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 as the 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 creditworthiness of cardholders, 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 ever, 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 an entry of Hadoop, everyone wants to dump their silos 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 on the 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?ilo
·        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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