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 | November 17, 2020
When contemplating Robotics or AI and Machine Learning, it is not true innovators but imaginative writers who could always trace their roots.
For several ages, intellectuals and writers have captivated a world where intelligent robots could play a central role in enhancing each sector of human life.
They have caused others to think about the possibilities and the splendour in such an effort. The people behind it have always tried to make machines more intelligent since the first computer was born.
Over the years, we have seen fantastic growth in the usage of enterprise app development services among several small, medium and large businesses.
An enterprise application covers them all, from boosting customer satisfaction to improving the decision-making process, boosting productivity, etc.
Article | November 17, 2020
The phrase “survival of the fittest” originated from Darwin’sevolutionary theory, describing the mechanism by which natural selection occurs. In business, the same principles apply; fast moving markets, new trends, new methods of customer engagement, new emerging technologies, all favour organisations that are agile and adaptable. Never has the role of the data analyst been more important, whether it be to profile new opportunities, optimise processes, derive propensity models for upsell, or the myriad of other business problems they solve in driving efficiency and creating competitive advantage.
Article | November 17, 2020
Storytelling is an art. It brings out the best of the teller and the listener. For centuries storytelling has been proved to be a successful way to reach out to the masses. However, a myth about storytelling is that it is considered only as a subject of literature. Well, we are here to break this myth.
The art of telling a story belongs to every field. Be it literature or science, electronics or computers, every field has its own story, even astronomy. Likewise, every little particle has a story to tell.
There is a story in every line of code written by software developers. The user tells a story, and the software developer writes it in a computer code language. Read further to understand more about storytelling in software development.
The Art of Storytelling in Software Development
When a user comes across a problem that needs to be simplified digitally, he explains the problem to the developer or salesperson.
For example, a shopkeeper needs to digitalize his inventory; he will talk about the problems he is facing while manually managing the inventory. This manual management could be handwritten or a basic excel sheet.
Now, when he tells his issue, he says that “I need software that easily tells me about the things I have in my inventory, goods that are to be stocked and also the goods that have and do not have demand.”
When the shopkeeper elaborates his problem and tells them in a story-like manner, the concerned person understands it better. Now imagine if they would say that, “I need software to know about the goods in the inventory.” Unfortunately, this simple sentence does not convey what exactly they are looking for. As a result, the salesperson would not be able to give them appropriate solutions.
Once the story behind the work is understood, the entire team puts in the effort and comes up with perfect solutions. From coding to the visuals of the software, everything works in synchronization. Thus, the story behind the work is of great importance.
Storytelling in software development has a structure. It has a beginning, middle part, climax, end, visuals, and imagination. The only difference in the storytelling tool is that it is known as understanding the need of the user (beginning), designing the solution (middle part), CTAs and ways to reach the target audience (climax), visually appealing and adequately working software delivery (end).
The success of storytelling in software development depends on how well the developer connects with the user’s problem. Once the entire team is on the same page, emotionally and practically, they deliver the most reliable solutions.
The Process of Storytelling in Software Development
Storyteller software is a tool that listens to the thoughts and ideas of the customer, understands their audience, and then transforms them into concrete solutions. Storytelling software uses the following steps.
● Understanding the user story
● Implementation of the story
Understanding the Story
As described earlier, understanding the story is learning the background of why the solution is being developed. Once the digital storytelling software is understood, the implementation of the solution becomes more apparent.
When the user tells his problem, there is always a story in it. When this story is told to the team, they give a set of solutions to the user. It is like giving them the menu to select the dish they like to be served.
Once they select the dish, it becomes an easy job to make it. But you need to ask the user why he is selecting the particular solution. Because the ‘why’ will answer almost every question that is needed to design the appropriate solution.
Implementation of the Story
The implementation of the story is the ‘show, don’t tell’ part. You need to display the solution on the screen for the user to understand. The user does not know the coding language, but he will understand that if I click this, this happens. And that understanding for the user is the implementation of the story.
We can also say that the implementation of the story is the architecture of the software. A well-built architecture will convey the story to the users. For example, when HRIS software is developed, the HR departments will know that the story behind the development was to ease out their tasks. These tasks could be anything from attendance to salary management.
Always remember, only a well-understood story can be implemented most dependably.
The conclusion of the story is the delivery and installation of the visual storytelling software. The user should be able to find all the answers to his problems in the best possible way. Their story that started with ‘once upon a time’ should end happily ever after’. And this is possible only when the entire team is connected emotionally and practically while working towards the solution.
The conclusion should also convey the climax of the story. The climax implies the CTAs or the final work that the software does after getting the desired data.
Only after you have reached a conclusion and the user is satisfied you will know that you have written an excellent story. This good story is the incredible software you built by gathering the plot, characters, problems, and other raw material from the user!
Benefits of Storytelling in Software Development
We know the process of storytelling in software development, but what do we gain from it? You must be thinking, can’t we listen to the demands, design a solution, and give the user what he wants? So, what is the need to understand the story behind the software?
Well, software delivered without understanding the story is merely a puzzle created. In the future, the software would not be able to answer the most important question -WHY? And that could prove that the software is a failure.
Here are some benefits of storytelling in software development.
● Straightforward Approach
Instead of beating around the bush or designing complicated solutions you will deliver the solutions in simpler steps. And this is because you have well understood the story behind creating the software.
● Understand the Big Picture
The user's story will let you understand what outcomes or expectations the user has from the software. The vision of the user and their expectations from the product can be understood only when the whole team has clarity on what and why they are working.
● Emotional Attachment
When software is developed practically and empathetically, the user also connects well with the software. People have to be given an emotion to get clicks on CTAs or use the software.
A survey was taken wherein people had to donate to a cause. As a result, two types of causes were created. One group was asked to donate food for an underdeveloped country, and the other group was asked to donate for a hungry child. The second group donated more, and many of them stated why they felt emotionally connected to the child.
Thus, the emotional connection of the team to design software is essential.
● Give Better Outcomes
Stories assure better results and excellent outcomes. They make sure that the team works best and the customers are motivated to take action. The story behind the software helps build the software in precisely the way it is asked for.
Thus, You Need to Tell a Story
Building a great story and connecting it with the user’s pain points is the best way to develop a creative solution. This helps paint a picture in the team's minds as to what is being built and why.
Storytelling in software development is essential as each software should convey an incredible story.
Frequently Asked Questions
What is storytelling in software development?
Storytelling in software development understands the story as a tool to design the software more accurately. The thoughts and requirements of the user are used as raw materials and utilized to create an efficient solution.
The story also helps in understanding the audience for whom the software is being developed.
What is the importance of storytelling in software?
Stories create better solutions and excellent outcomes. They motivate the audience to react in a certain way. They connect with the user and the audience on an emotional level. They are impactful and even let developers learn a lot.
They help create better software by keeping the team connected through the story.
What are the software storytelling tools?
Software storytelling tools are the elements that help implement the solution most productively. These are the user's point of view, emotionally connect, purpose, and the answers to the questions- what and why.
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The story also helps in understanding the audience for whom the software is being developed."
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