BIG DATA. Make your data ‘fast’ and you can achieve greater business intelligence without encountering certain challenges outlined here.
What's Agile Analytics For Financial Industries? And Why You Should Use It
Analytics projects can be a hit or miss without proper planning. Ensure you get the most from your Business Intelligence tools.
In a fast-paced, rapidly changing business landscape, intelligent data is the key to unlocking actionable goals and targets, more-streamlined strategies and ultimately better business decisions. Getting hold of that data, however, is often easier said than done.
As a business, you may have vast amounts of organisational and consumer data at your fingertips. But you might not have the ability to analyse it and fully extract its utility. This could be due to a variety of reasons but, on the whole, it often comes down to your BI capabilities.
Analytics projects can be a hit or miss without proper planning. To ensure you get the most from your BI tools, you need an analytics methodology that produces the intelligent data you need in the fastest possible time - in short, you need agile analytics on your side.
What is Agile Analytics?
Agile analytics is a type of development methodology used in business intelligence that’s shaped by its ability to adapt to rapidly-changing needs. It’s derived from the agile methodology that emphasises flexibility, adaptability and collaboration over rigidity, structure and fixed process.
Traditionally, the development cycle has been characterised by rigid processes, developments upheld up the contract and set plans.
Agile analytics deliberately veers away from this ‘fired-and-set’ style of approaching business data and favours a faster, more flexible style of data discovery and analysis that suits the fast-paced, changing nature of a business’s landscape.
Its primary aim is to find value in data as opposed to testing or proving a hypothesis while remaining adaptable through a rapid, iterative approach to data and BI development.
The Guiding Principles of Agile Analytics
There are a few core principles that help to guide the agile analytics method:
- Satisfying a business’s requirements through early and ongoing delivery of project systems, features or capabilities is priority number one.
- Welcoming evolving requirements, even late in the development process and harnessing the benefits of change requests.
- Delivering working systems or features frequently, usually every few weeks as opposed to months.
- Sharing project ownership with stakeholders, developers, project managers and users and working together throughout the project’s lifecycle.
- Valuing and incorporating insight and feedback from BI experts into the project’s development.
- Utilising the most time-efficient and effective methods of sharing information between different parties involved in project development.
- Measuring progress according to development and meeting of deliverables, not fixed deadlines.
- Recognising the balance between project scope, schedule and cost and working at a sustainable pace.
- Paying consistent attention to the best data warehousing practices to improve agility.
- Regularly reflecting on how the team can be more effective in their practices then tuning and adjusting behaviours as needed.
How Does Agile Analytics Improve BI?
So, agile analytics sounds like a great concept but you may be wondering if there’s any real impact on the end product - namely the business intelligence produced as a result.
In short, yes. The adoption of the agile methodology in BI and data analytics stems from real-life frustrations developers and stakeholders encountered when employing the traditional “analysis, design, construction, testing and implementation” lifecycle.
By adopting an agile approach, businesses are seeing faster returns on their BI investments and can adapt more swiftly in response to evolving business needs.
Agile analytics enables developers to deliver intelligent business data within a shorter period and with different iterations.
It also promotes greater transparency between stakeholders and the development team by allowing business users to be part of the development process and gain a better understanding of how the process works.
The shorter development cycles and reduced IT resources that characterise agile analytics mean that businesses see an increased rate of return on BI development projects.
Who Can Benefit From Agile Analytics?
Having agile business intelligence tools on your side can help you to make rapid, yet considered decisions, regardless of whether you’re an SME or a larger enterprise. While every industry can benefit from agile analytics, it’s proven particularly valuable to the financial industry.
The financial industry is under immense pressure. Huge changes in tech platforms, payment processing systems, financial service systems, and asset and risk management are taking place that requires comprehensive data management that’s heavyweight in structure but lightweight in capabilities.
On top of this, tracing and tracking malicious or illegal financial activities online is difficult for investigators who can’t always identify a linear paper trail.
The value-driven nature of agile analytics allows for a more open and freeflow financial data analysis to paint a more comprehensive picture of existing operations which can help organisations take smarter, more strategic action.
When paired with a capable power BI model like Finworx, agile analytics can be a potent tool for telling the story that retail and financial organisations want to read in their business data.