Sunday, 21 August 2016

How Industrial Internet of Things are impacting our lives

General Electric coined the term Industrial Internet in late 2012. It is effectively synonymous with the Industrial Internet of Things, and abbreviated as Industrial IoT or IIoT.

While many of us are familiar with the Internet of Things used by Nike+ FuelBand, FitBits, Hello Barbie, Nest and Samsung Fridge as connected devices, there’s much more going on in connecting industrial devices in the world of IIoT.

The Industrial Internet is still at an early stage, similar to where the Internet was in the late 1990s. The IIoT, through the use of sensors, advanced analytics and intelligent decision making, will profoundly transform the way field assets connect and communicate with the enterprise.

Industries impacted by IIoT are Manufacturing, Aviation, Utility, Agriculture, Oil & Gas, Transportation, Energy, Mining & Healthcare.

One of key opportunity that early adopters of the Industrial Internet are pursuing is the improvement of worker productivity, safety and working conditions.  Using drones or flying robots to monitor oil pipelines, chemical factories etc.

The IIoT will revolutionize manufacturing by enabling the acquisition and accessibility of tons of data, at lightning speeds, and far more efficiently than before.

A number of innovative companies have started to implement the IIoT by leveraging intelligent, connected devices in their factories.
  • GE’s latest locomotive have more than 250 sensors that measure 150,000 data points per minute giving location, weight, speed, fuel burn etc. for trip optimization,  remote diagnostics and yard planning.
  • Rolls-Royce’s TotalCare provides a suite of predictive maintenance and repair services for its jet engines, including monitoring engine health, and modifying engines to increase reliability and durability.
  • John Deere is building intelligence into its large combines, tractors and sprayers through sensors that make the machines into mobile platforms.
  • Amazon now operates one of the world’s largest fleets of industrial robots in its warehouses, where humans and robots work side-by-side, capable of fulfilling orders up to 70% faster than a non-automated warehouse.
  • Rio Tinto, a global mining company uses a remote command center to orchestrate the actions of huge drills, excavators, earth movers and dump trucks across multiple mining sites.
  • Aitbus has created a factory of future with IIoT, to track and visualize production process in real time.
  • Marathon Oil, at their refineries, employees wear a wireless multi-gas detector. This helps them by tracking their exposure to dangerous gases

With industrial internet of things, leading companies are improving operations, lowering costs, improving safety of workers.

Data generated by industrial equipment like Turbines, Jet engines and CT/MRI scanning machines when combined with big data analytics will give tremendous value to business.

IIoT & Big data analytics will change the game of competition while there are challenges like data security & interoperability in existing systems which have to be resolved.

To speed up the development & accelerate the innovation in IIoT, the Industrial Internet Consortium was formed by GE, Cisco, AT&T, IBM and is over 250 members from 30 countries.

The path to Industry 4.0 is via Industrial Internet of Things (IIoT) and is boosting the Digital Transformation in many ways.


Sunday, 14 August 2016

9 traits to become successful Digital Transformation leader

Digital Transformation is inevitable.

Across all industries, from consumer goods to health care, manufacturing to financial services, companies are going digital. Digital technologies from social media to mobile computing to big data to the Internet of everything are transforming businesses in these industries.

But how many of them are successful? As per surveys this number is less than 10%. Are you one of them? It all depends on how you lead the digital transformation efforts.

Here are some of the traits you need to show to take the organization on the digital ride:
  1. Start the Digital as company wide initiative and not in small departments. Get the commitments from the board of directors and CEO for digital.
  2. Develop a Vision & Mission statements that are in line with company growth strategy. Ensure it does not remain only as floor branding and marketing. Describe how the company will actually change: How will you engage differently with customers? How will you rethink your operations?  What new business models are possible?
  3. Bring in the cultural change that is required for Digital Transformation. People are extremely important in this roller-coaster ride. Many employees have seen visions come and go. Focus on communicating the vision and helping people know what it means to them
  4. Be customer obsessed.  With technology and customer habits changing so quickly, developing a deep and detailed view of customer behavior across all the channels is foundation of digital success.
  5. Experiment a fail fast approach with data driven measurements. As W Edwards Deming’s quote says – In God we trust, all others bring data”. Dare to rake risks and take decisions based on the data and move on.
  6. Connect with industry leaders & influencers to understand what work and what not. What are the current trends? Keep yourself updated.
  7. Simplify the subject so everyone in the company can understand and contribute to your efforts.
  8. Connect with partners who support your vision – and not only external third party technology vendors but your own customers and most important your employees.
  9. Lastly remember Digital is a long term commitment. Don’t cut the investments & pull back employees on billable roles in just one quarter saying it’s not working. This is the mistake most of the companies are doing. Nurture the efforts like a new born baby as she takes over a year to walk on its own. Have patience to see the results.

There are various successful examples of Digital Transformation in the industry from Starbucks to Uber, from Netflix to Nestle with their leaders showing these and many more characteristics.

My favorite saying for leaders now is “Uber them before they Kodak you”.

Sunday, 7 August 2016

How to evaluate Data Science models ?

In today’s Digital age,  insights received from data science are extremely important to deliver the best customer experience. 

Data Scientists use various techniques such as Regression, SVM, Neural network, Nearest neighbor, Naive Bayes, Decision Tree and Ensemble models.

These algorithms help to identify previously unrecognized patterns and trends hidden within vast amounts of structured and unstructured information. These patterns are used to create predictive models that try to forecast future behavior.

These models have many practical business applications: predicting patients at risk, they help banks decide which customers to approve for loans, and marketers use them to determine which leads to target with campaigns.

But how to determine if the predictive models you create are accurate, meaningful representations that will prove valuable to your organization?

There are various methods used by data scientists to measure the accuracy of the model:
  • Lift Charts & Gain Charts: 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.
  • ROC Curve: The ROC curve is the plot between false positive rate and True Positive rate.
  • Gini coefficient: This is the ratio of area between the ROC curve and the diagonal line & the area of the above triangle
  • Cross Validation: splitting the data into two parts, where one part is used for "training" your model, and the second part is used to make predictions. By this you can test the model on the data that was "not seen" by it previously, and check how it could possibly behave with external data.
  • Confusion Matrix: A table showing the number of predictions for each class compared to the number of instances that actually belong to each class. This is very useful to get an overview of the types of mistakes the algorithm made. This method shows accuracy, true positive, false positive, Sensitivity & specificity of the model.
  • Root Mean Squared Error: This is the average amount of error made on the test set in the units of the output variable. This measure helps you get an idea on the amount a given prediction may be wrong on average. This is most popular in regression techniques.
In general, the assessment used should be closely matching the business objectives. Using the right metric can have more influence on you model performance than the algorithm you use.


There are so many data points generated by Internet of Things, Mobiles, Social Media and all the Omni-Channels used for customer interactions. Only storing this data is useless , unless it is used by data scientists for generating insights that is used for next actions. 

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