digital transformation

How do you measure the success of Digital Transformation?

Posted by Sandeep raut on September 3, 2017 at 14:09

Digitization is disrupting every business and is spreading like a wild fire across every sector such as Banking, Financial Services, Insurance, Retail, and Manufacturing.

Digital Transformation does not happen overnight. It is a continuous process. That is why it is very hard to plan too far ahead in a digital transformation program. The technology is evolving so rapidly that your plans will certainly change.

How do you measure return on digital transformation in order to make the timely course correction and improve its success rate? It is even more important that people who will measure the progress know the actual meaning of digital and customer behavior. You will be surprised to know that how many employees/leaders take the customer journey themselves – buying an online policy on their own website, purchasing a merchandise or calling their own customer center to complain.

One of the ways is to break the long term plan into small doable projects with specific KPI’s. These should not last more than six months.

While traditional metrics of revenue, costs, customer satisfaction should be measured, companies should move beyond these quarterly revenue and margin guidance as they keep pulling them back to short term tactical focus. The new metrics have to be added to get the right control and visibility of progress.

Some of the new metrics which can be considered are:

· % of marketing spend that is digital

· Brand value in market

· Reach of organization in the market

· Digital maturity quotient of the employees including board and senior leaders

· % revenue through digital channels

· Contribution to digital initiatives from each department like purchasing, finance, HR, IT, Sales & Marketing

Customer Focus:

· Net promoter score

· Rate of new customer acquisition

· Number of customer touch points addressed to improve customer experience positively

· % increase in customer engagement in digital channels

· Reduction in time to market new products to customers

· Change in customer behavior over time across channels

Return on innovation:

· % of revenue from new products/services introduced

· % of the profit from new ideas implemented

· Number of innovative ideas reach concept to implementation

· Number of new products or services launched in the market

· Number of new business models adopted for different class of customers

· Rate of new apps and APIs to offer new products/services inside and outside the company

Always keep these metrics simple and measure right things and celebrate even the small successes so employees are motivated.

A digital transformation is a big culture change so there is plenty of fear which leads to resistance. Such inertia has to be changed with clear communication, as to why it is needed to change and what benefits it will bring to each department and employee.

A lack of urgency is the greatest obstacle businesses face when considering the value of digital transformation. Proper planning is important but more than that execution as per the KPIs you select, is what take you through.

Machine Learning - The brain of Digital Transformation

Posted by Sandeep raut on August 27, 2017 at 12:31

We are all familiar with machine learning in our everyday lives. Both Amazon and Netflix use machine learning to learn our preferences and provide a better shopping and movie experience.

Artificial intelligence (AI) has stormed the world today. It is an umbrella term that includes multiple technologies, such as machine learning, deep learning, and computer vision, natural language processing (NLP), machine reasoning, and strong AI.

Organizations are using machine learning for various insights they want to know about consumers, products, vendors and take actions which will help grow the business, increase the consumer satisfaction or decrease the costs.

Here are some top use cases for machine learning:

· Predicting & preventing cyber-attacks: With WannaCry making havoc in many organizations, machine learning algorithms have become extremely important to look for patterns in how the data is accessed, and report anomalies that could predict security breaches.

· Algorithmic Trading: Today many of financial trading decisions are made using algorithmic trading at higher speed, to make huge profits.

· Fraud Detection: This is still one of the key issues in all the financial transactions. With the help of deep learning/artificial intelligence, the identification and prediction of frauds have become more accurate.

· Recommendation Engines: In this digital age, every business is trying hyper-personalization using recommendation engines to give you a right offer at right time.

· Predictive Maintenance: With embedded sensors of Internet of Things, many of the heavy industrial machinery manufacturers are applying machine learning to predict the failures in advance, to avoid the costly downtime and improve efficiency.

· Text Classification: Machine Learning with NLP is used to detect spam, define the topic of a news article or document categorization.

· Predict patient’s readmission rates: By taking into consideration patient’s history, length of stay in hospitals, lab results, doctor’s notes, hospitals now can predict readmission to avoid penalties and improve patient care.

· Imaging Analytics: Machine learning can supplement the skills of doctors by identifying subtle changes in imaging scans more quickly, which can lead to earlier and more accurate diagnoses.

· Sentiment Analysis: Today, it is important to know consumer emotions while they are interacting with your business and use that for improving customer satisfaction. Nestle, Toyota is spending huge money and efforts on keeping their customer’s happy.

· Detecting drug reactions: With Association analysis on healthcare data like-the drugs taken by patients, history & vitals of each patient, good or bad drug effects etc; drug manufacturers identify the combination of patient characteristics and medications that lead to adverse side effects of the drugs.

· Credit Scoring & Risk Analytics: Using machine learning to score the credit worthiness of card holders, defaulters, and risk analytics.

· Recruitment for Clinical Trials: Patients are identified to enroll into clinical trials based on history, drug effects

With today’s advanced cognitive computing capabilities, image/speech recognition, language translation using NLP has become a reality which is used in very innovative use cases.

Machine learning is nothing new to us but today it has become the brain of digital transformation. In future, machine learning will be like air and water as an essential part of our lives.

Are you drowning in Data Lake?

Posted by Sandeep raut on August 19, 2017 at 17:01

Today more than even, every business is focusing on collecting the data and applying analytics to be competitive. Big Data Analytics has passed the hype stage and has become the essential part of business plans.

Data Lake is the latest buzzword for dumping every element of data you can find internally or externally. If you Google the term data lake, you will get more than 14 million results. With entry of Hadoop, everyone wants to dump their siloes of data warehouses, data marts and create data lake.

The idea behind a data lake is to have one central platform to store and analyze every kind of data relevant to the enterprise. With the digital transformation, the data generated every day has multiplied by several times and business are collecting this consumer data, Internet of Things data and other data for further analysis.

As the storage has become cheaper, more data is being stored in its raw format in the hopes of finding nuggets of information but eventually it becomes difficult. It is like using your smartphone to click photographs left, right and center, but when you want to show some specific photograph to someone it’s very difficult.

Data Lakes, if not maintained properly, have the potential to grow aimlessly consuming all the budget. Some companies have their data lakes overflowing on premise systems into the cloud.

Most data lakes lack governance, lack the tools and skills to handle large volumes of disparate data, and many lack a compelling business case. But, this water (the data) from your data lake has to be crystal clear and drinkable, else it will become a swamp.

Before getting into bandwagon of creating the data lake that may cost thousands of dollars and months to implement, you should start asking these questions.

· What data we want to store in Data Lake?

· How much data to be stored?

· How will we access this massive amounts of data and get value from it easily?

