Business Analytics
Business analytics (BA) is the practice of iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis. Business analytics is used by companies committed to data-driven decision-making.
Business analytics techniques break down into two main areas. The first is basic business intelligence. This involves examining historical data to get a sense of how a business department, team or staff member performed over a particular time. This is a mature practice that most enterprises are fairly accomplished at using.
The second area of business analytics involves deeper statistical analysis. This may mean doing predictive analytics by applying statistical algorithms to historical data to make a prediction about future performance of a product, service or website design change. Or, it could mean using other advanced analytics techniques, like cluster analysis, to group customers based on similarities across several data points. This can be helpful in targeted marketing campaigns in targeted marketing.
Specific types of business analytics include:
- Descriptive analytics, which tracks key performance indicators to understand the present state of a business.
- Predictive analytics, which analyzes trend data to assess the likelihood of future outcomes.
- Prescriptive analytics, which uses past performance to generate recommendations about how to handle similar situations in the future.
While the two components of business analytics -- business intelligence and advanced analytics -- are sometimes used interchangeably, there are some key differences between these two business analytics techniques:
The more advanced areas of business analytics can start to resemble data science, but there is a distinction. Even when advanced statistical algorithms are applied to data sets, it doesn't necessarily mean data science is involved. There are a host of business analytics tools that can perform these kinds of functions automatically, requiring few of the special skills involved in data science.
Business analytics tools come in several different varieties:
- Data visualization tools.
- Self-service analytics platforms.
- Statistical analysis tools.
- Business intelligence reporting software.
Self-service has become a major trend among business analytics tools. Users now demand software that is easy to use and doesn't require specialized training. This has led to the rise of simple-to-use tools from companies such as Tableau and Qlik, among others. These tools can be installed on a single computer for small applications or in server environments for enterprise-wide deployments. Once they are up and running, business analysts and others with less specialized training can use them to generate reports, charts and web portals that track specific metrics in data sets.
Once the business goal of the analysis is determined, an analysis methodology is selected and data is acquired to support the analysis. Data acquisition often involves extraction from one or more business systems, data cleansing and integration into a single repository, such as a data warehouse or data mart. The analysis is typically performed against a smaller sample set of data.
Analytics tools range from spreadsheets with statistical functions to complex data mining and predictive modeling applications. As patterns and relationships in the data are uncovered, new questions are asked, and the analytical process iterates until the business goal is met.
Deployment of predictive models involves scoring data records -- typically in a database -- and using the scores to optimize real-time decisions within applications and business processes. BA also supports tactical decision-making in response to unforeseen events. And, in many cases, the decision-making is automated to support real-time responses.
It’s no secret that using analytics to uncover meaningful insights from data is crucial for fact-based decision making. Yet when it comes to making analytics work, not all organizations are equal. The management of information – including the analytics used to transform it – is an evolutionary process, and organizations are at various levels of this evolution.
Those wanting to advance analytics to a new level need to understand the analytics activities across their organization, from both an IT and business perspective. Toward that end, an assessment focusing on eight key analytics metrics can be used to identify strengths and areas for improvement in the analytics life cycle.
SAS conducted a benchmark survey of more than 260 US companies across all industries to determine each organization’s proficiency across the key metrics. The results of this survey are presented in this paper to provide guidance for short- and long-term efforts needed to enhance analytical effectiveness. SAS is the leader in analytics. Through innovative analytics, business intelligence and data management software and services, SAS helps customers at more than 83,000 sites make better decisions faster.