbers have been growing in recent months and with cybercrimes becoming more sophisticated, SMBs with legacy frameworks will be severely compromised. 

Here are 4 ways SMBs can strengthen data security in 2021-

1. Cybersecurity training for employees

This is an old bug plaguing SMBs even in 2021. With the rapid transformation of remote workspaces, companies have prioritized collaboration over security. This has opened up employees to new security threats. Every team member should be educated and empowered to detect and avoid online frauds and instantly respond in case of a security compromise. The failure to report a breach quickly adds up to downtime and revenue loss. 

2. Secure cloud infrastructure

A strong cloud infrastructure is very important to any business. Unfortunately, improperly setup cloud systems can be a very weak point for hackers. You need to make sure that your cloud system is secured to keep them at bay.

Cloud has seen unanimous appreciation among small and midsize businesses. The major reasons are the cost-effectiveness and flexibility offered by the cloud infrastructure. While it’s theoretically more secure than physical devices, it has ultimately pushed SMBs to let their guards down. 

Cloud software can be broken into and that’s why businesses need to invest in industry-leading platforms to run their business. Microsoft 365 has been leading the office productivity market for decades and the recent push to the cloud suite should offer SMBs the security they deserve. 

3. System updates and data backups

Sometimes the most advanced security measure you can take is to cover the basics. Regularly updating systems and backing up data may seem like a tedious and irrelevant chore, but most hackers find loopholes either in outdated software or in legacy devices. In both cases, keeping the systems updated and backing up sensitive data can help you mitigate the risks. 

4. Create and enforce security policies

Far too many SMBs gloss over the importance of having a security and compliance policy, and strictly enforcing it on the floor. The documents should include a zero-trust protocol for vigilant data protection, virtual desktop infrastructure (VDI) for remote workforces, multi-factor authentication (MFA), and siloed access to data. 

It’s also important to roll out and practice response plans – from the chain of command to fast decision making. You need to make sure that these security policies are both properly structured and carefully followed in order to prevent any security risks.

MyTek: data security for SMBs

As a business owner, you need an industry expert who can offer you security solutions based on your business goals. As a Microsoft Partner, MyTek provides a comprehensive suite of security measures – from network security, firewall, asset tracking, Unified Threat Management (UTM) services, spam protection to access control, employee training, and enterprise mobility solutions. 

If you’re looking to bolster data security in 2021 and beyond, get in touch with us today.

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Using Analytics to Successfully Use Instagram Stories for Your Business

Using Analytics to Successfully Use Instagram Stories for Your Business

Analytics technology is essential to the success of modern marketing. In the past, marketers had to rely heavily on their gut instinct, because the performance of various strategies was harder to measure. In the digital era, however, data analytics has made it much easier to figure out what strategies perform optimally.

Instagram marketing is one of the areas that can be highly dependent on analytics technology. You need to know how to use analytics tools in every stage of the Instagram story funnel in order to get the best possible results. This article will delve into this in more detail.

Making the Most Out of Analytics in Your Instagram Story Strategy

Most people know that using Instagram for business is crucial. However, did you know that there are many apps for Instagram stories that can help promote your business? These apps rely heavily on analytics technology to provide the best solutions to their users.

Instagram stories help e-commerce business owners to promote their brand, engage with clients and potential customers, and grow their business in a fun way. However, you can’t just wing it. You need to use analytics technology to get the most out of your efforts.

This article will discuss everything you need to know about Instagram stories for business and how to use analytics to maximize your ROI:

What are Instagram Stories for business?

Instagram Stories appear at the top of the news feed and feature full-length vertical videos and photos. This makes them ideal for promoting a sale, event or new product. 

A key characteristic of Instagram Stories is that they are only visible for 24 hours, and this encourages fans to take a closer look.

Instagram Stories features.

There are many Instagram Stories features that you can use to promote your business. They are also a big data goldmine that you can tap to improve other aspects of your business.

Stickers are incredibly popular and you can select the ones that are the most appropriate for your brand. 

You can also incorporate hashtags into your Instagram Stories to make them more discoverable by potential clients. If you don’t know which hashtag to use, don’t worry! Instagram will suggest a few appropriate ones to consider.

If you have a brick and mortar store or warehouse, you can add your business’s location to your Instagram Stories feature. This can help to drive traffic to your store and result in increased sales.

Many people have Instagram on mute, so if your Instagram Stories post has sound effects, it’s a good idea to use captions. By adding the captions sticker, Instagram automatically creates captions so that your audience can hear what you have to say.

