As a one-stop shop for key business services and strategies, Futran Solutions knows exactly how to navigate challenges in countless domains to best serve our clients and their goals. Regardless of how fast technology changes or what software trends lie around the corner, we consistently aim for and achieve excellence through our artificial intelligence, software development, and data analytics savvy.
Our team focuses on maximizing business growth, cost effectiveness, and operational efficiency for all of our customers, and as the next step in our own development, we are excited to announce recognition of Futran Solutions by Clutch, The Manifest, and Visual Objects.
Clutch, a B2B research and reviews website that analyzes companies based on their market presence, strength of portfolio, and client feedback, recently named Futran Solutions among other leading data analytics companies in the industry.
Dev Gupta, a client of ours and editor of CS:GO related website, particularly praised our skills and support in a review on our profile. “Futran Solutions performed exceptionally in regards to project management,” Gupta touted.
“Futran Solutions and their team are skilled in a plethora of services. Instead of having to go to a different agency for various parts of the project, they had everything in one place – from initial requirements, deployment, release, and digital marketing,” he said. “With Futran Solutions, we received an end-to-end solution. We made a great decision choosing Futran Solutions.”
Sister companies to Clutch, The Manifest and Visual Objects also highlighted our range of services.
The Manifest, a news and how-to website that shares business knowledge and rankings, has ranked us among other top AI companies, attesting to our quality service. Likewise, the portfolio-focused Visual Objects website now showcases examples of our work, supporting Futran Solutions in backing us and other quality creative design and development agencies on the platform.
We very much appreciate these features of our services, strategies, and skills, and our team at Futran welcomes you to contact us with any questions or if you are interested in exploring future partnerships with us. We look forward to hearing from you soon!
https://futransolutions.com/wp-content/uploads/2021/03/The-Future-with-Futran-Guaranteed-Growth-and-Excellence.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-03-24 10:13:482021-12-08 18:10:46The Future with Futran: Guaranteed Growth and Excellence
Artificially intelligent or AI machines hit our imagination way before reality. Consider the Golem of Prague: a magically animated, mud-bodied robot with little to no imagination but insurmountable damage prowess. His character was written in the 1600’s – roughly 200 years before Frankenstein’s infamous monster. Of course, modern pages of robot-lore are filled with a laundry list of malevolent AI-powered machines ready to break out of 3D screens at movie theaters. Our imagination has even shaped AIs like Ultron and Skynet ready to threaten human extinction.
However, after all that hype and fear-mongering, what we really bring home is a mild-mannered AI-powered device playing songs and booking cabs with voice commands. You could term that split the difference between bestselling pop-culture content and ruthless consumerism.
We use the intelligence of AI machines like Google Assistant and Alexa to our heart’s content. After all, there’s the fear of missing out if not genuine use for such technology. But then, there’s the concern over privacy. It’s a just shadowy feeling of invasion though – like when you acknowledge that you might have left the front door open but are too lazy to get off the bed, so you trade off your sleep with what could be a potential disaster.
The industry is another dimension of AI in itself. Businesses continue to leverage AI and improve upon just about everything from chat support to workflow to brand intelligence. Aberdeen research indicates that one in every two organizations is leveraging artificial intelligence in some form or the other.
The Complexity of AI in Business
As we speak, AI is playing songs at our homes, gobbling up a hundred Excel sheets in seconds at the workplace and scaring us to the hilt in the movies. Effectively, AI participates in our lives all through the week – and on weekends, too. That’s smart to the point it’s scary.
Nevertheless, if you run an enterprise, you cannot possibly do without AI. But as is the case with every new technology, adopting AI into business can be trickier than it seems. You cannot just turn up at your technology provider’s and ask for an all-business-pervasive AI package. Businesses are feeling sufficiently roughed up even when it comes to implementing AI solutions to select verticals of operation.
Perhaps the most iconic hurdle to implement AI in business is the level of expectations of users. The best models of AI technology (Alexa and Google Assistant) are both affordable and accessible. This means regular folks all around the world now have hands-on experience of Artificial Intelligence making their lives easier and more entertaining. To wit, if your product is based on or makes use of any form or manifestation of AI, the experience has to be every bit as neat as Amazon, Google, or Facebook.
Concerns Over Privacy & Additional Regulations
Notwithstanding the gamut of applications and overwhelming contemporary relevance of AI, the technology is not free from concerns. So far, the outstanding concern over AI has been over privacy. Lately, there has been news of Alexa storing recorded conversations and Facebook displaying ads based on regular conversations between individuals. Such vaulting ambitions on the part of tech companies have aggravated the ‘AI-trumps-privacy’ stance of many consumers.
Plus, you have to consider something called the Turing Test. Wikipedia defines the Turing Test as
…the test of a machine’s ability to exhibit intelligent behavior equivalent to or indistinguishable from, that of a human.
The test itself was developed by a gentleman named Alan Turing in 1950. 68 years later, in 2018, we got to see something extraordinary at the Google IO – Google Duplex. Check out this clip:
Case in Point: Google Duplex
What we see in the clip above is either amazing or bone-chilling, depending on the way you look at it. While most tech enthusiasts rallied solidly behind Google and their technology (and how dumb they made Siri look), there were some that spotted an element of cheating in not immediately letting the respondent know that they were speaking to an AI assistant and not an actual human being.
To be honest, we already have AI chat assistants pretending to be humans and we can’t tell which is what. It’s just that the level of colloquial and sentential nuance with the ‘mm-hmm’s and ‘uh’s that the Duplex demonstrates at once injects a bunch of frightening realizations. If I recall this correctly, the only relevant examples of such trained AIs are Jarvis and Friday from the Marvel movies. That’s perhaps only one of the realizations that have been sending a chill down spines. And this value of shock will inadvertently play out every time we realize and materialize technologies from Sci-Fi movies.
The big question is about the necessity of something like Google Duplex. We understand the bit about the top brass of Silicon Valley decision-makers, who, in all probability, have shaped the theory of the Duplex. But is the average Google customer really that busy? Something tells me that technologies like Duplex are less about the ‘busi-ness’ of people and tell more of a story about their stark laziness.
Monetizing AI Machines: Cute or Cunning?
Cashing out on public laziness is not really close to being a danger – that’s just good enterprise. The real game where the plot gets murkier (and dangerous) is this: (how)does Google plan on monetizing a technology like Duplex?
