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Data Capture Firm Uses AI to Dissect Georgia Runoff Spending

georgia runoff spending

With majority control of the Senate at stake, it’s no surprise that the recent Georgia runoff elections were the most expensive political race in American history. Wherever campaign dollars flow freely, as they did in Georgia, accountability questions proliferate. Where did that money come from, where did it go, and what influence (if any) did those spending choices have on results?

Election law requires that campaign expenditures be made public. Even so, it often takes weeks or even months for analysts to sift through a dense forest of information and build usable datasets. Important decisions have to wait; only after a dataset has been given the green light can anyone even begin to search for associated outcomes and trends.

Many have wondered whether there is a legitimate place for artificial intelligence in harvesting public data repositories. Can machine-generated data really be trusted? And even if it can be, can it replace human analysts?

When Data is Public but Nearly Useless

Each TV and radio station is required to carbon-copy invoices for political advertisements to the Federal Communications Commission, which makes them public record. This sounds great on paper, but the FCC mandate is to merely publish the invoice documents — and each election comes with tens of thousands of invoices.

The invoices are important because of the dates, callsigns, and amounts listed on them page by page. The data allows analysts to build spending maps and scrutinize campaign behaviors, but the data needs to be aggregated in a spreadsheet first. A spreadsheet analysis would ordinarily mean going through this massive amount of invoices by hand.

A Better Use of Data

Of course, it’s not just the FCC receiving invoices — all businesses receive invoices when they trade with each other. Any company of size faces the same problem as the data analysts who would like to use the FCC data.

One automation software company that solves the problem of translating business documents to data for enterprises decided to unleash its automatic data capture on the FCC invoices for the Georgia runoff elections. The vendor Rossum, in partnership with analysts from the e.ventures fund, just published the resulting data set.

AI-Powered Reporting Follows the Money

Ever since the 1976 film “All the President’s Men,â€� many of us have internalized the admonition to “follow the money.â€� The practice remains a tried-and-true method for discovering unseen relationships and shedding light on patterns of activity and motivations that we might otherwise miss.

Data capture technology cannot read the minds of campaign managers — something for which we can all be thankful. But the new breed of automation based on artificial intelligence might at least enhance our ability to better see the tracks they’ve left.

Accelerating Data Collection

In addition to eliminating the time-consuming tedium of keystrokes and accelerating data collection, policy analyst Jordan Shapiro found that Rossum’s processing of the Georgia spending data produced a greater degree of granularity. This granularity, in turn, enabled her to better grasp the thinking that lay behind decisions made by the various campaigns.

Following the Spend

“As a political analyst, Rossum’s data about 2020 Georgia runoff election spending gave me the opportunity to get inside the heads of the campaigns to see which areas they thought were more or less competitive based on how much each candidate spent in that region,� says Shapiro. “Particularly helpful for my work was the ability to compare county-level spending patterns with a shift in vote share between November and January.�

Data Suggests a Shift from Red to Blue

Southern states have historically been fertile territory for Republicans seeking election, but that appears to be changing.

An NPR report from January 2020 found more Black Americans are moving south, and as they relocate, they are contributing to a shake-up in election results. In 2000, Rockdale County (southeast of Atlanta), for example, was a predominantly white area with a Black population of approximately 18%. Today, that same area has a 55% Black occupancy.

Changing Demographics

Shapiro notes that Georgia’s changing demography, heavy campaign spending by Democrats, increased voter mobilization at the grassroots level, and a tumultuous national political stage all played a hand in what many saw as upset victories. Political turmoil and civic unrest on the national scene rocked Republicans and set the stage for Democratic wins. Both races were reasonably close, with Ossoff winning his race by about 55,000 votes and Warnock winning his with around 93,000 votes.

Regardless, Rossum’s analysis found something even more important than spending-outcome correlations: reasons to default to AI-driven analysis.

Why AI-Enhanced Reporting Will Be Big

The mandatory availability of campaign spending records affords skeptics and naysayers an opportunity to fact-check any reporting that emerges after an election. Reports that have been compiled using AI-powered scanning techniques can be fact-checked just as easily as traditional reports constructed by workers furiously pounding away on keyboards.

As AI-powered processes continue to improve, confidence in this newer methodology is certain to grow as well. As that happens, voters can expect to see more accurate, useful information in the run-up to Election Day.

AI-driven reporting benefits democracy in at least four ways:

1. Enhanced Transparency of Public Data

Much data is the subject of public record, but too often, it is not readily available for analysis and therefore carries only a fraction of its potential. The problem with campaign spending records is that key dates and amounts are scattered through scanned documents instead of being aggregated in a spreadsheet that can be readily analyzed for new insights. This problem is common for many registries, FoIA data releases, and internal government operations.

2. Rapid Data Analysis

The speed advantage of AI-enabled reporting systems over traditional methods of computation will make relevant data available much earlier than in past election cycles. Earlier delivery of results could, in turn, open up new options for campaign managers to consider as constituents respond favorably or negatively to various messages.

3. Added Depth and Surfacing of Less-Obvious Correlations

As noted above, Shapiro was able to take Rossum reporting on election data and cross-reference it with migration data. In doing so, she discovered a trend in specific Georgia counties shifting from presumed Republican strongholds to surprise Democratic wins.

4. More Informed Policymaking and Decisions

Tightening the link between election results and policymaking can serve as a check on any politician’s temptation to drift away from the will of the people they serve. A democratically elected official can only disregard his or her mandate for so long. Faster access to accurate information allows voters more time to assess a politician’s voting record.

What’s Coming in 2022

With another election cycle coming entirely too soon, keep an eye out for new applications of AI. Both parties will use the technology to unearth patterns, analyze results, and suggest new political strategies. Policymakers will check proposals against their constituents’ latest voting trends.

Will 2022’s political environment be any less fraught than the 2020s? Maybe not — but it will be more data-driven.

Image credit: edgar colomba; pexels

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Data Integration: How Connecting Business Apps Boosts AI Capabilities

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Artificial intelligence (AI) and machine learning (ML) have become omnipresent in our personal lives. The same is true in the business world. Data integration can help businesses take their AI applications to the next level.

In many respects, AI is now a mature business technology. Consequently, it’s no longer the sole province of pioneering companies. Not only have advanced analytics powered by ML become popular. Chatbots responding to customer requests through a Natural Language Processing (NLP) AI are also on the rise.

However, many are leaving AI’s full potential untapped.

More than anything, AI needs data. ML algorithms ingest large data sets for training. AI is at its best when it extracts actionable information from a myriad of data points that no human could conceivably make sense of.

Businesses collect ever-larger amounts of data. Yet, most fail to harness all the data they have for their AI applications.

In 2021, a massive section of business operations happens in apps. These are now usually cloud services by Software as a Service (SaaS) companies. There are accounting platforms like FreshBooks. Customer Relationship Management (CRM) systems like Salesforce. Marketing powerhouses like HubSpot.

According to recent statistics, the average business uses 73 different apps. Furthermore, employees conduct over 80% of their work in the cloud. In 2020, the pandemic additionally magnified these trends.

Enormous amounts of data live on each of these platforms. With integrations, these services can take advantage of each other’s data. This enables countless business processes to take place in the cloud, from marketing to financial reporting.

Yet few businesses take advantage of all the possible integrations to unify their data and put it at the disposal of AI and ML.

Having a data integration strategy matters. Here’s why, what the challenges are, and how your business can benefit.

Why Strive for Data Integration? To Create and Harness Synergy.

In business, AI has three central tasks: process automation, generating-depth analyses, and engaging customers and team members.