Here are some guidelines to avoid drowning into data lakes.

· First and foremost - create one or more business use cases that lay out exactly what will be done with the data that gets collected. With that exercise you will avoid dumping data, which is meaningless.

· Determine the Returns you want to get out of Data Lake. Developing a data lake is not a casual thing. You need good business benefits coming out of it.

· Make sure your overall big data and analytics initiatives are designed to exploit the data lake fully & help achieve business goals

· Instead of getting into vendor traps and their buzzwords, focus on your needs, and determine the best way to get there.

· Deliver the data to wide audience to check and revert with feedback while creating value

There are many cloud vendors to help you out building data lakes – Microsoft Azure, Amazon S3 etc.

By making data available to Data Scientists & anyone who needs it, for as long as they need it, data lakes are a powerful lever for innovation and disruption across industries.

Do you want to hire a Data Scientist?

Posted by Sandeep raut on August 6, 2017 at 14:06

As mentioned by Tom Davenport few years back, Data Scientist is still a hottest job of century.

Data scientists are those elite people who solve business problems by analyzing tons of data and communicate the results in a very compelling way to senior leadership and persuade them to take action.

They have the critical responsibility to understand the data and help business get more knowledgeable about their customers.

The importance of Data Scientists has rose to top due to two key issues:

· Increased need & desire among businesses to gain greater value from their data to be competitive

· Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment

So it is extremely important to hire a right person for the job.Requirements for being a data scientist are pretty rigorous, and truly qualified candidates are few and far between.

Data Scientists are very high in demand, hard to attract, come at a very high cost so if there is a wrong hire then it’s really more frustrating.

Here are some guidelines for checking them:

· Check the logical reasoning ability

· Problem solving skills

· Ability to collaborate & communicate with business folks

· Practical experience on collaborating Big Data tools

· Statistical and machine learning experience

· Should be able to describe their projects very clearly where they have solved business problems

· Should be able to tell story from the data

· Should know the latest of cognitive computing, deep learning

I have seen smartest data scientists in my career who do the best job best but cannot communicate the results to senior leaders effectively. Ideally they should know the data in depth and can explain its significance properly. Data visualizations comes very handy at this stage.

Today with digital disrupting every field it has an impact on data science also.

Gartner has called this new breed as citizen data scientists. Their primary job function is outside analytics, they don’t know much about statistics but can work on ready to use algorithms available in APIs like Watson, Tensor flow, Azure and other well-known tools.

The good data scientist can make use of them to spread the awareness and expand their influence.

It has become more important to hire a right data scientist as they will show you the results which may make or break the company.

How Customer Analytics has evolved...

Posted by Sandeep raut on July 30, 2017 at 4:40

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza.

SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services.

In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics.

By the late 2000s, Facebook, Twitter and all the other socialchannels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.

With the digital age things have changed drastically. Customer is superman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience.

This tsunami of data has changed the customer analytics forever.

Today customer analytics is not only restricted to marketing for churn and retention but more focus is going on how to improve the customer experience and is done by every department of the organization.

A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.

From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.

Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.

Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before.

Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical.

There are various ways customer analytics is carried out:

· Acquiring all the customer data

· Understanding the customer journey

· Applying big data concepts to customer relationships

· Finding high propensity prospects

· Upselling by identifying related products and interests

· Generating customer loyalty by discovering response patterns

· Predicting customer lifetime value (CLV)

· Identifying dissatisfied customers & churn patterns

· Applying predictive analytics

· Implementing continuous improvement

Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time

Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect.

Tomorrow there may not be just plain simple customer sentiment analyticsbased on feedbacks or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time.

There’s no doubt that customer analytics is absolutely essential for brand survival.

Go Digital or Die - What will you chose?

Posted by Sandeep raut on July 23, 2017 at 15:19 in OSes

Just before 2007, we didn't have access to smartphones like iPhone or social media apps like Instagram, Whatsapp and even email was much more limited only to desktops.

Zoom in to Today - Digital Transformation has revolutionized everything we do. It has been one of the hottest topics for every business. It’s a subject which keeps the CEOs awake.

Today it is Digital or Die.

Digital is happening fast and forcefully, whether businesses are ready for it or not. You can’t hide from it. There is a possibility that five of out ten businesses like Blockbuster, Kodak and Borders that will become digital dinosaur because of their lack of ability to adapt.

Going digital is not about moving to a specific technology like Cloud or Big Data, Analytics but it is really about accommodating a change of how technology enables business. Billions of people across the world are attached to a global high-speed, real-time Internet.

There are over 7+ billion mobile connections worldwide. In couple of years, Millennials will make up half of the working population. They expect highly personalized products and services, they want instant-gratification and they are omni-channel, online anytime, anyplace and any device. Using Mobile firstas your strategy to go digital is no-brainer.

As technology becomes an increasing part of our everyday lives, it also becomes a vital part of business strategy to become more efficient in customer service and disrupt the market with exemplary customer experience.

Business models are changing, from products to services and have to have a sharp focus of extraordinary customer experience with digital, like Apple. To transform to digital, companies must place customer experience at the center of digital strategy.

Customers really want access to support via digital channels without the intervention of customer reps, unless they don’t find what they are looking for at the first point of contact or something goes wrong with the product which needs to be fixed quickly.

Burberry was one of the first players to turn their fashion shows into digital happenings. The company used the buzz around the events to lure its customer base, interact with and strengthen relationships with customers, and attract new ones.

Nike had moved on from a sports apparel company to fitness driven personalized wearables like FuelBand manufacturer.

Apple, Disney, Nordstrom and Nestle are just a handful of the household names that have mastered digital.

It’s a never-ending program of improvement. As important as the technologies and channels, are the employee training and mastering the skill set that empowers them to thrive in this more integrated and ‘digital first’ environment.

Working from home is adopted by many organizations and moving to cloud based systems enables your employees to do that more effectively. They can access all relevant work content and more.

Digital should not be bolt-on to home grown age old systems but must be central theme for every touch point to customer and internal processes.

Every company is a technology company today. The pace of digital is rising exponentially, making it very difficult to be the leaders in market. Your thereat is not your traditional competitor but someone who comes up with innovative ideas to steal your customers.

As Charles Darvin once said - It is not the strongest of the species that survives, nor the most intelligent that survives, it is the one that is the most adaptable to change

It is Digital or Die. You are an easy prey if you don’t change.

How to measure Digital Transformation maturity?

Posted by Sandeep raut on July 15, 2017 at 12:25 in OSes

The digital revolution has created significant opportunities and threats for every industry. Companies that cannot or do not make significant changes faster to their business model in response to a disruption are unlikely to survive

It is extremely important to do digital maturity assessment before embarking on digital transformation.