When you come to think of it, the same technology that books an appointment for a customer at a hair salon can also respond to the customer’s call on behalf of the salon. Add one value to the other and the receptionist could summarily be forced into hunting for an alternative career – in a market with fewer jobs for humans anyway.
Be that as it may, there’s no doubting that this is a remarkable point (somewhere close to the crest?) in the historical revolution called Artificial Intelligence. Businesses will adopt AI because of its manifold benefits, which, at least on face value, outweigh the concerns. Artificial Intelligence will help companies reduce costs, make decisions with greater certitude, cut down on ‘human errors’ and hustle through process speeds to deliver a real winning strategy.
Trend Picking Intelligence
AI Machines and systems play a big collective role in predicting customer trends. As these trends are fragmented into categories and the categories subsequently analyzed, innovations will rise out of nowhere.
We will witness rather sudden products and platforms, many from unsuspecting market players. Moreover, there’s always the blessing of artificially intelligent customer service interfaces and bots that eliminate the inefficiency and time lag of the ‘the-team’s-getting-used-to-it’ phase. Aberdeen research indicates that businesses that use AI machines save significant IT costs and serve customers faster.
The irony with Artificial Intelligence in business is this: the line was drawn way before we discovered any scope for debate on the technology. Whether we like it or not, this is what the future looks like. Businesses that avoid Artificial Intelligence today will suffer the same fate as businesses that avoided computers back in the day. Put plainly, only fools would miss out.
AI, Machines & the Future of Business
No one’s asking anyone to overlook a definitive gamechanger like artificial intelligence. But organizations must also weigh in, what I like to call from here on, the Cost-to-Human (or CTH) factor of it. By all means, go for it if the consequences are a) irrevocable but small or b) substantial but revocable. However, if the underlying cost of leveraging AI machines in business is both grave and irrevocable, step back and find an alternative (not necessary an alternative technology though). Now that we live in Industry 4.0, it must be the collective responsibility of us all to ensure human well-being before anything else. Jyoti Vazirani, CEO of Futran Solutions has previously written elaborately about the conflict between artificial and natural intelligence.
With that, let’s go back to the question I started this piece with. In all likelihood, the golden unicorn called AI is turning into an all-pervading, oversized behemoth. But not all giants torture and maim and kill. That’s what our collective psychology would surmise since the tale David and Goliath was written. However, recent literary evidence shows us that the opposite could be just as true (think Hodor and Wun Wun from GoT).
My point is AI could go either the Goliath way or the Hodor way. What’s certain is that it will go one day (just like Goliath and Hodor). The thing that really matters is how we treat it while it stays. At the end of it all, history will remember us for the way we treated humans when we had a set of options to choose from.
At Futran Solutions, we offer solutions in RPA and Artificial Intelligence. While respecting automation, Futran Solutions also participates in re-training of the employees who may lose their jobs to AI machines and systems.
Futran Solutions supports robotic process automation and artificial intelligence. We are a pro-technology company. And we believe that if a technology deserves to go viral, we must do our part in making it viral. We provide a range of RPA and AI solutions to industries across the board. Adjacently, we run a series of training programs to aid the displaced workforce.
Drop us a line to know how we can help you with RPA consulting and project implementation.
Krishna Vemuri is the co-founder of Futran Solutions and the CEO of the up and coming tech startup Onata. He writes on technology industry dynamics and the rather eclectic tantrums of his husky, Loki.
https://futransolutions.com/wp-content/uploads/2021/02/The-New-AI-Machines-on-the-Block-Is-the-Unicorn-Turning-Into-a-Behemoth-1.jpg256512Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-26 12:29:032021-12-08 18:11:36The New AI Machines on the Block: Is the Unicorn Turning Into a Behemoth?
The data on security breaches is overwhelming on many fronts. Over a billion records of consumers have been compromised since 2005. The total number of breaches in the period is threatening at around 8000. As late as 2017, big companies like Target, Equifax, and Neiman Marcus could not shield themselves from data breach attempts. Mind you, one of these is a top national credit reporting agency.
The data can get tossed around in an endless sell-and-resell loop of underground data piracy
Sensitive data can be used to steal bank accounts from customers
Identity thieves can use the data to update their existing records of targeted individuals
Adversarial nation states can use the data to disrupt peace or launder money out of the US
None of these constitutes stray casualty. The cumulative implications of the breaches are beyond grave. In fact, it is very difficult to quantify the damage dealt by these breaches to the society at large. That is where the General Data Protection Regulation (GDPR) swings into action. It gives consumers greater control over their own data while making corporates bite the bullet on their data processing practices.
What is the GDPR?
The GDPR is a regulation adopted by the European Union. It lays out the norms for data protection and privacy for the individuals that live in the European Union. It is one among the series of regulations that have helped formalize governance around security concerns of the average consumer.
In addition to strengthening consumer rights, GDPR aims at formalizing security standards that companies must establish to protect the data of their consumers.
Every organization functioning out of Europe and non-European organizations that collect the data of European citizens are expected to comply with the GDPR. The latest GDPR guidelines regulate how personal data is used, processed, stored, and deleted.
The GDPR also lays out that data subjects can request for both access and real-time usage information from organizations. If there’s any breach involving the personal data of users, it must be reported to the appropriate authority that oversees the regulation.
Security Governance: The Onus is on the Enterprise
At the crux of the GDPR is the impetus the regulation puts on enterprises to do all things necessary to protect consumer information. This has forced every enterprise software vendor to re-evaluate their policies regarding storage and management of sensitive user data.
This is where Robotic Process Automation (RPA) is impacting the industry in a big way. RPA platforms like Automation Anywhere are instrumental in offering comprehensive features in security and reliability. Starting with automation at once promises the following benefits for organizations:
Data encryption at all levels – when the data is in memory, in motion, or at rest.
A robust security framework (either built-in or third party) that guarantees security in the management and storage of user information. As a default practice, machine that store user credentials meant for critical purposes and the machines that run the software should always be exclusive.
Analysis of codes on both static and dynamic parameters, including manual pen testing for unbreakable application security.
Seamless enterprise based authentication system integration
Expansive logs of audits to support forensic analyses and audit processes
Secure operations that that make sure data is not exposed to business process threats during standard execution of processes
RPA platforms work with many ERP tools and in effect touch extensive sets of data within your organization. In case you are already using an RPA platform, make sure to check with them on GDPR compliance and the security measures they follow to ensure compliance.
