Business intelligence (BI) has been around for years in various guises. It was born from the idea of simple, centrally managed reports from a company’s key applications with all requests for data going to the firm’s corporate IT team. The central team would manage these requests individually and send out reports to each employee.
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While this model worked for critical applications and when data was needed occasionally, this model is not sustainable in today’s global business environment. In the effort to maintain synchronisation across the different environments, administrators have to manage constant data loads and metadata updates. The constant tending needed by big, traditional BI implementations results in restricted access to data and long wait times for the business teams. Worse still, this was a complete barrier to end user self-service or users understanding the data directly. Over time, this expensive and time-consuming effort results in a long queue of people in every department, waiting to get reports.
Resolving this is both a process and technology issue. Business users don’t want to wait for data that will help them become more efficient at doing their jobs. This leads them to take to their own tools for data discovery and visualisation, resulting in multiple islands of data and documents within the business. In large enterprises, the problem is even worse: thousands of users are doing it their way and coming up with their own views.
They can’t be blamed for this. After all, they see the value of information for their daily lives. However, while it might benefit them individually, this approach is not sustainable for the business. Any lack of proper governance around self-service analytics can increase reporting errors and leave companies exposed to inconsistent information According to analyst firm Gartner, only 10 per cent of self-service business intelligence initiatives will be sufficiently well-governed to prevent inconsistencies that adversely affect the business over the next couple of years.
no one is sharing data –which stifles creativity
This is due to the fact that individuals are working in silos – and no one is sharing data –which stifles creativity. On top of this, each person will be producing different answers to the same question. For business leaders responsible for decisions that can be worth millions of dollars or pounds to their companies, this represents an unacceptable state of affairs.
Getting the best of both worlds with cloud BI
There is a business requirement to satisfy around greater agility and access to data. However, this need can’t compromise the overall governance, consistency and trust in the data. Solving this calls for a more flexible model for delivering data and analytics results, while at the same time maintaining trusted and agile collaboration between centralised and decentralised teams. Cloud BI can help marry up the benefits of central governance with local flexibility.
With the ability to cut costs and increase efficiency all the way through the business, a cloud architecture is the key to meeting ever-increasing data and analytics requirements. For example, using cloud BI gives companies the opportunity to create virtual instances of data that link up different physical data into one place. These virtualised BI instances enable firms to extend analytical capabilities across multiple territories, departments and customers at a much faster pace. Think of it as networked analytics.
This wide reaching data access across the globe can support both local and aggregate views. This is important, as not every part of a business will run its operations or report on its activities in the same way, yet the central team can consolidate the financial results in a uniform way, as well. Ultimately, both the central team and the local users are working from the same data, but also working with that data in the ways that best suit them.
Cloud-based data discovery or visualisation technology lets individuals run analysis and add their own data, all the while still using a centralised tool. Better still, enterprises that move to a completely “networked” environment, using virtualisation to redefine the way BI is delivered and data consumed, can provide more insight back to the business.
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Using cloud, central IT teams can create a network of interwoven BI instances that share a common analytical fabric. This approach enables organisations to expand BI across multiple regions, departments and customers in a more agile manner.
This analytic fabric is governed and controlled, but still allows users to be consistent and productive in how they work with data. This represents transparent governance for the central team without the overheard that slows down end-users. When using a networked model, they can compare data and see what each other is doing. For example, a marketing manager may be doing one piece of analysis around results in their territory. The results and approach to getting them can be shared across the business, so that other marketers can make use of the same approach. Sharing these analytics ‘recipes’ helps improve performance across the business.
Multi-tenant cloud
One important requirement for this approach to work is that the BI platform has to be truly “multi-tenant”. In most cloud deployments, multi-tenant means that multiple customers can be hosted on the same physical infrastructure. However, cloud BI has to consider this in more detail, as it is not enough to simply host multiple silos of data on the same cloud instance and hope for the best. Instead, those instances have to be able to interact and share data between them, but without the end user seeing all the complexity that is going on behind the scenes.
Here, multi-tenant means networking virtual logical instances and applications together, so that the end user gets to see their results in context. However, any changes or additions each user makes cannot affect the central data. To be clear, this should not be based on data replication into new environments as this simply creates new silos; instead it is a logical instance that is virtually replicated, changed, adapted for each individual or group around the organisation. This is completely different than traditional BI or discovery, which physically replicate data.
One company already taking advantage of this approach is consumer packaged goods giant Reckitt Benckiser (RB). The company processes huge amounts of information from both its external data sources and internal applications to empower local sales organisation to sell better. Using networked BI, RB can do analytics across the globe and meet the needs of different teams. However, instead of having a big BI team responsible for creating and providing reports in each locale, all analytics are developed and run in the specific region, while central BI governs key data and maintains consistency for corporate metrics. This means that each region uses their own local data and virtual logical instances from corporate BI, giving them agility to run their business operations their way, while central IT can report on trusted data around overall global performance. The team at RB can roll this entire set of analytics to thousands of users in 22 different global locales in 24 weeks.
businesses can empower users to create more insight
As this example shows, firms can dramatically improve efficiency by moving their BI to the Cloud. By doing this, businesses can empower users to create more insight, as well as cutting costs.