AI for Testing

Winds of Change….

Software testing conventionally looks at validating requirements and, ignoring the correlation of data points that can be extracted from test results, test processes, defect logs, developer & tester performance, production incidents, code coverage, environment factors and sentiment of product users in real life situations. RTAP draws insights from these data points and enables AI (Machine Learning) based decisioning to reduce software defects, thus surpassing traditional testing practices and driving process and performance efficiencies.

Future of Testing….

Companies across the world are prioritizing their strategic technological adoption plans in the face of growing competition and pressure, which is positively contributing to the software testing industry. As the world embraces digital transformation and companies use newer techno-business models to sustain competition, the need for faster deployment of IT solutions will continue to rise. As digital transformation gains ground, organizations are investing more in application development to meet the demand of businesses. This has boosted spending on testing and quality assurance. This will translate into notable changes in the overall software development & testing process.

In this rapidly changing scenario, there is a paradigm shift in the way customers are building & testing products. The traditional methods of testing are gradually getting augmented by more controllable methods like Agile & SDET (Software Developer Engineer in Test). But the biggest change that is coming forth is from the realization that human effort in software testing has limited scalability and reliability. This directly challenges the continuation of our prevalent labor arbitrage model.

The beginning….

Imagine an AI (Machine Learning) software that analyzes every stage of the software testing life cycle and helps you take data driven decisions to reduce defects, optimize test case execution and reduce human effort. RTAP is such a product!

RTAP can deliver Improved quality, Faster time to market, and significant reduction in efforts with complete End to End test coverage. For the first time in the field of testing, it lays the foundation for taking a data driven, cognitive approach for reducing software defects.

It is an accepted fact in the technology industry that by simply adding more people in the software testing process, products are not going to get better. Increasing Head Count is simply not a scalable solution and heuristic methods of predicting QA outcomes is not a reliable method when you look at the sheer size and number of data points to be considered. There is also this need for progressive “defect reduction” as a key metric for evaluating the performance of test teams. In fact, some customer surveys have gone so far as to suggest that, in the future testers should be paid based on the defects they fix!

Results to look for….

RTAP improves testing effectiveness by reducing Overfit (removing redundant test cases) and Underfit (not testing key features). It uses machine learning models for Test Suite Optimization, Risk Based Testing, Traceability and Test Coverage.

RTAP uses AI models for making Predictions related to defects and helps testing team to incorporate “Shift Left” techniques that are so important for improving product quality.

RTAP also provides “What-If” analysis to simulate various parametric behaviors whereby helping the testing team to take corrective actions in advance to achieve testing objectives. It facilitates in prescribing corrective actions learned from the historical data, which can be used to reduce defects on a continuous basis. RTAP also is a self-learning product and gets smarter ( more accurate ) over a period of time by fine-tuning its machine learning models.

Only RTAP….

Backed by over 2 years of research, product development, data science and collaboration with one of the largest testing services providers in this world, RTAP has emerged as the only product in the market today that can be quickly configured for any customer’s environment to deliver AI enabled testing of software products.

RTAP is an integrated product and has all the necessary data management utilities, machine learning models and visualization elements necessary for this use case. RTAP reduces your “tools to solutions journey” and can quickly help you deliver software products in a smarter way.

At RTAP we still believe that this is only the beginning of better things to come….

SAP HANA – Scale Out Performance Test Results – Early Findings

Customer Performance Requirements

I was recently asked by a customer to recommend a solution that could solve the problem of performing analytics on data getting generated at ATMs and POS machines. The problem in essence was that while the data was getting generated there was no way to query it on the fly. Traditional databases like Oracle, DB2, SQL Server and others were not able to solve this problem with their current product set since these databases were not built with the purpose of analyzing large amounts of data online. The traditional databases also required constant tuning, indexing and materializing the data before you could run any sort of business intelligence query on it. Essentially someone had to prepare the data and make it query ready and to say the least this costs a lot of time and money which this particular customer was trying to avoid.

