Author: Classic Content Dev
‘Simplicity- the art of maximizing the amount of work not done- is essential.’
– Agile Principle #10
The first step to any agile project is preparing a product backlog- a list of prioritised stories required to meet the set goal. If done well, this step helps make release and iteration planning a lot easier, saving you time to execute them perfectly.
Thus, it won’t be wrong to call product backlog the backbone of a successful sprint. Indeed an essential step.
But, as expected, ensuring a perfectly managed, super-effective and result-driven product backlog is a bit tricky. Complex, yes, but not entirely difficult.
Here are a few solid tips from our Agile experts for product owners and teams that generally face difficulty managing an overflowing backlog!
1. Implement Layers of Decomposition
Include layers of decomposition in your product backlog, including objective, feature, and user stories, to make it more efficient.
This technique will help you have a high-level conversation amongst the team, promoting a high delivery rate. Many web tools, like Atlassian JIRA, come built-in with this facility, further easing your work-process.
2. Use A Problem Board to Segregate Work
Rather than piling every story involved in the roadmap on your backlog, try going for a definite number of stories at one go.
Using a ‘Problem Board’ is the best way to do this.
Define your business goal, a metric that could help measure the work done, and features that could help get it done effectively. Problem Board segregates them on the basis of the set-priority and helps assign them accordingly.
This way you’ll get your work done in real time, without worrying over the ever-piling list of ‘to-do’ work.
3. Tier your Backlog
In order to resolve issues accumulating in your sprint process, you may follow a tier-system to manage the feedback received.
The first level may work as a raw review stage, wherein the product owner may quickly decide whether to keep the issue for further scrutinisation or pass it through. If kept, the feedback may move to the next ‘unprioritised’ level and then to ‘ready to feature’ level.
This practice would help product owner resolve issues at a much faster rate.
4. Be DEEP when you INVEST
Using DEEP as in Detailed, Emergent, Estimated, & Prioritised feature is another perfect way to manage your Product Backlog effectively.
Likewise, you may also follow the INVEST (Independent, Negotiable, Valuable, Estimable, Small, & Testable) checklist for high-quality user-stories
Whoa! We went a little deep there, didn’t we?
5. Keep Your Product Backlog Ready for the Next Sprint
It’s always better to be prepared with the next sprint beforehand.
Thus, make sure you have at least two sets of Product Backlog materials ‘ready for sprint’. It would help save you crucial time, further increasing the productivity.
But, also ensure that the items on the next sprint are clear, testable, and feasible.
6. Groom Your Product Backlog Regularly
Your Product Backlog needs constant attention in order to remain in a fit condition.
Assign a time-slot for Product Backlog grooming each sprint and make sure to follow it religiously. The team may gather and understand the tasks ahead, priority shifts, upcoming challenges and organisation’s take on it, and move or break their stories accordingly.
7. Don’t Waste your Time on ‘Out of Scope’ Issues
If you think an issue can never be reached, just close it. As simple as that.
There is no need dragging an issue that lies deep down on your priority list when you have a number of fresh and important issues popping up in your backlog.
You may always use those issues as a research material later.
Being a Product Owner, thus, isn’t an easy task. Its full time and you need to give your 100% to keep your Product Backlog alive and working. But if you keep the above points in mind, it may help you be the best one out there!
Go ahead, try them out!
Author: Classic Content Dev
“Never doubt that a small group of thoughtful, committed people can change the world. Indeed. It is the only thing that ever has.” –Margaret Mead
Agile methodology, with its sharp planning strategies and high success rate, has witnessed a tremendous upsurge in its popularity last few years. The business world has wholeheartedly adopted this scalable, profitable and versatile method. Everybody wants to win; everybody wants to create a perfect agile team working in a perfect Agile process.
Only, it is not as simple as it sounds.
You may have an idea, resources and all the money in the world to build an awesome project, but it’s your team and the effectiveness of your Agile process that holds the complete power to make or break your project.
We have been providing successful agile teams to global organisations since decades and here’s what has helped us develop as one of the leading Agile development companies. Hope it can help you too!
1. Aim for an Autonomous and Self-organising Team
There is nothing that beats a self-sufficient team.
