What is the Difference Between Business Intelligence, Data Warehousing, and Data Analytics?

When listening to discussions of many of the core concepts of the big data world, it often can feel like being caught in a hurricane of technobabble and buzzwords. Three of the most relevant concepts to understand, though, are data warehousing, data analysis, and business intelligence (BI).

Individually, each of these concepts engenders one-third of an overall process. When that process comes together, a company can more efficiently collect data, analyze it and turn it into actionable information for decision-makers at all levels of an operation.

The What

Data warehousing is the most straightforward of the three concepts to understand. As the term suggests, it’s the process of taking collected data in a company and storing it in places where it can be kept secure and accessible. This means having access to either on-site database servers or off-site cloud storage platforms. Data analysis is the process of scanning through the available data an organization has in order to produce insights. Many people misuse this concept interchangeably with BI. The distinction is that data analysis tools help professionals.

handle the tasks of:

• Acquiring data from sources
• Prepping data for analysis
• Confirming data integrity
• Identifying statistically grounded methods for gaining insights
• Using computing resources to rapidly cull massive amounts of data
• Iterating through permutations of statistical models to generate insights
• Verifying that any generated insights are statistically valid

Business intelligence tools is about taking the raw insights gained using those data analysis tools and turning them into actionable information. BI platforms are designed to provide visualizations and data to stakeholders. For example, a U.S. retailer might offer its buyers in China real-time data streams of insights derived from scanning millions of influencers’ feeds on Twitter, Instagram, Facebook and other social media platforms. This allows the buyers to look at the insights and quickly make decisions about what’s likely to sell well in the upcoming fashion season.

Business analytics (BI) has been defined in many Predictive analytics 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 analysis software tools 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. While it might not sound like it, BI is different from analytics.

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 transaction 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–i.e. neither “good” nor “bad.” For instance, if you knew that rep X had received Y dollars worth or orders year to date, 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 (for instance) if compared to year-to-date sales target for rep X. 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 it questions and getting meaningful answers. For example, the simple command “sort in descending order” on a column of data in Excel representing year-to-date orders taken by sales rep 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, multidimensional slicing and dicing (XOLAP analysis), 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 territory? Or by demographics?

What is the untapped potential of sales territory X?

What would be the likely impact of revenue if I eliminated territory Y and relocated Y’s rep to territory X?

Are my reps balancing face time with their customers with “windshield time” in an efficient way? Is there a way to improve this?

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 between 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 Business analyst software tools projects fail not because of faulty technical implementation, but because of 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 project managers, CIOs or CTOs failing to look at it in light of the company’s mission.