Digital leaders must respond to the clear and present threat of digital disruption by transforming their businesses. They must embed digital capabilities into the very heart of their business, making digital a core competency, not a bolt-on. Creating lasting transformative digital capabilities requires you to build a customer-centric culture within your organization.

This requires new capabilities that organizations need to acquire and develop which include disruptive technologies like Big Data, Analytics, Internet of Things, newer business models.

Digital maturity model measures readiness of the organization to attain higher value in digital customer engagement, digital operations or digital services. It helps in incremental adoption of digital technologies and processes to drive competitive strategies, greater operationally agility and respond to rapidly changing market conditions.

Business can use the maturity model to define the roadmap, measuring progress on the milestones.

The levels of maturity can be defined as per multiple reports available and

adopt the ones which makes more sense to your business.

· Level 1 : Project based solutions are developed for a particular problem, no integration to home grown systems, unaware of risks and opportunities

· Level 2 : Departmentalized projects but still not known to organization, little integration

· Level 3 : Solutions are shared between the departments for a common business problem, better integration

· Level 4 : Organization wide efforts of digital, highly integrated, adaptive culture for fail fast and improve

· Level 5 : Driven by CXOs, customer centric and complete transformation changes happen to organization

Here are the 7 categories on which business should ask questions to all the stakeholders to gauge the maturity of Digital Transformation and identify the improvement and priorities.

1. Strategy & Roadmap - how the business operates or transforms to increase its competitive advantage through digital initiatives which are embedded within the overall business strategy

2. Customer – Are you providing experience to customers on their preferred channels, online, offline, anytime on any device

3. Technology – Relevant tools and technologies to make data available across all the systems

4. Culture – Do you have the organization structure and culture to drive the digital top down

5. Operations – Digitizing & automating the processes to enhance business efficiency and effectiveness.

6. Partners – Are you utilizing right partners to augment your expertise

7. Innovation – How employees are encouraged to bring the continuous innovation to how they serve the customers

Finally you know when you are digital transformed?

· When there is nobody having “Digital” in their title

· There is no marketing focused on digital within the organization

· There is no separate digital strategy than company’s business strategy

How Augmented Reality is improving Digital Age!!

Posted by Sandeep raut on July 9, 2017 at 7:04 in OSes

Augmented reality (AR) means amplified reality with graphics, sounds, haptic feedback and smell to the natural world as it exists. Virtual objects and information are displayed on top of the physical world, will make its way to our phones.

Just like the Internet of Things & Big data, Analytics, augmented reality is going mainstream.Search engines are already expanding on image search, allowing you to point your camera at something and search for information based on what the lens takes in.

Both video games and cell phones are driving & exploiting the development of augmented reality. Everyone from tourists to someone looking for the closest McDonalds can now benefit from the ability to place computer-generated graphics in their field of vision.

Unlike Virtual Reality, which creates a totally artificial environment like you are on the top of Eiffel tower or looking at Taj Mahal right now from your living room couch, augmented reality uses the existing environment and overlays new information on top of it.

Pokemon Go released in 2016 was the most successful game to use AR to superimpose Pokemon on physical background and all children and adults were mad chasing them in real world.

Recent innovation, Heads-Up Display (HUDs) glass with AR superimpose crystal-clear driving directions on top of the real world so you can easily navigate without taking your eyes off the road. It’s like Pokemon Go but all the adorable monsters have been replaced by driving directions.

Digital Marketing will get a boost with AR. A new augmented reality campaign from Pepsi Max have stunned people in London, giving experiences like a prowling tiger, a meteor crashing, an alien tentacle grabbing people on the street, the bus stop window serves as a scarily realistic screen to bring these scenarios to life.

With AR, you can view your living room on a smartphone and see how virtual furniture would fit into the real world and decide what is good to buy.

Artificial Intelligence has brought virtual assistants like Siri, Alexa, Cortana, Google to life but AR can put a face to it and beef up the experience. Microsoft Hololens is currently leading the AR headset race.

There are several industries that will benefit from AR applications, including healthcare, tourism and entertainment. However, it is retailers who are the ones to use it more. With AR, your retail website is brought to life with a 360° online presentation of your store. In-store, augmented reality can easily display information and other visuals on packaged items with a simple image scan.

Lego’s “Digital Box” Provides Customers with an Interactive 3D Digital Experience. Aside from kiosks in stores, soon they will have mobile devices to be equipped with the capability to instantly bring up relevant information about any product in real-time.

Fashion retailer Forever 21 had put up a giant billboard which features a model walking in front of an image of the crowd below. The model occasionally leans over, and pluck someone out of the crowd. Sometimes, she drops them in her bag and happily walks off.

French cosmetic super chain Sephora is one of the leaders in AR marketing area. Their mobile apps & AR mirrors allow people to see how clothing, jewelry, and accessories look on them.

Augmented Reality cleverly blurs the line between the digital and the real by way of specially designed apps and unique visual ‘markers’ to intuitively visualise 3D virtual forms in physical realms.

We are still in the very early days of AR, and all of the future possibilities are difficult to imagine at this point. As this technology advances and gets more affordable, it will be easier for businesses to take advantage of it. AR helps to bridge the divide between the digital and offline world.

How to build a successful Digital Transformation roadmap?

Posted by Sandeep raut on July 2, 2017 at 7:02 in OSes

Digital Transformation is a phenomenon that every company must deal with and it is a reality. It is a top priority for boardroom executives. Most companies know that digital transformation is vital to survival.

Customers are demanding new instant experiences, partners want more visibility & greater access, and employees want greater convenience and work from anywhere.

Many companies are claiming that they have started it but those initiatives are isolated or tactical. If not executed properly the only result is failure.

As you will look at weather reports, travel times, flight connections, hotel reviews before going on holiday journey, similarly you will need a road map for navigation from point A to point B.

The digital roadmap has 3 main leavers:

· Strategy: to make it completely clear how digital transformation support overall business strategy, define 30,60,90 days & beyond plan for measurements

· Technology: what are the tools and technologies you will need to go from current state to future state – big data, analytics, mobility, IoT, cloud, microservices etc, dedicated hardware, software innovation labs, standards, guidelines, security

· Processes & People: who are the leaders to drive the digital, what is the organization structure, operational integration of all processes, how to change to customer centric culture, training to employees and empower them

It is all about starting with baby steps, gaining trust from business by delivering quick value and celebrating and marketing the successes to generate internal buzz.

