Self-Service Business Intelligence and Analytics

Self-Service Business Intelligence and Analytics

Self-service means different things to different people, who want to interact and analyze data to discover new insights on their own and ability to consume it. Match self-service capabilities to your users: information consumers will be interested in reporting with little to no interactivity; more data-savvy users will want drill-down analysis, data querying, and report authoring capabilities; and analysts will want visual data discovery that helps them answer new questions as they arise.

Traditional BIs Vs QlikView :
Traditional BI suites usually remind us of rigid and complex UIs involving extensive usage of SQLs and resemble visual front-end extensions of databases.
Unlike traditional BI’s, QlikView sits on your existing data warehouses and infuses a broad set of self-service BI capabilities.Most traditional BI platforms don't fit into this. Controllers are usually not offered alongside models and views, that's why they are considered as visual extensions of your databases.Traditional report centric BI vendors have been vying to fit in the self service capability into their systems for a long time. Traditional BI architectures typically require mobile reporting layers.In contrast, QlikView offers a unified architecture for delivering cross platform Business discovery capabilities on mobile allowing users to benefit from a consistent experience across devices. Making full use of rich HTML5 features, QlikView offers appealing layouts, compelling visualizations and a bag full of touch interactions on new age mobile devices, thus delivering an user experience on par with native apps.



How QlikView offer Self-Service BI?
QlikView enables IT pros to deliver self-service BI, providing a competitive advantage to the business. With QlikView, IT allows business users to create their own analyses so they can arrive at innovative decisions. QlikView lets IT serve the business like never before—all while assuring strict data security, quality, and governance. Combined with the direct data access capabilities offered by QlikView’s Direct Discovery, virtually unlimited data can be addressed. It lets IT deliver a consistent, highly-responsive user experience, while freeing it up to focus on core competencies.

 QlikView enables IT professionals to deliver far more analytics and BI to the business than with any other approach. With its rapid application development capability, requests from the business for new apps can be answered much more quickly, changes to existing applications made easily and with its self-service approach, business users can get answers to their own questions without turning back to IT for a new report. This all contributes to the success of QlikView and makes IT groups who provide it to an organization true ‘champions to the business’.

ho want to interact and analyze data to discover new insights on their own - See more at: http://www.logianalytics.com/blog/5-keys-self-service-success#sthash.im9sszXi.dpuf

ho want to interact and analyze data to discover new insights on their own - See more at: http://www.logianalytics.com/blog/5-keys-self-service-success#sthash.im9sszXi.dpuf

Self-service means different things to different people - See more at: http://www.logianalytics.com/blog/5-keys-self-service-success#sthash.im9sszXi.dpuf

Self-service means different things to different people - See more at: http://www.logianalytics.com/blog/5-keys-self-service-success#sthash.im9sszXi.dpuf

Features Comparison of BI Tools

User Experience :

Microstrategy	Qlikview	Pentaho
MicroStrategy Web user interface adheres to an “Extreme AJAX” model where processing is shifted from the Web server to the Web browsers, making for a more responsive Web interface that increases user productivity and improves user adoption	Qlikview multiple Web interfaces intended for different deployment requirements. Because each interface has different capabilities, developers are typically forced to make tradeoffs between functionality and deployment requirements	Pentaho web interface offers very limited functionality. It lacks familiar Microsoft paradigms, making the end user experience less intuitive. Enterprise reports created using the Pentaho Web are limited to basic reports without any graphs, charts or crosstabs, severely limiting end user experience and self-service capabilities

Performance:

Microstrategy	Qlikview	Pentaho
ROLAP architecture which leverages the database for much of its processing. Data joins and analytic calculations are processed in the database whenever possible. MicroStrategy’s multi-pass approach provides the flexibility to answer any analytical question in the most optimal manner.	QlikView stores all data and performs all calculations in memory on the middle-tier server. QlikView does not fully leverage the relational database or the hard disk on the middle-tier. These aspects of the QlikView architecture result in inefficient resource utilization and limit QlikView’s scalability.	Pentaho ROLAP engine does not provide fully implemented multi-source ROLAP and multipass SQL engines. The Pentaho ROLAP engine is unable to leverage the database to its fullest extent possible, resulting in unnecessary network and hardware resources utilization

