Article | August 13, 2020
With Google Analytics, you can determine where the clicks to a certain website or webpage come from. However, this analysis isn’t the most precise method. For example, you can only find out whether traffic came from a specific source such as Twitter, but not whether the tweets from your own company were responsible for this linkage. In other words, you won’t be able to tell exactly which version of your call-to-action generated more clicks if both versions linked to the same URL. But there’s a solution: using UTM parameters.
Article | March 10, 2020
The saddest aspect of life right now is that science gathers knowledge faster than society gathers wisdom." Isaac Asimov This is a reprint from article in ReadWrite It might appear that data — the information you find in a scientific article, a history book, or a set of survey results — is just a collection of objective facts. The numbers, if sourced well, are supposed to be the hard truth untarnished by opinions, perspectives, or biases. In reality, this is rarely the case. Here is how AI can be just as biased as the humans creating it.
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 | April 28, 2020
The tech industry is almost always playing the balancing act by continuing to drive innovations and at the same time grappling with the side effect of those innovations in the global economy. Though every industry faces this challenge as it becomes more mature, the challenge is unique for the tech industry with the scale that tech is able to achieve and the evolutionary aspect of mixing digital and physical worlds.
Technology is evolving at such a rapid pace that it may go out of trend even before it is mentioned as a trending technology. But with the tremendous potential they bring with themselves, both for business and technology, it is time for the technology industry to make good use of it. While there are major questions around safety, privacy, sustainability, and trust, these questions can be answered by combining technical expertise with social awareness.
We’ll discuss how the impact of latest trends in 2020 will support and progress the infotech industry.
Table of Contents:
- Tech-Washing Fades in Favor of Real Strategy
- Growing Demand for Workforce Diversity
- Redefining IT Infrastructure with the Internet of Things
- Artificial Intelligence
- Demand for Automation
- Deep Fakes, 5G, and the Data Management Challenge
- Changing Reality of Emerging Technologies
1. Tech-Washing Fades in Favor of Real Strategy
With the vast influx of user friendly technologies it can now be said that every company is a tech company. Though the fact that technology is everywhere doesn’t necessarily change the underlying business model. A company cannot simply create new growth avenues by slapping a tech label on their product and expect to reap profits, which can be true for larger companies going public and struggling with the reality of the market.Then there are smaller businesses that are falling prey to marketing hyperbole. New trends like artificial intelligence and blockchain require significant investments and change to workflow. The smaller companies slowly realize the difference between buying new technologies and truly integrating them with their work culture.
Businesses will show more intent to integrate a technology into their work culture for strategic returns rather than buying a technology to use it as a crutch.
2. Growing Demand for Workforce Diversity
The technological workforce has been under the scanner for lack of diversity due to the unconscious bias along with other behaviour that is far more conscious, such as barrier to access for low-income students and even reports of outright abuse. In 2020, the call for improved diversity will continue to pay dividends, even if fully diverse and inclusive environments still lie further in the future. Going beyond the common conception of diversity, companies will also seek to bring in skill diversity. Companies are now increasingly seeking diversified expertise across all areas of IT framework – infrastructure, software development, cybersecurity and data.
In addition, companies now look for individuals that have some degree of work experience which indicates unwillingness to hire freshmen. Business are also looking for professionals that can speak the language of business and collaborate with other departments in order to drive technology-fuelled business results.
3. Redefining IT Infrastructure with the Internet of Things
The Internet of Things has emerged as one of the technological trends, along with cloud computing and mobile devices that will now be a permanent part of modern technology landscape. Digitization of environment and operations has gained pace due to the data value that comes with it. IoT is also bringing positive results for companies with both major and minor level IoT-related sales in the last year. Today, IoT as a managed services play is driving the most revenue in this category, but looking ahead to the next two years companies are predicting that analytics on data captured by IoT sensors – then shared with customers – holds the most financial promise.
I&O must get involved in the early planning discussions of the IoT puzzle to understand the proposed service and support model at scale. This will avoid the cascade effect of unforeseen service gaps, which could cause serious headaches in future.