The roadmap begins with a digital vision, mission & assessment of the digital maturity of your business today. Once the assessment and vision are completed, then next step it becomes possible to identify the systemic gaps that need to be filled. Then those steps can be built into the roadmap.

Here are the broad milestones of a successful digital transformation roadmap:

· Boardroom/Senior management buy in, decision to go Digital and drive it across organization

· Cultural alignment & commitment to Digital from board of directors to entry level employees

· Identify and assess the current state of the organization on Digital

· Put Customer first - Prepare customer journey maps to identify all the touch points with organization

· Find out pain areas at each touch point and respective stakeholders involved who can correct them

· Prioritize and break them in small projects to adopt fail fast approach. If anything did not work, just accept the failure, publish the learnings and move on.

· Seek partners to help you in your journey, who take shared risk and shared rewards

· Deploy agile implementation approach for quick results

· Market your successes to whole world and repeat the process for next pain area

Transformation programs may be massive and take place over multiple years, but understanding the ROI for each phase helps keep a multi-year journey on track. With a structured approach, all of the moving parts can be managed and progress sustained throughout this journey.

Finally, you know when you are digital transformed?

· When there is nobody having “digital” in their title

· There is no separate digital strategy than company’s business strategy

· There are no posters or marketing focused on digital within the organization

Enterprises that adapt, evolve and exploit this new digital reality will thrive, while those that do not, will be lost to the sands of time like Dinosaur.

What are Digital Twins?

Posted by Sandeep raut on June 27, 2017 at 5:42 in OSes

Digital Transformation has brought in all the new concepts and technologies at the hands of consumers and businesses alike.

Digital Twin is one of them. It is a virtual image of your machine or asset, maintained throughout thelife cycle and easily accessible at any time. It involves internet of things connected devices generating real time data in Big Data platform. This data is further analyzed in the cloud.

With a digital twin, machine manufacturers are able to use the power of digitalization to achieve improved efficiency and quality. This approach helps ensure optimized machine design and smooth operation.

Today, machine intelligence and connectivity to the cloud allows a huge potential of digital twin technology for companies in a variety of industries

Digital Twin allows the asset operator to predict precisely when maintenance will be required based on the unique conditions, experienced by that particular asset.

GE has built a digital wind farm collecting data from turbine sensors, which uses big data and the Industrial Internet to drive down the cost of renewable electricity.

Here are the several advantages of Digital Twin technology:

· Explore the impact of various design alternatives

· Do simulations and testing to ensure that product designs will meet requirement

· Understand how a projected change to a manufacturing process might impact costs or schedule

· see the current operating status along with any recent alarms and maintenance performed on a machine

· be instructed on how to perform proper maintenance procedures, for the specific problem they’re addressing

· Preventing the failure, or anticipating it and doing the required maintenancebefore failure occurs, can shorten outages

Digital twins give airlines a better idea of what happens when a jet flies through a flock of birds, or through dust storms in hot environments.

The digital twin, combined with advanced analytical tools and machine learning, will provide a platform that changes the traditional way of how we look at the analysis of asset’s condition and performance.

It will enable a new generation of advanced predictive analytics.

Top 5 uses of Internet of Things!!

Posted by Sandeep raut on June 11, 2017 at 7:56 in OSes

While many organizations are creating tremendous value from the IoT, some organizations are still struggling to get started. It has now become one of the key element of Digital Transformation that is driving the world in many respects.

It is really a time to look beyond the hype and get real about Internet of Things.

Just putting IoT in place may not help organizations but applying analytics is extremely essential for the success of IoT systems for better decision making.

Here are top 5 areas where IoT is making the disruption:

1. Wellness - IoT helps continuously monitor the patients and symptoms to early detection, diagnosis & accelerate breakthrough drug development. Wearables like Fitbit, Apple watch, and Samsung have all created new revenue streams from giving their users workout analytics and the ability to set daily health goals. Mobile apps around wellness have been around for years now to track your sleep, weight, nutrition, and more.

2. Safety and Security – Sensor based monitoring of elevators, escalators improves travelers safety at airports. Sensors, which are much cheaper these days, can let you know whether or not your water pipes are leaking or are about to burst. The droneswill allow the handful of rangers to quickly investigate reports of fires, than traveling into remote parts of the jungle over unpaved roads. Connected cars allows vehicle diagnostics and real time intervention from technicians for better safety.

3.

Marketing – with use of IoT, businesses can reach to right customer at at right time using geofencing. It is a virtual field in which apps are capable of sending alerts depending on your entrance or exit from a vicinity. With geofencing, your shopping experience can be more hyper-personalized to what you’re looking for. 1-800-Flowers covered the area around jewelry stores that were close to their flower shops to encourage customers to buy flowers with jewelry. Amazon Go is Amazon’s store concept without a check-out line.

4. Smart Cities & Smart Infrastructure – IoT is helping build the infrastructure which is really smart in quick response and improves the life of residents. Real time weather response systems, better traffic management, waste management, and optimal utilities management helps governments around the world. Smart Homes helps people more peaceful life.

5. Energy, Aviation & Manufa

cturing – Using IoT to do predictive maintenance can reduce downtime up to 50%. Companies like GE have put up 100s of sensors across the plant that provide round-the-clock monitoring and diagnostics of existing hardware. IoT enabled engines consume almost 15% less fuel than average jet engines, and also have reduced emissions and noise. Smart grids helps in increasing the reliability and efficiency of grid, avoid thefts.

In future IoT will continue to enhance our lives more and more by tracking our needs in real time giving opportunity to businesses to react accordingly and immediately.

Cybersecurity in Digital age

Posted by Sandeep raut on June 5, 2017 at 4:57 in OSes

You must have heard about the global cyberattack of WannaCry ransomware in over 200 countries. It encrypted all the files on the machine and asked for payment. Ransomware, which demands payment after launching a cyber-attack, has become a rising trend among hackers looking for a quick payout.

Every day it seems another news breaks about cyber-criminals hacking in and stealing data, & information. Private companies, government agencies, hospitals…no one is immune. Cybersecurity is no longer buried in the tech section of organizations, newspapers and websites - its front-page news.

With the penetration of the digital movement, cyber-attacks have also doubled year over year, making businesses and government sites more vulnerable.

In simple terms cybersecurity is use of digital technologies to protect company networks, computers and programs from unauthorized access and subsequent damage.

In recent times, every organization has launched a “go-digital” initiative. This has led to explosion of connected environments.

The growing mobility trend has sparked a rapid growth of endpoints that must be secured, and bring-your-own-device (BYOD) programs mean that employees could be accessing sensitive data on unsecured devices.

