Data creation and collection has exploded in recent years and it is universally accepted by businesses of all shapes and sizes that business intelligence (BI) and insight drawn from that data can be used as a business differentiator.
Successful business intelligence processes are centred on making sure they meet the requirements and needs of the business. However, the “age of the consumer” has completely changed the BI equation. Consumers want information and things to work quicker than ever and this is translating through to the business environment.
As a result of this changing behaviour and expectation, the demands for data and insight from the wider business are increasing and the speed at which data teams are required to deliver insight is getting faster and faster.
For some time business intelligence platforms could keep up with this pace, however, it is now becoming apparent that current BI practices and platforms are no longer delivering on the wider business’s demands.
The main reason for this is that collected data is often locked away preventing it from being shared across the business. Across all departments “open data” practices and better data flow can have far reaching effects. This is because data collected by one department may be able to provide insight to another, and therefore a system should be put in place to provide this. In order to redress and reconfigure business intelligence practices and platforms there must be a cultural as well as technological shift within businesses.
Changing mind sets
In order to implement “open data” practices across a business and improve data flow that can result in better and faster analysis a cultural change needs to take place as well as a technological one.
The most important change that needs to be instilled is transitioning the whole business, not just the data insights team, to adopt an agile approach to ways of working. Originating from software development, the principles that make up an agile working methodology can benefit all business areas. These principles include:
- Active user involvement
- The empowerment for all employees to make decisions
- Requirements evolve but timelines are fixed
- Make small changes to test and then adapt as necessary
- Focus on frequent delivery of products
- Complete each feature/task before moving to the next
- Adoption of Pareto’s Law (80/20) for efficiency and productivity
- Test early and often
- Collaborate and cooperate across all stakeholders
While some of these principles may require slight amendment depending on the team or business unit that they are being applied to, using these as a base, teams can instil more efficient and productive ways of working, particularly in regards to the way they look at data, make requests for insight and apply business intelligence to projects.
A new model for delivering insights
From a technology standpoint, in order to deliver BI that can quickly adapt to the changing needs of the business a new approach to how data is delivered is required. A new concept that is gaining traction in the business world is viewing data delivery as akin to a supply chain. Currently this is not how most data is accessed and delivered. At the moment, data architectures tend to be hierarchical and facilitated by process. In order to accelerate data delivery, a linear approach is required, essentially the creation of a data and insights supply chain to the business.
Current business intelligence systems are most often used to report on the historical state of the business as opposed to being used for demand planning. This is where the real differentiator in terms of data value lies, but does mean that the speed of data delivery and insight needs to increase. To do this, businesses must first have a data platform that provides a complete view of the data held. This includes:
- A full picture and understanding of the data collected and held by the business
- The integrity and quality of that data
- The gaps that exist in the data
Ensuring the big data and BI solution provides these three core aspects to the business is the first step. Once this is achieved data, information and insight can be arranged into a supply chain model that can then be segmented based on the requests from across the business enabling faster delivery.
The above model shows an example of a data supply chain model and demonstrates how the data, information and insight supply chain can be segmented based on the type of request made of it.
Why make this change?
Considering what is well accepted with regards to big data and business insights – that it provides a real competitive advantage – this question almost seems a little defunct.
However, the competitive advantage of adopting a data and insight delivery model within a supply chain is far reaching and bigger than some may realise. Faster and more intuitive insights can help businesses make data based decisions at a global level, it also means that they can take advantage of emerging trends much faster and be at the forefront of key changes, thereby getting ahead of their rivals. In addition, better access and view of data means that opportunities are opened up with regard to the monetisation of that data.