Agile Analytics Team

Extend your Data Analytics prowess with our Agile Analytics team. We bring you the values of DevOps for a more customer-centric, humane data analysis endeavour. Our analytics team works closely with you, empowering your organisation to be more data-driven with end-to-end support for data storage modernisation, business intelligence, data visualisation and advanced analytics.

The Gap in Data Analytics That We Aim to Address

Data Analysis has become more proactive, no doubt about that. However, if you compare it with agile software development, you’ll find out that it still lags, and it still has silos. As a result, there is a lack of synchronisation between the DevOps team and the Data Analytics team. Here are some specific issues that are caused by this lack of synchronisation-

Since the speeds of data analytics and application development don’t match, attaining sprint goals together becomes an issue. What this means is you can’t set the same goals for both teams.
When the Agile Team needs an estimate within a certain timeframe, the data team fails to give that - leading to delays.
Data science is unpredictable. Agile wants predictability. So applying traditional agile methodologies to DS results in failure.

How We Help You Take Advantage of Agile Analytics

We can use jargon here, quote Gartner… But let’s go back to the basics – the Agile Manifesto. Most Agile Data Analytics endeavours fail because people try to shoehorn the tenets of the Agile Manifesto into data analytics exactly the way they are.

We at Aceso Analytics tweak the Agile principles so that they don’t conflict with data analytics. Here’s how:

Individuals and Interactions - But in a Gentle Way

When it comes to Agile Analytics, micromanagement never works. Data analytics involves iterations. On top of that, there is a lack of adequate data to be analysed when a project starts. All these basically mean that the data analysts don’t have a clear view of what’s going to happen in the future. They can analyse the data at hand, and based on the outcome; they set future goals.

Hence instead of giving a shape to data analysis, our Agile Analytics framework urges the PM to ask the data analysts about current impediments and how the project manager can help. This is in line with the first principle of the Agile Manifesto. Let’s not be rigid when it comes to processes.

Delivering Working Software without Forcing Scrum

Data analysis is creative in nature. If you try to “time-box” it, you’ll get negative results. Hence our Agile Analytics makes sure that the work done by data analysts is broken down into smaller, independent tasks – without boxing data analysts within a specific sprint. If the independent tasks are marked as success – we deploy them into production. If those tasks fail to elicit expected results, we devise newer ways of defining individual problems based on the failures.

We at Aceso Analytics slant towards Kanban when it comes to Data Analytics since it is more flexible than Scrum.

Keeping the Research Work and Implementation Separate

Analysing enterprise data can be divided into – research and implementation. Even if it is possible to box the implementation phase in Scrum, keeping the research part free is extremely crucial. Data Engineers, in the actual world, work with messy data which needs to be cleansed and engineered, keeping the problem or requirement in mind. This phase can appear vague initially, and it is extremely challenging to estimate when the phase will come to an end. On the other hand, the implementation part is more quantifiable. This is why we at Aceso Analytics are in favour of implementing a hybrid of Kanban and Scrum – Kanban for research and Scrum for implementation.

The Key Is to Avoid Certain Principles That Are in Conflict With Data Science

Welcoming changing requirements is not possible when it comes to data analytics. Data analysts define the problem based on the requirement. Each phase of analysis – from data collection to data engineering and analysis – gets its shape based on the requirement. If the requirements change, the analysis has to start from scratch. Therefore, our Agile Analytics team avoids changing requirements when it comes to data analysis.

The End Result of Our Agile Analytics

So what exactly can you achieve with the help of our Agile Analytics team?

Faster data analytics without compromising on the creative part
We enable the data analytics team to keep in touch with application developers and people in the business to empower them to keep their analytics endeavour firmly aligned with business requirements.
Our Agile Analytics team keeps your data analysis endeavour flexible - adhering to Agile principles simultaneously.

Data Analysis is an art. Let’s not stifle it with rigid Agile methodologies. Leverage our Agile Analytics team and keep a healthy balance between Agility and creativity!