The prevalence of cloud based services and third party data storing has opened up new areas of risk.

As businesses adopt the new technologies like Big Data, Analytics, IoT & Mobility, the focus must be on how to safeguard the data spread across devices and cloud.

Cybersecurity must be a key factor during your journey to digitally transforming your business, just as you would ensure that your offices, brick-and-mortar store has locks and security systems of the highest quality, your digital storefront must have the same levels of security. If consumers do not trust these digital storefront with their data, or if that trust is broken because of a data breach, the cost to rebuild that trust is incredibly high.

The best way to protect yourself is to be suspicious of unsolicited emails and always type out web addresses yourself rather than clicking on links.

There are different types of attacks we have seen so far:

· Hackers target the software vulnerabilities that are yet to be discovered and patched

· Attack on mobile devices: malwares designed specifically for smartphones to steal data

· Data leakage: hackers steal the data by interrupting the traffic between organization and cloud environments

· Programming: hackers use malicious code on any server that gets replicated and allow them to delete, steal data

There are multiple ways to combat these cyber-attacks:

· Network defense: detect unwarranted traffic e.g. someone communicating with malicious host, malware entry into the network, unauthorized data transfer

· Detect user access violations: misuse of user access within the system, ensure proper authentications, use of antivirus, malware to prevent steal user information

· Mobile device protection: detect unauthorized devices or prevent hackers from compromising individual devices.

· Protect data in motion & rest: ensure data transfers protected within various environments

· Investment in securing IoT devices – today with everything is connected it is extremely important to secure all access points.

Today with machine learning organizations are in a very good position to know what users are doing that can affect the network and increase risk. Artificial Intelligence is used to constantly learn new malware behaviors and recognize how viruses may mutate to try and get around security systems.

Traditional IT security practices like network monitoring and segmentation will become even more critical as businesses and governments deploy IoT devices.

Recent events have highlighted the growing need for enhanced cybersecurity.

18 Big Data tools you need to know!!

Posted by Sandeep raut on May 27, 2017 at 14:29 in OSes

In today’s digital transformation, big data has given organization an edge to analyze the customer behavior & hyper-personalize every interaction which results into cross-sell, improved customer experience and obviously more revenues.

The market for Big Data has grown up steadily as more and more enterprises have implemented a data-driven strategy. While Apache Hadoop is the most well-established tool for analyzing big data, there are thousands of big data tools out there. All of them promising to save you time, money and help you uncover never-before-seen business insights.

I have selected few to get you going….

Avro: It was developed by Doug Cutting & used for data serialization for encoding the schema of Hadoop files.

Cassandra: is a distributed and Open Source database. Designed to handle large amounts of distributed data across commodity servers while providing a highly available service. It is a NoSQL solution that was initially developed by Facebook. It is used by many organizations like Netflix, Cisco, Twitter.

Drill: An open source distributed system for performing interactive analysis on large-scale datasets. It is similar to Google’s Dremel, and is managed by Apache.

Elasticsearch: An open source search engine built on Apache Lucene. It is developed on Java, can power extremely fast searches that support your data discovery applications.

Flume: is a framework for populating Hadoop with data from web servers, application servers and mobile devices. It is the plumbing between sources and Hadoop.

HCatalog: is a centralized metadata management and sharing service for Apache Hadoop. It allows for a unified view of all data in Hadoop clusters and allows diverse tools, including Pig and Hive, to process any data elements without needing to know physically where in the cluster the data is stored.

Impala: provides fast, interactive SQL queries directly on your Apache Hadoop data stored in HDFS or HBase using the same metadata, SQL syntax (Hive SQL), ODBC driver and user interface (Hue Beeswax) as Apache Hive. This provides a familiar and unified platform for batch-oriented or real-time queries.

JSON: Many of today’s NoSQL databases store data in the JSON (JavaScript Object Notation) format that’s become popular with Web developers

Kafka: is a distributed publish-subscribe messaging system that offers a solution capable of handling all data flow activity and processing these data on a consumer website. This type of data (page views, searches, and other user actions) are a key ingredient in the current social web.

MongoDB: is a NoSQL database oriented to documents, developed under the open source concept. This comes with full index support and the flexibility to index any attribute and scale horizontally without affecting functionality.

Neo4j: is a graph database & boasts performance improvements of up to 1000x or more when in comparison with relational databases.

Oozie: is a workflow processing system that lets users define a series of jobs written in multiple languages – such as Map Reduce, Pig and Hive. It further intelligently links them to one another. Oozie allows users to specify dependancies.

Pig: is a Hadoop-based language developed by Yahoo. It is relatively easy to learn and is adept at very deep, very long data pipelines.

Storm: is a system of real-time distributed computing, open source and free. Storm makes it easy to reliably process unstructured data flows in the field of real-time processing. Storm is fault-tolerant and works with nearly all programming languages, though typically Java is used. Descending from the Apache family, Storm is now owned by Twitter.

Tableau: is a data visualization tool with a primary focus on business intelligence. You can create maps, bar charts, scatter plots and more without the need for programming. They recently released a web connector that allows you to connect to a database or API thus giving you the ability to get live data in a visualization.

ZooKeeper: is a service that provides centralized configuration and open code name registration for large distributed systems.

Everyday many more tools are getting added the big data technology stack and its extremely difficult to cope up with each and every tool. Select few which you can master and continue upgrading your knowledge.

Top 7 Virtual Reality Industry use cases

Posted by Sandeep raut on May 21, 2017 at 13:15 in OSes

Today Digital Transformation has entered our life and we have subconsciously using it also in day to day life.

Virtual Reality technology has evolved dramatically in the past few years the costs of VR devices has gone down so it is all set to hit mainstream markets soon. While gaming applications like Pokemon Go have attracted most of the attention, there are many other use cases that could have a much larger impact on our lives.

Google Cardboard is a super low-cost headset ($15) to which a compatible, VR enabled mobile phone is attached to deliver the VR experience.

Other commercial product is Oculus Rift gear which has becomeextremely popular in gaming & business equally.

Here are some great VR use cases:

1. VR for Tourism: do you want to sit on your couch and climb up the Eiffel tower? Or walk on the glass horse shoe at grand canyon? Wild Within is VR app available for experience of travel through rain forest in Canada. Travelers around the world are able to experience a helicopter flight around New York City or a boat ride around the Statue of Liberty.

2. VR for Education: Over last decade eLearning had picked up very much. But it could not deliver hands on experience which is now possible with VR technology. Technicians can actually learn the real life examples and do their bit to solve the problems on the shop floor. Medical students can actually perform surgeries allowing them to make mistakes without any impact on actual patients.

