This year has just been crazy! Now gearing up for the next year. Some new ideas.. Some thoughts on product. Mostly user perspectives !
Happy holidays
Happy holidays
Sunday, December 20, 2015
Sunday, October 25, 2015
The User story
Lately I have started thinking more on the lines of stories. The more compelling your story is , the more is your product to succeed.
Its about solving certain problems of the target audience instead of delivering what you have. This is a classic problem , everyone wants to build there vision but guess what the user is the most important character of the story. Yes you have a wonderful design of a snow cone machine. But guess what I leave in Alaska! or yes you really have a great idea to improve wireline communication but guess what the world is going wireless.
A lot of times this is where the most of us as Product manager have to balance the creative engineering and UI design team and the customer expectations. Its always better to wear the user glasses when you are listening to them. It's super-easy to be defensive and go to "take it or leave it" attitude and its a different thing to understand what they are actually saying. May be you have great oranges from the best farms in the world but your customer is comparing those with apples he has..
I am still pondering on how to better understand the needs and come up with appropriate plans.
Its about solving certain problems of the target audience instead of delivering what you have. This is a classic problem , everyone wants to build there vision but guess what the user is the most important character of the story. Yes you have a wonderful design of a snow cone machine. But guess what I leave in Alaska! or yes you really have a great idea to improve wireline communication but guess what the world is going wireless.
A lot of times this is where the most of us as Product manager have to balance the creative engineering and UI design team and the customer expectations. Its always better to wear the user glasses when you are listening to them. It's super-easy to be defensive and go to "take it or leave it" attitude and its a different thing to understand what they are actually saying. May be you have great oranges from the best farms in the world but your customer is comparing those with apples he has..
I am still pondering on how to better understand the needs and come up with appropriate plans.
Sunday, September 27, 2015
Continued...
Here it continues...
Step 2.5 : Converging
Now that you have discussed absolutely everything from planets to minute organisms its time to zero in on one single implementation/ use-case. Without this you will in the circle.. Its important to look at all the aspects and scrutinize the idea before rejecting it. I am working on some material on how to reject ideas and will have it in some blog soon. (Though it doesnt sound right but thats what happens in this stage)
Step 3 : Prototype
Now that you have zeroed in on one idea , its time to prove the concept. Some people call it proof of concept , prototype etc. In this a small (may be non scalable) working model of how the end product will be. This can be used for investors , customers , technical documentation and most importantly to understand the loopholes you might have left!
Product launch depends on companies and it might be before the demo or after. Or there might not be an official launch step. It can be combines with one of the next steps.
Step 4 : Demo
This isnt the most difficult step if the prototype actually works fine. Well thats the ideal scenario. In reality it really depends on the kind of timeslines you are working. I definitely have some funny stories to share here.
Step 5 : Delivery
This is where a lot of logistics , teams get involved and really its mostly about the project management , inventories etc. Most importantly your customers might come back with feature enhancements or feature requirements. Everything is different on paper and demo. To meet the needs, perceptions it takes a lot of blood and sweat from multiple teams.
Step 6 : Support and so on.
Once the initial hurdle is gone and everything is ironed out, its more of ongoing support and bug fixes, feature enhancements , upscopes etc.
Sunday, August 30, 2015
The Journey : Concept to Launch!
Well Its a long long long Journey! But that doesn't make it boring.. Its a thrill! Its a journey where you spend sleepless nights..hours of hard work..
But its a wonderful feeling to actually see something you had just thought of! I think a good way to depict it would be an info-graphic.
But its a wonderful feeling to actually see something you had just thought of! I think a good way to depict it would be an info-graphic.
This journey can be different depending on the product , customer, if you are B2B or B2C , SaaS , PaaS etc. What I have worked on is the SaaS product. So a lot of it might be applicable for the B2B domain.
The Step 1 : Concept / Idea
Not all of us get our ideas from the falling apple, but these daya really a lot of ideas are falling around. It needs a Newton though who can see that idea! B2B a lot of ideas come as a part of Market research or new needs of the existing customers.
If you are lucky and convince all different organizations involved, you move to the next phase.
