Sunday, 24 September 2017

How the Digital has changed the way we communicate

Digital Transformation has impacted every business on earth and all aspects of life today. 

Communication is the most impacted area of our life.

Way back in 1997, most of the communication was in printed form – we used to write down our contacts in a diary, read the latest news in newspapers which still exists, there were printed magazines, retailers like Argos, Radio Shack, Ikea had printed catalog of their products. We used to get that free on the doorstep and then used to cut few coupons to get discounts at the store.

Now fast forward to 2017, Today we live in “always on, always connected” world with communication from all directions.

Social Media has stormed into our world. The moment you get up in the morning, you see alerts of birthdays of your friends on Facebook, messages from Instagram or official email communication on your smartphone.

You start for your office and you get google map alert of drive time to the office. While in office you get a variety of communications from senior management on company vision to various policies from HR, to transport and other facility related stuff from Admin.

In the evening when you head back home, you get relevant personalized communications while you are driving thru city on offers, discounts, sales and the list goes on.

One of the most prominent aspects of change we have experienced this digital age is the increased number of ways we can now communicate with each other.

Employee communication plays an important role to change the culture for digital. When employees feel like they have a stake in the outcome, have a clear understanding of their roles and responsibilities they are more engaged and effective.

Similarly, customer experience is the biggest driver for digital communication. They seek relevant, personalized communication in real time.

There are so many examples of digital revolution in Communication:
·       Thinking mobile first for young generations
·       Creating chatbots for quick query resolution instead of calling the customer service and waiting on line
·       Creating social media strategy for customer connect
·       Giving customer service on any device, any place and anytime
·       Providing news and reminders on smartphones

Communication to customers, external media, and influencers as well as to employees is important in this digital age.

Communication has changed a lot since last few decades.

Sunday, 17 September 2017

Digital Transformation in the Fashion Industry

Gone are the days when brand communication was mostly made up of ads that appeared on billboards, in magazines and/or on television. Today, all of this is augmented with Digital revolution.

The fashion industry is engaging with digital technology in new and different ways, in order to stay competitive and to engage with the ways that consumers are searching for jewelry, clothes, and accessories.

Technology is turning the fashion industry inside out. Today, consumers are most active as digital shoppers in the Fashion industry and are demanding a heartening digital experience across channels. People love the brick-and-mortar stores but also exploit online channels through social media, while on the go and online. These Omni-channel experiences should provide customers with a “wow” factor and Digital Transformation is the way to achieve this objective.

In today’s fashion world, competition is fiercer than ever, giving consumers’ far greater power & they demand only the very best customer service. Most of the fashion brands now have a social media presence on Pinterest, Instagram presence, tapping into our heightened engagement with imagery.

It can take many years to build a successful brand, but only a short time to destroy it. Fashion brands have always needed to be ready and able to respond to issues of uncertainty, risk, and reputation, all at varying times.

Burberry is the posterchild in digital for fashion that started with live streaming runshows. Then came iPads and mobile apps for consumers to try out different outfits.

In Paris, a window front invites passers-by to download the Louis Vuitton Pass app in order to interact with the window and explore.

L’OrĂ©al has put up a 'social wall' on its main website so consumers can share posts while shopping.

Harrods is the latest luxury retailer to transform its in-store experience with digital technology. They have many new super-high resolution stairwell displays at the flagship Knightsbridge, London store

Adidas has a store wall which shows shoe collections in 3D to see shoe designs from all angles.

With this availability of streaming big data and resultant analytics, fashion brands use the insights for hyper-personalization, align consumer experience and to track customer trends. The customer’s data is the core component of digital transformation in the fashion industry. So, hyper-personalization of mobile retail experiences will be huge in the near future.

Today, dressing rooms enhanced with augmented reality and social media features have transformed the shopping experience altogether. L’Oreal, Maybelline have already started testing special kiosks that enable shoppers to virtually try on makeup by simply taking a picture.

Even the most successful digital retail experiences are built from desktop experiences but the future is in mobile with a predicted 80% of sales traffic coming via this medium.

With digital at a side, fashion weeks across London, Paris, Milan & New York witness runway shows streamed online, Instagram & snapchat stories in real time, creating a close connection between consumers and brands. 

Sunday, 10 September 2017

How machine learning APIs are impacting businesses?

In this Digital age, every organization is trying to apply machine learning and artificial intelligence to their internal and external data to get actionable insights which will help them to be closer to today’s customer.

A few years back it was the field only for data scientists and statisticians, who used to analyze the data, apply several techniques and provide results.