How is RPA Easing up GDPR Implementation?
The first and absolutely unavoidable threat with manual processing of customer data is the guarantee of human errors. It does not really matter what level of security you follow. Even the slightest margin of error means that the organization is at the risk of non-compliance.
With RPA, you can automate the process defined by the legal and business teams to become GDPR compliant. Here is a collection of ways in which bots are helping enterprises with GDPR compliance:
Enterprise RPA platforms are loaded with audit logs which monitor every operational process, creating logs for users and events at every stage of a given process. When there’s a data breach, audit logs swing into action with recurring spells of root cause analysis. What follows is routine forensic analysis to recognize and thereafter report the breach.
Content that relates to specific internal or external events can be gathered concurrently in real time. This comes in especially handy in case an organization is attempting to decode a fraudulent activity.
Documentation of data
There’s a lot of data pouring in from devices, sensors, and systems at the office. From the organizations perspective, it must be able to document all the data that is held in its directory, along with the source of its origin. The organization must be able to submit updated reports to the authorities in charge of data protection. GDPR mandates companies to purge personal data once it has crossed the holding period.
This is another area where RPA can help organizations by using bots that automate the process of masking PII data that is identifiable across applications. For the PII data that does not adhere to established policy, Natural Language Processing (NLP) lets bots recognize such data and generate alerts that help in intercepting the issue.
GDPR makes it mandatory that subjects affected by data breaches be informed about it within 72 hours. For data breaches of a magnanimous nature, sending out information to everyone involved within 72 hours can become almost impossible. Imagine the case of Equifax, where 143 million users directly affected by the breach.
On the flip side, it is way easier to automate software bots to perform the job. In most instances, it does not even take 72 hours and makes sure the security governance timeframe is met.
Right to Access Information
European customers can request to access their information and know how an organization stores and uses the information. GDPR guarantees this right to all European consumers. If an organization wants to do this manually, it would need a dedicated team of individuals. Plus, every individual on the team must have access to such information.
It is way easier for bots to navigate through different systems and pull out data relevant to every user in question.
Right to Information Deletion
If a user requests an organization to dispossess their personal information, GDPR mandates the organization to delete such information promptly. Consider there is no automated process to do this. An employee or a team will have to access the information and then delete it from dozens of applications. Bots can not only pull out the relevant information on users but also email the report back to the concerned customers.
Some Data Cannot be Seen
There are legacy systems hiding data more than a decade old. Data can be accessed from these systems when needed. However, it’s never been as important to uncover sketchy data as it is now. RPA is the most convenient way to integrate the current technology platforms with legacy systems. Automation is also perhaps the only way to document and recognize available data that might be the cause of non-compliance.
Most companies are still taking their own sweet time understanding and dissecting the General Data Policy Regulation. At this, there is the threat of flooding of requests by consumers. Adhering to these requests will be compulsory. Doing it manually will mount up heavy costs on the administration. But the fact is responding to such requests might only be subject to a few well-defined requests. That makes it a great process for RPA to flex muscles.
The crux of it is organizations will have a hard time maintaining GDPR compliance in the absence of RPA. RPA solves security governance through GDPR wholly with the promise of zero errors.
https://futransolutions.com/wp-content/uploads/2021/02/RPA-and-GDPR-Security-Governance-in-the-Automation-Era.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-24 15:21:032021-12-08 18:11:39RPA and GDPR: Security Governance in the Automation Era
Self-driving cars. Intelligent bots. Neuro-tech psycho-development. Genetic editing. Geoengineering. Artificial lifeforms. Mobile supercomputing of a surreal standard. Welcome to Industry 4.0.
Industry 4.0 or the Fourth Industrial Revolution is not a purely industry-specific phenomenon. Like the three industrial revolutions of the past (water and steam-powered motors, electricity-powered assembly lines, and computerized systems), the fourth industrial revolution will challenge the very way we work, live, and connect with one another – and with bots. The adoption and amalgamation of distinct values like the internet of systems, internet of things, and the cyber-physical grid will redefine and reform the very fabric of industry as we know it.
First off, we need to understand that Industry 4.0 or the Fourth Industrial Revolution is not a gimmicky marketing stunt pulled off to sell a new technology. It’s a living phenomenon described in very fine detail by Professor Klaus Schwab, founder and executive chairman of the World Economic Forum in his book, The Fourth Industrial Revolution. In his book, Professor Schwab argues:
The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential peril.
While Schwab highlights that the dawn of the fourth industrial revolution promises exponential growth, he also points out a set of risks that constitute the “potential peril” in the above statement. We’ll get to that in a minute.
What constitutes Industry 4.0?
By now, it is understood that both computing and automation have leaped to the next level with Industry 4.0. Core principles of robotics are connected to computer systems, which are in turn equipped with ML (machine learning) algorithms. The resultant loop can learn, control, and execute robotics and allied operations with minimum (almost negligible) human supervision.
Then, there’s the introduction of what is called the “smart factory.” Within the setup of a smart factory, cyber-physical systems have the capability to make decentralized decisions while monitoring physical processes. The wireless web connects the physical systems enabling interaction with one another and with humans.
By this structure, a setup can be considered within the Industry 4.0 structure when it has the following four attributes:
Independent (or decentralized) decision making: removing the “approval” cap off simple, obvious decisions for cyber-physical systems, allowing them to be as autonomous as possible.
Sound technical assistance: assisting humans with technical aid for tasks that are either too labor-intensive or too dangerous for humans along with the general ability to assist humans with decision making and problem-solving.
Transparency of information: contextualizing information and datasets through systems that create virtual copies of the physical world through sensor data.
Heterogeneous network interactions: devices, sensors, machines, and humans connecting and communicating with one another in a complex yet seemingly manageable environment
The concerns with the major shift in Industry 4.0
If your organizational infrastructure has woven through the above elements, you are already riding the Industry 4.0 wave. But like it has happened every time a new technology has set in, a set of concerns follow the benefits.
First off, the natural extension of allowing new systems entry into existing data banks brings the threat of security breaches. The more access you give to these systems, the more data security issues you will have to deal with. Plus, proprietary production knowledge presents an adjacent concern related to IT security.
