Gartner’s 2012 predictions for business intelligence focus on the challenges around Cloud, alignment with business metrics and a balanced organizational model between centralized and scattered. Forrester has looked toward 2012 with everything from the rise of individualized BI tools to Cloud to mobility to Big Data. At the recent Gartner BIIM Summit, industry panelists discussed issues such as BI spending under IT and finding the right people with BI skills.
There’s no doubt that coming years will be an exciting time for business intelligence and information management. While there are many factors that will continue to influence and shape the industry data quality, rising storage and network requirements, IT capabilities and business requirements, we have identified what we think are the top five BI trends for the year ahead.
Analytics is the next progression of modern BI. It uses algorithms to search for patterns and explanations from historical data to predict future activity for better business and decision making. Organizations using analytics are more likely to substantially outperform their competitive peers.Analytics will help companies differentiate themselves, it will allow them to run more efficiently, make the most of their customers and increase profitability. Analytics provides organizations with actionable intelligence. Analytics is going to continue to grow at a dramatic pace. The three biggest likely trends around analytics to be are:
Optimization: the combination of business rules for optimized decision management
Consumable analytics: the visual presentation of increasingly complex data;
New data analytics: the analysis of new types of data, such as social media, location information, etc.
Just like any other systems like business intelligence system, the companies getting the rewards and gaining true value from analytics are the ones who have made a suitable investment, especially in establishing an enterprise grade solution.
BI in the Cloud
Cloud BI is nothing but a combination of software as a service(SAAS) and Platform as a service(PAAS). Most SaaS-based BI will be delivered in the form of analytic applications; that is, BI built into a SaaS application that solves some specific business problem for an end user. I've noticed that most analysts and journalists today do not distinguish (in their research and writings) between SaaS BI, BI for SaaS, and BI for PaaS. Typically, it's all called "Cloud BI" and it almost always means SaaS BI.
BI for SaaS: building BI functionality into a SaaS-based application for the purpose of delivering specific, data-driven functionality within that application
BI for PaaS: delivering a reporting and analytic service that can be easily built into PaaS-based applications and managed by the PaaS
PaaS will do for software developers what SaaS has done for business users.
As Cloud computing continues to dominate the IT landscape, however market researchers like Gartner is skeptical of Cloud BI take up, predicting that cloud offerings will make up just 3 per cent of BI revenue by 2013. Reason is user adoption will lag far behind the expectations of vendors. Cloud BI will continue to chip away at on-premises BI, but it’s still a long road ahead.
Decision makers are still questioning the Cloud. The greatest challenge for organizations is the logistical issue of moving data into the Cloud initially. They need to look at the security network and bandwidth, the quality of the data they are transferring and planning to analyze and think about a usable interface.
Once data has been transferred to the Cloud, there are numerous cost-effective BI and big data tools available for organizations to take advantage.
Mobile business intelligence offers huge advantages for organizations, particularly those with increasingly mobile and remote workforces. It means that staff and management are never disconnected from the tools that help them make business decisions.
We will see in 2012/13, based on the need to make decisions when and where they need to be made, mobile BI go mainstream. A large number of companies are rapidly undertaking mobile BI owing to a large number of market pressures such as the need for higher efficiency in business processes, improvement in employee productivity (e.g., time spent looking for information), better and faster decision making, better customer service, and delivery of real-time bi-directional data access to make decisions anytime and anywhere. Common Mobile BI types are
Mobile Browser Rendered App: Almost any mobile device enables Web-based, thin client, HTML-only BI applications. These apps are static and provide little data interactivity. Data is viewed just as it would be over a browser from a personal computer.
Customized App: A step up from this approach is to render reports and dashboards in device-specific format. In other words, provide information specific to the screen size, optimize usage of screen space, and enable device-specific navigation controls.
Mobile Client App: The client app provides full interactivity with the BI content viewed on the device. In addition, this approach provides periodic caching of data which can be viewed and analyzed even offline
Mobile BI is a cost-effective and sensible addition for organizations that rely on business intelligence to make important decisions and define future directions. Due to the ease of consumption, more C-level executives will see the value of better business decision making, more often, when and where they need it.
In-memory analytics tools query data in RAM, unlike their conventional BI counterparts that query data on disk. Because accessing data from memory is up to a million times faster than accessing it from disk, in-memory analytics greatly speeds up response times. As memory becomes cheaper, we’ll continue to see the increasing popularity of in-memory analytics. Qlikview, Spofire and Tableau are few sample In-memory analytics tools. The benefits of in-memory analytics include:
Dramatic performance improvements: Users are querying and interacting with data in-memory which is significantly faster than accessing data from disk.
Cost effective alternative to data warehouses: This is especially beneficial for midsize companies that may lack the expertise and resources to build a data warehouse. The in memory approach provides the ability to analyze very large data sets, but is much simpler to set up and administer. Consequently, IT is not burdened with time consuming performance tuning tasks typically required by data warehouses.
Discover new insights: Business users now have self-service access to the right information coupled with rapid query execution to deliver new levels of insight required to optimize business performance, without the IT bottleneck.
Users are able to download large amount (up to 1 terabyte) of data onto their own computer and explore that information for proving theories and making business decisions throughout an organization.
Considering the speed, ease and affordability with these tools, we are already seeing the adoption of in-memory analytics and its popularity will continue to grow at a similar pace.
As its popularity and adoption grows, however, it’s important to remember that ‘quick-fix’ and ‘short cut’ tools are no substitute for quality data. To ensure an organization’s BI and analytics are accurate, in-memory analytics tools should be used in conjunction with a structured, quality data warehouse solution.
In information technology, big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools. The challenges include capture, curation, storage, search, sharing, analysis, and visualization. The big data usually represented as 3Vs(Volume, Velocity and Verity) or three-dimensional, i.e. increasing volume (amount of data), velocity (speed of data in and out), and variety (range of data types and sources).
Big Data analytics analyzes the micro-details of business operations including unstructured data coming from sensors, devices, third parties, Web applications, and social media - much of it sourced in real time on a large scale. Using advanced analytics techniques such as predictive analytics, data mining, statistics, and natural language processing, businesses can study big data to understand the current state of the business and track evolving aspects such as customer behavior. There are two types of big data analytics say
Deductive analytics: This consists of a top down understanding of the rules of the business. These rules are drawn from assumptions that business leaders take for granted. This approach tend to miss changes, the new and disruptive customer behaviors or trends, or even changes in trends, that may shift the business rules.
Inductive analytics: This is driven by observation of real time (or near real time) data, events and behaviors. New shifts, changes from the norm are quickly detected and scrutinized.
Big Data will help organizations better manage risk and improve the customer experience, fundamentally changing the way information is managed and used. The Big Data is set to change the information landscape and, for those who use it, will get strong competitive advantage and insight.
We expect to see banks, telecommunications, government agencies and retail take up Big Data technology in the coming months.