Data Analysis and In Memory Technologies, let’s connect the dots:

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SPEED is one of the important aspect of Data Analysis. Wouldn’t it be great if you query a data source, you get your answers as soon as possible? Yes? Right! Of course, it depends on factors like the size of the data you are trying to query but wouldn’t it be great if it’s at “SPEED OF THOUGHT“?

So Here’s the Problem:

Databases are mostly disk based and so the bottleneck here is the speed at which can get access to data off the disks.

So what can you do?

Let’s put data in a RAM (memory) because data-access via memory is faster.

If it’s sounds so easy, why didn’t people do it earlier? And why are we talking about “In Memory” NOW?

1) BIGGER Data Size/sets and so today with more data, it takes more time to query data from databases. And so researchers have looked at other approaches. One of the effective approach they found is: In-memory

(And I am not ignoring the advances in Database Technologies like Parallel databases, But for the purpose of understanding “Why In-memory”, it’s important to realize the growing size of data sets and a viable alternative we have to tackle the problem: In memory. And also I am not saying that it’s the ONLY way to go. I am just trying to understand the significance of in-memory technologies. We, as data professionals, have lot’s of choices! And only after evaluating project requirements, we can talk about tools and techniques)

2)  PRICE of Memory: Was the price of RAM/memory higher than what it is today? So even though it was a great idea to put data in memory, it was cost-prohibitive.

So Let’s connect the dots: Data Analysis + In Memory Technologies:

What’s common between Microsoft’s PowerPivot, SAP HANA, Tableau and Qlikview?

1) Tools for Data-Analysis/Business-Intelligence 2) Their Back End data architecture is “In Memory”

So since Data Analysis needs SPEED and In-Memory Technologies solves this need – Data Analysis and Business Intelligence Tools adopted “In-memory” as their back-end data architecture. And next time, when you hear a vendor saying “in-memory”, you don’t have to get confused about what they’re trying to say. They’re just saying that we got you covered by giving you ability to query your data at “speed of thought” via our In-memory technologies so that you can go back to your (data) analysis.

That’s about it for this post. Here’s a related post: What’s the benefit of columnar databases?

your comments are very welcome!

The role of Sentiment Analysis in Social Media Monitoring:

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I’ve posted tutorial/resources about the Technical Side of Sentiment Analysis on this Blog. Here are the Links, if you need them:

LingPipe (Java Based) | Python | R language resource | Microsoft’s Tool “Social Analytics

Apart from this, I’ve used other Tools per project requirements and It’s been fun designing and developing projects on “Sentiment Analysis” primarily using Social Media Monitoring. Having worked with clients on projects that use “Sentiment Analysis” – I reflected about the role of Sentiment Analysis in Social Media Monitoring. And in this blog post, I am sharing these reflections:

What is Social Media Monitoring?

Social Media Monitoring is a process of “monitoring” conversations happening on social media channels about your brand/company.

Is it NEW? Not really. The idea of monitoring or gathering data about what is being talked about the brand/company is not new. Earlier, it was newspapers and magazine-articles and now, it’s the social media channels including online news, forums and blogs and thus the name given to this process is “Social Media Monitoring”

brand monitoring social media

What is Sentiment Analysis?

Analyzing data to categorize it under a “sentiment” (emotion).

Example. Is this review saying positive, negative or neutral thing about our product.

sentiment analysis positive negative neutral

side-note: Sentiment analysis is often categorized under “Big Data Analytics”.

What’s the Role of Sentiment Analysis in Social Media Monitoring?

We’ve seen that in social media monitoring, we gather all online conversations about a brand/product/company. Now wouldn’t it be great to take the data that we have and bucket it under “Positive”, “Negative” or “Neutral” categories for further analysis?

So few questions that can be answered after we have results from sentiment analysis:

1) Are people happy or sad about our product?

2) What do they like about our product?

3) What do they hate about our service?

4) Is there a trend or seasonality in sentiment data?

Among other business insights that may be not be easily answerable with just plain text data.

Thus sentiment analysis is one of the step in social media monitoring that assists in analyzing sentiment of all the conversations happening on the social web about a brand/product.

That’s about this for this post. Here’s a related post: Three Data Collection Tips for Social Media Analytics

your comments are very welcome!

How conditionally formatting your data in Excel can help you save time in answering business questions?

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Visual analytics is amazing – it helps “data enthusiasts” save time in answering questions using Data. Let’s see one such example. For the purpose of the blog post, I am going to show how to do it in Excel 2010:

Problem:

Here’s the Business Question: What was sales of Tea in North Region in 2012 Q1

Here’s the data:

SALES DATA(2012 Q1)  East West Central North South
Coffee  $  7,348.00  $  7,238.00  $  1,543.00  $  9,837.00  $    1,823.00
Tea  $  9,572.00  $  8,235.00  $  3,057.00  $  8,934.00  $  13,814.00
Herbal Tea  $  5,782.00  $  8,941.00  $  9,235.00  $     392.00  $    1,268.00
Espresso  $  9,012.00  $  2,590.00  $  4,289.00  $  7,848.00  $       340.00

So it’s easy to give out answer using the data: $8934

But let me CHANGE the business question:

WHICH Products in WHAT regions are doing the best?

Now this questions is not as easy as the previous one? WHY? because you’ll have to manually go through each number in a linear fashion to answer the question. Now imagine a bigger data-set. It’ll take even more time.

Solution

What can Excel Power users and Data Enthusiasts do to answer the new business question in an efficient way? Well, let’s see what conditional formatting can do it:

Excel Visual Analytics Conditional formatting

Now with the Data Bars, it’s easier to just glance at the report and see best performing products and regions. For instance, it’s very easy to spot that Tea is performing best in South among all products and region.

So how do you create data bars?

1. Select the data

2. Home > Conditional Formatting > Data Bars

Excel Visual Analytics Conditional formatting 2

3.Done! you’ll see this:

Excel Visual Analytics Conditional formatting

4. You can play with other options here to see what suits the best for your needs. But I just wanted to point out that there is a way for you to highlight the data in a way that helps you save time in answering business questions using data

Conclusion:

Visual analytics is a great way to quickly analyze data. In most cases, Human brain is much faster at interpreting the visual results as oppose to text/numbers – so why not use it to your advantage. And tools like Excel have inbuilt functionality to help you do that!

Resource: Introduction to Data Science by Prof Bill Howe, UW

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Introduction to Data Science course taught by Bill Howe just started on coursera platform. Having studied the Data Intensive Computing in Cloud course at UW taught by Prof Bill Howe, I can say that this course would be great resource too!

Check it out: https://www.coursera.org/course/datasci

Introduction to Data Science