Another popular feature of Instagram Stories is the music sticker that can be used with videos. This allows you to add a suitable soundtrack to enhance the theme of your Instagram Stories post. There are thousands to choose from, so there is definitely something to suit your brand.

If you’ve created a great Instagram Stories post and don’t want it to expire after 24 hours, you can highlight it and keep it on your profile for as long as you like.

You can also create fan polls and questions to engage your followers, which is a great way of driving traffic to your Instagram profile.

Want to sell a product or promote a sale in your Instagram Stories post? Simply use the product sticker that takes followers directly to your store. 

Finally, if you have 10,000 or more Instagram followers or a verified Instagram business account, you can insert links to your stories. This is an excellent marketing tool and an easy way to improve visibility.

Compelling reasons to use Instagram Stories to promote products.

The Instagram Stories feature is a fun and exciting way to attract followers to your brand.

It is easy to use and work wonders by helping to create the brand’s visual identity.

An example of this is how it can incorporate a color theme. If your logo is red, you might want to use red in all your Instagram Stories posts to accentuate its impact and unify your brand’s tone.

Instagram users like the platform because it is very visual and they enjoy viewing short, interesting videos and eye-catching photos.

If you are able to create Instagram Stories posts that compel fans to explore your brand further, you have a much better chance of increasing revenue.

Since Instagram allows you to schedule your Stories posts to go live at a specific time, you can attract your target audience when they are most likely to be using Instagram.

Of course, because many other brands are using this feature, it makes sense that you should, too. After all, why should your brand be left behind?

What Role Can Analytics Play in Your Instagram Stories Marketing Efforts?

You will realize much higher returns from your Instagram stories if you use analytics strategically. However, you will only get more value by leveraging analytics if you know what metrics to focus on. Some of the most important Instagram stories variables that you need to look at and integrate with your analytics dashboard include:

ImpressionsLink clicksRepliesProfile visits

You want to look at each of these variables both individually and in the context of other metrics when using analytics to optimize your strategy. A high number of impressions with a low number of clicks suggests that your story is not engaging enough. A lack of impressions means that you are not doing an adequate job generating reach for your Instagram story.

Your analytics platform will make it a lot easier to assess the impact of your Instagram story strategy. However, you have to know what to look for.

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Building the data science driven organisation from the first principles

Building the data science driven organisation from the first principles

In this blog series, a companion to our paper, we’re exploring different types of data-driven organizations. In the last blog, we discussed main principles for building a data engineering driven organisation. In this second part of the series focus is on how to build a data science driven organization. 

The emergence of big data, advances in machine learning and the rise of cloud services has been changing the technological landscape dramatically over the last decade and have pushed the boundaries in industries such as retail, chemistry, or healthcare. In order to create a sustainable competitive advantage from this paradigmatic shift companies need to become ‘data science driven organizations’.1 In the course of this article we discuss the socio-technical challenges those companies are facing and provide a conceptual framework built on first principles to help them on their journey. Finally, we will show how those principles can be implemented on Google Cloud.

Challenges of data science driven organizations

A data science driven organization can be described as an entity that maximizes the value from the data available while using machine learning and analytics to create a sustainable competitive advantage. Becoming such an organization is more of a sociotechnical challenge rather than a purely technical one. In this context, we identified four main challenges:

Inflexibility: Many organizations have not built an infrastructure flexible enough to quickly adapt to a fast changing technical landscape. This inflexibility comes with the cost of lock-in effects, outdated technology stacks, and a bad signaling for potential candidates. While those effects might be less pronounced in more mature areas like data warehouses, they are paramount for data science and machine learning.

Disorder: Most organizations grow in an organic way. This often results in a non-standardized technological infrastructure. While standardization and flexibility are often seen as diametrically opposed to each other, a certain level of standardization is needed to establish a technical ‘lingua franca’ across the organization. A lack of the same harms collaborations and knowledge sharing between teams and hampers modular approaches as seen in classical software engineering.

Opaqueness: Data science and machine learning are fundamentally data driven (engineering) disciplines. As data is in a constant flux, accountability and explainability are pivotal for any data science driven organization.2 As a result data science and machine learning workflows need to be defined in the same rigour as classical software engineering. Otherwise, such workflows will turn into unpredictable black boxes.

