Article | November 17, 2020
Expert cites machine learning advancements creating immediate, actionable value to drive data literacy, elevate cognitive insights and increase profitability in kind.
In today’s tumultuous business-scape amid increasingly intricate, and often vexing, marketplace conditions, curating and mining data to drive analytics-based decision making is just no longer enough. For competing with maximum, sustained impact and mitigated opportunity loss, it’s rapidly monetizing data that’s now the name of the game—particularly when spurred by artificial intelligence (AI). Indeed, emerging AI methodologies are helping forward-thinking companies achieve and sustain true agility, fuel growth and compete far more aggressively than ever before.
AI is critical as a means toward those ends and also certainly with respect to aptly predicting, preparing and responding to prospective crises as with the COVID-19 pandemic the globe is currently immersed in. In fact, Gartner recently cited the need for “smarter, faster, more responsible AI” as its No. 1 top trend that data and analytics leaders should focus on—particularly those looking to “make essential investments to prepare for a post-pandemic reset.” Novel coronavirus matters aside, Gartner underscored just how impactful AI will become, predicting that, “by the end of 2024, 75% of enterprises will shift from piloting to operationalizing AI, driving a 5X increase in streaming data and analytics infrastructures.”
“To innovate their way beyond the post-COVID-19 world, data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts,” said Rita Sallam, Distinguished VP Analyst, Gartner.
However, employing AI techniques like machine learning (ML) and natural language processing (NLP) to glean insights and render projections is simply no longer “enough” to get the job done—especially for organizations seeking to compete efficiently on a national, multi-national or global scale. Today’s organizations must endeavor toward a culture of AI-driven data literacy that directly and positively influences their top and bottom lines.
“To help data monetization-minded enterprises better future-proof their operations and asset-amplify their data value chain, there are a few key ways to implement and elevate machine intelligence so that it’s far smarter, faster and more accountable than protocols past,” said Microsoft alum Irfan Khan, founder and CEO of CLOUDSUFI—an AI solutions firm automating data supply chains to propel and actualize data monetization.
Below, Khan details five benefits of leveraging AI data-driven insights and technology in a way that will create actual and actionable value right now—the kind of insights that enable new and evolved business models and empower companies to increase both revenue and profitability.
Manifesting new market opportunities
Today’s machine learning capabilities allow people to sift through data that previously could not be accessed, all at speeds faster than ever before. Present technology offers the opportunity to wholly analyze image, spoken or written inputs rather than just numerical, helping companies better find connections across these diverse data sets. This generates and maximizes value in a number of ways. Relative to the bottom and top lines, not only can it significantly reduce expenses, but it can also create new market opportunities. With COVID-19 as one recent example, algorithms speedily sifted through an extraordinary amount of data to identify diseases and potential cures that presented as similar, which allowed those methodologies to be readily tested against the coronavirus.
Machine learning advancements also help companies better monetize their data and establish new revenue streams. In the above example, of course patient information would not be shared or sold in any way, but other highly valuable data points can be gleaned. This includes determining that a certain drug is only effective on woman between certain ages—critical insights for pharmaceutical developers and physicians.
Emerging AI data processing protocols are far more rapid than prior iterations of machine learning technology, as are the resulting solutions, discoveries and profit-producing results thereof.
Reconcile emotions with actualities
Data generates value, which leads to the generation of money. It’s that simple. Previously, it was difficult, if not humanly impossible, to sift through mass amounts of data and pinpoint relationships. There existed very rudimentary tools like regression and correlation, but today’s analytics call for gaining a true understanding of what extracted data actually means. How do you convert data into a story you can actually tell? Often, decisions are made based on emotional foundations. Leaders are using data to either validate their gut or disagree with their instincts. Now, they are getting quicker insights that decisively validate or invalidate their thinking, while also prompting them to ask new questions. So, garnering meaning out of a company’s own data provides tremendous advantages.
“Human nature is such that unless we can see it touch it feel it, it’s hard to understand it,” Khan says. “We as data scientists haven’t done a really great job of explaining AI-driven data technology in simple terms. Telling a story with data or demonstrating actual results is where real power and understanding lies.”
Scale statistical models for actionable models
We often separate our data as factuals, asserting “this is what happened.” Neural networks connect the “human decision-making process” to those factuals—a simulation practice that helps us make better decisions. Previously, we would look at data sets like demographics, customer behaviors and such in silos. But when these multiple data sets are connected, it becomes quite evident that no two humans—or customers—are exactly alike.
