It is that time of year again – time for the BI predictions for next year. Fortunately, last year’s predictions were pretty spot on so there are only a few reasons to update them. So, using 2012's predictions are a base, here’s a look at what we expect to see in BI this year:

1. The best tool for each BI job trumps IT standards

BI has traditionally been ruled by over insistence on enterprise wide standards and a single version of the truth. These will continue to be important, but they won’t be the Holy Grail. In 2013, expect IT to start embracing agile BI tools, such as ones based on flexible in-memory models, in addition to enterprise-grade BI tools and standards. For information workers who need information anytime and anywhere, agility concerns will trump standards.

2. Ready or not, information workers will demand more BI control

The current BI approach — a never-ending stream of business requests that IT cannot keep up with — has become unsustainable. Information workers get frustrated with the time lag between when they make requests and when IT fulfills those requests, so they demand more control over BI. End user self-service features of BI tools, such as semantic layers and search capabilities, will become increasingly critical when selecting and deploying BI tools and solutions.

3. BI tools that support the right amount of managed end user self-service will become popular

Self-service BI allows casual and power BI users to fulfill the majority of their own requirements, such as ad hoc queries and exploration of entity relationships, without IT hindering business processes. But it's not that simple: No management or control is unacceptable, but too much control will also become less and less popular. Organizations need to find just the right amount of control that balances the needs of end users and IT. The winning solution will combine the flexibility and agility that self-service brings with behind-the-scenes monitoring and adjustments that improve performance.

4. Mobility is no longer a "nice to have" — it will become the new BI mantra
Information workers can no longer wait to make decisions until they "get back to the office" — that may be too late. Forrester expects that, as BI goes mobile, certain robust mobile features will become the norm: multiple visual query methods, leverage of GPS signals for geo location and geospatial analytics, animated displays, sensor-based queries, and integration with other enterprise mobile ERP applications.

5.Cloud BI will slowly and steadily chip away at on-premises implementations

Cloud BI solutions remain immature, and heavy integration and customization of enterprise BI platforms, tools, and applications will not go away anytime soon. But the story of cloud BI is not all bad news. Interest in cloud BI solutions certainly exists, due in part to the need to lessen the burden on internal IT resources and the desire for elasticity and low initial investment cost. This will not happen overnight, but in the coming years Forrester expects the cloud BI market to continue expanding as leading BI vendors get in the game, blurring the lines between on-premises and cloud deployments.

6.BI-specific DBMSes will gain popularity

Alternative database management system (DBMS) engines architected specifically for agile BI will emerge as one of the key fundamental agile BI technologies that BI pros should closely evaluate and consider. These specialized, BI-specific DBMS databases — those that are designed specifically for BI reporting and analysis — currently have lower adoption rates when compared with their bigger, older, more versatile, jack-of-all-trades RDBMS cousins. But do not expect these low adoption rates to continue; BI-specific DBMSes started to become mainstream in 2012 and the trend will continue in 2013. Forrester expects that more than 20 percent of all BI applications will be based on this technology within the next two years.

7. Big data will move out of silos and into enterprise IT

The amount of data available is growing faster than enterprises’ ability to deal with it. But big data approaches — techniques and technologies that make handling data at extreme scales affordable — can transform this data into insights. Big data will disrupt the data management landscape by changing fundamental notions about data governance and IT delivery, where a single version of the truth ceases to be absolute and becomes relative and contextual. While big data remains for many an unrealized ideal, IT started taking big data initiatives more seriously in 2012 and will continue to do so in 2013.

8. Data exploration will become the new bread and butter of BI suites

Schema-driven BI applications based on a relational data model lockdown severely limit what can be analyzed and reported. Organizations must create these applications specifically for the business questions at hand in order to navigate the data effectively. This is not sustainable, as one can never foresee every single question that will ever be asked of a model-driven BI application and prebuild that logic into data models. Information workers will demand the ability to explore data without preconceived notions, specific questions, or fixed, prebuilt data models in mind.

9. An enterprise "tax" on top-down BI initiatives will become more popular

Put the emphasis and priorities on top-down approaches. However, charge an x percent "tax" on every top-down project and use that "tax" to build up strategic architecture (data warehouse, etc.) incrementally.

10. Hadoop-based BI applications will start encroaching on mission-critical territory

Given the current state of Hadoop software (immature at best functionality that we have come to expect from any DBMS environment, like transactional controls, multiphase commits, and rollbacks of incomplete transactions), most Hadoop-based BI implementations are not being used for mission-critical applications. We are, however, beginning to see a few glimpses that this may change in the near future. We see a few companies that are willing to invest in custom coding that addresses current Hadoop limitations in order to achieve long-term cost savings from eliminating commercial software license and support costs and eliminating the reliance on proprietary hardware.