Turning Data into Actionable Information
Business intelligence (BI) has been defined in many ways. By the earliest definition (1958), business intelligence was seen as “the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal.”
A broader and perhaps more current definition of this discipline is this: business intelligence is the process of collecting business data and turning it into information that is meaningful and actionable towards a strategic goal. Or put even more simply, BI is the effective use of data and information to make sound business decisions.
Business intelligence encompasses the following elements:
Reporting: the process of accessing data, formatting it, and delivering it inside and outside the organization
Analysis: identifying patterns and establishing relationships in a group of data
Data mining: the extraction of original information from data
Data quality and interpretation: the greater or lesser correlation between data and the real-world objects they represent
Predictive analysis: a branch of data mining, it attempts to predict probabilities and trends
Reporting and analysis are the central building blocks of business intelligence and the arena in which most BI vendors compete by adding and refining features to their solutions.
The general process of business intelligence is as follows:
Gathering data and organizing it through reporting
Turning it into meaningful information through analysis
Making actionable decisions aimed at fulfilling a strategic goal
Data: The Raw Material
The raw material of business intelligence is the data that records the daily transactions of an organization. Data may come from such activities as interactions with customers, management of employees, running of operation, or administration of finance. According to the traditional model, data from daily transactions is recorded in three main transactional databases: CRM (customer relation management), HRM (human resource management), and ERP (enterprise resource planning). For instance, a sales transaction would be recorded and stored as a piece of data in the CRM database.
A piece of data, in itself, is neutral. It is neither “good” nor “bad.” For instance, if you knew that a sales representative had received 500 dollars worth of orders last year, you wouldn’t necessarily know whether it’s a cause of panic or celebration.
Just like raw material, data needs to be processed through analysis to become meaningful. The same piece of data in the example above would become meaningful if compared to the sales target for the sales representative or the performance of other sales representatives. By doing this, the piece of data has become part of the process of analysis.
Analysis: Contextualizing the Data and Answering Questions
Analyzing data means asking questions and getting meaningful answers. For example, the simple command “sort in descending order” on a column of data in Excel representing orders taken by sales representatives would answer the questions “Who is taking the most orders? The least orders?” The sort command has contextualized the data, making it much more meaningful in terms of the strategic goals of the business.
Of course, analysis in BI is much more complex and varied than this. The powerful and interactive analysis tools of today’s better business intelligence solutions make it easier to ask data an increasing number of questions and getting meaningful answers–including “what-if” scenarios, mashing up of data with geographic mapping, and much more.
For example, data analysis features can answer such questions as:
How is my product performing by product line? What about by location? Or by demographics?
What is the untapped potential of a new sales location?
What would be the likely impact of revenue if I changed store display??
In any case, the goal of even the most sophisticated analysis features is always the same: enabling decision-makers to understand data, to spot patterns among numbers, to identify trends and the reasons behind them–simply put, to contextualize data and answer questions about it.
Making Decision and Taking Actions that Are Strategically Relevant
Interestingly, most BI projects fail not because of faulty technical implementation, but because of the lack of a strategic focus. Business intelligence should be a lever that enables a company to lift itself more efficiently towards its strategic goals. But all too often, BI becomes an end-in-itself proposition, with managers failing to look at it in line with the company’s mission.
Cyclops provides brick-and-mortar retailers with insightful shopper analytics. To find out more about Cyclops, visit our website: https://dayta.ai.