Technology is now allowing us to understand trends on a factual level and then project outward. In the health realm, some companies are using this key learning to project whether or not a person is likely to suffer a certain affliction. It’s also allowing for far more efficacious “if this then what?” scenarios. If a diabetic person takes insulin controls, then their diet the treatment protocol will change. This is enabling highly personalized medicine. But the same processes, principles and benefits hold true in non-health categories as well—encompassing all industries, across the board.
Future-proof, anti-fragile data supply chains
From data connectors to pipelines; data lakes to statistical models; AI to Quantum; visual storyboards to data driven automation; ML to NLP to Neural Networks and more, there are highly effective methods for future-proofing your data value chain. The data supply chain is quite complex and, to make it future-proof and non-fragile, it requires thoughtful processing from the point of creation to the point of consumption of actionable insights.
It starts with data acquisition—garnering a wide variety and volume of data from a number of internal and external sources where data is being generated by the millisecond. Once the data is identified and ingested, it needs to brought to a central point where it can be explored, cleansed, transformed, augmented and enriched and finally modelled for use toward a purpose. Then comes statistical and heuristic modeling. These models can be of different types using different algorithms yielding different levels of accuracy in different scenarios. Models then need to be tuned and provided and environment for continuous feedback, learning and monitoring. Finally, is the visualization of outcomes—an explanation demonstrated by drawing cause-effect relationships that highlight where the most impact happens. This leads to a conclusion on how a set of problems can be solved or opportunities uncovered.
“Most organizations have some data and drive different levels of business process improvement and strategic decisions with it,” Khan notes. “However, few use data to the fullest. The right approach to data valuation and monetization can uncover limitless possibilities, including customer centricity, operational efficiency, competitive advantage, strategic partnerships, efficient operations, improved profitability and new revenue streams.”
Up to now, we have been able to write algorithms, generate immense amounts of numerical or written data and make sense of it. However, there is a significant amount of data that comes as images or voice, which has not been easy to process and manage until recent developments. The applications for the processing of visual and auditory inputs are endless. In fact, retail and finance industries have been early adopters of this technology—and with good reason. They’ve seen costs go down, engagement go up, sales increase and benefitted from other highly substantial points of monetization.
Now, a large department store can digitize their video data every night and determine that “X” amount of people saw “X” number of jeans, but they had to walk further to get to it. As a result, the department store can put those items closer to the door and walkways to determine if sales increase in kind.
Even the education realm is tapping AI-driven data. The technology is tracking retina movement to discern if kids are engaged amid the remote learning paradigm ushered in by the pandemic. They’re exploring how to measure the retina to determine whether or not a child is actually engaged in the lesson.
In radiology, they are starting to convert visual data and track it to gain a deeper understanding of digital images and video. MRIs are better able to track brain tumors—whether they are growing or shrinking and at what rate and if they are getting darker or lighter in terms of the regions. This kind of AI-driven learning is helping doctors better detect cancer and treat it more rapidly. Video data processing of the human eye can also be used to determine if a person is drunk, fatigued or even has a disease. Voice machine learning has also keenly evolved. Originally, voice recognition was being utilized to discern if a person was actually suicidal, which could be accurately predicted by inflection points in a person’s voice. Now, if that person can be captured on video, it is deemed to be about 20 times more accurate.
“All of this possibly had previously demanded a hefty price tag using systems and solutions of yore,” Khan notes. “Today, integrating multiple processes across hybrid multi-cloud environments has made data processing and analytics much more accessible and outsourceable. This negates the need for companies to purchase cost-prohibitive servers and other machine hardware.”
As one of the world's leading experts on building transparency into supply chains, Khan doesn’t just talk the talk, he’s walked the walk. As a revered marketplace change agent, he’s known for driving business transformation and customer-centric turnaround growth strategies in a multitude of environments. In addition to engineering partnerships with MIT, Khan has successfully led organizational changes and process improvement in markets across the Americas, Europe, Middle East and Asia.
“New AI solutions and trends will eliminate patchwork processes that cause data, and interpretations thereof, to get lost in translation or, even worse, remain entirely undiscovered,” Khan says. “Next-Gen platforms are solving such problems by executing all functions required to create and govern AI products— single-source systems that pull data, transform, model, tunes and recommend actions with cause-effect transparency.”
For niche players, today’s leading-edge AI technology also aptly provides for vertical industry specialization. “Emerging solutions enable common data models, compliance and interoperability requirements that, in turn, accelerate model validation, refinement and implementation that’s specific to a given sector or marketplace,” notes Khan. “All of this ultimately drives speed to insights on previously unsolved problems, which reveals untapped opportunities and automates workflow integrated cognitive solutions.”