Data Culture (or lack thereof): Most organisations have a permission culture where data is managed by a team which then becomes a bottleneck on providing rapid access as they cannot scale with the requests. However, in organizations driven by data culture there are clear ways to access data while retaining governance. As a result, machine learning practitioners are not slowed down by politics and bureaucracy and they can carry out their experiments.

Personas in data science driven organizations

Data science driven organizations are heterogeneous. Nevertheless, most of them leverage four core personas: data engineers, machine learning engineers, data scientists, and analysts. It is important to mention that those roles are not static and overlap to a certain extent. An organizational structure needs to be designed in such a way that it can leverage the collaboration and full potential of all personas. 

Data engineers take care of creating data pipelines and making sure that the data available fulfills the hygienic needs. For example, cleansing, joining and enriching multiple data sources to turn data into information on which downstream intelligence is based. 

Machine learning engineers develop and maintain complete machine learning models. While machine learning engineers are the rarest of the four personas, they become indispensable once an organization plans to run business critical workflows in production. 

Data scientists act as a nexus between data and machine learning engineers. Together with business stakeholders they translate business driven needs into testable hypotheses, make sure that value is derived from machine learning workloads and create reports to demonstrate value from the data.

Data analysts bring the business insight and make sure that data driven solutions that business is seeking are implemented. They answer adhoc questions, provide regular reports that analyze not only the historical data but also what has happened recently.

There are different arguments if a company should build centralized or decentralized data science teams. In both cases, teams face similar challenges as outlined earlier. There are also hybrid models, as a federated organization whereby data scientists are embedded from a centralized organization. Hence, it is more important to focus on how to tackle those challenges using first principles. In the following sections, we discuss those principles and show how a data science and machine learning platform needs to be designed in order to facilitate those goals.

First principles to build a data science driven organization

Adaptability: A platform needs to be flexible enough to enable all kinds of personas. While some data scientists/analysts, for example, are more geared toward developing custom models by themselves, others may prefer to use no-code solutions like AutoML or carry out analysis in SQL. This also includes the availability of different machine learning and data science tools like TensorFlow, R, Pytorch, Beam, or Spark. At the same time, the platform should be open enough to work in multi-cloud and on-premises environments while supporting open source technology when possible to prevent lock-in effects. Finally, resources should never become a bottleneck as the platform needs to scale quickly with an organization’s needs.

Activation: Ability to operationalize models by embedding analytics into the tools used by end users is key to achieve scaling in providing services to a broad set of users. The ability to send small batches of data to the service and it returns your predictions in the response allows developers with little data science expertise to use models. In addition, it is important to facilitate seamless deployment and monitoring of edge inferences and automated processes with flexible APIs. This allows you to distribute AI across your private and public cloud infrastructure, on-premises data centers, and edge devices. 

Standardization: Having a high degree of standardization helps to increase a platform’s overall efficiency. A platform that supports standardized ways of sharing code and technical artifacts increases internal communication. Such platforms are expected to have built in repositories, feature stores and metadata stores. Furthermore, making those resources queryable and accessible boost teams’ performance and creativity. Only when such kind of communication is possible data science and machine learning teams can work in a modular fashion as it has been for classical software engineering for years. An important aspect of standardisation is enabled by using standard connectors so that you can rapidly connect to a source/target system. Products such as Datastreamand Data Fusion provide such capabilities. On top of it,  a high degree of standardization avoids ‘technical debt’ (i.e. glue code) which is still prevalent in the majority of most machine learning and data science workflows.3

Accountability: Data science and machine learning use cases often deal with sensitive topics like fraud detection, medical imaging, or risk calculation. Hence, it’s paramount that a data science and machine learning platform helps to make those workflows as transparent, explainable, and secure as possible. Openness is connected to operational excellence. Collecting and monitoring metadata during all stages of the data science and machine learning workflows is crucial to create a ‘paper trail’ allowing us to ask questions such as: 

Which data was used to train the model? 

Which hyperparameters were used? 

How is the model behaving in production? 

Did any form of data drift or model skew occur during the last period? 

Furthermore, a data science driven organization needs to have a clear understanding of their models. While this is less of an issue for classical statistical methods, machine learning models, like deep neural networks, are much more opaque. A platform needs to provide simple tools to analyze such models for confident usage. Finally, a mature data science  platform needs to provide all the security measures to protect data and artifacts while managing resource usage on a granular level.