For all this, data from connected business platforms is invaluable.

Let’s take a core office technology as an example: business phone services. Modern business telephony is cloud-based, thanks to Voice over Internet Protocol (VoIP) technology. For businesses, this has countless advantages, some more obvious than others. VoIP has enabled multiple communication channels, from video conferencing to live chat.

But VoIP business telephony also enables AI applications.

For example, NLP AIs can transcribe voice interactions in real-time. This can be applied to every single conversation between company representatives and customers. Then, ML algorithms can run advanced analytics such as sentiment analysis on the transcripts. The result? Company representatives can receive an instant evaluation of their performance. They can go over the conversation and learn.

But by integrating with other platforms, this kind of AI-generated data becomes even more useful. Connect your cloud business phone system with a CRM, and you can add every transcript to a customer profile. This immediately boosts possibilities for personalization. In turn, this can increase your chances of conversion or retention.

You can also integrate the data into a personnel management platform. For instance, sentiment analyses of a company representative’s conversations might be frequently negative. You’ll see this in their performance evaluation. Then, you can tackle the problem. Targeted training could, for example, showcase positive examples from other transcripts.

Or imagine you’re using sentiment analysis algorithms to sift through online reviews. By itself, this will give you valuable insights into customer attitudes and expectations. But integrating it with data from your digital marketing platform, and you can go much further. AI can then correlate email campaigns or social media strategies with sentiment trends in online reviews.

Getting Crucial Information in Real-Time

Speed is another core advantage of integrating data to boost AI performance. Integration increases the amount of information you can access. What’s more, access becomes easier and much faster.

Let’s go back to the business telephony example. The transcripts from customer calls are in your CRM. Imagine someone calling with a problem they were having with one of your products. A few days later, the same customer calls again. Now, the AI assistant in your business phone service pulls the relevant CRM records. Before your representative even picks up, they get a full run-down of this customer’s case: How they’ve described their problem. Personal information from demographics to billing history. And even what conversational strategies in the last call worked best.

All this saves your customer from having to give a long-form recap of their problem. For one, this reduces frustration on their part. For another, it also allows your representative to address them in a personalized manner. Overall, the exchange will be more efficient and satisfactory for both parties. Your business saves resources and increases customer satisfaction at the same time. All thanks to AI integration.

Generate Positive Feedback Loops

A final significant advantage of business app data integration for AI is positive feedback loops.

This means that by integrating platforms, you generate new data. On this basis, you can refine your strategies and grow your platforms. Leading to more data.

Take chatbots as an example. These days, countless companies use them in their online stores and on their websites. They are powered by NLP AIs. Their usefulness hinges on whether they can provide relevant information to customers.

For this, context is crucial. Customer behavior can provide that context. By supplying chatbot AIs with behavioral analytics, you can increase their usefulness.

For this, you can feed customer data from e-commerce and website analytics platforms and feed it to an AI. Have the AI find patterns and use these patterns to train your chatbot. On this basis, it will give suggestions and make recommendations to customers. Then, track the further behavior of these customers. It will give you even more data – to refine your chatbot.

This feedback training between chatbot AIs and e-commerce analytics helps increase communication efficiency. And seriously boost customer experience and thus retention and sales.

The Challenges of Data Integration

As the examples above make clear, data integration entails countless benefits for AI. But it also poses several challenges in its implementation.

First, businesses looking to integrate data face the hurdle of siloed technologies. Data silos are inaccessible for ML algorithm training. Nor can trained models analyze their contents for decision-making.

Second, the readability of data is a central issue. Many platforms have idiosyncratic data structures and lack metadata or context. They might even save files in proprietary formats.

Third, the trustworthiness of data is often in question. A study in the Harvard Business Review has highlighted this. In it, almost 50% of companies admit to data integrity problems. Issues with false, inconsistent, or stale data are frequent. And even more troubling, more than half of enterprise data fails to provide value altogether, due to siloing.

There are several avenues businesses can take to avoid these pitfalls and overcome these challenges to effectively harness integrated data for AI. The first of these is data mapping to establish a single source of truth.

Data Mapping and Single Source of Truth

Strictly speaking, data mapping is a subfield of data integration. It describes the process of connecting a data snippet in one system to a corresponding item in another. For example: Mapping the “name” field in a CRM record to the “caller ID” field in a business phone system.

The goal of this is to unify data while maintaining its integrity.

Crucially, this also helps businesses create single sources of truth. This refers to a single master record, which can boast high accuracy and sees frequent updates. Other systems will derive their data from here.

This doesn’t mean that all data needs to be stored in a single record. But for each item, only one ultimate source should exist. Especially highly sensitive data – like names, addresses, or social security numbers – should be treated like this. This makes it much easier to control and audit how this information is handled while still harnessing its value.

The good news is that modern AI-based cataloging tools can maintain such master records. They can parse information from all your platforms and deduce credible metadata. They can also automate data cleaning and highlight inconsistencies. This is a crucial first step in dealing with data complexity.

Dealing with Data Complexity

As mentioned above, data readability is a major hurdle to overcome. At its core, that means dealing with data complexity.

More and more raw data is available. This raw data is available in countless different formats. These formats range from social media posts over e-commerce statistics to server logs.

In addition, context is lacking for a lot of this information, especially natural language data. Take a thread of social media posts, which may include abbreviations and typos. Many statements might not make sense without the accompanying emoji, GIFs, or videos.

Plus, all this information is scattered across an ever-growing number of platforms. And these platforms, in turn, may have dozens, if not hundreds, of third-party integrations.

Using AI to Overcome Data Integration Challenges

The good news is that AI systems are becoming better and better at handling these challenges. This means you can use AI to generate input data for AI.

First off, AI can conduct data mapping automatically with precision and speed. In addition, the mapping mechanism can weed out issues such as duplicates and missing values. This helps maintain data integrity and speeds up data unification.

Furthermore, AI makes it possible to automate regular exports of data siloed in various places to central data hubs. It can infer data structures and parse uncommon formats. And crucially, it can propagate and consolidate data across systems, drawing from a single source of truth.

Recently, AI and ML algorithms have become adept at parsing situational context. If the schema structure for the incoming data is unknown or only partially known, they will either judge on a pattern basis, or parse the content to decipher a schema.

AI-Assisted Integration or Self-Service APIs?

One crucial question of data integration remains, though. How exactly can a business go about it?

It’s easy to say that AI can do this and that, without specifying exactly how it does so.

Generally, there are three ways in which businesses can approach data integration.

First, enterprise-scale businesses probably have the resources to hire and task data analysts with establishing a system of integration and analysis. They have the required knowledge in coding and data mining to set up a system that is custom-tailored to their employers’ specific needs. With the right access to the administrative space of various business apps, little should stand in the way of success.

Second, you can opt for self-service analytics platforms. A growing number of platforms aims to make analytics easily available for non-specialists. Many of these come in the form of cloud APIs. Their aim is to democratize AI analytics. Following this philosophy, some platforms even offer chatbot-like features. These help you create an integration procedure conversationally.

And finally, you can – at the very least – harness the integrations that are already at your disposal. Many major business apps already offer native integrations with each other. Even more, they are compatible with bridges such as Zapier. In many cases, taking the time to explore the options of integrations that you have in your existing app ecosystem can already pay off big time. Prioritize platforms that offer AI analytics and automation capabilities, and then proceed to map out the nexus of available integrations.

Final Thoughts

Data integration is a challenge. But it’s a challenge worth tackling to reap the rewards.