“Overall, AI is ushering in a new and more sophisticated era of data literacy,” he continues. “It’s a new paradigm founded on automated, comprehensive and holistic data discovery, which is fostering elevated cognitive insights and actionable strategies that positively impact the top and bottom line.”
Perhaps the future mandate for AI should not only focus on becoming smarter, faster and more accountable than predecessors, but actually bridge the gap between human intuition and data-backed decisions. Doing so will assuredly advance an organization’s ability to transact with utmost trust.
Article | July 15, 2021
The Software as a Service (SaaS) sector is one of the most essential industries globally, and it's always more complex than general customer marketing. Of course, we do not mean that customer marketing is effortless. However, it's relatively more traditional than SaaS marketing.
Many people are not familiar with the right strategies for selling non-physical products. As selling such products can possess its own set of challenges, SaaS marketing gets even more demanding.
What is SaaS Marketing?
Let’s begin with the basics of SaaS — Software as a Service or SaaS allows users to use cloud-based applications. Businesses or users hire SaaS applications for a variety of purposes.
SaaS marketing is nothing but the process to market such applications. Marketers use various strategies to sell SaaS software and achieve the highest possible conversion rates.
However, there are several significant challenges in this quest. First, SaaS tools don’t have any physical presence, and selling something that doesn’t physically exist is not easy.
Furthermore, we live in a world where SaaS platforms are constantly changing. With newer features and updates being launched every day, marketers struggle to nail down even their most basic payment app before the next version is launched.
If your company sells software, you must adopt the right SaaS marketing strategy to get more customers and, ultimately, higher conversions.
But how will you get started? And, most importantly, how to overcome those three biggest SaaS Marketing challenges?
Let’s find out!
The Three Biggest SaaS Marketing Challenges
The following are the three biggest marketing challenges faced by SaaS marketers.
● Earning The Loyalty from Customers
● Getting Noticed
● Dealing With Conventional Complainers
If your company faces the same B2B SaaS marketing challenges, it will be fair to say that you are doing well. Their occurrence is pretty common. After working with several B2B companies, ranging from 2 million annual revenue to 2 billion, you will notice that most of them face the same challenges mentioned above.
The demand for cloud-based services is increasing day by day. And eventually, this has birthed thousands of SaaS companies all around the globe. Locations beyond Silicon Valley are seeing the rise of several new SaaS providers ranging from government agencies to small and mid-size start-ups.
As a result, the competition is mounting at an incredible pace, and the niches are getting overcrowded. Suppose you do not invest in improving your company’s digital presence, brand identity, and messaging. In that case, you are more likely to drown in the ocean of the current marketplace where new companies are entering every single day.
Here’s how you can deal with these core SaaS Marketing Challenges:
Being a SaaS marketer, analyze your strategy by asking yourself this question — How can your company address the customers’ needs in areas that your competitors are not targeting?
The SaaS marketplace is constantly evolving, and even the slightest functionality or design improvement in the competitor’s platform is enough to take away the sleep of your company leadership.
Traditionally, most SaaS marketers sell their product by convincing companies that they have a problem, and only our SaaS platform can fix it. But, now things have changed, and there’s a lot to compete with.
Several market analysts have claimed that smart process apps are overlapping with SaaS. While SaaS companies are focusing on only the US market (and ruling it as well), traditional software companies have taken their business to Asia and Europe and have established themselves there.
As a consequence, SaaS marketers are faced with more than just indifference and ignorance. Being a SaaS provider company, you must make the most out of the predicted surge in growth and go beyond the traditional SaaS marketing strategy. Plus, you need to develop newer ways to stand out in the competition of sameness and simultaneously reach more customers.
Earning Customer Loyalty
There was a time when data migration was one of the most challenging tasks for SaaS marketers. Thankfully, we are past that time now. Today, companies can easily migrate data from their existing system to the cloud base. Moreover, they will have a painless process to manage it post the migration.
But, here’s a catch! If you can do this with ease, your competitor can also do that. This drastically increases the importance of customer acquisition and customer retention. And for this reason, you must have an effective SaaS marketing strategy that aims at earning customer engagement and loyalty.
Dealing with Conventional Complainers
Many industries are yet to accept the benefits of cloud computing. However, if you target them in the right way, your state-of-the-art SaaS platform can replace their incumbent software system.
Someone has spent years building that existing software system, and they are more likely to lose sleep because of this takeover. Such people, in most cases, are the decision-makers and won’t be keen to relinquish their hold on the existing system. It would help if you won them to sell your product. Your strategy should be able to handle an onslaught of their objections. They might claim that data migration is so complex that it’s not worth their time and money, or they will even say that the cloud is not secure enough. You have to do your homework and be prepared to tackle these hurdles.