The degree of stability and system reliance required for successful cyber-physical bonding will be very high. Such states can be difficult to obtain as well as maintain. Additionally, we have the challenge of maintaining the integrity of manufacturing and production processes with dwindling human control.
Perhaps the most crucial challenge in all of this will be the loss of high-paying human jobs. We simply cannot brush under the carpet the corporate accountability for humans in a world of bots. Finally, there’s always the risk of running into expensive production outages arising out of technical inconsistencies.
First-time blues matter less than the expansive potential
The singular most challenging part of implementing new technology across industries is the lack of experience. The systemic shortage of manpower in implementing these new changes would be augmented with the general reluctance of stakeholders in trusting new technologies upfront. Low trust naturally translates into lower investments.
Irrespective of the initial round of suspicion, there’s a lot of benefits offered by the 4.0 model. More importantly, the benefits outlive the concerns by a long shot. Safety of human workers in dangerous work environments can be dramatically improved. We will exercise far greater control over the supply chains when there is processed data at every level of the delivery process.
One thing is certain. The cyber-physical combination kind of guarantees increased productivity and faster deliveries. With that, the revenue, market share, and profits – all shoot up.
Industry 4.0 demonstration city
Mayor John Carnely of Cincinnati, Ohio has declared that Cincinnati, Ohio will be an Industry 4.0 demonstration city. The primary objective of the proclamation is to create a hub for investment and manufacturing within the Industry 4.0 environment. The resolution also thanked the contributions of Prof. Jay Lee and the Center for Intelligence Maintenance Systems for their positive contributions to the global revolutions surrounding Industry 4.0. The Cincinnati move is not just strategic but also historical given the potential leverage it provides to the manufacturing hub in the city.
Besides the Cincinnati move, reports suggest that emerging economies like India will reap great benefits of Industry 4.0 practices, largely because of readily available resources and the willingness to participate in new technologies.
Are we ready for the transformation?
To be honest, most industries will follow conventional practices unless there’s a standout example of blazing success in the Industry 4.0 model. But I am afraid much time will be lost with respect to realizing the largest gains promised by the revolution. Companies that show the courage to dive in fast will well and truly emerge as the frontrunners in maximizing profits.
There are additional issues that play beyond the willingness to adapt to change. Legacy systems cannot be overhauled in a night. The efficacy of tenured systems with proprietary applications should also continue to be just as important as they are today.
The analysis of big data and dark data will be one of the first challenges that the 4.0 architecture will need to address. We presently have immense volumes of data generated by digital systems, existing sensors, and other existing equipment. Much of these data banks that are presently unaccounted for will influence decision making in the industry.
Robotic Process Automation and Industry 4.0
That we are generating more volumes of global data than ever is one side of the story. ERP systems, bots, sensors, and even cookies are collecting pools of data that is currently unused. Most manufacturers are concerned about making this connection between machines from the production unit and back office systems. Finding a way to channel the unstructured data requires more than conventional computing. It requires software-based intelligent automation.
The best bet about RPA is that it seamlessly gels in with the existing IT setup inclusive of hardware and legacy applications. Large volumes of enterprise data can be indexed and structured using RPA. When clubbed with AI, RPA presents learning capabilities with advanced data models to organize and classify all the information, thereby making it valuable. On the whole, the platform visualizes these insights and eventually helps in predicting the future.
Notwithstanding the magnanimity of the promise presented by the Industry 4.0 model, the realization of success can only come with the right RPA solutions and resources. Futran Solutions specializes in delivering composite RPA solutions and resources. To expand upon a wholesome list of RPA offerings, Futran recently partnered with Automation Anywhere – a leading platform for implementation of automation solutions across the industry.
Jyoti Vazirani is the co-founder and CEO of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.
https://futransolutions.com/wp-content/uploads/2021/02/The-New-AI-Machines-on-the-Block-Is-the-Unicorn-Turning-Into-a-Behemoth.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-19 10:40:222021-12-08 18:11:51Industry 4.0: The Era of Cyber-Physical Systems and Intelligent Analytics
Only one event commands the same absolute degree of certainty as death – disruption of the status quo. Unless you concede that transformation is the new status quo. In every industry.
We now live in an era where the promise of unprecedented growth is almost unchallenged. It takes no second guessing to formulate that such growth cannot be realized without an increasingly digitized workforce headed by business leaders that are not afraid to weave AI, RPA, and cognitive machine learning technologies into existing processes.
But like Jyoti Vazirani had stated in a previous article, the more we lean toward automation and machine learning and integrate them to our core business processes, the faster we realize that there’s an expansive human workforce calling for imminent, preferably immediate consideration. What happens to the corporate accountability of our human resources? More importantly, what happens to our human resources?
For the start, let’s not bluff ourselves with arguments that make rats and bats crack up. Intelligent, emotionless bots are far more ruthless when it comes to beating known standards of efficiency and productivity. It’s time the high and mighty leaders in business work to create the right synergy between humans and bots.
The audacity of bots
What has for long been an expectation and a speculation is now looking at us in the face as a guarantee of sorts. Bots will outperform humans, they’ll champion cognitive functions, and in the very near future even develop the capacity for thought, albeit with a debatable degree of independence. That explains why even as business leaders are running hay and hill behind robotic process automation, the workforce at large is unwelcoming of the change.
The madness of reaping early-bird profits from automated processes is so extremely insane that almost no business leader is ready to acknowledge the unavoidable employment crisis, let alone starting a meaningful dialog on it.
Crisis #1: Saving the value-based ecosystem
In the last two decades, organizations have successfully embedded a sense of value in their core missions and brand philosophies. Fortunately, the largest part of this change was brought about by the technology companies. If your mind is already reading out names of tech giants, you see my point.
Ironically, the first and perhaps the biggest industry that will be faced with the workforce imbalance ushered by bots is the technology industry – because of obvious reasons. How, when, and if at all the industry stands up to the challenge is shrouded under expansive dubiety.
If the technology industry – the very one that created the bots in the first place – does not act swiftly to save the value-based corporate ecosystem, it will become doubly difficult for other industries to follow suit.
Crisis #2: Maximizing convenience and minimizing pain
That’s just what bots will do. For their corporate masters. Will the effect be the same for lower-rung employees? Hardly. Here are some more questions that must be answered:
Will we create new jobs for displaced employees – jobs that we didn’t know existed?