Be ready to answer question for the business case, $$$$ , technology, feasibility etc etc
Step 2 : Brainstorming
This is the DIVERGING step. This is where all great brains are put to work! Every one comes up with the usecases , Hows and Whats of the product. Probable features , (Trust me , its super crazy : You might end up discussing why not come up with a Telecom infrastructure for Mars! )
This is where everyone is super passionate about their thoughts and the walls of conference rooms are not sufficient to write down all the ideas.
There is one more step (Very important though!) before the prototype and lets go through it in the next blog
Friday, July 31, 2015
Shifting Gears
Definitely one has good idea but what about productization ? Idea to Product is an exciting journey! Especially if you are a part of startups/smaller organizations..Luckily in my short span of Product Experience , I have seen this for 2 products (launched) and a couple of not launched (I don't want to call them failed) ideas.
Going forward Lets discuss about Products.
Sunday, June 28, 2015
Learning from the past
There is this saying about not learning from past, wait what happens to people who actually learn from past?
Use case 1 : Network : Definitely my favorite one! So what kind of data comes here ans whats the prediction we are talking about. So for network analysis the data is coming from network testing equipment , MME logs , all the call data records, data in the OSS. Now there is literally huge data if all of these are combined. We are talking of 100s of thousands of sites. A platform that will take the data from all the possible sources and analyse it. Over the period of time, some patterns will be seen from the data and definitely some predictions can be made. There is a lot of scope in this field and with SDN (Software defined network) the network will get the flexibility to implement such changes.
Use Case 2 : Baby monitoring systems : If the trackers track the time when your baby sleeps, drinks , yawns (why not!) , cries , plays. Over the period of time you will be able to know the moods, sleep cycles. And also the mothers will get to plan their activities accordingly. This data will be more and more accurate over the period of time. (I am not sure if crowd sourcing will help here, for example using the data from babies with the same age and their sleep cycles, I am not sure)
Use Case 3 : Connected House : Yes the highly useful one. What time one leaves to office, what is the optimum temperature set in the house as per the different weather conditions. What are the food habits as per weather. Who are frequent , known visitors, who are the banned ones! The grocery stock, automated promotions. (Guess when you are almost running out of detergent and you get a promotion from Tide, which brand are you going to buy???). Home organisation, closet organisation (Very difficult to understand the pain unless you actually have atleast 10 shades of black dresses in your closet! ) , Food choices , family time monitoring. Automated lights and the list will never end,
TAAA DAAAA! Yes they can predict the future. We all have heard words predictive analyctics, machine learning etc. What exactly are we talking about?
Wikipedia says : "Predictive analytics encompasses a variety of statistical techniques from modeling, machine learning, and data mining that analyze current and historical facts to make predictions about future, or otherwise unknown, events"
Yes. Now that we have tons of data , how can we find patterns and learn from the past. As George Santayana rightly said, the ones who do not learn from the history are ....
Crazy analytics comes into picture here. A lot of data modelling and research is going on and honestly I am not the best person to talk about it.
What I simply understand is its equally important to process the data in a right way and make sure sensible patterns are generated from the patterns. This is where the values lies. The unused data is as good as garbage.
Lets go through some use cases of predictive analytics
Use case 1 : Network : Definitely my favorite one! So what kind of data comes here ans whats the prediction we are talking about. So for network analysis the data is coming from network testing equipment , MME logs , all the call data records, data in the OSS. Now there is literally huge data if all of these are combined. We are talking of 100s of thousands of sites. A platform that will take the data from all the possible sources and analyse it. Over the period of time, some patterns will be seen from the data and definitely some predictions can be made. There is a lot of scope in this field and with SDN (Software defined network) the network will get the flexibility to implement such changes.