Linear Scaling for Big Data in Real Time

In my opinion the only way to do this was using an in-memory database like SAP HANA which was built with the purpose of running analytics on live data. I did have some doubts about HANA’s scalability and requested SAP for guidance. They briefed me about a recent scale-out, testing of HANA where they simulated 100 TB of actual BW customer raw data over a SAP Certified configuration of a 16 node cluster with 4 IBM X5 CPUs each having 10 cores and 512 GB memory. The test data consisted of 100TB test database with one large fact table (85TB, 100 billion records) and several dimension tables. 20x data compression was observed, resulting in a 4TB HANA instance, distributed equally on the 16 nodes (238GB per node). Without indexing, materializing the data, or caching the query results, the queries ran between 300 to 500 milliseconds, which in my opinion close enough to real time. There were also ad-hoc analytic query scenarios where materialized views cannot be easily used, such as listing top 100 customers in a sliding time window, and year-to-year comparisons for a given month or quarter.

In my opinion these tests demonstrate that SAP HANA offers linear scalability with sustained performance at large data volumes. Very advanced compression methods were applied directly to the columnar database without degrading the query performance. Standard BW workload provides validation for not only SAP BW customers, but any data mart use cases. This is the first time I have encountered a solution offering BW the potential to access raw transactional ERP data in virtual real-time.

Data Management Architecture for Next-generation of Analytics

Readers of this blog may be also interested in knowing that new business intelligence optimized databases such as HANA have inherent architectural advantages over traditional databases. Old database architectures were optimized for transactional data storage on disk-based systems. These products focused more on transactional integrity during the age of single CPU machines connected through low-bandwidth distributed networks while optimizing the use of expensive memory. The computing environment has changed significantly over last decade. With multi-core architectures becoming available through commodity hardware, processing large volumes data in real-time over high-speed distributed networks is becoming a reality due to products such SAP HANA.

All in-memory Database Appliances are Not Created Equal

Apparently some solutions in the market like Oracle’s Exadata, also cache the data in Exalytic/TimesTen for in-memory acceleration. However, TimesTen is a row-based in memory database and not a columnar database like HANA which are faster for business intelligence applications. Oracle also uses these databases for in-memory cache, not like HANA which is the primary data persistence layer for BW or data mart. Therefore in my opinion, Oracle’s solution is more suited for faster transactional performance but creates data latency issues for real-time data required for analytics. From a cost and effort perspective it will also require significant amount of tuning and a large database maintenance effort when doing ad-hoc queries (sliding time-window or month2month comparison…etc) because you are trying to re-configure an architecture that is meant for transactional systems to deploy for analytics.

I hope this blog is useful and provides general guidelines to people interested in considering new database technologies like SAP HANA.


Just what Happens to us every day

It is such a simple thing, happens to us every day and we accept it all the time. Can you believe it, every moment someone is overtaking you! I am sure some of you find this idea a little over the top, but I have always believed that the greatest ideas can be explained in the simplest manner….

Let’s us take a common example, while driving a vehicle, people overtake us or we overtake people. Seems familiar, right? Everyone has done it. Now think about it for a minute, in modern day roads there are lanes you are supposed to observe, speed limits, traffic rules etc., you know the drill. But still if someone is in a hurry, likes to show off his car, does not like the way you are driving, is simply reckless or has an urgency, he simply overtakes you.

Same thing applies in business

So just like the road, in business everyday your competitors are trying to overtake you or get ahead.

If you really look at a business, it is a simple pursuit to gain something, usually at your competitor’s expense. To do this successfully, you need to have a product that is relevant today, make people want it and make sure it gets to them easily. This applies to everything you do in enterprises or in your work life.

The real question is, do you have what it takes to get ahead?

AI for it…

At RTAP, we have written many self-learning algorithms which on the principles of “overtake” tells you what you need to do to stay ahead. We have applied these machine learning models to age old methods of engineering and testing software products with remarkable success.

The concept is very simple, yet surprisingly innovative. Just like to get ahead, you chose the best route, avoid traffic bottle necks and most importantly drive responsibly – with RTAP’s AI for Testing application you can figure out the best way to build software products.

Just like you rely on modern day GPS systems to tell you the fastest route to reach your destination factoring in traffic conditions, RTAP uses AI technology to help you build better software products.

While there will always be someone with a faster car, with RTAP our goal is to make sure you arrive at the party on time and in good shape.