Business Impact: many data science projects fail to go beyond pilot or POC stages according to McKinsey.4 Therefore, the ability to anticipate/measure business impact of new efforts, and choosing ROI rather than the latest cool solution is more important. As a result it is key to identify when to buy, build, or customize ML models and connect them together in a unified, integrated stack. For example, if there is an out of the box solution which can be leveraged simply by calling an API rather than building a model after months of development would help realising higher ROI and demonstrating value. 

We conclude this part with the summary of the first principles. The next section will show how those principles can be applied on Google Cloud’s unified ML platform, Vertex AI.

First principles of a data science driven organization

Using first principles to build a data science platform on Google Cloud

Adaptability
With Vertex AI, we are providing a platform built on the first principles that covers the entire data science / machine learning journey from data readiness to model management. Vertex AI opens up the usage of data science and machine learning by providing no-code, low-code, and custom code procedures for data science and machine learning workflows. For example, if a data scientist would like to build a classification model they can use AutoML Tables to build an end-to-end model within minutes. Alternatively, they can start their own notebook on Vertex AI to develop custom code in their framework of choice (for instance, TensorFlow, Pytorch, R). Reducing the entry barrier to build complete solutions is not only saving developers time but enables a wider range of personas (such as data or business analysts). This reduced barrier helps them to leverage tools enabling the whole organization to become more data science and machine learning driven. 

We strongly believe in open source technology as it provides higher flexibility, attracts talent, and reduces lock-in. With Vertex Pipelines, we are echoing the industry standards of the open source world for data science and machine learning workflow orchestration. As a result, allowing data scientists / machine learning engineers to orchestrate their workflows in a containerized fashion. With Vertex AI, we reduced the engineering overhead for resource provisioning and provided a flexible and cost-effective way to scale up and down when needed. Data scientists and machine learning practitioners can, for example, run distributed training and prediction jobs with a few lines of Python code in their notebooks on Vertex AI.

Vertex AI Architecture

Activation
It is important to operationalize your models by embedding analytics into the tools used by your end users. This allows scaling beyond traditional data science users and bringing other users into data science applications. For example, you can train BigQuery ML models and scale them using Vertex AI predictions. Let’s say business analysts running SQL queries are able to test the ability of the chosen ML models and experiment with what is the most suitable solution. This reduces the time for activation as business impact can be observed sooner. On the other hand, Vertex Edge Manager would let you deploy, manage, and run ML models on edge devices with Vertex AI.

Standardization
With all AI services living under Vertex AI, we standardized data science and machine learning workflows. Together with Vertex Pipelines every component in Vertex can be orchestrated, making any form of glue code obsolete helping to enhance operational excellence.  As Vertex Pipelines are based on components (containerized steps), parts of a pipeline can be shared with other teams. For example, let’s assume the scenario where a data scientist has written an ETL pipeline for extracting data from a database. This ETL pipeline is then used to create features for downstream data science and machine learning tasks. Data Scientists can package this component, share it by using GitHub or Cloud Source Repository and make it available for other teams who can seamlessly integrate it in their own workflows. This helps teams to work in a more modular manner and fosters team collaboration across the board. Having such a standardized environment makes it easier for data scientists and machine learning engineers to rotate between teams and avoid compatibility issues between workflows. New components like Vertex Feature Store further improve collaboration by helping to share features across the organization.

Accountability
Data science and machine learning projects are complex, dynamic, and therefore require a high degree of accountability. To achieve this, data science and machine learning projects need to create a ‘paper trail’ that captures the nuances of the whole process. Vertex ML Metadata  automatically tracks the metadata of models trained and workflows being run. It provides a metadata store to understand a workflow’s lineage (such as how a model was trained, which data has been used and how the model has been deployed). A new model repository provides you a quick overview of all models trained under the project. Additional services like Vertex Explainable AI help you to understand why a machine learning model made a certain prediction. Further, features like continuous monitoring including the detection of prediction drift and training-serving skew help you to take control of productionized models.

Business Impact: as discussed earlier it is key to identify when to buy, build, or customize ML models and connect them together in a unified, integrated stack. For example, if your company wants to make their services and products more accessible to their global clientele through translation, you could simply use Cloud Translation API if you are translating websites and user comments. That’s exactly what it was trained on and you probably don’t have an internet-sized dataset to train your ML model on. On the other hand, you may choose to build a custom solution on your own. Even though Google Cloud has Vision API that is trained on the same input (photos) and label, your organisation may have a much larger dataset of such images and might give better results for the particular use case. Of course, they can always compare their final model against the off-the-shelf solution to see if they made the right decision. Checking feasibility is important, so when we talk about building models, we always mention quick methods to check that you are making the right decisions.