Use Case 2 : Baby monitoring systems : If the trackers track the time when your baby sleeps, drinks , yawns (why not!) , cries , plays. Over the period of time you will be able to know the moods, sleep cycles. And also the mothers will get to plan their activities accordingly. This data will be more and more accurate over the period of time. (I am not sure if crowd sourcing will help here, for example using the data from babies with the same age and their sleep cycles, I am not sure)
Use Case 3 : Connected House : Yes the highly useful one. What time one leaves to office, what is the optimum temperature set in the house as per the different weather conditions. What are the food habits as per weather. Who are frequent , known visitors, who are the banned ones! The grocery stock, automated promotions. (Guess when you are almost running out of detergent and you get a promotion from Tide, which brand are you going to buy???). Home organisation, closet organisation (Very difficult to understand the pain unless you actually have atleast 10 shades of black dresses in your closet! ) , Food choices , family time monitoring. Automated lights and the list will never end,
Sunday, April 26, 2015
And more data
Now that we have a realization that BIG data is really BIG. One more blog for it! :P
Data is the new oil. Its the treasure and somehow I can relate it to uncle scrooge. The way he used swim in his money, one needs to find the data in this huge pile. Its very easy to get lost!
So there is so much data , one will feel like uncle scrooge!
Just a Caution : DO NOT GET LOST!
There is just so much data that unless we know the right way out , its very easy to get lost!
So first about managing Big data.HDFS and MapReduce.
Hadoop which is a software framework uses HDFS and MapReduce to analyze the data.
This is done on clusters of commodity hardware.
Lets just quickly understand HDFS : Hadoop Distributed File System :
- So a big problem is converted to chunks and then resolved :)
- As its easier to solve bigger problems when we look at one piece at a time. Similarly its qucik and cost effective to get the analysis done on small elements
- Is HFDS , a hard drive ? No its the service that is used when you have crazy amount of data
MapReduce : so this is where people write programs. So that they can process massive amounts of data in parallel and most importantly across multiple processors. (One need not wait for a year to get the output!)
-
Data is the new oil. Its the treasure and somehow I can relate it to uncle scrooge. The way he used swim in his money, one needs to find the data in this huge pile. Its very easy to get lost!
So there is so much data , one will feel like uncle scrooge!
Just a Caution : DO NOT GET LOST!
There is just so much data that unless we know the right way out , its very easy to get lost!
There are two unanswered questions from the previous blog. First is how is one going to process the data and second one is how is it important anyways?
So first about managing Big data.HDFS and MapReduce.
Hadoop which is a software framework uses HDFS and MapReduce to analyze the data.
This is done on clusters of commodity hardware.
Lets just quickly understand HDFS : Hadoop Distributed File System :
- So a big problem is converted to chunks and then resolved :)
- As its easier to solve bigger problems when we look at one piece at a time. Similarly its qucik and cost effective to get the analysis done on small elements
- Is HFDS , a hard drive ? No its the service that is used when you have crazy amount of data
MapReduce : so this is where people write programs. So that they can process massive amounts of data in parallel and most importantly across multiple processors. (One need not wait for a year to get the output!)
-
Sunday, March 29, 2015
Data Here .. Data there...everywhere ..
Was thinking of an old song
Old MacDonald had a farm, E I E I O,
--
With a quack quack here and a quack quack there,
Here a quack, there a quack, ev'rywhere a quack quack.
--
It perfectly suits the data situation we have! here data ..there data..,everywhere data data !!
By now we have seen that from the tons of connected devices we are generating exponential quantities of data! We are currently using data and trust data more for decision making than human expertise. Its more reliable and unbiased input. So lets look at the basics of the data and from the IoT standpoint.
Now some basic questions arise
a. How exactly is this data stored? (Remember old days of storing content on CDs and guess what happens when you lose it?)
b. How is one going to process this enormous data?
c. Whats the use of this data anyway?
and so on...
--
Starting from very very basic , what is so BIG about the data?
So these 4 Vs make data BIG. First and the easiest is the quantity of the data. Its HUGE. For the sake of understanding , lets take the example of Facebook.
I remember reading somewhere last year that facebook users every minute share 2460000 pieces of content.
Now the second factor is variety, Gone are the nice microsoft access days where you knew what to expect. The mantra is expect the unexpected!
Veracity : The third V is not very commonly used but definitely is required. Going by dictionary.com , the meaning of veracity is "conformity to truth or fact; accuracy". This plays a critical role in decision making as its important to rely on accurate data.