We the People
As humanity evolved, we stepped out into what we often call as a bright future. A future where every discovery added luster to our way of life. We also made bulk of our knowledge pervasive and accessible to everyone. Now with everyone discovering new things and finding out new ways to make things better, the rate at which we started growing in technological terms, was huge.
At this rate in about 50 years from now, most of us will not have a clue about how things work. A simple example of this can be found in our own generation. For example, most of our grandmothers knew how to sew clothes and most of our children do not. Now this may seem like an inconsequential thing, but by the time we get to be grandparents, your children will be saying that my father knows how to write a computer program, but my kids have no clue.
Let me give two other examples to drive home this point. When I was growing up, in my early school years a lot of emphasis was given on hand writing skills. In fact, we had note books to practice hand writing and they were part of our daily homework. Consequentially most people who are 50+ now have a fairly legible and more often than not beautiful hand writing. But if you fast forward this to present times, most children have very little practice in improving their hand writing skills. They simply prefer to use the computer key board to type in letters. If this goes on for say another 30 years, very few amongst us will have the ability to write in a conventional way.
In a similar way, with the advent of self-driving cars it is quite easy to imagine that in a not too distant future, we lose our ability to drive just like many people are simply not able to drive cars having a stick shift to change gears and can only drive automatic cars.

Are we included in the era of IOT?
So we are having two sets of reality here. One the technological progress will far outpace us and two we will be losing touch with things around us and how they work.
These day’s people talk about internet of things. In simple words it means that all manmade objects around us are getting smarter or have some intelligence built into them so that they can communicate with each other. The idea here is to make life easy and safe for all of us with IOT enabled world in play.
We have often heard in science fiction stories about aliens being able to communicate with each other using “telepathy”. Basically by understanding each other’s thoughts and taking appropriate actions. In a similar manner, in the era of IOT, the objects around us are able to communicate through signals of different sorts. But what about us?
If you closely observe the evolving scenario, we are not included in it. Just imagine a time in the near future where you are surrounded by objects which can communicate with each other, but not you. Because the simple truth is as human beings, in a biological way we will never be able to receive machine signals and process it efficiently. This is true for all life forms, because by nature we are designed differently. I am not saying that you will not be able to give voice or other types of commands to make the objects around you work for you. But you will not be able to internalize the relationship with smart objects around you. In many ways you will never know how what the objects are thinking. Also you will always need another object like a smart phone to communicate with these already smart in the era of IOT. Without such an interface you are absolutely helpless. Try taking the TV remote away from a child’s hand when his favorite program is going to be on air and see what happens. If that is his daily routine, he will surely get agitated because he does not know any other way of interacting with the TV.
If look what is happening here, in the fast evolving IOT era, the objects are able to communicate with each other without you, but you are not able to communicate with them just by yourself. So in my opinion in a fully IOT enabled world, we will just be going through our lives in way which is hard for us to even imagine.
Just look at a modern car for that matter. It is truly connected in every sense. As soon as you get in, it will do many things by recognizing you in an innate sort of way. It will do the same thing a little differently (Example: Seat Adjustment) for some other person it recognizes through a unique object like a “key”. Now if look closely, one object the “Car” is recognizing the other object “Key” and that’s it! You are really not in this picture! Even without you these two objects will do exactly what they are trained to do.