Conclusion

Building a data science driven organization comes along with several socio-technical challenges. Often an organization’s infrastructure is not flexible enough to react to a fast changing technological landscape. A platform also needs to provide enough standardization to foster communication between teams and establish a technical ‘lingua franca’. Doing so is key to allow modularized workflows between teams and establish operational excellence. In addition, it is often too opaque to securely monitor complex data science and machine learning workflows. We argue that a data science driven organization should be built on a technical platform which is highly adaptable in terms of technological openness. Hence, enabling a wide set of personas and providing technological resources in a flexible and serverless manner. Whether to buy a solution or build a solution is one of the key drivers of realising return of investment for the organisation and this will define the business impact any AI solution would make. At the same time, enabling a broad number of users allows activating more use cases. Finally, a platform needs to provide the tools and resources to make data science and machine learning workflows open, explanatory, and secure in order to provide the maximum form of accountability. Vertex AI is built on those pillars helping you to become a data science driven organization. Visit our Vertex AI page to learn more.

1. Data science is used as an umbrella term for the interplay of big data, analytics and machine learning.
2. The Covid pandemic is a prime example as it has significantly affected our environment and therefore the data on which data-science and machine learning workflows are based.
3. Sculley et al. (2015). Hidden technical debt in machine learning.
4. https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/global-survey-the-state-of-ai-in-2020

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How AI and IoT Solutions Can Improve Your Business

How AI and IoT Solutions Can Improve Your Business

In today’s modern era, AI and IoT are technologies poised to impact every part of the industry and society radically. Because most businesses devote their primary efforts to developing their brand, software applications, or network, new technologies are apt to transform how they operate. In addition, as companies attempt to draw better significance from the huge datasets gathered by linked devices, the potential of AI is accelerating the wider implementation of IoT.

While businesses invest heavily in digitization, they are also incorporating AI throughout their IoT initiative, evaluating prospective future IoT ventures, and looking for ways to get greater value from current IoT deployments. Moreover, with the help of an AI development company, businesses can avoid unforeseen downtime, increase operational productivity, develop new services and products, and boost risk control.

Benefits of AI and IoT in Businesses

AI and IoT, when combined, are incredibly powerful technological forces. The advantages of IoT and AI could be combined to reap the full benefits of both. The following elucidates the same:

Improved Protective Measures.

Security and protection are the most important aspects for a business, given the recent growth in data thefts and loss of valuable data. The AI-powered IoT platform protects your confidential info and prevents third parties from accessing it. Various firms are using machine-to-machine interaction to identify inbound attacks and send out automatic answers to cybercriminals. In the financial sector, for instance, unlawful activity in ATMs is detected by IoT sensors and quickly reported to law authorities.

Successful Execution of Business Analysis.

There must be a perfect equilibrium between demand and supply. AI assists in enhancing stock control and relieving inventory strain by allowing you to understand when you need to refill in advance. This is helpful for merchants that sometimes accumulate too many things only to discover later that they can’t sell them all. Moreover, this demonstrates how much more precise it is than traditional approaches. There are IoT solutions that can assist them in collecting data and performing analytics for inventory management.

Improved Risk Management.

We have covered how AI and IoT platforms aid with security. When it concerns risk control, which involves dealing with economic damage, employee safety, and cyber attacks, the two handle issues with ease and respond quickly so that similar circumstances do not occur. For instance, Toshiba, a Japanese computer device and service provider, uses data obtained from portable tech combined with AI to ensure safety at work.

Automated Production Efficiency.

IoT implementation simplifies your organization and aids in creating precise forecasts, both of which are critical for increasing corporate efficiency. Moreover, investing in the IoT is critical in today’s world since the technology may help you identify repetitive tasks and those that take up so much time. A good example of this is Google’s drop in data center cooling costs, which they may achieve through the use of AI and IoT. You, too, can discover which of your organizational operations require some fine-tuning in order to avoid sacrificing productivity.

What Do You Need to Get a Deeper Understanding of the Internet of Things (IoT)?

Sensors and devices in the Internet of Things gather data that could be used to provide meaningful insights. Most businesses, on the other hand, struggle to grasp how IoT may help them grow their businesses, and they need assistance when initiating IoT operations. IoT operations have the ability to lower company costs, improve processes, and provide specific company insights. Also, IoT consulting services might help companies reach their full potential.