3. VR for Sales: Traditionally automakers have the showroom to show the cars to the customers and explain their features and sometimes a test drive is also possible. But customization of how the interior will look as per their choice was not possible which now can be done via VR. Audi is experimenting this in London, where customer can configure their Audi with accessories as they want and drive virtually in real time.

4. VR in Gaming: who does not know the excitement Pokemon Go had created and reached 50 million users in record time of 22 days. Using AR/VR technology games have changed the life of seniors as well as teens. Game of Thrones has capitalized on VR and gone viral in various countries.

5. VR in Designing: product designing is tedious task and changes to products based on the competition or customization is time consuming. This is where VR helps designers. They can now create the products easily, configure all the features and test them out. It is more popular in construction of buildings to see how the interior will look like.

6. VR in Marketing: With Digital Marketing ads are becoming more intrusive. The best marketing campaigns use VR to create successful campaigns as users get completely immersed into the content, and create memorable experiences. Coca Cola created a virtual reality sleigh ride. New York times releases multiple immersive documentaries in their app. Finnair is showing their Airbus 350 via VR to attract more customers.

7. VR in Sports coaching: The potential for VR in sports in endless. You get all the benefits of real-world interaction, but in a controlled environment. Showing is so much more effective than explaining, and experiencing something first-hand is that much more powerful again. Football, Cricket.

Virtual reality technology holds enormous potential to change the future for a number of fields, from medicine, business, and architecture to manufacturing. We are on the roller coaster ride !!

Internet of (Medical) things in Healthcare

Posted by Sandeep raut on May 15, 2017 at 4:16 in OSes

Over the past few decades, we’ve gotten used to the Internet and cannot imagine our lives without it. Millennials and new age kids don’t even know what is life without being online.

With the disruption of Digital Transformation, Internet of Things have added lots of opportunities to business and consumers like us, equally.

IOT means connecting things, extracting data, storing, processing and analyzing in big data platforms and making decisions based on analytics. It helps in predicting certain outcomes thereby helping with taking preventive actions.

The popularity of wearables, such as fitness trackers, blood glucose monitors and other connected medical devices, has taken healthcare by storm. Connected devices have become a prevalent phenomenon in the consumer space and have made their way into healthcare.

Healthcare is fast adopting IoT & changing rapidly, as it reduces costs, boosts productivity, and improves quality. IoT can also boost patient engagement and satisfaction by allowing patients to spend more time interacting with their doctors.

There are a number of opportunities for the internet of things to make a difference in patients' lives. IoT-enabled devices capture and monitor relevant patient data and allow providers to gain insights without having to bring patients in for visits. Adding sensors to medicines or delivery mechanisms allows doctors to keep accurate track of whether patients are sticking to their treatment plan and avoid patient's readmission.

Patients are using these connected medical products to capture ECG readings, record medication levels, sense fall detection and act as telehealth units.

Diabetes self-management includes all sorts of gadgets and devices, which control glucose levels and remind patients to take their insulin dose. The newest wearables are even capable of delivering insulin on their own, according to health condition indicators.

Remote patient monitoring is one of the most significant cost-reduction features of IoT in healthcare. Hospitals don’t have to worry about bed availability, and doctors or nurses can keep an eye on their patients remotely. At the same time, patients usually feel more relaxed at home and recover faster.

Smart beds are a convenient solution for patients who have trouble adjusting bed positions on their own. This kind of IoT tool can sense when the patient is trying to move on their own and it reacts by correcting the bed angle or adjusting pressure to make the person more comfortable. Additionally, this frees up nurses, who don’t have to be available all the time and can dedicate extra time to other duties. Many hospitals have already introduced smart beds in their rooms.

At Boston Medical Center, IoT is everyday life:

Newborn babies are given wristbands, allowing a wireless network to locate them at any time.
They have installed wireless sensors in refrigerators, freezers and laboratories to ensure that blood samples, medications and other materials are kept at the proper temperatures.
Hospital has more than 600 infusion pumps which are IoT enabled. BMC staff members can now dispense and change medications automatically through the wireless network, rather than having to physically touch each pump to load it up or make changes.

At Florida Hospital, when patients go in for surgery, they're tagged with real-time location system (RTLS) badges that track their progress through from the pre-op room to the surgical suite to the recovery unit so relatives can track the patients from outside.

Philips GoSafe can be worn as a pendant and it helps to detect and alert falls in elderly people.

There are few challenges as well in implementing IoT:

Data security & lack of standard security policy
Hospital’s internal system integration with IoT data
Further changes and improvements in IoT hardware

The Internet of these Medical Things is a game-changer as future will be connected, integrated & secure healthcare industry.

Terminator or Iron Man – What will AI bring in future?

Posted by Sandeep raut on May 7, 2017 at 6:35 in OSes

In the age of Digital Transformation, Artificial Intelligence has come a long way from Siri to driverless cars.

If you have used a GPS on Google Maps to navigate in your car, purchased a book recommended to you by Amazon or watched a movie suggested to you by Netflix, then you have interacted with artificial intelligence.

Artificial Intelligence is the capability of a machine to imitate intelligent human behavior which relies on the processing and comparison of vast amounts of data in volumes with help of big data analytics, no human being could ever absorb.

Stephen Hawking, Elon Musk, Bill Gates have recently expressed concern in the media about the risks posed by AI.

According to them, AI will soon replace all kinds of manual tasks and make humans redundant. This could be true in some sense but still this is a far cry from the current maturity levels of AI, which is still at the stage of figuring out real-world use cases.

Today machines can carry out complex actions but without a mind or thinking for themselves. Smartphones are smart because they are responding to your specific inputs.

The world’s top tech companies are in a race to build the best AI and capture that massive market, which means the technology will get better fast, and come at us at faster speed. IBM is investing billions in its Watson, Apple improving Siri, Amazon is banking on Alexa; Google, Facebook and Microsoft are devoting their research labs to AI and robotics.

Together, they will swirl into that roaring twister, blowing down the industries and businesses in its path.

Within maybe few years, AI will be better than humans at diagnosing medical images and converting speech to emotions. But it can also be stealing millions of records from a government agency to identify targets vulnerable to extortion.

Soon you’ll be able to contact an AI doctor on your smartphone, talk to it about your symptoms, use your camera to show it anything it wants to see and get a diagnosis that tells you to either take a couple of Tylenols or see a specialist.

In all the fairy tales we have seen so far, good almost always wins over evil.