Harnessing all the data at your business’ disposal is vital in 2021. It will help you understand your customers better, meet their needs, and increase your sales.

The best way to go about this is to use AI to improve AI through data integration.

You can either manually explore your existing integration options, find an easy-to-handle cloud API, or approach the task with the help of a professional analyst.

Putting the required systems and automated processes in place will take some time and effort. At the end of the day, it will be worth it.

You will be able to boost the quality of your algorithms, establish positive feedback loops, and gain insights at an unprecedented level. With decisions based on the insights, you’ll be able to steer your business through the times to come.

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How is Conversational AI Improving Customer Experience?

Conversational AI

The Conversational AI allows the program to be a part of human-like interactions. This set of technologies empower the applications to send automated replies. It is yet another example of the exponential rate of innovations happening in the artificial intelligence field.

As a result, businesses are investing in conversational AI technologies like Chatbots to serve customers round-the-clock. Although the benefits of using this advanced technology are innumerable, you need to answer certain questions while assessing a conversational AI solution.

Conversational AI is Still Evolving

We are still undergoing the phase of revolution wherein innovators are bridging the gap between the artificial and natural interactions among humans and computers. Constantly, developers are empowering Conversational AI technologies to decipher human actions and mimic human-like conversations.

According to research, the Conversational AI market size is expected to reach US Dollars 15.7 billion by 2024. This clearly depicts the interest of investors in this technology and gives a sign of a lucrative future scope for businesses.

The incorporation of context, relevance, and personalization after deciphering various languages and tones is the end goal of this set of technologies. Chatbots are integral components of these technologies. Consequently, they undergo continual enhancements.

Conversational AI is not the Same as Traditional Chatbots

What do you like more, scripted TV shows or reality shows? Traditional chatbots are the scripted ones and Conversational AI chatbots are the non-scripted ones. The former one works with scripted dialogues whereas the latter one works with the context.

When scripted traditional chatbots are created, developers feed the dialogues with proper keywords. The bots are able to respond with the most appropriate reply out of the many replies added to their memory.

When a user sends a particular text, the chatbot identifies the keywords and sends in the scripted replies. This adds tons of burden on the owner of the chatbots. Hence, they update the conversations to make them look realistic.

The traditional scripted chatbots are not able to converse in real-time with users by understanding the context of the whole conversation. As a result, this compromises the customer services of the businesses.

This particular loophole is looked after by the chatbots powered by conversational AI. They hold the capability to engage in any dialogue after grasping the context of the whole conversation. They do not follow a script because they have in-built conversational capabilities in the software. Let’s understand how they work in detail.

Work Process of the Conversational AI

Conversational AI works with a combination of technologies. With the integration of advanced technologies, Conversational AI performs the function of interacting like humans. Here are the steps involved in the work process of these technologies:

1. Accept the Inputs

The first step involved in the functioning of Conversational AI is to accept the inputs from users. These inputs can be in the form of text or speech. If the inputs are in the written form, text recognition technology is applied. On the other hand, if inputs are spoken phrases, then voice recognition technology is applied.

2. Comprehending

Text and voice recognition is done with AI technology natural language understanding (NLU). After the application reads the inputs, the user intent is understood before forming any kind of response. Usually, businesses can use conversational AI for comprehending responses in various languages. In a nutshell, this is one of the most difficult steps in the work process of a chatbot.

3. Creating Response

In this step, the Natural Language Generation (NLG) is used to create responses in a language that humans understand. After deciphering the intent of the human, dialog management is used to create responses. Finally, it converts the computer-generated responses into human-understandable language.

4. Delivering Response

Finally, the response created in the previous step is shared with the users in the expected form. Either the system delivers it as a text or conducts the production of human speech artificially. Are you able to recall the voice of Alexa or Google Assistant? They generate their responses by following this process only.

5. Learn from Experience

Conversational AI also has provisions for improving their responses for future interactions by learning from their experiences. By accepting suggestions, the application learns to deliver better responses in future conversations.

Technologies used in Conversational AI

The Conversational AI platforms use a set of technologies at the right times to complete the work process. All these technologies are empowered by Artificial intelligence. Let’s understand these technologies in brief.

1. Automatic Speech Recognition (ASR)

The application interprets the spoken phrases by deploying this technology. Adding to this, it converts the speech into texts for the app. Voice assistants like Alexa, Google Assistant, etc. use Automatic Speech recognition.

2. Advanced Dialog Management

This technology helps in forming the response to the conversational AI app. Dialog management arranges this response for the next technology. Further, converts it into something which humans can understand.

3. Natural Language Processing (NLP)

Conversational AI uses natural language processing along with its two subsets. The first one is Natural language Understanding which understands the meaning as well as the intent behind any text. It can decipher texts shared in multiple languages as per the programming.

Both chatbots, as well as voice assistants, use this technology. After ASR, voice apps apply NLU. The second one under the NLP technology head is Natural Language Generation. Conversational AI uses this in the last stage of the work process by Conversational AI.

It creates the responses by converting the computer-generated replies into a language that is understandable for humans. This technology deploys dialog management to conduct this task seamlessly.

4. Machine Learning (ML)

Machine learning is great at understanding a set of data. In conversational AI also, machine learning is used to understand the interactions that have happened over time. Also, ML identifies better responses to these interactions.

Therefore, it understands user behavior and guides the app to create better responses. Humans also join machine learning in this task and together make the Conversational AI app a better interactor for customers.

Benefits of Using Conversational AI for Better Customer Engagement

Businesses are struggling for quite a long time to improve their customer engagements. As a consequence, conversational AI tools like Chatbots have become an integral part of websites and apps. Hence, the developers are working hard to incorporate conversational AI in their solutions.

Conversational marketing has become a proven corporate strategy for millions of businesses operating across various domains including healthcare, tourism, education, etc. Let’s find out what exactly can Conversational AI do to empower customer engagement:

1. Never-ending Scalability

Contrary to human customer support executives, Conversational AI can provide solutions to as many customers as possible at one time. Therefore, you can scale up your operations to any limits. Moreover, it can provide human-like interactions around-the-clock without any interruptions.

2. Acts as a Supportive Wing

In an organization, teams work together towards achieving organizational goals. Conversational AI technologies work with human experts and take their burdens away. They do those tasks which are humanly not possible at the same consistency as that of Conversational AI. This leaves room for human experts to entertain customers only when required.

3. Reduces Cost

Investing in conversational AI solutions might seem an added expenditure to you. But in the long run, the functions it performs reduces your cost. You will not have to pay employees for all the shifts to satisfy customers with real-time conversations. These applications prove to be immensely cost-effective for businesses.

4. Offers Data Insights

As mentioned above, machine learning understands the past experiences and interactions to improve your Conversational AI potential for future interactions. This allows businesses to get an insight into the data.

Hence, you will be able to know your customers’ preferences, behavior, and requirements. Furthermore, you can utilize this data for various other purposes to improve your plans and strategies.

5. Improves Productivity

The primary reason for investing in conversational AI solutions should be the need to improve productivity. It enhances overall productivity with uninterrupted, credible, and prompt customer services.

24×7 support and human-like interactions decrease the risk of losing customers. Hence, conversational AI is capable of providing better customer engagement and ultimately a rise in customer retention rate.

Leverage Conversational AI in Omni-Channel Approach

Investing in conversational AI might seem lucrative after reading about its work process and benefits. Before taking the final call, make sure to identify the channels where you are going to leverage this technology.

When it comes to the customer experience journey, we need to take care of many gateways. With conversational AI solutions, you can provide live chats, social media interactions, messaging on various platforms like Whatsapp, SMS, etc., as well as emails.