Cognitive Systems
To top this we are once again seeing the rise of cognitive systems. Basically these are devices which can not only sense things but can also make sense out of it. Sort of think like human beings. The word Artificial Intelligence has been tossed around for several generations now. In that sense it is nothing new. Simple things like a “wheels” or “pulleys” or “gears” are also early stage forms of cognitive systems because they allow us do things better through principles of mechanical advantage. In the sense, they all try to amplify human ability.
The modern day cognitive systems also attempt the same thing but use the power of computers to do things more quickly, more accurately and without any bias or human error. If you look at any machine learning model, whether is supervised (guided), unsupervised (observed) or reinforced(causal) the basic idea remains the same. Amplify human capability by making machines learn initially through human inputs so that they can error free results every time. It is almost like training a circus Lion, after some time it can do the most difficult trick like clock-work without any mistakes. But if you increase the height of the table or add more props, the animal is not able to do the trick.
I am sure my comparison of machine learning with training a circus animal may not go well with many people in the scientific community but my goal here to bring out these things in an easy to understand way and not really bring down the wonderful work our data scientists and engineers are doing in this space. In the real world cognitive systems can be thought of as a circus Lion which knows all the tricks one can think off and have the ability to perform it flawlessly every time.
At RTAP we build these types of systems for many use cases and our customers talk to us about many specific types of gains. These are either gains due to understanding their business drivers better, gains due to planning ahead based of one of our predictive models or doing something in advance based on a good or bad response they had due to an earlier action. But broadly these gains are in the area of productivity or efficiency. In simple words by using RTAP our customers save either time or money.
With the availability of large amounts of data and the ever increasing computing power to process it, RTAP enabled systems are able to think and do things more efficiently than human beings. In fact, the rate at which companies are adopting this system is so rapid that within the next 3 years pretty much every computer application will have some amount of AI technology built into it. To make this point clear, let me once again give a real life example which many of us are experiencing every day. If you look at your email “Junk” box, you will realize that the application has cleverly identified on its own that people bothering you with offers, mass email campaigns, objectionable content are all dumped in the “Junk” box. This is a classic example of AI technology being used, because somebody designing this email system has trained the computer (through a software program) to identify this pattern and take action. Now for those of you who do not know how to build such a system but still want to have the satisfaction of using it all you have to do is, next time you open up your email application add some more rules to your “spam” mail settings. There you go, now you are also an important player in the AI revolution.

The future as I see it…
In the world around things are gradually becoming more powerful and smarter than human beings. The future is really getting so bright that it will become impossible for us to make sense of it.
Also there is nothing much we can do to stop the rate at which we are making progress. But is still in our control is to try and not lose touch with the basic ways of doing things. A lot of responsibility is on us, in terms of remembering the importance of exploring the world with our physical and mental abilities. For example, playing a sport(s) is so important in an era of video games. Doing our daily chores and finding out how things actually work is also very important. Exploring the nature, practicing co-existence and learning through experimentation all become more meaningful in the modern world.
While we must make progress to find innovative ways to provide basic things like food, water, clean air to the citizens of this world, we must not forget that our responsibility to leave a better world for future coming generations. All this will require advances in technology even beyond things RTAP like systems are trying to do. But while we do all this, let us try to stay in touch with the real world.

Machine Learning & You

Finding you….
We are living in an era of always on devices. Very few amongst us will remember the last time we turned off our cell phone! But what we do not realize is that we are not only “live” on the digital highway all the time, we are also creating a lot of data on it through our, posts, comments, text messages etc. Can you imagine that a just over 25 years ago, you were doing none of these things and therefore not leaving behind any sort of digital foot print. Now in a matter of seconds, digitally you are all over the place.

Digital Footprints….
Think about it, so many people, but each of us leaving behind digital footprints similar to the others but unique in our own way. We at RTAP believe that this opens up a huge opportunity in the area of exploring the human mind and behavior through machine learning technology.

Dream Come True….
They say marketing is all about having a piece of your consumer’s mind. Therefore, if one is able to find out people having similar needs it will open up unimaginable marketing opportunities. For example: similar product preferences, healthcare needs etc. All this can be attempted now, move effectively with machine learning technology.

Getting Wiser: Machine Learning….
We at RTAP believe that, the availability of data in real time from a large number of sources gives us the ability to build cognitive based systems. Since RTAP is designed on the principles of machine learning, it helps you become smarter as times goes by. It allows you to bunch people, products and preferences in a more predictable manner thereby helping enterprises to offer products (and services) as per needs and desires of the consumer.

Future based on Past: Supervised Learning….
Supervised learning algorithms make predictions based on a set of examples. For instance, historical data on car colors can be used to guess future color preferences before they come out of assembly lines. Each example used for training is labeled with the value of interest—in this case the car color. A supervised learning algorithm looks for patterns in those value labels. It can use any information that might be relevant—demographics, buyers age, the type/model of the car, competitor data—and each algorithm looks for different types of patterns. After the algorithm has found the best pattern it can, it uses that pattern to make predictions for unlabeled testing data—next color choices for cars under production.