These IoT consultants assist firms in understanding IoT technology and developing a plan to improve operations and products. There are also some consulting firms that provide development services for IoT solutions.

Take Away

In the business sector, AI and IoT technologies guarantee the success of data-driven approaches. They are seen as liberating forces because they allow businesses to handle jobs that only humans can handle. In addition, businesses that execute on data insights are becoming pioneers and industry experts at the end of the day. Likewise, entrepreneurs that seek expert assistance are more likely to thrive. They may be able to optimize their operations, cut costs, and gain insight into their business.

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Why Data-Conscious CIOs Use Gartner to Evaluate Software

Why Data-Conscious CIOs Use Gartner to Evaluate Software

Data analytics is the lifeblood of modern business. Every large organization has a CIO, who appreciates the need to invest in big data solutions.

There are a lot of companies that offer solutions for data-driven businesses. One of the most popular is Garnter.

Gartner is a Reliable Company for Businesses that Need Big Data Solutions

Gartner is an information technology research and advisory company. It provides research on the world’s major hardware, software, and services vendors to help tech buyers make more informed decisions about how they can best spend their budgets. It is a great company for organizations that need big data solutions.

Gartner has been around for over three decades and it’s grown into a powerhouse of information and resource for data-driven businesses. With Gartner, your company will have access to a wealth of knowledge that can help you better evaluate software vendors and find out how to refine your own software products based on customer feedback.

Why Gartner?

Gartner uses its research to provide clients with guidance on the market and to help identify and evaluate business opportunities. Its research is rigorous, objective, and often changes over time. Gartner has been involved in the IT business for decades, so they have the credentials to conduct in-depth research and offer valuable insights.

Gartner produces independent research about how enterprise buyers interact with vendors. Gartner works with several major software providers, and this lets it get early access to sales data.

Gartner uses this data to develop reports about how buyers perceive top vendors. These insights will help give you a better chance of winning the business of customers that are actively using software in your industry. It will also give you reviews from peers to help your team choose their own software vendors for internal purposes.

Why do CIOs use Gartner to evaluate software and improve their data analytics strategy?

Scalability and insight. Before you even launch a new product, you want to make sure that the partner you’ve selected can scale with your company’s growth. For example, you want to make sure your cybersecurity is able to scale concurrently with your number of employees, as well as the number of endpoints in your infrastructure. In that case, you’d be interested in Gartner endpoint protection.

Gartner has the knowledge and insights to assess companies across all industries to help you determine the right partner to help your business grow. They also have a lot of big data resources that can help businesses operate more efficiently.

You’ll have access to a resource that can help you assess the companies you choose for your enterprise needs and help you create a plan to meet your compliance requirements.

To get the right type of software for their business’s data analytics needs

As a CIO or IT director, you’ll be better off making a choice based on your company’s needs and not on vendor hype. It may take a lot of research on different vendors to understand the product and its intended use, but you’ll be able to make an informed decision that will benefit your business.

CIOs are charged with efficiently running a business and making sure that the right technology is at the right place, at the right time, and that it helps grow it. You also need to protect your business data – for example, in a Gartner survey, 98% of brands are negligent when it comes to their big data security.

To find out how popular a software is in the market

Understanding what software products your customers like and need is a huge indicator of your position in the market. However, it isn’t easy to gather feedback from your customers.

You need to rely on a trusted source to provide insights into what your customers think about your product and what you should improve. Gartner provides this type of research to help companies gather insight into their products.

With the Gartner Peer Insights product, you can see which software partners are trusted by your customers. Gartner also provides a wide range of free tools and research to help you understand what software your customers prefer and why they might do so.

To get an overview of the vendor’s company and what they’re all about

The annual Gartner Symposium/ITxpo is one of the largest events in the IT industry. It takes place in Las Vegas every year during the second week of November.

The symposium features CIOs and industry luminaries offering their perspectives and the latest industry trends. The Gartner Peer Insights reviews website also hosts a Gartner Magic Quadrant report covering all major software providers.

All of these benefits mean that Gartner is a widely trusted source for technology professionals and business leaders. As the world’s leading independent technology research and advisory company, Gartner provides unbiased and actionable information that helps companies across all sectors innovate and prosper.

Gartner is a Great Company for Businesses That Need Big Data Solutions

Big data has become very important for businesses that are trying to grow in 2021. Gartner is one of the companies that offers resources for data-driven businesses.

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