Therefore, businesses are using the omnichannel approach. Under this approach, they use multiple engagement channels and offer a seamless and intuitive customer experience. It allows businesses to offer their customers a proactive engagement and prompt responses.

Conclusion

Across the world, businesses are deploying high-end artificial intelligence technologies. This, in turn, offers business solutions to enhance the engagement of customers. Therefore, we can these technologies to offer an improved experience to your users. Conversational AI holds the potential to strengthen customer and business relationships. All you need is to explore it efficiently!

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Why Your Business Needs Non-Stop Software Security

software security

Have you ever lost 30 minutes of creative works on your computer? Or has it suddenly occurred to you that you have a great piece of data that will augment a business proposal, only to discover that the data is missing? Oh – how frustrating!

Data loss occurs for various reasons

  • 78 percent – Hardware or system malfunction
  • 11 percent – Human error
  • 7 percent – Software corruption or program malfunction
  • 2 percent – Computer viruses
  • 1 percent – Natural disasters
  • 1 percent – Other acts.

Impact of critical data loss across global enterprises

Meanwhile, research reveals that global enterprises lose a whopping sum of 1.7 Trillion dollars due to data loss and downtime. And this excludes disruption of business activities, the loss of productivity, the diminished customers’ loyalty, the break of investor’s confidence, the cost of time spent on reconfiguration, and lots more.

While it may be difficult to establish a precise impact of data loss and downtime on organizations, it’s obvious that it would, sure, have a radical negative effect.

With a seamless increase in web adoption and constant acceptance of new technologies, both small and large scale businesses have been able to share important data as regards their products and services — using the web-as-a-service, Waas.

Hackers can compromise corporate networks

Meanwhile, hackers are seriously looking for ways to compromise the corporate network of several industries. As a matter of fact, the Verizon Data Breach Report reveals that 15.4 percent of reported incidents were related to malware and web application attacks.

Also, many of the most fatal breaches that covered the media in the past few years were caused by web-application and software security vulnerabilities. A very good example is the Equifax breach.

Simply put, “business websites possess the greatest threat to organizational security.�

Watch your data loss due to website and software patches

A sizable number of business sectors have experienced (or will experience) data loss due to website and software patches. This has reduced the efficiency and productivity of these organizations to the barest minimum. Little wonder why 70 percent of firms that experience data loss run out of business within one year of the attack. (DTI)

You may not know when the next attack could occur, but taking proper precautions can hamper or completely abolish a hacker’s attempt at gaining access to your business website.

Why your business website needs software security programs

1. Monitoring and detection

How satisfying will it be to have effective and efficient protection of your business website against the worst threat ever?

Using a software security program means your business web is on the watch, and any single vulnerability will be detected on the spot.

Software security companies provide website security scanners that check your website at predetermined intervals to detect any malicious action. You can rest assured that you’ll receive an alert as well as the next line of action when this happens.

Not only does website security monitoring protect you and your customers, but it protects your website’s rankings by checking a variety of different blacklists, and notifying you if you have been placed on one.

2. Performance optimization

Do you know that Google, Bing, and other search engines, use site speed as a ranking factor?

We live in a world where nobody is ready to wait for anything. We have become accustomed to business websites and apps working instantly and perfectly. As a matter of fact, a study reveals that 47 percent of customers abandon business websites that take more than 3 seconds to load!

Performance optimization is a major reason why your business website needs software security programs. Besides SEO, a site performance typically revolves around reducing the overall size of web pages. This includes the size of the files and perhaps, more importantly, the number of them.

3. Fast disaster or data recovery

In an age where data is king, the idea that data can be lost so easily should be enough to encourage businesses to take steps to protect it.

The U.S National Cyber Security Alliance found that 60 percent of companies are unable to sustain their businesses over six months after a data breach.

According to the Ponemon Institute, the average price for small businesses to clean up after their businesses have been hacked stands at $690,000; and, for mid-sized businesses, it’s over $1 million.

Recent events have proven that nobody is safe from the threat of data breach — not large corporations, small businesses, startups, government agencies or even presidential candidates.

When a crisis occurs, there would be one of the two scenarios:

  1. You run a licensed app/piece of software and the vendor is responsible enough to issue an update/patch when issues are reported.
  1. You run a custom software delivered by your software development company and you ask for the software to be enhanced. That is going to take just as little time but chances are your custom software will ever be hacked is drastically lower. Just because the hacker would need to spend even more time looking for vulnerabilities than the AQ department of your software developer.

Even if your website is secure, a misconfiguration or simple mistake can lead to data loss. Only a sure backup plan can save you if your custom files are overwritten or tampered with.

A website security provider can offer secure remote storage, automatic backup scheduling, and an easy recovery process without disturbing your workflow. Decent software companies offer a fast and easy way to recover all the files you need in a very short time.

4. Regular software update

A software update, also known as a service pack is a periodically released update to software from a manufacturer, consisting of requested enhancements and fixes for known bugs. A software update is mainly to present security vulnerabilities in their existing items.

You may think that you do not have anything to protect on your business website but the reality is that security software gives protection for your data. Data is valuable for the sustenance of your business. Top software security programs keep your data secure by providing regular updates to keep you safe from malicious attempts.

Summing It Up:

Since 60 percent of businesses that are affected by a breach in business websites or data will shut down in 6 months, cybersecurity experts, thereby, recommend that you have an effective software security program to save yourself and your business from this calamity.

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Employee Scheduling Trends that Deserve to Continue Even After the Pandemic

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It’s been a long time since you could assume that the majority of your team is at it from 9 to 5. The “global village� means that work doesn’t end when the sun sets or markets close in your time zone, and the rise of flexible working patterns made it even more complex to coordinate employee schedules.

The best employee scheduling strategies consider employee preferences as well as employer needs and consumer demands, but the enormous number of moving parts – operational needs, budget, regulations and compliance – can make it all very difficult to manage.

COVID-19 has only exacerbated the situation in many industries. Employees who are high risk may be unable to work, or can only take shifts with little contact with the public or when only a skeleton staff is present. Workers grappling with unpredictable childcare needs and unreliable transport can cause even more last-minute changes than usual.

Scheduling conflicts can cause bad feeling in a company, but it doesn’t have to be that way. New advances in tech and better communication between employees and managers help enterprises get employee scheduling right, which improves employee experience and in turn pushes up employee retention and satisfaction.

The exigencies of COVID-19 pushed new trends in employee scheduling, which may be worth continuing even when the pandemic fades into memory. Here are a few scheduling trends from 2020 that are worthy of sticking around.

Scheduling is becoming more flexible

Scheduling that is more flexible is also more complex, but flexibility is crucial for a happy, motivated workforce under pandemic conditions. Employees with more flexible scheduling arrangements report higher wellbeing, more engagement, and more effectiveness at work than those stuck in inflexible scheduling.

For example, mothers working remotely with flexible, efficient schedules that match their availability are three times more likely to have positive wellbeing than those with inflexible, inefficient scheduling.

Although employees may be coping, everyone has their own challenges. “In driving new mindsets and behaviors (such as adapting to a new virtual-working model) at scale, it’s important to engage employees in a continual two-way dialogue that takes into consideration their specific needs, allows them to configure their own journeys,� says Jonathan Emmett, associate partner at McKinsey. Even people who love their jobs need accommodation for whatever else is going on in their lives.

Self-scheduling software invites employees to choose their own shifts, make last-minute changes, book vacation days, and check their schedules independently and remotely. This helps employees to feel more in control, which is especially important during such unstable and uncertain times, increasing employee engagement and satisfaction.

