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.


Sunday, 30 July 2017

How Customer Analytics has evolved...

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 social channels 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 analytics based 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.

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