Deployment and Administration

Microstrategy	Qlikview	Pentaho
Provides organizations a platform that is quick to implement and deploy as well as easy to maintain and administer, fueled by a single code base that offers the advantage of reusable business logic across the entire platform. MicroStrategy’s single BI server provides efficient, centralized administration for IT and fewer moving parts which translate into less downtime.	QlikView lacks a common reusable metadata layer that is shared across documents. This creates a maintenance challenge as developers are typically forced to continually and manually synchronize metric definitions and security profiles across documents.	Pentaho lacks a unified and reusable metadata layer creating maintenance challenge and promotes “multiple versions of the truth.” The administration console provides control over only a subset of administrative tasks. Have fewer tools to centrally monitor and manage the BI applications, thus more administrators per number of end users. Lacks enterprise features like clustering and load balancing, increasing the administration complexity and increasing the IT workload.

Drawback:

Microstrategy	Qlikview	Pentaho
Reusable metadata is easier to maintain  requiring less redundancy, end users  have more self-service capabilities that offload work from the IT staff, t provides a comprehensive suite of administrative tools requiring fewer IT administrators	Developers are forced to create  redundant metadata objects as the metadata objects they create cannot be  reused across multiple reports, causing unnecessary development and maintenance  efforts.	Developers are forced to create redundant metadata objects as the metadata objects they create cannot be reused across multiple reports, causing unnecessary development and maintenance efforts.

Business Analytics: Good Data and Poor Data

Today, most organizations use data in two ways:
Transactional/Operational use (“running the business”), and Analytic use (“improving the business”).

Good business demands GoodData.
Business analytics can provide amazing insights into how an organization is operating -- in hindsight, with insight and with foresight. But one must be attentive to the quality of the data being analyzed and put first things first. Step one is to check the validity of the data, ensure its quality and completeness. Step two is to ask those key questions that help provide the information needed to make informed decisions. Internal auditors, armed with analytic technologies of their own, can provide a huge amount of assistance in determining data quality and addressing the risk of drawing incorrect conclusions based on bad data.

It is of great value to any enterprise risk management program to incorporate a program that includes processes for assessing, measuring, reporting, reacting to, and controlling different aspects of risks associated with poor data quality.

Data quality is a critical prerequisite to effective business analytics. Poor data quality jeopardizes the performance and efficiency of operational systems. It undermines the value of analytic and business intelligence systems upon which
organizations rely to make key decisions. Decisions based on poor data can result in direct financial loss.

Business leaders need to pay serious attention to the accuracy, quality and reliability of their data. The most obvious cause for poor data quality is data entry. If an organization has no standards or IT controls for how data is entered into a system, the data will quickly reflect their lack. It is in this way that duplicate entries are made in master data.

While Business Intelligence tools can create beautiful and compelling dashboards and graphics, if the data that the tools rely on is of poor quality, their results are
meaningless, or at least potentially badly flawed.
Data cleansing and data quality are important in order to ensure quality results from business intelligence analytics.

You can never fully predict how business users will want to analyze their data, so give them complete freedom to drill down in any direction they choose.Deliver them something good in a week as opposed to something great in six months.Good data is attained by integrating multiple data sources, deriving a ‘single version of the truth,’ and putting that good data (and unstructured content) into a data warehouse where the BA/BI tools can perform their magic. DDD (data-driven decision making) begins and ends with good data.Analytics applications that nicely present dashboards, scorecards, historical trends, predictive analys, and give me actionable insights, can all benefit from good data. Good data begins with data integration, data quality, and a good data warehouse.