AI is bringing intelligence to scheduling

AI is stepping into many more HR use cases. Now managers can use AI tools to predict changes in consumer demand, and plan ahead to meet altering workforce needs.

For example, surging customer numbers in the winter holiday shopping season can require more retail assistants; a sunny day could tempt more diners to a cafe in the park, needing the addition of more waiters; rolling out a new product version might prompt you to increase customer service agents to answer user questions, etc.

With AI and machine learning, HR teams can analyze employee strengths and weaknesses to understand which employees work best together. With these insights, you can construct the strongest possible on-schedule teams for every situation and place the right person on duty at the right time.

Employees expect remote and mobile scheduling

Managing employee scheduling manually, even with an Excel spreadsheet, has long been a joke, but today, employees and HR managers simply can’t live without remote and mobile access to cloud-based scheduling tools that sync automatically to allow use anywhere.

The COVID-19-driven shift to WFH only underlined the importance of cloud-based systems for scheduling. We live our lives on our phones, from ordering dinner to taking out a mortgage, so it’s understandable to assume that scheduling software would include a mobile app.

“You want to make it easy for your staff to access their schedules from anywhere. This isn’t possible with desktop software,� writes tech expert Neil Patel in his scheduling tool drill-down. Beyond mobile-friendliness, he continues, “The best tools will also have shift swapping, employee self-service tools, HR features, labor cost management, leave management, attendance tracking, team messaging, overtime control, time clocks, etc.�

In today’s dynamic work environments, HR needs the ability to respond to scheduling changes on the fly, ensuring that they don’t cause your entire month-long schedule to fall apart, and requesting that someone else to step in without breaking your own rules or creating a sense of injustice among your workforce.

Employers are upping the ante in communication

Employee scheduling flows more smoothly with excellent communication that increases trust relationships, creating a virtuous circle where efficient scheduling itself raises trust.

Employee trust is high at the moment, with “my employer� as the most trusted institution and 73% of workers agreeing they trust businesses to protect them by adapting scheduling and sick-leave policies as necessary. But you can’t take this for granted.

Employers need to keep up and even improve employee communications. “Given the present state of low trust, business will have to fill a further void, that of credible information,� says Richard Edelman, CEO of Edelman Holdings. “For CCOs, it is time for you to initiate regular briefings for employees by your chief scientist or medical officer, to provide trustworthy content that can be shared with employee families or community.�

Enterprises should continue communicating around scheduling, asking how employee needs may have changed (e.g. working parents may prefer a night shift now) and accommodating them as much as possible.

Encourage employees to share their concerns; create more channels for communication between employees and managers and among employees themselves; and open up the conversation around mental health and anxiety, to reinforce trust and improve your understanding of factors that may influence scheduling.

Not all scheduling changes prompted by COVID-19 should fade away

Employee scheduling has never been easy, and with more moving parts, increasing globalization, and the new stresses of COVID-19, it’s only gotten more complex. But necessity is the mother of invention, and so we’ve seen new tech and trends emerge of using AI for intelligent scheduling, supporting scheduling on the hoof, enabling flexible scheduling, and building communication into schedule planning.

Holding onto these new best practices after the crisis of coronavirus has passed can make companies stronger and more resilient in the long term.

Image Credit: depositphotos _19

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Multi-Cloud Cost Optimization

Multi-Cloud Cost Optimization

The popularity and confidence in cloud computing platforms continues to grow unabated.  More and more businesses are moving mission-critical workloads to public clouds.  Forbes recently projected that by 2021, 32% of IT budgets will be spent on public cloud platforms.  Forbes also points out that cloud spending has grown 59% on average since 2018.

The Recent Trends of Multi-Cloud Optimization will Continue — Elevating the Importance of a Multi-Cloud Strategy.

The elasticity of cloud platforms provides great potential from an engineering perspective but great challenges from a cost-containment perspective. Traditional engineering teams using on-premises infrastructure are not accustomed to considering cost in a pay as you go environment. When migrating from limited on-premises hardware to the comparatively infinite expanse and variety of cloud, cost containment, tracking and optimization have to be considered.

Cost discipline, by necessity, becomes part of engineering awareness and vigilance — a requirement for businesses looking to exploit the new paradigm.

The Multi-Cloud Way

Many businesses already have a presence in multiple cloud platforms, either due to a strategy, or more likely, due to organic growth.  The benefits of cloud technology include the lack of a reliance on a single provider, agility, scalability, high availability, SaaS services, and PaaS platforms. These higher quality services, along with the pay as you go billing model, is very attractive.

Controlling the associated costs requires a well thought out multi-cloud strategy.

A multi-cloud cost strategy considers workload placement by factors.

  • Workload/platform optimization. Does the application utilize sufficient platform features to justify placement there?  Conversely, does the availability zone provide needed features for the workload?  How can inter-region bandwidth charges be balanced against fixed availability zone costs in a distributed deployment?
  • Performance. Can the workload be placed on a platform, region, or server-class with overall lower performance without impact?  Workloads that can tolerate lower average performance can benefit from right-sizing the computing environment.  Similarly, for storage; can the workload tolerate lower performance or even object storage to lower costs.
  • Availability. Are some workloads tolerant of low (or at least not high) availability?  Can they be placed on cloud excess capacity when available?  Most cloud platforms have far cheaper preemptible instances for workloads that can tolerate it ( e.g., ETL / batch jobs that can snapshot progress).
  • Serverless. Does the workload require a dedicated server?  Similar to shopping for excess capacity, serverless offerings have the potential for cost savings by not maintaining a running server and only incurring costs based on resource consumption on a highly granular basis.

Hybrid cloud strategies also can have an important impact on cost. Hybrid cloud, using on-premises capacity along with public cloud resources, should be considered when excess on-premise capacity exists — or where public cloud offerings aren’t cost-competitive.

For many businesses, compliance requirements will make a hybrid approach necessary. For others, hybrid cloud deployments are simply the result of a phased migration of workloads to the cloud, which may take many months or years.

The basic promise of the public cloud, the efficient consumption of resources on-demand as an operational expense vs. large capital plus operational expense, isn’t guaranteed to make sense under all circumstances.

Cloud Cost Assessment

If some workloads are already running on the public cloud, the first step is quantifying the costs of existing workloads and services over time as a baseline. Quantifying the cost-baseline is key to getting a detailed profile of consumption and waste beyond simple aggregation of spending.  Once this baseline is established, it can serve as a starting point for identifying problem areas and building an understanding of how cost relates to system usage.

It is critical to correlate current costs to internal teams or projects to enable accountability.

It is critical for cost control to correlate current costs to internal teams or projects to enable accountability and identify the “low hanging fruit.� The correlation can be very difficult without the assignment of tags/labels to cloud instances as a general policy for teams that are deploying cloud workloads.

One of the benefits of a high-level of cloud automation is the ability to tag workloads transparently so that cost traceability can be achieved consistently. The benefits of cloud workload orchestration in the context of day to day operations (CI/CD processes) are discussed later.

Cloud providers offer tools that can assist with cost analysis. For example, AWS has its “Cost Explorer� and its “Cost and Usage Report.�  These are particularly useful in combination with AWS cost allocation tagging.

Azure offers “Cost Management� from the Azure console, which can provide detailed reports. Azure also uses resource tagging to associate cloud resources with accounts (and other indicator-like “projects�).

Google Cloud has a similar service. In addition to the native tools, cloud management platform vendors such as Flexera, Cloudbolt, CloudApp and others provide cost analysis tools across multiple cloud platforms.

