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.