Are we sure that bots guarantee a better and more sustainable corporate future?
Is there a long-term reskilling program that gives employees the flexibility to try out newer careers?
Does the combined human-bot workforce turn out to be as effective as it looks on paper?
Do business leaders even care?
There’s no definitive answer to any of these questions. But we must remind ourselves that history will remember us not for creating bots, but for what we did to humans after bots were created.
Crisis #3: Weighing down by Peter Principle
The Peter Principle is a benchmark corporate ideal laid out by Canadian educator Dr. Laurence J. Peter. It states that in organizational hierarchies, employees rise up the ranks through promotion until they are promoted to a position for which they are incompetent. In effect, it highlights the logical assumption that save a few exceptions, one individual cannot have mastery over many diverse fields within a corporation.
The principle also states that every position in the hierarchy of an organization will at some point be filled by people who are incompetent to fulfill their job roles in those respective positions. Dr. Peter also stressed upon the fact that such outcomes might not be related to the general incompetence of employees. It’s largely because new positions might require additional skill-sets which cannot always be imparted through training.
The Peter Principle has been as true lately as it was in 1968 when the term was coined. But there were no automated bots in 1968. So the principle applies exclusively to human competence, and to wit, to human employees.
Here’s how the principle unfolds with the bot-human amalgamated workforce:
It will still apply to humans only because bots are not likely to receive promotions
A large part of the human workforce will be saved from the Principle because they will no longer exist as a part of the workforce
Of the humans that will still be a part of the workforce, very few will be engaged in the lower rungs of the hierarchy; the long chain of systematic promotion to the top will break and the Peter Principle will lose much of its premise
Since there will be very few human employees, most of them will comprise of the top management of the organization. We can safely assume they will come with enough training required for their positions
If you’re reading this right, the Peter Principle might cease to even exist if the human-bot work grid is laid out at large. A solid, 50-year old tried and tested corporate theory based solely on logical deduction, human psychology, and organizational observation might die a sudden death without people even noticing. If this doesn’t send the alarm bells ringing in wild abandon, no corporate activist screaming off rooftops ever will.
Back to the drawing board
Let’s resume thought with the truth.
The sun is setting on thousands of employees. Nothing in the corporate universe is as lucrative as the prospect of making big money in small time. Most corporations impart skill-training to employees not really to help them grow their skill set. But to extract greater competency from the same resource. If they can replace skill training with an additional program on an automated bot and save both time and money, make no mistake – they will.
You could argue that the Top 100 companies might actually retrain staff for new jobs. I say yes, they very well may. But the best of the organizations have laid off staff by the hundreds for much smaller reasons. Plus, retraining might not automatically amount to retaining. More like “we teach you fishing and there’s a lake at 6 o clock.”
Like Jyoti had written earlier, the human workforce cannot be saved without government intervention. Might sound grim, but it’s just as necessary. Tech and other industry giants will plant so many trees that no one will ever talk about the human workforce they uprooted. They’ll make so many of those feel-good inspirational videos that no reporter will cover the plight of the jobless that were once employed with them. But are the governments even listening? They won’t unless people who care about jobs speak up. For very soon, jobs will make way for tasks in the most cruelly literal way.
Futran Solutions supports robotic process automation. We are a pro-technology company. And we believe that if a technology deserves to go viral, we must do our part in making it viral. We provide a range of RPA and AI solutions to industries across the board. Adjacently, we run a series of training programs to aid the displaced workforce.
Drop us a line to know how we can help you with RPA consulting and project implementation.
Krishna Vemuri is the co-founder of Futran Solutions and the CEO of the up and coming tech startup Onata. He writes on technology industry dynamics and the rather eclectic tantrums of his husky, Loki.
https://futransolutions.com/wp-content/uploads/2021/02/What-Does-Corporate-Accountability-Stand-for-When-Bots-Do-All-the-Work.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-16 10:35:492021-12-08 18:11:55What Does Corporate Accountability Stand for When Bots Do All the Work?
Machine learning seamlessly is integrating with other industries right before our eyes. Like with so many so many other industries, more data means greater effectiveness in the pharma industry. A McKinsey report estimates that machine learning and big data could generate a combined business value of $100B annually. The value optimization involves optimal innovation, better decision making, greater efficiency for clinical/research trials, and additional tool creation for medical professionals.
What is the source of so much data? Regular streams like research and development, clinics, physicians, patients, and caregivers do their bit. The disparate origin points of data sets form a large part of the problem when we talk of synchronizing all the data and improving the healthcare industry as a whole. The core of the problem is to find ways to effectively collect different types of data sets for better treatment, analysis, and ultimately treatment.
Today, the applications of Machine Learning are sprouting in manifold ways. All these applications give us a glimpse of a future where the analysis and synchronization of data is already a reality. Here’s a collection of some of the most important applications of machine learning in the pharma industry.
1. Behavioral Modification
With machine learning, personalized medication will soon be a reality. Such treatment is based on individual health data along with a decent dose of predictive analysis. In fact, this is one of the most hotly worked on topics on machine learning and behavioral modification.
At the moment, this area is dominated largely by a combination of genetic information and supervised learning. This basically allows physicians to choose from a set of diagnoses. The next decade is crucial for health optimization with the help of machine learning. Micr biosensors will see a further rise in the application and there will be a similar rise in use remote monitoring capabilities.
A gamut of startups is emerging in fields like cancer identification and treatment. While the success of these innovations is still far from desirable, there is a good chance of a significant breakthrough in the coming decade.
2. Research and Clinical Trial
Machine learning has already tasted decent success in shaping direct research and clinical trial. The application of predictive analytics in identifying candidates for clinical trials could see a lot of additional data pouring in compared to the volume of data we see today. Clubbed with genetic information, this will help in quicker and more cost-effective trials in the coming days.
The application of Machine Learning also spans over access to real-time data for heightened safety. One critical area is the monitoring of biological signals for any visible sign of harm or fatality. McKinsey suggests that there is a whole lot of other applications that help in augmenting the efficiency of a clinical trial. This includes the discovery of the best sample sizes for greater efficiency.
3. Drug Manufacturing
ML has tremendous scope in the early stages of drug discovery. The application starts at the initial screening of compounds and moves over to the predicted success rate based on a number of biological factors. We are also looking at discovery technologies in R&D like next-generation sequencing.