Cloud Cost Control

It is critical to raise awareness in teams that use cloud resources of the cost behavior of their workloads so the impact of design and operational decisions can be understood in context. Teams may be consuming large compute instances, retaining unneeded logs or other data on cloud storage, or not tearing down idle resources.

Even with all the benefits of a multi-cloud strategy, the tracking and forecasting associated with the operation of workloads hosted on multiple cloud platforms is a challenge. Add to that the unpredictability of workload scale, one of the major benefits of cloud architectures, and the complexity can become overwhelming.

A strategy for dealing with cost control is needed, potentially along with controls that can overlap with modern DevOps practices.

A casual survey of cloud billing models may lead to the impression that they are the same — but actual costs can be highly workload-dependent.  Using the baseline measurement to identify cost hot spots, compare public cloud billing models to identify significant savings.

The complexity and effort to migrate and maintain services on multiple cloud platforms is significant and requires a significant benefit. The costs and benefits are highly workload-dependent. Because of this dependency, any multi-cloud strategy will benefit from a multi-cloud orchestration layer.

The orchestration layer will provide a degree of portability and make it easier to exploit new cloud providers and changing cost advantages. In addition, discounts provided by cloud providers can provide significant savings for organizations.

Flexera reports that less than half, much less in some cases, of customers, exploit cloud discounts such as AWS spot instances — meaning Azure low priority instances and Google ad hoc negotiated discounts.

Besides operational automation, the adoption of a multi-cloud orchestrator that integrates with modern DevOps practices can provide cost containment benefits.

An orchestrator with a declarative “infrastructure as code� approach makes templates a reviewable part of the release process. Cost containment policies can be applied to the template during review to effectively deny the deployment of problematic workloads. Labels or tags are then applied automatically for cost tracking.

For example, the attempted use of inappropriate-instance-types can be denied far in advance of any damage being done. Furthermore, a competent orchestrator will be capable of applying user/group or even time-specific barriers to workload deployment.

In addition, an orchestrator can limit scaling behavior — thus ensuring that complex deployments are completely cleaned up. Cleaned up deployments are critical to avoid zombie-cost-sources like abandoned unattached storage.

Summary

The journey to an optimal, cost-efficient multi/hybrid cloud strategy is a complex one. It is important to understand current costs, including on-premise workloads. Understanding the current costs will be your foundation for advancement and growth. You’ll understand which of the various platforms have provided the tools you require.

Automation will play a key role in standardizing and controlling the approved interactions and workload placement on various platforms and provide a degree of workload portability.

Portability is key because the world of cloud providers never stands still — and cloud billing models vary over time — requiring adaptability.

Finally, besides ongoing cost auditing, a practice of manual and automated orchestration-template-review must be in place to avoid unpleasant billing surprises.

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5 Tips for Making Onboarding Global Employees Easier than Ever

onboarding global employees

Onboarding is the process companies use to integrate new employees into their teams, the keyword being “integrate.â€� Employees need to become one with the company quickly. The employee who fails to assimilate both the needed information and culture will be headed for the door — taking the company’s initial investment with them.

Onboarding any employee is a challenge. Onboarding an internationally-based employee is a different beast entirely — requiring special care and teaching ability.

Creating a solid onboarding strategy can help you avoid the loss of promising talent at home and abroad. Here are five tips you can use to make onboarding your global workforce a little easier:

1. Prepare to pay up.

The first question of onboarding will always be about payment processes. For global workers, those can be complex. Different countries have different rules and regulations for cross-border payments. Not all banks are prepared to facilitate such transactions.

Implementing an international payroll system is a huge undertaking, but it’s necessary to ensure employees are paid promptly. Lean on a payroll partner to handle all the taxes and withholding required by the team member’s home country.

If you want your international workers to trust your business, you need to back up your word with hard cash. A global payroll system is part and parcel of being reliable to international employees.

2. Be proactive — and reactive.

Your onboarding plan must be comprehensive and tailored to each of your international locations. Onboarding begins with pre-boarding and lasts throughout the first year, with a half-dozen or so key milestones along the way.

Step one is to provide access to your online onboarding portal as soon as an employee accepts your offer. Ensure this portal has accessibility features, such as translation tools for people whose primary language is not English.

As soon as possible after hiring, employees should be introduced to important company individuals and to their primary team. Company introductions tend to focus on the home country; make sure to include information about your company’s global workforce as well. Explain the roles and responsibilities of existing team members to give your new hire a sense of their place in the business.

Check-in at the one-month, three-month, six-month and one-year marks. Don’t just throw your onboarding program out there to see what sticks. Provide substantive feedback from new and seasoned hires.

Remember not to rely solely upon your HR manager to gather feedback. Fellow employees, supervisors, and mentors might provide the most in-depth insight. Even interns and contractors can provide new hires with meaningful feedback, though you may need to prompt them to provide it.

Don’t forget to solicit feedback, too. What did your new team members like about their onboarding experience? What are their suggestions for improving it? Welcome both positive and negative feedback, as long as the information is constructive.

3. Mind your language.

There’s no beating around the bush: American English is full of idioms, which can be problematic for non-native speakers. Add in industry catchwords, abbreviations, and slang, and you can confuse even fluent global hires.

The fast-paced nature of business conversations compounds this challenge. For global workers who aren’t native English speakers, reading is likely easier than listening to company leaders speak. Put onboarding details in writing online where it can be accessed at any time — including team meeting minutes. Having a portal with all information available at all times gives global hires more time to read and absorb information.

As with pre-boarding materials, make meeting minutes available in employees’ native language when possible. If nobody on the team can translate, invest in professional translation services. The payoff is worth the price: Your new hires will more intimately understand the nuances of your company.

Be understanding if a new team member stumbles over their words when communicating with you. Remember, starting a new job is intimidating. If their language skills aren’t perfect, chances are good they will flub a phrase or two. Don’t take it as a demonstration of their skills.

4. Address cultural differences.

New hires aren’t a one-size-fits-all group, so onboarding shouldn’t be a one-size-fits-all process. Engagement is the key to retention, so make sure you provide a culturally meaningful onboarding experience.

If you’re building a global workforce based in a number of foreign countries, it can be tough to grasp the nuances of each culture your workers hail from. Sometimes, the easiest way to bridge cultural divides is by encouraging employees of different cultures to educate one another. Encourage them to ask questions if they don’t know what is being said.

Start things off on the right foot. Hold icebreakers where workers can talk about their backgrounds in a productive way. Facilitate “did you knowâ€� and “would you rather sessions.â€� Questions are not only less intimidating in this climate — but they make it easy for team members to get to know each other.

It’s vital that collaborative teams understand one another on both personal and professional levels. There’s no easy way to bridge cultural gaps, but one of the best is through casual interactions. Make space for chit chat, fun activities, and other diversions to bring workers closer together. The result will be teams that are more understanding, better informed, and more efficient than they would be otherwise.

5. Watch the clock.

Navigating time zones can be tough for global teams. The more offices you have, the more time zones you have to work with.

If offices are in three or more time zones, consider scheduling meetings and events using Coordinated Universal Time (UTC). UTC is a time standard, not a time zone. Employees can calibrate their local time to UTC without the need to Google everyone else’s time zone. There are various calendar apps and scheduling apps that will make these conversions for you so that you don’t have to stop and calculate time.

Regardless, the responsibility is on you to schedule times that work well for everyone. No new employee wants to be embarrassed by missing a meeting because it’s at 3 a.m. local time — or someone miscalculated the time or date. International workers’ hours may not always align with your own, but giving them some breathing room is crucial for getting off on the right foot.