Today many of the organizations are using APIs to access the ready-made algorithms available in the market as they make it easy to develop predictive applications. In fact, you don’t even need to have an in-depth knowledge of coding or computer science to introduce them into your apps.

APIs provide the abstraction layers for developers to integrate machine learning into real world applications without worrying about which technique to use or how to scale the algorithm to their infrastructure.

These APIs can be categorized broadly into 5 groups:
·        Image and Face Recognition: It understands the content of the image, classifies the image into various categories, detects individual objects and faces, detects labels and logos from the images.
·        Language Translation: Translate text between thousands of languages, allows you to identify in which language any text that you need to analyze was written. Some APIs allows organizations to communicate with the customer in their language.
·        Speech Recognition and Conversion: Today most of the customer service is handled by Chatbots with underlying APIs helping simple question and answer. Speech to text APIs are used to convert call center voice calls into text for further analysis.
·        Text /Sentiment Analytics using NLP: With the rise of Social Media, consumers easily express and share their opinions about companies, products, services, events etc. Companies are interested in monitoring what people say about their brands in order to get feedback or enhance their marketing efforts. These APIs can identify, analyze, and extract the main content and sections from any web page. They further help in to analyze unstructured text for sentiment analysis, key phrase extraction, language detection and topic detection. There are some tools also helps in spam detection.
·        Prediction: These APIs, as the name suggests helps to predict and find out patterns in the data. Typical examples are Fraud detection, customer churn, predictive maintenance, recommender systems and forecasting etc.

Google Cloud, Microsoft Cognitive Services, Amazon Machine Learning APIs & IBM Watson APIs are the leaders in the market.

With growing number of free/reasonably priced APIs and tsunami of data generated every day, the race is on as to which is the best Machine Learning API.

These machine learning APIs are not yet perfect or matured and they will take some time to learn and act accurately. But they allow faster time to market-based on ready availability, rather than asking data scientist to code the algorithms.

In future, machine learning will lead to revolutions that will intensify human capabilities, assist people in making good choices and help navigate through the world in powerful ways, like Iron Man's Jarvis.

Sunday, 3 September 2017

How do you measure the success of Digital Transformation?

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.

Sunday, 27 August 2017

Machine Learning - The brain of Digital Transformation

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.

Saturday, 19 August 2017

Are you drowning in Data Lake?

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.

Saturday, 12 August 2017

Why Data Visualization matter now?

Data Visualization is not new, it has been around in various forms for more than thousands of years. 

Ancient Egyptians used symbolic paintings, drawn on walls & pottery, to tell timeless stories of their culture for generations to come.

Human brain understands the information via pictures more easily than writing sentences, essays, spreadsheets etc. You must have seen traffic symbols while driving…why do they have only 1 picture instead of writing a whole sentence like school ahead, deer crossing or narrow bridge? Because you as driver can grasp the image faster while keeping your eyes on the road.

Over last 25 years technology has given us popular methods like line, bar, and pie charts showing company progress in different forms, which still dominate the boardrooms.

Data visualization has become a fundamental discipline as it enables more and more businesses and decision makers to see big data and analytics presented visually. It helps identify the exact area that needs attention or improvement than leaving it to the leaders to interpret as they want.

Until recently making sense of all of that raw data was too daunting for most, but recent computing developments have created new tools like Tableau, Qlik with striking visual techniques, especially for use online, including the use of animations.

There is a wealth of information hiding in the data in your database that is just waiting to be discovered. Even historical complicated data collected from disparate sources start to make sense when shown pictorially. Data Scientists do a fantastic job of analyzing this data using machine learning, finding relationship but communicating the story to others is the last milestone.

In today's Digital age, we as consumers generate tons of data every day and businesses want to use that for hyper-personalization, sending right offers to us by collecting, storing & analyzing this data. Data Visualization is the necessary ingredient to bring power of this big data to mainstream.

It is hard to tell how the data behaves in the data table. Only when we apply visualization via graphs or charts, we get a clear picture how the data behaves. 

Data visualization allows us to quickly interpret the data and adjust different variables to see their effect and technology is increasingly making it easier for us to do so. 

The best data visualizations are ones that expose something new about the underlying patterns and relationships contained within the data. Data Visualization brings multiple advantages such as showing the big picture quickly with simplicity for further action.

Finally as they say “A picture is worth a thousand words” and it is much important when you are trying to show the relationships within the data.

Data is the new oil, but it is crude, and cannot really be used unless it is refined with visualization to bring the new gold nuggets.

Sunday, 6 August 2017

Do you want to hire a Data Scientist?

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 at analytics, 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.


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