In today’s demanding economic environment, companies that can develop and deploy analyt­ics quickly have a significant competitive edge. They can use analytics to detect patterns and changes in markets, learn customer preferences, be alert to fraudulent activity, and more. With the advent of cloud computing, users quickly gain access to new data sources and analytic techniques, enabling companies to final­ly unleash their analytics – they are no longer constrained by the limits of their on-premises computing, database platform, data warehouse, and data storage capacity. However, to avoid even more data siloes, data governance concerns, and other issues, organizations should consider a hybrid analytics archi­tecture that brings together on premises and cloud, enabling a more controlled journey to the cloud, while enjoying the flexibility, power, and speed they need to handle a range of analytics demands.

Predictive Analytics will be a Key Differentiator for Modern Apps: Embedded predictive analytics—whether em­bedded in existing applications and workflows, on devices, in memory or in real-time analytics systems—will become the key differentiator for modern apps. Consumers will expect their software to anticipate their needs, driving requirements for predictive capabilities in all apps. Spatially aware apps will become more intelligent and more common – the “who is near me” functionality of apps like Waze and Tinder will be­come more intelligent and prevalent in other use cases. App creators looking to embed will be faced with the key ques­tion: build, buy or partner? Leading innovators will integrate existing technology to get to market the fastest.

Making Sure Your Dashboard has the Right Func­tionalities: Dashboards allow visual integration of complex datasets, taken from an unlimited number of sources – all displayed in visually comprehensible and intuitive formats. The flexibility needed in the dashboard interface is one that allows users at various levels to view relevant cross-or­ganizational content, and collaborate across departments, continents, and time-lines. Without need for coding or complex IT requests, your dashboard allows you to define and manipulate views into a wide variety of display and presentation formats.

Business intelligence (BI) delivers critical performance analytics and insights to workers, empowering them to make faster and better business decisions. However, enterprise-wide penetration of BI is still surprisingly low. This is par­tially due to the misperceptions that business intelligence is costly, difficult to use and deploy, and slow to deliver real business value.

“Self-service BI” is shattering these perceptions. It deliv­ers low-cost, rapidly deployed decision-support, enabling any worker, regardless of job role, geographic location or depart­ment, to work from a reliable and up-to-date set of data, pre­sented in a context and detail level relevant to job role and appropriate to data access privileges.

With data locked away in disparate systems, when busi­nesses require rolled-up reporting, workers must often manu­ally copy and paste data into spreadsheets, merge and trans­form it with crosstabs and spreadsheet formulas, and then present the reports to business decision-makers. What often ensues is an argument over the veracity of the numbers. Or­ganizations mired in this “spreadsheet morass” can spend more time arguing over the accuracy of the data than they spend making the required decisions. With self-service BI, data from source systems is automatically extracted, trans­formed and loaded into a data model that resolves conflicts. Data is regularly updated. The result is a “single version of the truth.” Users can then set aside the debate over the verac­ity of the numbers and instead focus on collaboration, ana­lyzing promising opportunities, identifying root causes of waste and optimizing existing products and processes. Once departmental data silos and “spreadmarts” are eliminated, workers find that data transparency, consistency and trust­worthiness help them work smarter and more effectively.

Data Visualization Integration: Data visualization forms another option of integrating Big Data with existing data warehouse architectures. This integration is accomplished through newer visualization applications as Tableau and Spot fire. In this visualization category we also encounter can­didates as R, SAS or the traditional technologies as Micro strategy, Business Objects and Cognos. All these tools can directly leverage the semantic architecture from their integra­tion layers and create a scalable interface.category  we also encounter candidates as R, SAS or the traditional technologies as Micro strategy, Business Objects and Cognos. All these tools can directly leverage the semantic architecture from their integration layers and create a scalable interface.