This post is part of an article on Search-based BI published in Information Management. In this post, I’ll discuss a new innovation in Business Intelligence: Search-Based BI. Enterprise Search is one of the technologies that will transform tomorrow’s Business Intelligence. Based on some recent integration projects for our clients, we found that Enterprise Search engines have the capacity to simplify and improve BI in large organizations. Search-Based BI is capable of making Business Intelligence more user friendly and accessible to a growing number of knowledge workers.
How-to make BI more user friendly?
We will see more and more evidence in the BI space that Enterprise Search engines are revolutionizing BI architectures. Here are ten reasons for this:
1. Search engines are very flexible in handling any type of format and information, whereas Data warehouses are mostly rigid in nature because of the single structured view of data.
2. They’re able to cope with continuously evolving data structures. Indexing both existing and new data does not require extensive data-modeling. This in contrast with the modeling of the Data warehouse which is time consuming not only when the model is created but each time new data is added.
3. They enable content-driven dimensional navigation: at each step of navigation Search engines propose different possibilities to filter results according to the content of the datasets that are being indexed and analyzed in near real-time.
4. Unlike solutions based on Relational Database Management Systems, they are able to analyze data without the need to know the various data types: for exemple a Search engine can easily search for any event that occurred at a specific point in time.
5. End users are now all familiar with the “Google” interface and, as a consequence, are much more independent from IT departments if they can access decision support data through a Search engine.
6. When external and unstructured data is needed to support decision making, traditional Data warehouse architectures are limited and Search engines can help to fill the gap.
7. Search engines include functionality to automatically generate categories and clusters, hence improving the contextualization and meaning of data.
8. Most of today’s Search engines include some basic functionality to aggregate and analyze data.
9. Search engines enable end-users to expose relationships and to find patterns in data without the necessity of the perfectly formulated question or query.
10. They can work with existing information systems, like Data warehouses and provide a federated view of data without compromising on performance. At the same time, the federated business view can encompass new data sources and provide cross-domain data navigation.
Easy integration and presentation
Over the past couple of years, Capgemini has implemented multiple Enterprise Search engines for a growing number of customers. Based on technologies, like Exalead, Endeca and Autonomy, billions of documents are being indexed on a daily basis from multiples sources, e.g. Enterprise Content Management, Enterprise Resource Planning, Customer Relationship Management, etc. In most of the cases, information is being collected in near real-time and presented to end-users through highly friendly and helpful interfaces. Both structured and non-structured data sources, like information residing in Data warehouses, are being indexed and loaded into the Enterprise Search repository.
Agility and performance
Because of the nature of Enterprise Search engines, i.e. one single product to extract, load, index, and present data, the time required to implement a Search-based BI solution is heavily reduced compared to the time that is needed to design and build a traditional BI system. Furthermore, performance is not an issue in Search-based BI, neither in terms of number of users nor in volume of data. Future BI systems, integrating non-structured and external information, will benefit from the proven scalability features of Search engines. Within Enterprise Search environments, volumes are measured in petabytes and exabytes instead of gigabytes and terabytes. Take for instance Google, which is indexing billions of documents daily on the Internet while providing access to a tremendous number of concurrent users.
Search-based BI is leveraging investments in existing BI systems and is capable of getting the long-awaited business benefits out of existing Data warehousing environments. The combination of BI and Enterprise Search will preserve the strengths of both and mitigate the drawbacks of each. Enterprise Search technologies have some really nice features to make existing BI systems user friendly, agile and flexible. Also in terms of Costs of Ownership and Return on Investment, Enterprise Search engines are the way to go. My point is this: During the past couple of decades, BI has seemed to avoid any disruptive innovation. To support tomorrow’s business requirements, BI must evolve to a next stage. Search-based BI is one of those exciting new innovations capable of helping traditional BI to make the change. In my next post, I will discuss the way Enterprise Search can help businesses to extend beyond the rigid structured data approach.