Give your team members power to utilise their expertise and skills to the fullest. Encourage them to take impromptu measures rather that sticking to the laid out plans. Train them to take initiatives and do more than just completing the tasks in hand. Allow them to take suitable and actionable decisions for the good of the project.
Raise an autonomous team rather than promoting a group of yes sirs.
2. Pick the Right Agile Model for your Project
Different projects support & compliment different Agile models based on their nature. Pick the most fitting one.
You are allowed to play with the different sets of agile concepts for different projects. Agile XP or Scrum, Lean development or whatever, pick the approach that best suits the project in hand. In Classic Informatics, we like to try our hands with different base factors for the same model. For example, for projects that are focused on time to task completion, we use time estimates as measuring component whereas, for projects with a high focus on the quality of execution and different priorities on different stories, we like to use story points.
3. Hold Short, Sweet and Super-effective Meetings
Regular meetings (scrum, sprint refinement, retrospectives & more) are essential to make any agile project a successful one. It helps channelise better communication within the team, monitor progress, map the road ahead, and dig out and correct any roadblocks witnessed in real-time.
But tackling the list of things stated above would mean a super-long meeting (even longer if the team is quite large), right? Honestly, the answer is no.
Train yourself to use simple yet effective Agile hacks. As an example, we use printed cue cards. Hand them out to the assignees and ask them to write 3 points each for the ‘completed subtasks’ and ‘to-do’s’, and then go for the scrum meeting. It’s easier, effective and a lot time-saving.
One such other example that we use is JIRA to Slack notification integration. We have configured notification of important events in JIRA to be automatically pushed into Slack channels. This method allows team members to be updated in real time about sprint progress without having to invest time asking and waiting for responses.
Don’t drag the meeting unnecessarily. Just review, discuss, resolve, and move on.
4. Avoid Going for Long Sprints Unless Absolutely Necessary
Longer sprints, while being thorough, carry a higher risk of deviating from the original objectives and planned outcomes. For release critical & strictly time-bound projects, longer sprints are the real killers. You start, you execute, then you get lost and then start re-doing stuff.
The best plan is to adopt shorter sprints with much value for time and delivery approach. Take a feasible time-frame (usually 2 weeks to 4 weeks) and execute tasks according to set time estimates or story-points. It also creates a psychological impact as everyone knows that time is short and it’s not affordable to slack off; thereby increasing team productivity.
With that said avoid making your sprints super-short also as it invites fatigue, takes away breathing space from the team and appears chaotic that can’t be easily managed.
5. Swap Individual Responsibilities with Teamwork
While it comes under ‘Agile’ framework, your agile team is just what it is- a team. It may have individuals with individual talents, skills, expertise, and roles, but at the end, they all work together to achieve one target.
Create an environment that isn’t entirely focused on individual responsibilities but on the team as a whole where everyone is accountable for the project in hand. By doing this, you’ll be ensuring a complete transparency in the system and everyone in the team will be able to recognise their efforts.
6. Befriend Automation
Leverage the powerful of automation processes for a smooth and successful sprint.
Rather than wasting time on process related facilitation, allow your team to get the real work done. Automate everything else. There are several applications that are solely focused on automating the Agile process; take Atlassian JIRA for example. They have taken Agile automation to a whole new level. This would enhance actual work done in the designated time slot and at the same time keep the team engaged & interested.
By implementing one such automation, that is one-click, single point access to entire project inventory for our teams, we have increased our sprint productivity by more than 30%. Just like that. Simple but powerful, isn’t it?
Creating a simple but powerful agile process can be a bit tricky, but not entirely impossible. You need to pay attention to minute details, align them with your project requirements and build an agile team that is both productive and inspiring.
It will take some time, of course. And patience- a lot of it.
Author: Classic Informatics
Learning from data is virtually universally useful. Master it and you’ll be welcomed nearly everywhere! —John Elder
The industry has seen a huge leap in the way it processes data. From being primarily focused on collecting data and deriving an actionable insight from it as seen a few decades ago, now it works mostly on the future predictions a.k.a. forecasting. Indeed, advancement.