The last one is velocity : I think this one is self explanatory and the same facebook example is valid here. The data is generated at lightening speed.
--
Now going to the first question : How on earth this enormous data is stored?
Ok , so first of all , the data is huge and comes in all shapes and sizes so the servers have to be always "READY"! Now there are two things that we want to know about the Physical storage.
1. It must be resilient (As in should be able to recoil once bent!) : So the system should be resilient to failures. That means it should change when you have enough resources which are redundant
2. There should be redundancy : Redundancy to eliminate a single point of failure. Ask any girl why carry two pairs of shoes while travelling, the answer is as simple as that
--
Old MacDonald had a farm, E I E I O,
--
With a quack quack here and a quack quack there,
Here a quack, there a quack, ev'rywhere a quack quack.
--
It perfectly suits the data situation we have! here data ..there data..,everywhere data data !!
Source : http://shmsoft.blogspot.mx/2014/03/big-data-cartoon-data-is-new-oil.html
By now we have seen that from the tons of connected devices we are generating exponential quantities of data! We are currently using data and trust data more for decision making than human expertise. Its more reliable and unbiased input. So lets look at the basics of the data and from the IoT standpoint.
Now some basic questions arise
a. How exactly is this data stored? (Remember old days of storing content on CDs and guess what happens when you lose it?)
b. How is one going to process this enormous data?
c. Whats the use of this data anyway?
and so on...
--
Starting from very very basic , what is so BIG about the data?
I remember reading somewhere last year that facebook users every minute share 2460000 pieces of content.
Now the second factor is variety, Gone are the nice microsoft access days where you knew what to expect. The mantra is expect the unexpected!
Veracity : The third V is not very commonly used but definitely is required. Going by dictionary.com , the meaning of veracity is "conformity to truth or fact; accuracy". This plays a critical role in decision making as its important to rely on accurate data.
The last one is velocity : I think this one is self explanatory and the same facebook example is valid here. The data is generated at lightening speed.
--
Now going to the first question : How on earth this enormous data is stored?
Ok , so first of all , the data is huge and comes in all shapes and sizes so the servers have to be always "READY"! Now there are two things that we want to know about the Physical storage.
1. It must be resilient (As in should be able to recoil once bent!) : So the system should be resilient to failures. That means it should change when you have enough resources which are redundant
2. There should be redundancy : Redundancy to eliminate a single point of failure. Ask any girl why carry two pairs of shoes while travelling, the answer is as simple as that
--
Sunday, January 25, 2015
A new year a new Beginning!
Wishing everyone a very Happy new year!
Now that we have a clear understanding of different wireless protocols , signalling and technologies , IOT. This year lets move on to the practical applications of everything we know so far!
Now lets look at more details on IOT networks and data. I am still working on the list for this year. May be it will be a good idea to go with the flow. This year at CES IOT was big. From Google's Nest, Samsung SmartThings to Self driving cars. IOT was really big!
So this year we will have a lot coming.
Another piece of the puzzle is DATA , we are collecting huge data from different applications, now its time to understand the data and make sense out of it.
-----
CES Overview 2015
This I think Samsung SmartThings , Nest and self driving cars were my biggest takeway. Definitely drones cant be ignored.
There were some interesting things like LG Washer , 3D printing.
Overall thing are getting smarter, Things are getting connected.
And we do have lots coming our way
Now that we have a clear understanding of different wireless protocols , signalling and technologies , IOT. This year lets move on to the practical applications of everything we know so far!
Now lets look at more details on IOT networks and data. I am still working on the list for this year. May be it will be a good idea to go with the flow. This year at CES IOT was big. From Google's Nest, Samsung SmartThings to Self driving cars. IOT was really big!
So this year we will have a lot coming.
Another piece of the puzzle is DATA , we are collecting huge data from different applications, now its time to understand the data and make sense out of it.
-----
CES Overview 2015
This I think Samsung SmartThings , Nest and self driving cars were my biggest takeway. Definitely drones cant be ignored.
There were some interesting things like LG Washer , 3D printing.
Overall thing are getting smarter, Things are getting connected.
And we do have lots coming our way