Business is an increasingly international game. Building a global team is the only way to rise to the challenge. By onboarding your international hires well, you’re setting your business up to win in the 21st century.

Image Credit: andrea piacquadio; pexels

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Mindful Technology Use: The Next Digital Revolution

mindful technology digital revolution

For the past couple of decades, we’ve seen an impressively powerful technology revolution. In just 20 years, we’ve gone from having less than half the U.S. population with internet access to having the vast majority of Americans rely on the internet for work, socialization, and leisure for most of their day. The movement has been to develop more technology, use more technology, and integrate technology into more areas of life.

Mindful Technology

For the most part, these changes have been positive. Workers are more productive than they’ve ever been before. People are able to talk to friends and family inexpensively, no matter where they are in the world. And, of course, we get a chance to search for movies, TV shows, and even memes we’d otherwise never dream of seeing.

But the next digital revolution may be a more nuanced one. Instead of pushing for “more� technology, it may be time to scale back—at least in some ways. It may be time to spark a revolution of “mindful� technology use. But what is this concept, exactly, and why is it so important for our health, productivity, and daily interactions?

Mindful Technology Use

You may associate the term “mindful� with “mindfulness meditation,� and you’re not too far off. In case you aren’t familiar, mindfulness meditation is the practice of mindfulness, or paying attention to the present moment. In the course of daily life, our minds tend to wander; we drift between an annoying song stuck in our heads, a grocery list, an imaginary argument with someone who upset us earlier, and random stimuli in our environment, all during an important work meeting. Mindfulness encourages us to be presently conscious, if only in brief, fleeting moments between these competing distractions.

Mindful technology use follows a similar principle. The idea is that we’re constantly afflicted with technological distractions, and we’re tempted to use technology far more often than is warranted—and far more often than is healthy.

Some people have advocated abandoning technology altogether, such as quitting social media or abandoning email in favor of traditional phone calls. But the productivity-increasing potential of technology is far too powerful for this to be a smart move.

Instead, our goal should be to become more aware of how and when we’re using technology—and only use technology when it benefits us to do so.

Non-Mindful Technology Use

It’s perhaps easiest to understand what constitutes “mindful� technology use when we illustrate “non-mindful� technology use.

A perfect example of non-mindful technology use: losing time in an infinite scrolling social media feed. Facebook, Reddit, Twitter, and dozens of other social platforms now utilize a mechanism known as “infinite scrolling.â€� The users can endlessly keep discovering new content by scrolling — possibly forever. Nearly all of us have fallen victim to mindless scrolling at some point, forgetting that we’re spending time doing this and losing ourselves in consumption.

How much time would you estimate you have lost in your scrolling adventures?

Here’s another example of the non-mindful use of tech. Have you ever found yourself bored for a moment, whether it’s waiting in line or dealing with an unnecessary meeting, and found yourself opening an app on your phone without thinking about it? Suddenly, you’re in the middle of using an app — you didn’t choose this. You didn’t think about it. You just did it. Unconsciously. You maybe even started playing one of your games.

In these contexts, technology functions as a kind of 301 redirect for our minds. We automatically follow this pattern of behaviors, even if it’s not good for us. And the fact that most digital apps are specifically designed to be addictive just makes us more vulnerable.

All of the data about the consequences of  mindless scrolling are complex:

  • Wasted time. For starters, we waste time. We spend too many hours on apps that are meant to provide us with temporary entertainment. We end up dwelling on apps meant to increase our productivity in a way that renders us unable to do any “realâ€� work.
  • Lost attention and focus. We also lose our attention and focus. If we’re compelled to open an app and start scrolling every time we’re bored, we’re practically unable to pay attention in conversation or focus on our more important work.
  • Bad habits. Mindlessly using technology leads to bad technology habits, which can follow us for years if not addressed. For example, we’ve all conditioned ourselves to drop what we’re doing and respond to notifications whenever we receive them—at least at some point.
  • Mental health issues. Some forms of non-mindful technology use are associated with mental health afflictions. For example, chronic social media users tend to be more inclined to feel lonely, depressed, and anxious.

Principles of Mindful Technology Use

Mindful technology use sounds great. But it’s also a bit vague. So what does mindful technology use look like? How can we achieve it?

The principles of mindful technology use include:

  • Simply learning more about the effects of technology can make you a more mindful technology user. If you know that an app has the potential to be addictive, you’ll be inclined to use it less frequently or in less repeatable patterns. If the claims a productivity app makes are dubious, you’ll consider using an alternative.
  • Mindful technology use is also about minimalism. That doesn’t mean restricting your use of technology or using as little as possible; instead, it means avoiding wasted technology use. It means not using more apps than you can reasonably handle and focusing on the tech tools that are most beneficial for you.
  • You need to be transparent and aware of your own habits if you’re ever going to improve. That’s why mindful technology use is heavily focused on awareness. Consider tracking how much time you spend on each of your most popular apps and documenting instances where you feel like you’re not in control of your own use of technology.
  • Mindfully using technology also requires intention. You shouldn’t be using technology because you feel like you have to or because they’re a part of your habits or routine; you should be actively choosing to use technology if and when it suits you.
  • Analysis is the gateway to improvement across all these tenets. You have to understand your own behaviors, feelings, and attitudes if you’re going to change them.

Changing Bad Habits

It can be difficult to change a bad habit—especially if it’s been deeply ingrained and reinforced for many years. However, there’s always time to change your patterns of behavior.

With technology use, most of our patterns rely on triggers and/or repetition. For example, when we receive a notification, we look down at our device; this is a trigger that encourages a natural response, and it’s all too common now that most of us are working remotely. If the trigger continues, your response will likely continue.

Breaking a bad habit reliant on a trigger requires breaking the trigger in some way. Ideally, you’d get rid of notifications entirely and only check your communication channels when you truly intend to do so. However, reducing or changing your notifications may also help.

Repetition is another issue. If you can engage in the same sequence of actions repeatedly, you’ll easily build a habit, whether you mean to or not. For example, you may mindlessly tap an app on your phone, knowing its location so familiarly that you don’t even have to look at it.

Again, you’ll want to break the pattern. In this case, that could mean moving the app to a different location on your smartphone, so you’re forced to think about whether you truly want to open the app or whether you’re doing this mindlessly.

Toward a More Mindful Future

Almost anyone can benefit from practicing more mindful technology use. It’s challenging to break bad habits and resist the natural tendency to engage in behaviors encouraged by modern tech. However, it’s extremely rewarding to regain control of your own mind, health, and productivity.

Image Credit: armin rimoldi; pexels

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Why the Edge is Key to Unlocking IoT’s Full Potential

edge unlocking IoT

To IoT’s great benefit, edge computing is about to take the spotlight. Consider that each day billions of devices connected to the Internet of Things come online. As they do, they generate mountains of information. One estimate predicts the amount of data will soar to 79.4 zettabyes within five years. Imagine storing 80 zettabytes on DVDs. All those DVDs would circle the Earth more than 100 times.

In other words, a whole lot of data.

Indeed, thanks to the IoT, a dramatic shift is underway. More enterprise-generated data is being created and processed outside of traditional, centralized data centers and clouds. And unless we make a course correction, the forecasts could come unglued. We must make better use of edge computing to deal more effectively with this ocean of data,

Network Latency

If we do this right, our infrastructure should be able to handle this data flow in a way that maximizes efficiency and security. The system would let organizations benefit from instantaneous response times. It would allow them to use the new data at their disposal to make smarter decisions and — most importantly — make them in real-time.