This is a common and practical example of machine learning. With RTAP’s machine learning platform we can help you look at several specific types of supervised learning like: classification, regression, and outliers. These topics are discussed briefly below:
Classification: Here we use data to predict a type. There are different types of classification algorithms like binomial, polynomial etc. depending upon the number of possible choices. Based on this we can further carry our cohort analysis to bunch up outcomes.
Regression: When a value is being predicted, as with prices of goods and services, supervised learning is called regression.
Outliers: Sometimes the goal is to identify things that are amiss or not fitting into a pattern or expected result. In credit card fraud detection, for example, a large purchase from an unknown location can trigger an alert of something being wrong. The most common way of doing this is to find out a known buying pattern for a particular credit card customer and if things look absolutely different from that pattern trigger an alert.

Tidy up to Find Things: Unsupervised Learning ….
In unsupervised learning, data points have no labels associated with them. Instead, the goal of an unsupervised learning algorithm is to organize the data in some way or to describe its pattern. This can mean grouping it into clusters or finding different ways of looking at complex data so that it appears simpler or more organized. RTAP for Life, which is our solution to analyze large amounts of unstructured genetic data to find disease pathways looks at many complex sets of algorithms, so simply arrive at an acceptable data structure or pattern for biologist to determine the presence or absence of a particular disease. Similar algorithms are also available in RTAP for Work to look at enterprise data on a number of use cases.

Experience Counts: Reinforcement Learning….
In reinforcement learning, the algorithm gets to choose an action in response to each data point. The learning algorithm also receives a feedback, indicating how good the decision was. Based on this, the algorithm modifies its strategy in order to achieve the highest reward. Reinforcement learning is common in robotics and Internet of Things applications. At RTAP, we are building on such cognitive algorithms to help businesses get smarter over a period of time and continuously learn from their hits and misses.

Take Away for Now:
We started with the being “ON” all the time and the data we are generating on a continuous basis. We then explored ways of finding your digital footprint and the importance it bears to manufacturers and service providers. We then looked at the enormity of this problem and figured out a way to make our computers (machines) do all the computational work for us to learn from this. We also talked a little bit about what we are doing at RTAP using Machine Learning Technology. The topic of cognitive sciences or Artificial Intelligence is nothing new, it has been discussed and applied in various ways over several generations. So what has really changed for this generation is the availability of large amounts of data in real-time and the ability of our modern day computers to process it. If I have to sum it up in a few words, what really has changed is you and the power of silicon.

Let’s look at Money…

What is money?

It almost sounds like an existential question, like something which has been there since time immemorial. But believe it or not it has been around just for a few thousand years. Also we invented it as a concept many years ago to help us exchange things.
Now the rhetorical question is why are we looking at money in the same old way?
The answer is very simple: we have been conditioned by the establishment guys (people who make, hold or trade in money…. A bank is a good example) from our childhood days to do so. In reality, we cannot imagine life without money. But in my opinion, time has come to have a second look at money as we know it because its value has become questionable.

Let me explain this briefly….
To start with we invented money to get rid of the “barter system”, which essentially was a way of exchanging things you have for things you want. Since the holding pattern and perceived value of your “haves” and “wants” was not clear, we resorted to equating this to the most precious metal of that time, namely gold. So we said, let us see how much a measure of gold can buy and later translated that to minting gold coins. The exchange of goods and services for gold coins went on for a number of years until someone came up with a brilliant idea of that time the “symbolic money” or the money as we know it now. The idea here was, to make money using non-gold materials but hold its value in equivalent gold bullion. This went on for a few years at best. While there is no record of who or which country or group, started defaulting first (in the sense not holding enough gold reserves to back their currency), very soon pretty much everyone started not backing their currency to the full with gold bullion. As of now, we are just printing money. Some less, some more but everyone has joined the party now.

So what does it mean to you….
What is the real value of money you hold or plan to earn as time goes by? Sadly, the answer does not foretell anything good for you. You are just too far down the bread line to really bother the establishment to change their ways, so all they will do is keep printing money and every time take a little off it. So your money in the bank or wherever it is, in which ever form can be now equated to a water bottle with a small hole in it and even if you do not drink off the bottle the water inside is slowly becoming less and will eventually be empty. The hole right now is very small so you do not notice it, but with the kind of debt countries are amassing, the hole is getting bigger. A lot of things are responsible for this erosion in value but to name a few devaluations, spending more than one can afford and plain simple wastefulness caught up with it.

