You could be a small online clothing store wanting to figure out which products to put on discount. Or you could be a large construction firm trying to consolidate project data to spot any signs of scope creep.
In either case, the one thing that can provide the info and answers you need is a business intelligence (BI) solution. The software is valuable if you want to obtain actionable insights, no matter how big or small your company is, or what industry it belongs to.
However, there is no single best approach or tool for implementing BI.
For a business owner or stakeholder exploring BI tools, the question to ask is what kind of BI do you need—traditional or self-service BI?
What is traditional BI?
Traditional BI is the “old-school way” of implementing data analytics tools. It typically requires a complex IT environment, space for data warehousing, and near-constant involvement from IT staff.
The important thing to note here is that “old-school” doesn’t mean “outdated” or “inferior.” It just denotes how large companies have implemented data analytics tools since BI was introduced to the business world.
The evolution of traditional BI
The first comprehensive BI systems were developed by IBM and Siebel Systems (acquired by Oracle in 2006) between 1970 and 1990. The initial versions were data warehouses that served as central repositories of integrated data from one or more different sources.
These data warehouses were technically complex and required extensive IT staff to maintain and manage. They needed staff specialized in BI to extract insights out of data and create analytical reports for separate departments and business functions. In other words, owning and operating BI was a costly affair sustainable for resource-rich businesses solely.
With time, BI vendors started developing tools that were not as cost-intensive or technically complicated—and the dashboard emerged. Dashboards still depended on IT staff to get the data in place, but they made BI somewhat accessible for users to generate reports and run queries themselves.
Big tech companies such as Microsoft, IBM, Oracle, SAP, and SAS were the vanguards of this BI era and dominated the “leaders” space in Gartner’s 2010 Magic Quadrant for BI Platforms (full document available to Gartner clients only).
What is self-service BI?
Self-service BI is the approach of implementing data analytics that enables users to access and use data without statistical, analytical, or data handling expertise.
This approach depends on BI tools that allow users to filter, sort, analyze, and visualize data to extract insights without the dependence on developers or data or IT specialists.
The idea is simple—grant users direct access to intelligence and help them slice and dice data as per the need.
The evolution of self-service BI
Technology innovation is redefining what is expected of BI tools on the market. Accordingly, vendors are continually reinventing and improving their tools to expand the capabilities for governance and scalability.
Machine learning and artificial intelligence are entering data preparation, data modeling, and insight generation processes. Natural language programming is providing users a search-like experience instead of a “query-building” process. Finally, innovations in cloud technology are enabling the categorization and processing of data in volumes never possible before.
Changes in this space have been so rapid that Tableau and Qlik, which were market “challengers” in 2010, became Analytics and BI Magic Quadrant “leaders” in nine years (full document available to Gartner clients only). For small and midsize businesses, a host of new software vendors such as Dundas BI, Looker, BOARD, and Sisense emerged, leading the self-service BI market as our FrontRunners.
Traditional vs. self-service BI—a comparison
As a business owner or stakeholder exploring BI tools, the question for you remains—which of the two is right for your business?
Traditional BI implementation is comprehensive and resource-intensive whereas self-service BI will mean a ready-to-use tool. But there is more to both the approaches.
For a broader evaluation of both the options, here is a detailed comparison between the two:
|Traditional BI||Self-Service BI|
|IT setup||Majorly IT-driven with near-constant involvement of IT and data specialists. Legacy deployment with multiple components, each requiring specialized personnel to implement and maintain.||Once implemented, it frees up the IT staff to focus on other infrastructural requirements. Also, reduces the number of specialized IT personnel needed for maintenance.|
|Agility||Access restricted to IT personnel and data specialists. Market opportunities might remain trapped inside week- or month-long cycles of queries and reports.||Users can do data analyses and generate reports and insights in real-time. They can also test hunches about trends and correlations by modeling data on-the-fly.|
|Kind of data||Need to structure the data before it can be utilized.||Harness data in various formats from multiple sources.|
|Kind of reporting||Focused on answering questions about what happened in the past or is happening right now. Limited capabilities of on-demand reporting.||In addition to historical reporting, provides predictive and prescriptive reporting with a forward-think approach. Diverse on-demand reporting capabilities.|
|Data governance||Close involvement of IT staff and data specialists ensures cleaning, proper storage, and security of data, and addresses concerns about data governance.||Need data governance policy to define processes for cleaning and storing data, considerations for data modeling, and privileges for data access.|
Is self-service BI the obvious “better” choice?
On the face of the comparison, self-service BI might look like the winner. However, that is not always so.
In layman’s terms, traditional BI is like on-premise software: It’s costly and cumbersome to set up and needs hands-on IT involvement but you can make the tool completely as per requirements and have absolute control over it.
Self-service BI is like cloud-based software: It’s ready-to-use and cheaper initially but could have limitations on customizability, scalability, and data governance.
If you choose to go with self-service BI for your business, following are some recommendations to help you build a strong analytics foundation:
- Confirm the value of self-service BI by aligning the BI initiatives with organizational goals and capturing anecdotes about measurable, successful use cases.
- Create a formal process for collaboration between users and the IT staff to help each party understand what it needs from the other.
- Ensure that data governance is flexible enough to enable and support the free-form analytics explorations of users yet structured enough to protect against the risks of data breach.
- Create a formal onboarding plan to train users on applying the new self-service tool to their specific business problems.
If you are still unsure of what BI approach to choose, you can get in touch with a Software Advice BI expert for a free consultation and pricing information of BI software tools best-suited to your business.
Note: The applications selected in this article are examples to show a feature in context and are not intended as endorsements or recommendations. They have been obtained from sources believed to be reliable at the time of publication.