That’s not what we have nowadays.

In fact, when IoT devices ship their data back to the cloud for processing, transmissions are both slow and expensive. Too few devices are taking advantage of the edge.

Traffic Jam: The Cloud

Instead, many route data to the cloud. In that case, you’re going to encounter network latency measuring around 25 milliseconds. And that’s in best-case scenarios. Often, the lag time is a lot worse.  If you have to feed data through a server network and the cloud to get anything done, that’s going to take a long time and a ton of bandwidth.

An IP network can’t guarantee delivery in any particular time frame. Minutes might pass before you realize that something has gone wrong. At that point, you’re at the mercy of the system.

Data Hoarding 

Until now, technologists have approached Big Data from the perspective that the collection and storage of tons of it is a good thing. No surprise, given how the cloud computing model is very oriented toward large data sets.

The default behavior is to want to keep all that data. But think about how you collect and store all that information. There is simply too much data to push it all around the cloud. So why not work at the edge instead?

Cameras Drive Tons of Data – Not All of Which We Need

Consider, for example, what happens to the imagery collected by the millions of cameras in public and private. What happens once that data winds up in transit? In many – and perhaps most – instances, we don’t need to store those images in the cloud.

Let’s say that you measure ambient temperature settings that produce a reading once a second. The temperature reading in a house or office doesn’t usually change on a second-by-second basis. So why keep it?  And why spend all the money to move it somewhere else?

Obviously, there are cases where it will be practical and valuable to store massive amounts of data. A manufacturer might want to retain all the data it collects to tune plant processes. But in the majority of instances where organizations collect tons of data, they actually need very little of it. And that’s where the edge comes in handy.

Use the Edge to Avoid Costly Cloud Bills

The edge also can save you tons of money. We used to work with a company that collected consumption data for power management sites and office buildings. They kept all that data in the cloud. That worked well until they got a bill for hundreds of thousands of dollars from Amazon.

Edge computing and the broader concept of distributed architecture offers a far better solution.

Edge Helps IoT Flourish in the era of Big Data

Some people treat the edge as if it were a foreign, mystical environment. It’s not.

Think of the edge as a commodity compute resource. Better yet, it is located relatively close to the IoT and its devices. Its usefulness is precisely due to its being a “commodity� resource rather than some specialized compute resource. That most likely takes the form of a resource that supports containerized applications. These hide the specific details of the edge environment.

The Edge Environment and Its Benefits

In that sort of edge environment, we can easily imagine a distributed systems architecture where some parts of the system are deployed to the edge. At the edge, they can provide real-time, local data analysis.

Systems architects can dynamically decide which components of the system should run at the edge. Other components would remain deployed in regional or centralized processing locations. By configuring the system dynamically, the system is optimized for execution in edge environments with different topologies.

With this kind of edge environment, we can expect lower latencies. We also achieve better security and privacy with local processing.

Some of this is already getting done now on a one-off basis. But it hasn’t yet been systematized. That means organizations must figure this out on their own by assuming the role of a systems integrator. Instead, they must embrace the edge and help make IoT hum.

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5 Things 2020 Taught Me About Remote Leadership

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Only one year ago, I shared how the trend was moving to remote work. According to a survey from CloudApp, more than 50% of younger generations were working from home at least part of the week, new startups were launching remote, and companies like GitLab were carrying the torch of possibility.

Little did I know how much that would be accelerated due to a global pandemic. In March, we were thrust into the unknown, and “2 years of digital transformation talks were crammed into 2 weeks,� said Satya Nadella, CEO of Microsoft. The tech world moved remotely. Here is what I have learned over the year leading a marketing org and company that previously was not remote.

1. Find your comfort zone

I started like most of you. Unsure of what to do and how to make it work. My first day was spent in my basement on an IKEA kids chair and laptop on my lap.

Day 1 of remote work.

Some remote setups are better than others.

I was literally and physically out of my comfort zone with my nice desk, big monitors, and complete quiet. It has taken time to adjust, to find a groove. I still haven’t quite figured it out and may not ever figure it out until we return to the “old normal.�

What I have learned is that it’s important to adapt and find peace with a new situation. At the very beginning, my team and I did 10-minute standup chats every morning. It was a chance to replace the familiar morning conversations that happen casually at the start of work. Those have gradually faded to a normal weekly cadence, but was a helpful way to stay connected.

I take productivity breaks at home, make sure to play with my kids during that time, so they aren’t desperate for my attention during an important meeting. I also try to separate work and home as much as possible, but I have definitely had a toddler join me on plenty of Zoom calls. These things have helped me to find some sort of comfort zone with change.

Once you find a new normal spot, you will be able to lead better. You can find ways to help others when you have taken care of yourself.

2. Capture the moment

Remote Startups
Be nimble as a remote leader.

Leading marketing at CloudApp, in which screen recorder and screenshot for mac and PC products help remote workers stay connected, I saw a unique moment to capture an audience and help them along the way with some remote work tips and tricks. We put out dozens of content pieces, including podcasts, webinars, blog posts, and guides. The content exploded and had over 100k views directly tied to it over a 45-day span.

Obviously, this moment was a chance for our company to lead and help in the situation. In my 15 years, I have found there are constantly opportunities like this for companies to step up and help their community. It’s important to be flexible and build in time for campaigns that capture a cultural moment in time.

These campaigns generally run hot for a few months and then peter out, but provide a good opportunity to build global awareness of your brand and strengthen ties with your community. Going through this exercise of trend content will also help you to learn how nimble your team is and how you can try and create success with similar campaigns in the future.

3. Over-communicate

remote team video conference
Meet often with your remote team. Photo by Anna Shvets from Pexels

It’s amazing the amount of side, informal conversations you have on a daily basis when you are side by side with your team. In remote work, those meetings are gone. How can they be replaced? I’ve taken a combination of technology and virtual meetings to do so.

Slack or Microsoft Teams can compensate for some conversations; just make sure to use them wisely. It’s important to block off time for yourself to not be available on these channels.

1:1s and team meetings can provide opportunities to give pass downs from other teams and stay connected as a team. It’s important to protect these on your calendar and not continually reschedule or cancel.

4. Project Management

All projects and campaigns should have a process to ensure they are launched on time and have good results.

Kick-off call – this can be a great time to identify the expected outcomes and timeline for a project or campaign. Everyone who is involved in cross-functionally should be invited to the kick-off call. I also love to use this time to introduce how we will track success along the way.

Project Management software – Having a place to track updates and make assignments is key to scaling, especially with multiple projects running simultaneously. Asana and Jira are both great options for project management.

The key is clear outcomes and milestones along the way. It is also helpful to have a lead for the overall project to help coordinate and ensure updates are put into the project management software.

Quick updates – these can be done with a CloudApp screen recording, a 15 min stand up meeting, or just over email/slack, whatever your company preference is. The key is to have some sort of check-in on measurement to ensure progress and accountability.

Post mortem – sometimes these can be too fluffy. Including things that went massively wrong along with the wins can be helpful in refining the process and making it smoother the next time around.

5. Have fun and celebrate

last minute gift ideas
Don’t forget to celebrate.

I still do a terrible job at this. I am a much more fun leader in person than I am remote. What I have learned, though, is that there needs to be time to celebrate. The best thing we do at CloudApp is a Cloud9 channel in Slack. This is a place that every organization can celebrate small and big wins.

Finding time to celebrate asynchronously and also in team meetings really creates a culture that wants to continue winning and connects to a leader who can help to continue that focus.

Image Credit: rebrand cities; pexels

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