What can you do to change things for good….
Now that I have your attention, let us examine what it will take for us to push the reset button on everyone who have done this to you. First of all, let us see if it is possible to do this, you being a small guy in the big bad world of financial establishments.
As a starting point, let us see if we can devise a way to go back to our good old barter system. Where we exchange goods or services for things we want. For example, I will mow your lawn if you let me pluck some roses from your garden. Or for that matter another good example would be I will give you an Orange, I have, for the Apple you have. Now if you look at these transactions we have taken the actual value of these goods and services in terms of money out of the equation and replaced that with our perceived value for it. Something which both parties feel in their own way as acceptable to each other. But by doing so, you have done a very important thing, you have taken the establishment out of the equation. So yes, if we can come up with a worldwide exchange for barter system to flourish successfully once again, you can take care of many of your “haves” and “wants” without getting bothered by the diminishing value of money as we know it.

We at RTAP’s are discussing this with several thinkers both within and outside of the financial services industry to develop an algorithm which can look at equivalence of all things which can be traded effectively in such a platform. Like us, many people now believe that an alternative to money as we know it is not only possible but will definitely be there in the future. There are several companies who are already looking at different types of what is popularly called as “crypto currency” to handle buying and selling. Most however have resorted to once again bringing the base value back to money as we know it and simply call it with an in-house name and make it available in a digital fashion. That method does cut off a lot of intermediaries, but is still far away from some of the ideas I have brought up here.
The hope here is to cut the reliance on money as we know it as much as possible and still transact with each other for goods and services based on what we feel it is worth to each other. For example, let us consider a barter between two large enterprises; a personal computer manufacturer and a computer chip maker, where for a certain number of computer chips a certain number of computers are exchanged. We feel something like this can become possible in the future.

The Road Ahead….
On the flip side, it is important to mention here that already there are a number of tax and regulatory organizations who are setting up ground rules to do such a trade. The existing crypto-currency companies are facing a host of issues from these bodies, but like every beginning this is one also will eventually find an acceptable end. It will take time and a fresh look at money as we know it to bring about this change. We at RTAP are very optimistic that with this new realization about money we will be able to develop new technology platforms for exchanging goods and services at close to its true value for the person doing it in the future.

It will take many years for this system to be adopted universally, but until then we can keep our thinking caps on, collaborate with each other and see if we can get something without using money.

The RTAP Story, one more thing…

With RTAP, our goal was to create a simple, easy to use platform that will allow users to manage data, analyze it and visualize it in a way that makes sense. We knew that if we can pull the pieces together, it will give the power of analytic discovery to so many people within an organization.

But while doing that we wanted to make the adoption process easy. This is very important and as software makers we owe it to our customers.

What more can you do to make it easy:

I often see software applications that need additional things for it to work. Starting with things like proprietary programming languages, operating systems, specialized hardware and a myriad of add-ons. Think about it, as software engineers we are in the business of making things easier for our users and somewhere down the line we seem to have forgotten that and have gone about designing products in a way where there is the least amount of care for our users. But fortunately, all is not lost and software makers are slowly responding to the calls from users who want it to be simple and easy. In my opinion the harbinger to this revolution was none other than Apple. From the very beginning they did their best to make their products easy to use and adopt, and the most recent example is their best-selling product, the iPhone (and its variants iPad, iPod etc.). If you think of it from an engineering perspective, it is clearly one of the most sophisticated products ever designed by man so far, but yet in its form factor and ease of use it still remains unbeatable. But there is something else which Apple started doing many years ago which fortunately most personal computer manufacturers have recently followed suit. It is the start-up process. Apple was the first company to make it so easy that all you had to do was push the start button and more or less with little assistance their devices would be fully operational in minutes. They were the first to make going easy, the new cool thing!

I am sure we all remember the days when we were setting up computers, routers or for that matter any electronic device and struggling so hard to make it work that invariable we had to make a call to the manufacturer’s help desk or customer service center. Thanks to companies like Apple, we do not have to do that anymore, since they have forced every other manufacturer to follow suit. They have raised the bar and now consumers are simply not willing to accept products which are hard to install.