This is what we have seen in the movies like I, Robot or Avengers: Age of Ultron. But Will Smith or team of avengers does not know that till end of the story. That’s where we are now: face to face with the demon for the first time, doing everything we can to get through the scary plot alive.

Today many companies are using AI for improving their business:

· Geico is using Watson based cognitive computing to learn the underwriting guidelines, read the risk submissions, and effectively help underwrite

· Google Translate applies AI in not only translating words, but in understanding the meaning of sentences to provide a true translation.

· IBM Watson is the most prominent example of AI based question answering via petabytes of data retrieval that helps in various areas like finance, healthcare & insurance.

As Humans we are programmed from childhood either by nurture or nature to do things the way we do. All the nine emotions we have learned since then are the inseparable part of our lives.

Currently we are in the control of the planet because we are smartest species compared to all the animals.

But when, and if machines learns to love or hate, work in peace or retaliate in anger, then it’s not too far that, with the ability to consume & digest the vast amount of data, they will become more smarter & start taking control of the planet.

Only then we will be able to know that AI is helping us like Iron Man's Jarvis or planning to eradicate us like Terminator!!

5 Ways to Improve the Model Accuracy of Machine Learning

Posted by Sandeep raut on April 30, 2017 at 4:12 in OSes

Today we are into digital age, every business is using big data and machine learning to effectively target users with messaging in a language they really understand and push offers, deals and ads that appeal to them across a range of channels.

With exponential growth in data from people and & internet of things, a key to survival is to use machine learning & make that data more meaningful, more relevant to enrich customer experience.

Machine Learning can also wreak havoc on a business if improperly implemented. Before embracing this technology, enterprises should be aware of the ways machine learning can fall flat. Data scientists have to take extreme care while developing these machine learning models so that it generate right insights to be consumed by business.

Here are 5 ways to improve the accuracy & predictive ability of machine learning model and ensure it produces better results.

· Ensure that you have variety of data that covers almost all the scenarios and not biased to any situation. There was a news in early pokemon go days that it was showing only white neighborhoods. It’s because the creators of the algorithms failed to provide a diverse training set, and didn't spend time in these neighborhoods. Instead of working on a limited data, ask for more data. That will improve the accuracy of the model.

· Several times the data received has missing values. Data scientists have to treat outliers and missing values properly to increase the accuracy. There are multiple methods to do that – impute mean, median or mode values in case of continuous variables and for categorical variables use a class. For outliers either delete them or perform some transformations.

· Finding the right variables or features which will have maximum impact on the outcome is one of the key aspect. This will come from better domain knowledge, visualizations. It’s imperative to consider as many relevant variables and potential outcomes as possible prior to deploying a machine learning algorithm.

· Ensemble models is combining multiple models to improve the accuracy using bagging, boosting. This ensembling can improve the predictive performance more than any single model. Random forests are used many times for ensembling.

· Re-validate the model at proper time frequency. It is necessary to score the model with new data every day, every week or month based on changes in the data. If required rebuild the models periodically with different techniques to challenge the model present in the production.

There are some more ways but the ones mentioned above are foundational steps to ensure model accuracy.

Machine learning gives the super power in the hands of organization but as mentioned in the Spider Man movie – “With great power comes the great responsibility” so use it properly.

Beyond SMAC – Digital twister of disruption!!

Posted by Sandeep raut on April 22, 2017 at 13:38 in OSes

Have your seen the 1996 movie Twister, based on tornadoes disrupting the neighborhoods? A group of people were shown trying to perfect the devices called Dorothy which has hundreds of sensors to be released in the center of twister so proper data can be collected to create a more advanced warning system and save people.

Today if we apply the same analogy – digital is disrupting every business, if you stand still and don’t adapt you will become digital dinosaur. Everyone wants to get that advance warning of what is coming ahead.

Even if your business is doing strong right now, you will never know who will disrupt you tomorrow.

We have seen these disruption waves and innovations in technologies – mainframe era, mini computers era, personal computers & client-server era and internet era. Then came the 5 ^thwave of SMAC era comprising Social,

Mobile, Analytics and Cloud technologies.

Gone are the days when we used to wait for vacations to meet our families and friends by travelling to native place or abroad. Today all of us are interacting with each other on social media rather than in person on Facebook, Whastapp, Instagram, Snapchat and so on.

Mobile enablement has helped us anytime, anywhere, any device interaction with consumers. We stare at smarphone screen more than 200 times a day.

Analytics came in to power the hyper-personalization in each interaction and send relevant offers, communications to customers. The descriptive analytics gave the power to know what is happening to the business right now, while predictive analytics gave the insight of what may happen. Going further prescriptive analytics gave the foresight of what actions to be taken to make things happens.

Cloud gave organizations the ability to quickly scale up at lower cost as the computing requirements grow with secure private clouds.

Today we are in the 6 ^thwave of disruption beyond SMAC era - into Digital Transformation, bringing Big Data, Internet of things, APIs, Microservices, Robotics, 3d printing, augmented reality/virtual reality, wearables, drones, beacons and blockchain.

Big Data allows to store all the tons of data generated in the universe to be used further for competitive edge.

Internet of Things allows machines, computers, smart devices communicate with each other and help us carry out various tasks remotely.

APIs are getting lot of attention as they are easy, lightweight, can be plugged into virtually any system and highly customizable to ensure data flows between disparate systems.

Microservices are independently developed & deployable, small, modular services.

Robotics is bringing the wave of intelligent automation with help of cognitive computing.

3D printing or additive manufacturing is taking the several industries like medical, military, engineering & manufacturing by storm.

Augmented reality / virtual reality is changing the travel, real estate and education.

Wearables such as smart watches, health trackers, Google Glass can help real time updates, ensure better health & enable hands-free process optimization in areas like item picking in a warehouse.

Drones have come out of military zone and available for common use. Amazon, Dominos are using it for delivery while Insurance & Agriculture are using it for aerial surveys.

Beacons are revolutionizing the customer experience with in-store analytics, proximity marketing, indoor navigation and contact less payments.

The new kid on the block is blockchain where finance industry is all set to take advantage of this technology.

As products and services are getting more digitized, traditional business processes, business models and even business are getting disrupted.

The only way to survive this twister is to get closer to your customers by offering a radically different way of doing business that’s faster, simpler and cheaper.

A to Z of Analytics

Posted by Sandeep raut on April 16, 2017 at 4:02 in OSes

Analytics has taken world by storm & It it the powerhouse for all the digital transformation happening in every industry.

Today everybody is generating tons of data – we as consumers leaving digital footprints on social media, IoT generating millions of records from sensors, Mobile phones are used from morning till we sleep. All these variety of data formats are stored in Big Data platform. But only storing this data is not going to take us anywhere unless analytics is applied on it. Hence it is extremely important to close the loop with Analytics insights.