Precision medicine, a genre that involves mechanism identification for multifactorial diseases, is the frontrunner in this race. Since a lot of this research is based on unsupervised learning, the game revolves around identifying data patterns without the use of prediction of any kind.
4. Identification and Diagnosis of Disease
Most of the present efforts in Machine Learning research for the pharma industry is geared toward disease identification. In a 2015 report released by the PRMA, the number of cancer medicine and vaccines in trial went over 800. The larger challenge, however, is to make justified use of all the data that comes out as a result of these studies.
It is here that the need for biologists working with information scientists and machine learning experts will become extremely vital. It doesn’t come as a surprise that the bigger players were the first to jump on the bandwagon. IBM Watson Genomics came into existence in 2016. It partnered with Quest Diagnostics to take rapid strides in precision medicine.
5. Epidemic Control
At present, AI and ML technologies are also being used to monitor and foretell epidemics around the world. The predictions are based on satellite data, real-time updates on the social media as well as other sources. Scientists have already made use of artificial neural networks and support vector machines to predict outbreaks of malaria. The analysis took into account factors like average monthly rainfall, temperature, data points, and the number of positive cases.
The prediction of outbreaks (and their severity) becomes even more important in third-world countries where epidemics claim lives in the hundreds. This also alerts governments to implement prevention protocols and if needed quick treatment measures, too.
Futran Solutions works with a talented pool of resources that are ML experts in the pharma industry. Call us today to know how we can help you with project implementation and digital consulting in machine learning.
Jyoti Vazirani is the co-founder of Futran Solutions. She is a certified SAFe Agile coach and an out and out machine learning enthusiast.
https://futransolutions.com/wp-content/uploads/2021/02/Five-Applications-of-Machine-Learning-in-the-Pharma-Industry.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-12 09:30:452021-12-08 18:11:59Five Applications of Machine Learning in the Pharma Industry
(Futran solutions is a company that offers RPA solutions. While respecting automation, Futran Solutions also participates in re-training of the employees who lose jobs owing to RPA)
Is AI thrusting upon us and our future generations a new technological, psychological and socio-economic change? Slowly but surely, are we humans becoming redundant for office, society and the universe at large? More terrifyingly, are machines really going to take control of humans? I believe we have looped this routine too many times on Hollywood to make it true? But we would all be too stupid to ignore the fact that the seeds have been planted already!
As companies prepare to make their businesses AI-powered right from smart virtual assistants to robotic process automation, we are looking at a tech-future with inevitably dwindling demand for human resource and Natural Intelligence.
At this point, we are a bit hard-pressed to figure how our LIMITED(!) natural intelligence will compete with the GOD-LIKE-POWER(?) of artificial intelligence. For once, let’s be honest and not pretend that there is no competition. There is tremendous competition already and it’s swelling up as we speak.
AI & NI: The Battle
When we speak about the competition between Artificial Intelligence (AI) and Natural Intelligence (NI), we are not expressly looking at a Terminator styled battle between human-made intelligent robots from the future and gym-vest wearing men and women of our times. In all reality, it will be the battle of employment between AI-powered robots (that are fast and accurate) and Natural Intelligence (NI) powered Humans of present times (that are slow and error-prone). A single AI-powered robot can potentially leave an entire team of 50, 500, or 5000(!) unemployed. The war has been waged; the winner is known! Only time confirms the facts!
Commercial manufacturing is already automated to a very large extent. Now imagine a situation where the smallest of factories are automated. The army is automated and no country has human armies. Governments are automated. The judiciary is automated where deep learning interprets cases and generates the judgments in seconds. And entertainment, too-imagine that you can virtually produce your own Marvel(like) movie for private viewing and that displaces thousands of people working for months together. Transport, heavy engineering, and several other industries-all automated to a point of no return.
If such a future were to really transpire, Tesla would become a company with one employee. Elon, alone and his giant team of robots with greater caliber than human intelligence fill everything from boardrooms to shop-floors. Similar fates will embrace Amazon, and Google, and Microsoft. In that hypothetical situation, since all the workers are laid off and replaced with robots, humans shall stay back in their homes for the lack of work. They shall no longer be able to afford the luxuries, necessities, wants, and pleasures that they did when they were employed.
They’ll reach a stage where they have no money to BUY!
No jobs will soon mean no difference in affordability and no need to travel from Los Angeles to San Francisco in 30 minutes. No need to travel to Singapore in an hour. On the flip side, the sole shareholders of the big companies will lose business faster than we can imagine because no one will be able to buy their service/product. It’s pretty much the story of Bee Movie that was released in 2007. From that point, humans while battling machines, start living like our ancestors; growing their own food in their backyards, flocking to one another like never before to form the small habitats of self-reliant communities!
The situation has been discussed in detail about several eminent personalities of the Silicon Valley. While some people see no net change occurring, others have warned of grave consequences.
How Does it Unfold?
Where do we stand speaking from a totally neutral plane of judgment? For the start, it is always wise to read from the pages of history when throwing light on the future. Remember the time when the coal mines were shut down and all the workers in the coal mines summarily lost their jobs! America became a global hero for taking the first big step in the fight against global warming at the cost of the joblessness of thousands of Americans.
The mine workers were never re-trained and re-employed. Not by the holier-than-thou corporates. Not by the holiest-among-the-holy governments of the world! So was the case when the factories were shut down!!
Aren’t mining and technology two totally different coins. Yes, on the face of it. But when you dig deeper, it is controlled by the same industry dynamics and the overall economics is same for every industry.
Companies will make a mad rush toward profits for shareholders, as the main job of the CEO is to increase the wealth of the investors. (A suggestion from us is; let the company’s performance also be measured by give-back-to-customer-index every year like the dividends to the shareholders, under the control of SEC!) To show investors an unimaginable P/E (price to earnings) ratio, corporations will lay off workforce by the hundreds and the thousands.
For effective integration of Natural Intelligence and Artificial Intelligence, Governments may have to intervene with a legislation in favor of the “rehabilitation of the displaced workforce.” Without such planning mandated as a law, it will be easy for corporates to replace the people with AI-powered bots.