Data prediction is like extrapolation; going back through past data, spotting a trend, and predicting where it will go in a few decades from now.
There is a reason why Predictive Analytics is one of the hottest trends amongst organizations, big or small. Apart from a greater understanding of different processes, indulging in predictive analytics helps businesses approach growth opportunities and risk factors better.
Like your secret weapon to success.
Hence, we build a ‘Predictive Model’. A model that we try to base our findings and expectations upon. A perfect model that could help guide our business to where you want it to be.
Only it isn’t an easy task. Having worked with 50+ successful predictive models till date, we would like to share a few tips and techniques that helped better our approach. Hope they help perfect yours too.
1. Determine the Performance of Your Current Model
The first step is to analyse where you must have gone wrong with your model set if you have one. First timers, stay tuned. We have actionable suggestions for you as well (refer point no. 2, 3, 4, 7, 8, and 9).
There are basically two types of glitches a Predictive Model usually experiences- high variance and high bias. High variance refers to a situation where your model performs exceptionally on the training set but is suffering too much on hold-out set. In the case of high bias, both training and test data sets don’t work in the favor of your model.
It’s better that you find out the lapse present in your model and correct or redo it in real time for an improved business foresight.
2. Avoid Common Mistakes
This technique best fits those building a Predictive Model for the first time.
It’s always better to take a BI/Big Data expert’s opinion before you begin framing a predictive model. Talk to people with experience in different versions and styles of predictive models, do your own research and understand what lapses and roadblocks you may face. Learning from successful models also could help you avoid any easy mistake.
Learning from successful models also could help you avoid any easy mistake.
3. Hire an Expert To Handle Your Project
Building a Predictive Model isn’t an easy task. You need a creative problem-solver on board, preferably an expert with years of experience in data handling, to take care of the extensive steps involved in predictive modeling- from collecting data, analysing them to utilising them to frame an actionable forecast.
4. Add More Data and Features
Adding data and features to the model helps decrease the degree of bias (or variance in the case of features), increasing the flexibility of the model.
But avoid adding features to your model if you are opting out of adding data and your original data set is pretty small in terms of data points.
5. Do Feature Selection
Almost opposite of the point above, this suggestion is for models with high features and not enough data points.
It drives variance down while adding to the flexibility of the model. The best way to do feature selection is to do it methodically, removing only the extra noisy and in-informative ones. In the case of models with enough data, you won’t even have to do the manual selection, the model will automatically handle the unimportant features.
In the case of models with enough data, you won’t even have to do the manual selection, the model will automatically handle the unimportant features.
If you can’t think of any other method, use regularisation. This way you can have a better control over the features involved in the model. Regularisation guides algorithm in using fewer yet apt features, and in implementing them smartly.
This way you can have a better control over the features involved in the model. Regularisation guides algorithm in using fewer yet apt features, and in implementing them smartly.
7. Bootstrap Aggregation (Bagging)
This method majorly focuses on reducing the high variance of the model.
A bunch of variations of the same model is created and a split test is performed on them. Thus, a working predictive model is chosen.
This process, while being thorough, is quite intensive on the computational front. It takes up a lot of memory.
Boosting is a slow, complicated yet perfect method to reduce bias in a model. It involves creating successive training models that learn from the errors in the model preceding it.
Just like in the bagging technique, you may suffer from the high computation time and memory costs.
9. Reduce, Reuse, and Recycle
Make your models as sustainable as possible.
Reuse the cleansed data in different analyses. Reduce the burden on the model by automating report process. And recycle the knowledge obtained in future projects.
10. Change the Model (In case any of the above techniques don’t work :))
Banging your head (and your money) on a predictive model that doesn’t work is going to take you nowhere.
Be smart and change your model class. If you were working on a linear one, switch to the neutral network.
This change will help you know where you were lacking. There are times when a particular algorithm suits specific data sets, and your class might not fit that criterion. It is always better to look for a different model that suits your requirements.
The way to build a perfect predictive model isn’t an easy one. But always remember:
Predictive Model’s mission is to engineer solutions. As for the data employed and the insights gained, the tactic in play is: “Whatever works.”- Eric Siegel