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Why Analytics Makes Tesla Better Than Jaguar

Why Analytics Makes Tesla Better Than Jaguar | Business Intelligence & Analytics Scoop | Scoop.it
The Tesla isn't a perfect car, especially in a market still dominated by gas guzzlers. But the company's widespread use of analytics to study its vehicles improves the customer experience and offers a lesson to automobile industry mainstays still resting on their laurels.
Lakshmi Chaudhari's insight:

This article explicates how Tesla’s customer analytics initiative has helped it grow its customer loyalty and market share in an industry still dominated by gas guzzlers.

 

The actions they took along with the impact are as below:

Action - Tesla fully instruments its cars by default, connecting them wirelessly to their corporate offices for analysis.

Impact - It results in far higher customer satisfaction score and better targeting of resources to customer satisfaction. It helps Tesla better anticipate and correct problems before they damage the firm.  


Action - Use of sensor data, customer contact and analytics.

Impact - Tesla has grown faster than the rest of the market, its customer loyalty is far higher, and its car has been rated the best in the world – ahead of cars and firms that have been in the car business for more than a century.


Action - Tesla itself runs its most active forums on its own car.

Impact - It gives Tesla a running sense of what excites and annoys customers and, in turn, gives Tesla a massive advantage over firms that don't host or monitor forums on their cars.

 

Conclusion: It is important to capture more real-time information about customers, and do more with that information at an executive level. If you're building something as revolutionary as the Tesla electric car, good product and customer analytics may be the only real insurance against failure. 

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Big Data -- Big buzzword or big change? - YouTube

Big Data -- Big buzzword or big change? - YouTube | Business Intelligence & Analytics Scoop | Scoop.it
In this Google Hangout, industry experts explore how Big Data is changing business and our lives. In a world where more and more data is being monitored and ...
Lakshmi Chaudhari's insight:

This is an excellent Google Chat from thought leaders in Analytics. My insights include:

 

1)      Mainframes VS Hadoop: Future of mainframes is bright

A key consideration is to distinguish between hype and reality and focus on value. Many Fortune 500 and 250 companies still run mainframe databases. These mainframes serve as the system of record for many companies. They are reliable, cost effective per unit of measurement and automated. They can serve not only as a source, but also as a platform on which analysis is performed using new advanced technologies.  Another advantage of using mainframes is that they are relatively “hacker-proof” as compared to alternative open source technologies.  As long as mainframes are able to grow and accommodate operational bounds, the future of mainframes looks bright.

However, the analytics required will eventually dictate the platform used. Supporting mainframes with Hadoop externally can serve to be a more flexible architecture. The move to the big data world of distributed databases can help avoid silos.

 

2)      Leaders in Big Data Adoption:

The biggest adopters are Internet companies for obvious reasons.

The second biggest adopters are in Retail mainly due to the tight margins that they operate on.

Lastly, Financial Services is heavily adopting big data in fraud detection and risk management.

 

3)      Ethics in Big Data with respect to data privacy:

Companies have to mitigate the reputational risk that comes with excessive use of customer data to the point where customers are spooked and move to competitors.

 

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tdwi-8-considerations-utilizing-big-data-analytics-with-hadoop-107015.pdf

Lakshmi Chaudhari's insight:
Excellent paper explaining the key considerations while using Hadoop for Big Data initiatives. 

1)      Although Hadoop doesn’t replace the data warehouse, it can complement it, especially for storing disparate data types. Hadoop can be an important part of the analytics ecosystem, but open source or as-is Hadoop and MapReduce may not be the best choice for advanced analytics.

2)      Move from ETL to ELT (extract, load, then transform): One Hadoop use case is to preprocess data in Hadoop and then bring relevant data into the warehouse or to an in-memory server or other platform for analysis.

3)      Leveraging logical data warehouses to create virtual views of data from relational and big data sources without data movement accelerates time to insight and reduces IT workloads.

4)      Business value from big data analytics can only be realized if results from the model are integrated within the business processes to help improve decision making. The models need to be operationalized to handle new data and reviewed periodically.

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5 Business Analytics Tech Trends and How to Exploit Them

5 Business Analytics Tech Trends and How to Exploit Them | Business Intelligence & Analytics Scoop | Scoop.it
Big Data. Faster infrastructure. Falling costs. Mobility. Social media. CIOs at John Hancock, Shopzilla and other organizations say these IT trends are transforming how their companies process data to gain valuable business intelligence.
Lakshmi Chaudhari's insight:

1) Technology leaders should adopt the attitude that more data is better and embrace overwhelming quantities of it. This was particularly interesting because another school of thought that emerged at an analytics conference I attended recently focused on investing only in collecting data that can provide insights.

