Self-service business intelligence (SSBI) is a technique for data analytics that allows business teams to access and utilize company data regardless of their background in data analysis or knowledge of data mining. It allows end users to employ statistical analysis to pose questions and solve their own problems.
When users across the enterprise can analyze data to make informed decisions, it eases the demands on BI or IT personnel to provide data meeting a sometimes endless stream of ad hoc requests. Self-service BI users can devise their own queries and obtain information for reports or other purposes, allowing technical teams to focus on bigger issues.
Making SSBI Work
In order for self-service BI tools to be productive and reliable for employees without BI training, it's essential that the interface used to access data tools be relatively straightforward and intuitive. Visual control elements such as dashboard graphics make navigation easier. However, some training is usually required so that a variety of end users can navigate the interface to explore data and compose their own specific data queries.
Before users can start to utilize data in their own decision making, the IT team normally has to first set up the data warehouse, specialized data marts, and the necessary self-service BI tools. Once levels of access are granted, and minimal training provided, end users should be able to generate information and satisfy their own data requirements quickly and easily.
SSBI helps to create a mindset where employees are more likely to seek out data when making decisions, rather than judgments based on intuition or assumptions. However, there needs to be a degree of data governance in place to avoid repetition and confusion. This involves strictly defining permissions for who has access to what data or data objects, including editing, copying, and sharing reports or queries. There should also be policies regarding security, confidentiality, and preserving data integrity among the self-service BI users.
Learning Statistical Analysis
As a component in business intelligence, statistical analysis requires consideration of every data point in a sampling. In statistics, this is a representative collection of a larger population. For example, the Pareto Rule states that 80 percent of events likely come from 20 percent of the possible causes. The other 20 percent of events and 80 percent of causes are likely to be statistically insignificant.
Techniques for statistical analysis can normally be divided into the following five distinct steps:
Define the data required for analysis.
Determine the relationship of the sample to the population.
Construct a model that summarizes this relationship.
Verify or disprove the conclusions of the model.
If proven, use predictive analysis on applicable scenarios to suggest future actions or outcomes.
The purpose of statistical analysis is finding trends. For instance, a large service company may employ statistical analysis of unstructured customer feedback to identify prevalent consumer expectations. Understanding the customer and using personalized information can increase revenue by providing improved customer experiences.
Self-service BI tools can provide data warehousing benefits and analysis to most aspects of a business, without specialized training in business intelligence or statistical mathematics.
The Answer to Everything
This may not be false if you've gathered exhaustive records on every aspect of your business and loaded them correctly into your data warehouse. This provides incredible transparency and accuracy. However, the key to successfully utilizing statistical analysis is that you must ask the right questions.
If you're hoping to just spit out some numbers and discover mind-blowing revelations, you're going to be disappointed. Instead, analysis can suggest and clarify business theories and rules. A large company with millions of contact points could quickly amass a huge amount of information. This can actually obscure the facts if you aren't careful about the data used in your sampling.
Success requires that you have a very clear idea of what you want before you start exploring the data. If you don't know the objective or understand the information, you won't recognize the answer. This is vital in using statistical analysis.
Self-service BI platforms don't necessarily eliminate the occasional need for specialists, especially in the early phases of adoption or particularly complex or important analysis projects. But self-service BI tools are becoming both more flexible and more instructive. Many products will include and even explain packaged statistical formulas. But training programs on BI, statistics, and selected vendor products can certainly speed up the learning curve.