Business intelligence (BI)
Business intelligence (BI) is a technology-driven process for analysing data and presenting actionable information to help corporate executives, business managers and other end users make more informed business decisions. BI encompasses a variety of tools, applications and methodologies that enable organizations to collect data from internal systems and external sources, prepare it for analysis, develop and run queries against the data, and create reports, dashboards and data visualizations to make the analytical results available to corporate decision makers as well as operational workers.
The potential benefits of business intelligence programs include:
- Accelerating and improving decision making
- Optimizing internal business processes
- Increasing operational efficiency
- Driving new revenues and
- Gaining competitive advantages over business rivals.
BI systems can also help companies identify market trends and spot business problems that need to be addressed.
Business intelligence allows a company to make effective important strategic decisions. By using the business intelligence model a company strategically plan how to make the overall operations of the company better for them and easier for their customer to use their services while keeping ahead of their competitors.
By using the data and information collected on their customers they can analyse what aspects of the company the customer uses the most and where they can improve in other parts of the company.
Business intelligence combines a broad set of data analysis applications, including:
- Ad Hoc Analysis and querying:
Ad hoc analytics is the discipline of analyzing data on an as-needed or requested basis. Historically challenging, ad hoc analytics on big data sets versus relational databases adds a new layer of complexity due to increased data volumes, faster data velocity, greater data variety and more sophisticated data models.
2. Online analytical processing (OLAP)
OLAP (online analytical processing) is computer processing that enables a user to easily and selectively extract and view data from different points of view. For example, a user can request that data be analyzed to display a spreadsheet showing all of a company’s beach ball products sold in Florida in the month of July, compare revenue figures with those for the same products in September, and then see a comparison of other product sales in Florida in the same time period. To facilitate this kind of analysis, OLAP data is stored in a multidimensional database. Whereas a relational database can be thought of as two-dimensional, a multidimensional database considers each data attribute (such as product, geographic sales region, and time period) as a separate “dimension.” OLAP software can locate the intersection of dimensions (all products sold in the Eastern region above a certain price during a certain time period) and display them. Attributes such as time periods can be broken down into sub attributes.
OLAP can be used for data mining or the discovery of previously undiscerned relationships between data items. An OLAP database does not need to be as large as a data warehouse since not all transactional data is needed for trend analysis. Using Open Database Connectivity (ODBC), data can be imported from existing relational databases to create a multidimensional database for OLAP. Two leading OLAP products are Hyperion Solution’s Essbase and Oracle’s Express Server. OLAP products are typically designed for multiple-user environments, with the cost of the software based on the number of users.
BI technology also includes data visualization software for designing charts and other infographics as well as tools for building BI dashboards and performance scoreboards that display visualized data on business metrics and key performance indicators in an easy-to-grasp way.
BI applications can be bought separately from different vendors or as part of a unified BI platform from a single vendor.
Hadoop is an open source software framework for storing data and the ability to run applications of commodity hardware.
Hadoop was initially started in 2003 but moved to the new subproject and what we know as Hadoop today in 2006 by Doug Cutting, Hadoop was named after Cutting’s son’s toy elephant.
Hadoop has the power to be able to handle endless amount of data and is able to handle endless amounts of jobs and tasks.
The reliability and the fact it saves companies from building their own data centers and piling money into building for a fraction of the price they can hire Hadoop to store their data for them.
Many multinational companies makes use of HADOOP. These are a few of them.
Four trends influencing the face of BI:
- Unstructured data
Unstructured data is a vast, vast unrealized and untapped natural resource. When I say unrealized, I mean everyone recognizes it’s out there, and it’s a rich vein to be mined, but many executives maybe sitting right on top of the gold without even realizing it’s down there.
The ability to extract insights from unstructured data — which is the essence of Big Data — represents opportunities for real business returns. The insights that lie in Big Data are key to competitiveness in today’s economy — offering insights to predict market shifts, understand customer behavior, optimize supply chains, and develop product innovations.
Executives, managers and professionals who are able to make better and faster decisions more often will have the edge in today’s economy.
In a survey Unisphere Research conducted among 264 data managers about a year ago, it was found almost unanimously that unstructured data — which is defined as business documents, presentations and social media data is on the rise, and ready to engulf their current data management systems.
The trouble is, management does not understand that the challenge is coming, and fails to recognize the significance of unstructured data assets to the business. So there’s lots of work to be done here.
2. Cloud-based BI and analytics
BI can be expensive to purchase, implement and maintain. Cloud may change all that. Cloud is opening up business intelligence and analytics to more users — non-analysts — within organizations. There already is a drive to make BI more ubiquitous, and the cloud will accelerate this move toward simplified access.
To be sure, we’re still only in the early stages cloud-based BI and analytics. A survey of 200 companies by Saugatuck Technology concludes that only about 13 percent of enterprises worldwide — including all industries and all sizes of enterprises — have cloud-based BI/advanced analytics solutions in place and in use. But this is about to change.
3. Mobile BI and analytics
More enterprises are embracing access to data analytics via mobile apps. Having analytics available in a simple app fashion could be a major boost for efforts to “democratize” analytics in organizations.
The key is to keep things simple and understandable, and mobile apps can go a long way in delivering this. Analytics can be offered through simple, single-purpose mobile apps, whose utility is quickly and easily grasped by business users.
4. Visual analytics
Some also refer to this as “3D data visualization.” Perhaps even 4D would be a better way to describe it, since it enables a look across time — the fourth dimension.
Visual analytics provides something more powerful than 2D charts, and providing deeper understanding. They are typically interactive, 3D diagrams that enable decision-makers to see at a glance what is trending.
A stunning example of visual analytics is Google’s work-in-progress, a 3D map of the universe called, “100,000 Stars.” It enables you to zoom in on our solar system, and then zoom over to the closest adjoining star and its solar system. Click on specific stars and planets, and you will get a brief description.