Futran Solutions specializes in RPA and is a proud partner of AA (Automation Anywhere). We provide solutions to our clients tailored to the RPA niche. In addition, as a part of our integrated solutions, we also offer re-training to the part of the workforce that will be affected by RPA. This allows employers to use the employees’ skill elsewhere and helps the latter explore other realms of technology without a day of unemployment.
Both the bridge and the boatman help us cross the river. The bridge promises to make the journey smoother and shorter. So we choose the bridge. But while the bridge is being built, the system must reward the years of service by the boatman and find him another career option before he wakes up jobless one day. How about training and rehabilitating him as a toll collector?
We, at Futran Solutions, support AI while respecting Natural Intelligence.
Jyoti Vazirani is the CEO of Futran Solutions. She can be reached at vjyoti@futransolutions.
https://futransolutions.com/wp-content/uploads/2021/02/AI-NI-And-The-Future.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2021-02-01 09:43:112021-12-08 18:12:15AI, NI And The Future!
Automation is not a new phenomenon. It comes with its own baggage of promises of development, prosperity and what not. In today’s world, robots do most of the physical work and that too, at a cheaper rate than humans. But it is also capable of cognitive functions like sensing emotions or even driving. But how quickly would this automation become reality? It is important to know these automation key trends to predict future development.
Automation of activities has enabled growth both at an individual level and at the business level. Will these impact on the employment and global economy?
Here are some of the automation key trends to watch out for in the IT domain in 2020.
Automation of technology to replace humans
The technology used for automation of tasks that once required humans is known as Hyperautomation.
The application of machine learning and artificial intelligence is increasing to automate processes. A range of tools can also be automated in Hyperautomation. It can be also referred to as sophisticated automation.
The main goal of Hyperautomation is to increase AI-driven decision making. This can be achieved by including robotic process automation(RPA) and intelligent business management software (iBMS). While it’s not the main goal, but Hyperautomation often results in the creation of a digital twin of an organization (DTO). This is one of the important automation key trend of all.
Focus on immersive experience.
Technologies like AR (Augmented Reality) and VR(Virtual Reality) come under immersive experience. This is also known as Multiexperience.
It is far apart from the traditional method of single point interaction. It covers multisensory and multi-touchpoint interfaces like computer sensors.
For example, Dominos pizza added a smart speaker communication and pizza tracking feature to their app.
Easy access to technical and business expertise
The focus is to gain easy access to technical and business expertise without any extra cost or training. This is often known as Democratization. It is mainly based on application development.
Some of its key factors are data, analytics, and citizen access. Citizen access is also known as design and knowledge. This has also led to an increase in the number of data scientists, programmers and more.
Democratization would enable developers to rather rely on AI-driven development to generate code or automate testing.
Physical human augmentation
Changing or enhancing physical capability is Human Augmentation. A physical capability can be changed or enhanced by implanting or hosting a technology within or on the human body. This is used to enhance a human’s cognitive or physical experiences.
Human augmentation mainly falls into 4 categories: Sensory augmentation, appendage and biological function augmentation, brain augmentation and genetic augmentation.
Cognitive augmentation enhances the ability of the human body to function, think and make better decisions.
Traceability and Transparency
Ethics, integrity, openness, accountability, and consistency are the five key elements this trend requires. As the evolving technology increases development and prosperity, it also creates a trust crisis.
Most of the times AI and ML are used to make a decision in place of humans. This evolves trust crisis. Such a condition drives the need for ideas like explainable AI and AI governance.
Edge Computing is a technology where sources of information are placed closer to information processing and content collection. This reduces the latency by keeping traffic local and distributed.
This includes IoT. IoT stands for Internet of Things. Moving the key application and services closer to people and the devices is known as Empowered edge .
Provide Distributed cloud
The goal is to provide cloud facilities to users from outside the physical data centers. No matter if these cloud facilities are from outside the data centers, they would still be controlled by the cloud provider.
All the aspects of cloud service is the responsibility of the cloud provider. Owing to this, cloud centers can be located at different locations. This not only reduces latency but also solve stubborn technical issues.
Semiautonomous to fully autonomous things
The exploitation of AI to perform tasks usually done by humans is performed by Autonomous things. The Spectrum of intelligence ranges from semi-autonomous to fully autonomous things. It includes a variety of environments such as air, sea, and land.
Autonomous things include drones, ships, robots, and other appliances. These things are also used in collaborative swarms. For example, drones can be used in the Olympic games.
Evolving technologies like autonomous things and Hyperautomation, lead to economic development. It also offers transformational opportunities in the business world. However, it leaves security vulnerable.
AI security could be vulnerable and could be a potential point of attack. Security teams must address these challenges and be aware of how AI will impact the security space.
Creativity and strategy are still human skills even though the world is getting automated rapidly. Bots can automate a multitude of designs and admin tasks but cannot automate creative thinking. It’s simply a matter of identification as in which things are done manually and which things need your input and imagination
https://futransolutions.com/wp-content/uploads/2020/04/Automation-2020-9-Key-Trends-To-Watch-Out-For.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2020-04-28 10:22:322021-12-08 18:12:36Automation 2020: 9 Key Trends To Watch Out For
The Big data is the vast volumes of data generated from a number of industry domains. Big data generally comprises data collection, data analysis and data implementation processes. Through the years, there’s been a change in the big data analytics trends – businesses have swapped the tedious departmental approach with data approach. This has seen greater use of agile technologies along with heightened demand for advanced analytics. Staying ahead of the competition now requires businesses to deploy advanced data-driven analytics.
When it first came into the picture, big data was essentially deployed by bigger companies that could afford the technology when it was expensive. At present, the scope of big data has changed to the extent that enterprises both small and large rely on big data for intelligent analytics and business insights. This has resulted in the evolution of big data sciences at a really fast pace. The most pertinent example of this growth is the cloud which has let even small businesses take advantage of the latest technology.
The modern business is floating on a stream of never-ending information. However, most businesses face the challenge of extracting actionable insights from vast pools of unstructured data. Despite these roadblocks, businesses are deriving from the tremendous opportunities for growth presented by big data. Here is all that would count as the hottest big data analytics trends of 2019.
Booming IoT Networks
Like it’s been through 2018, Internet of Things (IoT) will continue to trend through 2019, with annual revenues reaching way beyond $300 billion by 2020. The latest research reports indicate that the IoT market will grow at a 28.5% CAGR. Organizations will depend on more structured data points to gather information and gain sharper business insights.