So, be excellent and well prepared. And, the chances are, you will convince them.
How to Overcome SaaS Marketing Challenges?
Any SaaS Vendor must dig deep into all the significant concepts of marketing. But, before getting started with it, it's essential to learn about the customer journey. It helps SaaS marketers to deploy and integrate an effective strategy that works well with all the following stages of the customer journey:
The reality is, there are hundreds of marketing strategies that can produce great results. However, the following ones are the best of all when it’s about marketing your SaaS products.
Develop an Effective Content Marketing Strategy
Be it any online business, content marketing is one of the most important marketing aspects. It can be beneficial for your SaaS company by letting your audience understand the advantages of your product and improving your online presence.
You can also use various social media platforms to extend your approach for offering the same information. Credibility, trust, and existence are the critical aspects of every SaaS marketing niche, and content marketing is the most effective way to spread your valuable information and earn trust.
Set Realistic Goals
Directionless movements are a total waste of time. Instead, all successful SaaS marketing strategies begin with defining clear and realistic goals. To do that, you must figure your business most comprehensively. Then, set tractable and specific goals keeping the key metrics and KPIs in mind. And lastly, work on how the marketing and sales team can align to get the best results.
Remember, bigger goals are more challenging to achieve. You can break them down into smaller ones to ease the process.
Offer a Free Trial of Your SaaS Product
This helps to attract new customers and make them aware of the benefits of your SaaS product. It will also help you with lead generation.
Make sure to optimize the conversion rate during this trial period. And, present the best customer service to your prospects so that they will be with you for long.
Frequently Asked Questions
What are the most effective SaaS marketing techniques?
The following are the most effective strategies to overcome various SaaS marketing challenges in 2021:
● Develop a content marketing plan
● Offer free SaaS trials
● Focus on SEO
● Refine your Call-to-Action
● Refine your PPC campaigns
● Strengthen SaaS review websites
Why is Saas marketing important for businesses?
Even though SaaS marketing is challenging and requires exceptional marketing strategy, it comes with its own advantages. They include:
● Customer marketing & customer communication
● Long term customers
● Brand awareness
● Short sales cycles
● Lead generation
What are the biggest challenges in SaaS marketing?
The following are some of the biggest and the most common SaaS marketing challenges:
● Earning loyal customers
● Standing out from the crowd
● Dealing with conventional complainers
● Getting noticed
● Generating value
"name": "What are the most effective SaaS marketing techniques?",
"text": "The following are the most effective strategies to overcome various SaaS marketing challenges in 2021:
Develop a content marketing plan
Offer free SaaS trials
Focus on SEO
Refine your Call-to-Action
Refine your PPC campaigns
Strengthen SaaS review websites"
"name": "Why is Saas marketing important for businesses?",
"text": "Even though SaaS marketing is challenging and requires exceptional marketing strategy, it comes with its own advantages. They include:
Customer marketing & customer communication
Long term customers
Short sales cycles
"name": "What are the biggest challenges in SaaS marketing?",
"text": "The following are some of the biggest and the most common SaaS marketing challenges:
Earning loyal customers
Standing out from the crowd
Dealing with conventional complainers
Article | August 12, 2020
If you think the conventional way of designing and testing an Internet of Things (IoT) device is still relevant today, you might be wrong. Tens of billions of IoT devices surround us today. Billions more will connect to the internet in the next few years. On top of that, IoT deployment is diversifying from consumer-based to mission-critical applications in the areas of public safety, emergency response, industrial automation, autonomous vehicles, and healthcare IoT. While IoT devices offer great convenience, having large numbers of them in a small space increases complexity in device design, test, performance, and security.
Article | August 23, 2021
What’s the core of those drone-supported Amazon deliveries, online food orders, the ability to watch your favorite shows on Netflix, and virtually augmented monitoring of your upcoming trip to Disneyland?
They constitute a significant part of almost every evolution we see around us. But how are the developers managing to yield so much from computer programming? How are they able to enrich so many lives through their creations all over the world?
The answer is simple — Artificial intelligence (AI). Undoubtedly, AI is one of the leading technologies now, and it has the power to transform every bit of any business’ functionality.
The software industry is not behind in making the most of AI and delivering intelligent and intelligent software. On the contrary, modern enterprises are convinced to adopt an entirely new software development paradigm to stand out from the competition.
Traditionally, machine learning was predominant in the Software Development Lifecycle (SLDC). Even though it could encode numerous tasks in a computer program, it took relatively more time to be finalized. It required developers to put the exact requirements together first and hand them over to engineers. And then, engineers programmed the code accordingly.