 

2) The capacity of today's computers to process much more data in memory allows for faster results than when searching through data on disk-even if you're crunching only gigabytes of it.

 

3) Technology costs less, especially with open source technologies. Once, open-source tools were available only for basic reporting, but now they offer the most advanced predictive analytics.

 

4) BI is going mobile.

 

5) With the explosion of Facebook, Twitter and other social media, new analytics applications have emerged to support statistical techniques such as natural language processing, sentiment analysis, and network analysis that aren't part of the typical BI toolkit.

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Big Data Analytics: Answers from Big Data

Keynote video, presented at the SAS Analytics Conference 2012, by William Hakes, CEO and Co-Founder of Link Analytics. Big Data Analytics is not just hype. B...
Lakshmi Chaudhari's insight:

The world's data is doubling every 1.2 years. Big Data might not be only a hype after all!

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Visualization techniques revolutionize modern business intelligence gathering

With so much data to be analyzed, organizations want a new way to be able to conceptualize the business intelligence they have gathered. New visualization techniques are making this happen.
Lakshmi Chaudhari's insight:

1. The data visualization solution must provide for self-service BI that really empowers the user to achieve goals in his/her business.

2. Every visualization feature must be easy to use and fill a functional role.

3. Executive dashboards need to be simple to easily identify problem areas but with an ability to drill on demand to locate problems as they arise.

4. There are essentially 3 types of visualizations:

 

Strategic dashboards that focus on high level measures of performance. They usually feature static snapshots of data on a daily, weekly or monthly basis and have little user interaction.

 

Analytical displays that are designed for detailed analysis. They include extensive historical data, still mostly periodic snapshots.

 

Operational dashboards that need dynamic environments using real-time or near real time data. 

.

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Knowing Hadoop Is Not Enough for Big Data

Knowing Hadoop Is Not Enough for Big Data | Business Intelligence & Analytics Scoop | Scoop.it
In the retail space, I was one of the early adopters of big data. I am told by the vendors I was the first in the Midwest and tied for first in the industry overall. Years ago I came upon hadoop,
Lakshmi Chaudhari's insight:

Hadoop is a great tool but just knowing Hadoop is not enough to know big data.

Normally when a company fails it is for two reasons:

1. They get someone who knows Hadoop and systems admin and think that now they can do big data. Big mistake.

2. They don't understand that big data is not a department function, it is a companywide initiative.

Why is knowing hadoop not enough? Simple, hadoop is merely a tool for processing data.

Many companies want someone who knows strategy, marketing, IT, hadoop, R, java, mahout, data science, economics, data viz and be able to present to the CEO on all this, oh and you need to be an expert in all of these. Fact is, they don't exist. Yes, you can find someone who knows all of these, but an expert in all of them, no. The skills are so wide that you need to break it down into groups to get the best in that group.

Often companies treat big data as a data or IT issue, so naturally they put the project in IT or analytics. At first when building this will seem to make sense but as it grows, it will not make sense and will limit benefits if left in those teams only. Big data initiatives must span the enterprise rather than individual departments.

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Big Data and Key Performance Indicators | SmartData Collective

Big Data and Key Performance Indicators | SmartData Collective | Business Intelligence & Analytics Scoop | Scoop.it
(Image) We sometimes get so caught up in the hype of big data – the huge, fast moving, complex and diverse data sets – and the potential value they can deliver to companies, that we forget its little brother.
Lakshmi Chaudhari's insight:

We shouldn’t forget traditional KPIs in all the hype about big data. In fact, traditional KPIs such as revenue growth, profit margins, customer loyalty, relative market share or staff engagement are vital components of any big data initiative and therefore more important than ever before.

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Tell a Meaningful Story With Data – Think Insights – Google

Tell a Meaningful Story With Data – Think Insights – Google | Business Intelligence & Analytics Scoop | Scoop.it
In this article, see how brands are taking data and weaving that into interesting and meaningful stories for their audiences and consumers.
Lakshmi Chaudhari's insight:

Data analysis is not about graphics and visualizations; it’s about telling a story. Data is powerful. But with a story, it’s unforgettable. Data should be used to tell a meaningful story that resonates both intellectually and emotionally with an audience.

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