Industry insiders believe that the future of tech belongs to the company that builds the first quantum computer. No surprise that every tech giant including Microsoft, Intel, Google and IBM are racing for the top spot in quantum computing. So, what’s the big draw with quantum computing? It allows seamless encryption of data, weather prediction, solutions to long-standing medical problems and then some more. Quantum computing allows real conversations between customers and organizations. There’s also the promise of revamped financial modeling that helps organizations develop quantum computing components along with applications and algorithms.
Analytics based on Superior Predictive Capacity
More and more organizations are using predictive analysis to offer better and more customized insights. This, in turn, generates new responses from customers and promotes cross-selling opportunities. Predictive analysis helps technology seamlessly integrate into variegated domains like healthcare, finance, aerospace, hospitality, retailing, manufacturing and pharmaceuticals.
The concept of edge computing among other big data trends did not just evolve yesterday. Network performance streaming makes use of edge computing pretty regularly even today. To save data on the local server close to the data source, we depend on the network bandwidth. That’s made possible with edge computing. Edge computing stores data nearer to the end users and farther from the silo setup with the processing happening either in the device or in the data center. Naturally, the entire procedure will see an organic growth in 2019.
Unstructured or Dark Data
Dark data refers to any data that is essentially not a part of business analysis. These packets of data come from a multitude of digital network operations which are not used to gather insights or make decisions. Since data and analytics are increasingly becoming larger parts of the daily aspects of our organizations, there’s something that we all must understand. Losing an opportunity to study unexplored data is a big-time potential security risk.
More Chief Data Officers
The latest trendy job role on the market is that of a Chief Data Officer. Top-tier human resource professionals are looking for competent industry professionals to fill this spot. While the demand is quite high, the concept and value of a CDO are largely still undefined. Ideally, organizations are preferring professional with knowledge in data analysis, data cleaning, intelligent insights and visualization.
Another Big Year for Open Sourcing
Individual micro-niche developers will invariably step up their game in 2019. That means we will see more and more software tools and free data become available on the cloud. This will hugely benefit small organizations and startups in 2019. More languages and platforms like the GNU project, R, will hog the tech limelight in the year to come. The open source wave will definitely help small organizations cut down on expensive custom development.
https://futransolutions.com/wp-content/uploads/2018/10/How-Big-is-Low-Code-in-the-Insurance-Industry-Today.jpg356711Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2018-10-29 16:42:052021-12-08 19:40:45Seven Hottest Analytics And Big Data Trends For 2019
Dark data is the kind of data that does not become a part of the decision making for organizations. This is generally the data from logs and sensors and other kinds of transactional records which are available but generally ignored. The largest portion of the yearly big data collected by organizations is also dark data.
Dark data does not usually play a vital role in analytics because:
Companies do not want to use their bandwidth on additional data processing
There’s a lack of technical resources
Organizations do not believe dark data adds any value to their analytics
All of these are valid reasons for the data taking the back seat. But today we have a string of data-centric technological advances. Together, they present a heightened ability to ingest, source, analyze, and store large volumes of data. With that, it becomes important for organizations to recognize this largely untapped volume of data.
The conventional way to use this data would be to systematically drain all of it into a waterhouse of data. This is followed by the identification, reconciliation, and rationalization of the data. The reporting follows soon after. While the process is pretty methodical, there might not be as many projects that truly call for such a need.
The Immense Volume of Dark Data in Enterprise
At the moment, we have solid evidence to suggest that as much as 90% of all data used in enterprises could be dark. Since industries are now storing large data volumes in the ‘lake’, it should be natural to tag the data appropriately as it gets stored. Perhaps the key is to extract the metadata out of this data and then storing it.
Profiling and exploring the data can be done using one or a combination of tools that are already available in the market. Cognitive computing and machine learning can further increase processing power and open up possibilities of making intelligent use of dark data.
Dark data may or may not have an identifiable structure. For example, most contacts and reports in organizations are structured. But over the course of time, they add up to the pile of dark data. Unstructured data can be small bits of personally identifiable info like birth dates and billing details. In the very recent past, this type of data would remain dark.
Machine learning can help organize this data in an automated manner. It can then be connected to other attributes of data to generate the complete view of the data. Using geolocation data is slightly trickier though. While it is extremely valuable, the lifespan is rather short. A collection of historical geolocation data sets can be further leveraged using machine learning to aid in predictive analysis of data.
Recognition of regular data as dark data
Other sets of data often considered “dark” in the past include data from sensors, logs, emails, and even voice transcripts. The longest stretch they would get in terms of application would be vested in troubleshooting purposes. Not many would look to make such data a part of actual decision making. Now that we can convert voice or text (and vice versa) and use the data to gather intelligence, there are many use cases that draw advantage of data traditionally considered dark.
An IDC estimate suggests that the total volume of data could be somewhere close to 44ZB (zettabytes) in 2020. This data explosion will be influenced by many new data generators like the Internet of Things. And unless we light up this data with new technology and processes, a large volume of it will continue to stay dark.
The first and obvious step will be to make all the dark data available for exploration. The second step is to categorize the data, scrape out the metadata and do a quality check for all the extracted data. Modern tools for data management and data visualization provide the ability to explore the data visually. This determines whether or not the data can be illuminated to remove the visual noise.
The myriad advances in Artificial Intelligence (AI) will definitely aid in uncovering the secrets of the oft-ignored “dark data”. However, the trick is still in using the data prudently. Wrong use of data will inadvertently result in incorrect predictions and may invite regulatory sanctions.
The vastness of dark data demands handling by Big Data and AI experts. In addition, there needs to be a clear plan about the application of the data once it is sorted. At Futran Solutions, we work with a pool of incredibly talented Big Data and Artificial Intelligence experts who can help your organization make the most of dark data. Contact us today to talk solutions in big data and artificial intelligence.
https://futransolutions.com/wp-content/uploads/2018/08/Making-of-a-Storm-What-Happens-to-Dark-Data-in-Analytics-and-Big-Data.jpg300600Priyadarshan Patilhttps://futransolutions.com/wp-content/uploads/2021/09/Futran-logo-Color-300x61-1.pngPriyadarshan Patil2018-08-03 10:34:062021-12-08 18:13:10Making of a Storm: What Happens to Dark Data in Analytics and Big Data?
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