With RTAP, we have tried to remember these things and designed a product which does not require any proprietary hardware or software to work. We use a responsive design methodology, so you can use it off any device like a phone or tablet etc. and the user interface automatically adapts to the size. It is available on the cloud as a service, so you are really not bothered with the installation, upgrades, infrastructure requirements and maintenance issues. Not only that, it comes pre-loaded with the intelligence to solve industry specific use cases, so all you have to do to get insights is really to put in your data and you have it all done from that point onwards.

We are sure in future people will build more intelligent systems using technology similar to RTAP, but hopefully all us will remember to make it simple and easy for our customers to use these things.

Let’s keep it simple.

The RTAP Story, Continues…

With RTAP, our goal was to create a simple, easy to use platform that will allow users to manage data, analyze it and visualize it in a way that makes sense. We knew that if we can pull the pieces together, it will give the power of analytic discovery to so many people within an organization.

But while doing that we wanted to make sure that we build and offer our product in an optimally engineered and usable way.

Let me explain what we mean by that..

Offer the Right Amount:

It is very important to make sure that what you offer to your customers is the right amount. Because that often determines their ability to buy or consume it. I will cover this topic through introspection of what we offer as an analytics platform versus what is typically available from other vendors of similar solutions. We have always believed in offering a RTAP instance that is sufficiently capable of solving an use case. Basically it comes with the right amount of “smart” technology and “power” to do a particular thing very well. Yes, it fails to do any other thing or solve any other analytics problem. But at least our customers are guaranteed that it is doing what it is mean to do or bought for. Every day we see and explore other analytics platforms and feel that they are often over engineered and sorely miss the intelligence to solve any particular analytics problem. So you are offered a bunch of tools and pretty much asked to figure out how to use them effectively to solve your problems. What this means is you are not only getting more than you need but you are definitely paying more for it. By offering the right amount in every product category you sell, you have an opportunity to capture market share in every segment and have many more satisfied customers.

There are many example of this in the world we live in, take different sizes of tooth paste for example or for that instances different models of cars. The list goes on and I am sure if you think about it you can see many good example for companies who have done an excellent job in this area.

We work with a number of companies on this topic and have developed unique algorithms that help you size the product which you want to sell. So once again the key word here is to offer the right amount. The corollary is also true, regardless of what you buy, buy the right amount.

I will share more stories as we go along.

Until then, let us do the right things

The RTAP Story, The Beginning…

With RTAP, our goal was to create a simple, easy to use platform that will allow users to manage data, analyze it and visualize it in a way that makes sense. We knew that if we can pull the pieces together, it will give the power of analytic discovery to so many people within an organization.

Here are some examples of what actually our customers were able to do with RTAP:

Be Precise in meeting market needs:

When we started analyzing data for one of our customers in the telecommunications business using RTAP, we realized that different customers were looking for different types of service and corresponding price threshold levels. Not only that an analysis of external data of visitors entering United States let us discover a specific demand area which was not addressed before. Based on these facts our customer could create specific talk-time packages as well as a unique time-bound package for visitors. So just by analyzing data, our customer could not only be precise in terms of what to server to whom, but went on to discover a new market. Now they are able to constantly tailor their product offerings based on changing market data. Being precise not only helps you gain market share but also stops wastages or what is typically called “over bundling”.

Better Way to do things:

People often discard things which are literally staring them in the face. Thereby missing the opportunity. So we always tell customers that regardless of the time and effort involved, the first thing to do is always to get in all the data which is relevant into RTAP and them build descriptive analytics dashboard on it. Simply putting the facts out in a clear and meaningful way can by itself make a positive difference and allow people at all levels to make the right decisions. We have many examples of customer success stories in this area, but one that immediately comes to mind is the work we are doing with one of world’s top University and Research Institution on bio-conductor data analytics. Basically we are analyzing unstructured genetic data to find disease pathways. Kind of like finding out what specific genetic makeup is an early stage indication of a particular disease. RTAP for Life as this platform is code named someday in the near future hopes to revolutionize disease tracking as well as treatment methodologies by organizing life science data to promote preventative medicine. Sounds very complex right! We felt the same way when we got started but believe me when I tell you, that all it really took was organizing the data in a manner where one could make sense out of it. So the importance of looking at what you have in different angles and lights should never be dismissed. You are bound to see things which you never saw before and that is all it takes to find a better way to do things.

I have more customer stories, but hopefully I will cover those next time.

Until then, let’s all keep looking!