- Ross Winser, Senior Research Director, Gartner
The next wave of IoT will require expert understanding of digital BizOps. Business will have to treat IoT projects more like an expansion of infrastructure.This will dictate networking structures, storage options, data policies, and security decisions. The stage for IoT, as for cloud computing and mobile devices, is set and ready to bring in advance IoT digital transformation.
4. Artificial Intelligence
Cloud computing lowered the barrierdeveloping software and distribution, while mobile devices extended the reach of software, thus increasing the software’s ability to drive activity. This created a new challenge in conducting said activity and acting on the data being collected. Artificial intelligence with software-driven routines and compute resources that can run advanced algorithms, takes software to another height. It is clear by now that AI needs a different kind of oversight compared to other software given the challenge of programming bias and unreasonable outputs. Like any other software, AI requires solid inputs and these inputs are often massive datasets rather than highly specific data points. But, there is still a need to maintain the data quality. AI opens up new opportunities for businesses as well as job roles as it continues to disrupt the infotech landscape.
5. Demand for Automation
SMBs require automation mainly in the areas of integration of platforms, application and data, while the large corporation who also focus on integration, have more internal resourcesto lean on. But, whether the integration is in-house or outsourced, the next step is automation. Today’s automations open doors to new opportunities like cloud systems offers tools from the provider; IoT gathers inputs from varied sources, and AI suggests insight-driven actions. With a number of technologies disrupting the tech landscape, companies can build complex automation. But as automation goes more complex, AI will play more a monitoring role.
Cybersecurity is not an emerging trends but it has been around for a while. Though the technologies revolving cybersecurity are upgrading as new threats keep appearing. Hackers keep finding news way to exploit the toughest of measures.
The attitude towards cybersecurity has shifted drastically from defensive to a more aggressive approach. Another drastic change has come in enterprises now treating cybersecurity more as an important component of business and not another function of IT. At large enterprises, this usually takes the form of a CISO managing a team of resources, and the division is more clear.
The first step, which many enterprise overlook, is to define the risk tolerance. The next steps is to fill the skill gap that exists due to a varied areas that come under the security umbrella. Finally, there must be metrics to measure the return on a more significant investment.
READ MORE: HOW TO MITIGATE ROBOTIC PROCESS AUTOMATION IMPLEMENTATION WITH LOW-CODE DEVELOPMENT
7. Deep Fakes, 5G, and the Data Management Challenge
Deep Fakes have the potential to wreak havoc on the society, personal lives, politics, careers, and beyond.Forging video and voice software appear to convincing people of doing things that they normal won’t do. Such software in the hands of bad elements would mean inviting trickeries and handing over important personal information to strangers. As long as deep fake applications exist – and they will continue to exist and proliferate – the need for sophisticated data management will skyrocket in the coming years. And data volume, already completely exponential, is only going to mushroom with the more expansive rollout of 5G networks next year and beyond. The entry of 5G will increase the absorption of data exponentially.
In addition to bringing us all faster broadband speeds and more reliable mobile networks, the proliferation of 5G will also accelerate advancements in smart city, smart vehicle, smart manufacturing, and scores of IoT-intensive technologies hungry for 5G. Just about every industry that touches our daily lives will be transformed – for the better – by the technology evolution that will define 2020.
- Daniel Newman, CEO,Broadsuite Media Group
The two trends will bring new data management challenges. There will be increasing need to identify data with its true source as well assecure the mountains of information speeding along these networks.
8. Changing Reality of Emerging Technologies
The excitement around emerging technologies is highand even though the impact on an operational levelhas been positive for business for building better practices for evaluating early-stage topics and accelerating adoption, its been chaotic at a tactical level. Companies have had less time to observe and evaluate which technology is profitable, while the constraints of resources and skill gap exacerbates.
The trend has been that companies in the business of technology are starting to pull back on adopting new technology as part of their portfolio. This slight tap on the brakes suggests that classic situation where companies move too quickly into a new technology discipline or business model only to have a reality check in year two or three. Though companies are excited about new technologies including AI, IoT, 5G, drones, blockchain, and quantum computing, they reserve their excitement since new trends can still take off overnight.
READ MORE: HOW TO LEVERAGE IOT IN MANUFACTURING TO USHER IN INDUSTRY 4.0