However, AI came with its advantages. As a result, it is reshaping the modern world of automated testing, Agile test software, and ultimately the entire software development. So if you see bots accompanying computer programs to make software development even easier, faster, and smarter in the future, it will be because of AI.
So if you are already thinking of potential changes AI will bring to your software development process and how you can reap all the benefits of AI software development, stay tuned!
Area of AI Software Development
Artificial intelligence has a significant impact on various aspects of software development, for example, software testing, coding, designs, etc.
Let’s now discuss what role AI will play in the current and future of software technologies by reshaping the major software development areas.
Software Design Process will Improve
Designing software is one of the most complicated and error-prone stages of software development. Therefore, specialized skills and the right experience are crucial for designing and planning software development projects to come up with an absolute solution.
Moreover, the software designs are mostly subjected to dynamic changes as clients may suggest changes in different stages of software development. AI-powered systems such as AIDA (Artificial Intelligence Design Assistant) can eliminate such complexities in the design process.
Time & Money Saving Software Testing
Traditionally, software testing takes a lot of time, especially when there are changes in the source code. Plus, it's costly, too! But in the end, it’s one of the essential software development stages as it ensures product quality. Therefore, there’s no room for error.
Thankfully, there’s AI and a variety of software testing tools. Testers can utilize them to develop test cases and carry out regression testing. This kind of automated testing is relatively faster, smarter, and astonishingly time and money-saving. On top of all, it's error-free!
Easy Data Gathering and Analysis
Data gathering and data analysis are the most fundamental stage of any software development lifecycle and need a significant amount of human intervention. The project team has to come up with all the information necessary for the software development, and clients' input can be dynamic.
Automated data gathering through various AI tools such as Google ML Kit can be the best option to ease the process. It can take care of specific data-gathering processes without the need for significant human intervention.
Say Bye to Manual Code Generation
Generating huge codes requires a lot of labor, time, and money. Therefore, simplifying the code generation process is significant because code writing is crucial for any software development life cycle.
While traditional code generation can fall short in identifying the target goals effectively, automated code generation can be a game-changer. This is because AI tools typically generate snippets of reusable codes and write code lines as instructed. As a result, they save a substantial amount of money, labor, and time.
Benefits of Artificial Intelligence in Software Development
Incorporating artificial intelligence in software development can do wonders. Considering the incredible impact of AI on software development and the possibility of incredible transformations in the future software technologies due to AI, here are some promising benefits of AI software development.
Enhanced accuracy in estimates
Conceptual decision making
Error-free end product
Easy bugs and error detection
Improved data security
The software development landscape is rapidly changing, and AI has a lot to do with it. Being an enterprise, you need to understand the benefits of AI and how it is enriching human lives worldwide.
It's hard to deny the tremendous pressure on the current software development industry from the demand for applications. However, it’s one of the fastest-growing industries, and AI can simplify it with secure, unique, and scalable solutions. Unquestionably, AI software development is the future, and adopting it is the best decision enterprises can make.
Frequently Asked Questions
What are the things to consider when adopting AI for software development?
It would help if you consider the following factors to reach new heights with AI software development:
Cloud is necessary for AI
AI solutions are much more than implementing machine learning algorithm
AI is near real-time or real-time
Big data is required for AI
Machine learning-powered AI solitons may need frequent retraining
What are the real-world examples of integrating AI into software development?
Here are some examples of AI tools that several organizations are using for efficient AI software development:
Stack Overflow AutoComplete
Google Bugspot Tool w3C
What are the top machine learning and AI tools software developers should consider?
Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development:
Google Cloud’s AutoML Engine
Azure Machine Learning Studio
"name": "What are the things to consider when adopting AI for software development?",
"text": "It would help if you consider the following factors to reach new heights with AI software development:
Cloud is necessary for AI
AI solutions are much more than implementing machine learning algorithm
AI is near real-time or real-time
Big data is required for AI
Machine learning-powered AI solitons may need frequent retraining"
"name": "What are the real-world examples of integrating AI into software development?",
"text": "Here are some examples of AI tools that several organizations are using for efficient AI software development:
Stack Overflow AutoComplete
Google Bugspot Tool w3C"
"name": "What are the top machine learning and AI tools software developers should consider?",
"text": "Generally, Machine learning software, Deep Learning software, AI platforms, and Chatbots are the four major types of software. Apart from the tools mentioned above, developers should consider the following AI tools for the enhancement of software development:
Google Cloud’s AutoML Engine
Azure Machine Learning Studio"