Here is my version of A to Z for Analytics:

Artificial Intelligence: AI is the capability of a machine to imitate intelligent human behavior. BMW, Tesla, Google are using AI for self-driving cars. AI should be used to solve real world tough problems like climate modeling to disease analysis and betterment of humanity.

Boosting and Bagging: it is the technique used to generate more accurate models by ensembling multiple models together

Crisp-DM: is the cross industry standard process for data mining. It was developed by a consortium of companies like SPSS, Teradata, Daimler and NCR Corporation in 1997 to bring the order in developing analytics models. Major 6 steps involved are business understanding, data understanding, data preparation, modeling, evaluation and deployment.

Data preparation: In analytics deployments more than 60% time is spent on data preparation. As a normal rule is garbage in garbage out. Hence it is important to cleanse and normalize the data and make it available for consumption by model.

Ensembling: is the technique of combining two or more algorithms to get more robust predictions. It is like combining all the marks we obtain in exams to arrive at final overall score. Random Forest is one such example combining multiple decision trees.

Feature selection: Simply put this means selecting only those feature or variables from the data which really makes sense and remove non relevant variables. This uplifts the model accuracy.

Gini Coefficient: it is used to measure the predictive power of the model typically used in credit scoring tools to find out who will repay and who will default on a loan.

Histogram: This is a graphical representation of the distribution of a set of numeric data, usually a vertical bar graph used for exploratory analytics and data preparation step.

Independent Variable: is the variable that is changed or controlled in a scientific experiment to test the effects on the dependent variable like effect of increasing the price on Sales.

Jubatus: This is online Machine Learning Library covering Classification, Regression, Recommendation (Nearest Neighbor Search), Graph Mining, Anomaly Detection, Clustering

KNN: K nearest neighbor algorithm in Machine Learning used for classification problems based on distance or similarity between data points.

Lift Chart: These are widely used in campaign targeting problems, to determine which decile can we target customers for a specific campaign. Also, it tells you how much response you can expect from the new target base.

Model: There are more than 50+ modeling techniques like regressions, decision trees, SVM, GLM, Neural networks etc present in any technology platform like SAS Enterprise miner, IBM SPSS or R. They are broadly categorized under supervised and unsupervised methods into classification, clustering, association rules.

Neural Networks: These are typically organized in layers made up by nodes and mimic the learning like brain does. Today Deep Learning is emerging field based on deep neural networks.

Optimization: It the Use of simulations techniques to identify scenarios which will produce best results within available constraints e.g. Sale price optimization, identifying optimal Inventory for maximum fulfillment & avoid stock outs

PMML: this is xml base file format developed by data mining group to transfer models between various technology platforms and it stands for predictive model markup language.

Quartile: It is dividing the sorted output of model into 4 groups for further action.

R: Today every university and even corporates are using R for statistical model building. It is freely available and there are licensed versions like Microsoft R. more than 7000 packages are now available at disposal to data scientists.

Sentiment Analytics: Is the process of determining whether an information or service provided by business leads to positive, negative or neutral human feelings or opinions. All the consumer product companies are measuring the sentiments 24/7 and adjusting there marketing strategies.

Text Analytics: It is used to discover & extract meaningful patterns and relationships from the text collection from social media site such as Facebook, Twitter, Linked-in, Blogs, Call center scripts.

Unsupervised Learning: These are algorithms where there is only input data and expected to find some patterns. Clustering & Association algorithms like k-menas & apriori are best examples.

Visualization: It is the method of enhanced exploratory data analysis & showing output of modeling results with highly interactive statistical graphics. Any model output has to be presented to senior management in most compelling way. Tableau, Qlikview, Spotfire are leading visualization tools.

What-If analysis: It is the method to simulate various business scenarios questions like what if we increased our marketing budget by 20%, what will be impact on sales? Monte Carlo simulation is very popular.

What do think should come for X, Y, Z?

From Bullock Cart to Hyperloop – Digital Transformation of Travel

Posted by Sandeep raut on April 8, 2017 at 16:16 in OSes

Remember when you were teenager and wanted to go on vacation with parents-you were asked to go to travel agent and get all the printed brochures of exotic locations?

Then came the dot.com wave and online booking sites like Expedia, Travelocity, Makemytrip paved so much that took travel agencies out of equation.

We used to send holiday postcards to our friends and families back home, which are gone out of business due to social media postings on Facebook, Instagram.

Lonely Planet used to be the traveler’s bible, but now we go to tons of websites like TripAdvisor, Priceline which provide us with advice and reviews on hotels, tours and restaurants.

Now I am able to book my flight online, have my boarding pass on my phone, check in with machines, go through automated clearance gates and even validate my boarding pass to board the plane

The travel industry, like many others, is being disrupted by great ideas powered by digital technology and innovation.

Some of the digital innovations travel industry taken so far:

· Online booking sites like Expedia, Travelocity, MakeMyTrip, Trivago

· Mobile optimization with Wi-Fi enablement

· Targeting and hyper-personalization with Big Data Analytics

· Digital discounts on travel by Kayak, Tripadvisor

· Smartphones for research vacations, deals, feedbacks

· Wearables like Disney band for payments, room keys

· Bluetooth beacons to guide travelers in the vicinity at airports

· Virtual reality – see the places without even getting out of home

All such digital footprint of customers are collected and then analyzed by big data analytics to hyper personalized the experience.

With extensively networked digital properties and deep hooks into customer data collected via travel booking sites and social media channels, travel companies are delivering customized dream vacations according to the likes and preferences of today’s travelers.

Today’s trend is towards spending money on memories & experiences instead of material possessions.

Accordingly, travel companies are investing in their digital storefronts and omni-channels to keep today’s hyper-connected travelers snapping, sharing, researching and reviewing on the fly – leaving immense data footprints for marketers to leverage.

Bluesmart is a high-quality carry-on suitcase that you can control from your phone. From the app you can lock and unlock it, weigh it, track its location, be notified if you are leaving it behind and find out more about your travel habits.

Thomas Cook have introduced virtual reality experiences across select stores.

Digital disrupters like Airbnb have already put tremendous pressure on hotels.

Starwood Hotels have launched “Let’s chat”, enabling guests to communicate with its front desk associates via WhatsApp, Blackberry messenger or iPhone before or during their stay.

World has gone from Bullock Cart to Hyperloop today. The future will belong to those using data-based intelligence to offer better experiences, encourage exotic longer and more frequent stays, and build long-term loyalty