Page Text: Ad hoc reporting
Data management tools
Better decision-making starts with better data. Data management tools help clean up "dirty data ," organize information by providing format and structure and prepare data sources for analyses.
Functionality
Description
Data quality management
Helps organizations maintain clean, standardized and error-free data. Standardization is especially important for business intelligence tools that integrate data from diverse sources. Data quality management ensures that later analyses are correct and can lead to improvements within the business.
Extract, transform and load (ETL)
Collects data from outside sources, transforms it and then loads it into the target system (a database or a data warehouse). Because primary data is often organized using different schemas or formats, analysts can use ETL tools to normalize it for use in analytics.
Data discovery applications
The ability to sift through data and come to meaningful conclusions is one of the most powerful benefits of adopting business intelligence tools. Data discovery applications help users make sense of their data, whether it be through quick, multivariate analysis during OLAP or via advanced algorithms and statistical computations during data mining.
Functionality
Data mining
Sorts through large amounts of data to identify new or unknown patterns. It is often the first step that other processes rely on, such as predictive analytics. Databases are often too large or convoluted to find patterns with the naked eye or through simple queries. Data mining helps point users in the right direction for further analysis by providing an automated method of discovering previously neglected trends.
Online analytical processing (OLAP)
Enables users to quickly analyze multidimensional data from different perspectives. It is typically made up of three analytical operations: data consolidation, data sorting and classification ("drill-down"), and data analytics from a particular perspective ("slice-and-dice"). For example, a user could analyze sales numbers for various products by store and by month. OLAP allows users to produce this analysis.
Analyzes current and historical data to make predictions about future risks and opportunities. An example of this is credit scoring, which relies on an individual's current financial standing to make predictions about their future credit behavior.
Semantic and text analytics
Extracts and interprets large volumes of text to identify patterns, relationships and sentiment. For example, the popularity of social media has made text analytics valuable to companies with a large social footprint. Understanding semantic trends is a powerful tool for organizations evaluating purchase intent or customer satisfaction among users of these channels.
Reporting tools
In the words of John W. Tuckey, “the greatest value of a picture is when it forces us to notice what we never expected to see.” Reporting applications are an important way to present data and easily convey the results of analysis.
Business intelligence users are increasingly business users—not IT staff—who need quick, easy-to-understand displays of information. In response, software vendors have been working to mask the complexity of these applications and increasingly focus on the user experience.
Functionality
Visualizations
Helps users create advanced interactive dashboard representations of data via simple user interfaces. The ability to visualize information in a graphical format (as opposed to words or numbers) can help users understand data in a more insightful way. In addition, new interactive tools can help teams use analytics and manipulate reports in real-time.
Dashboards typically highlight key performance indicators (KPIs), which help managers focus on the metrics that are most important to them. Dashboards are often browser-based, making them easily accessible by anyone with permissions.
Report writers
Allows users to design and generate custom reports. Many CRM and ERP systems include built-in BI reporting tools, but users can also purchase standalone applications, such as Crystal Reports, to create ad hoc reports based on complex queries. This is especially helpful for organizations that constantly use analytics and need to generate new reports quickly.
Scorecarding
Scorecards attach a numerical weight to performance and map progress toward goals. Think of it as dashboards taken one step further. In organizations with a strategic performance-management methodology (e.g., balanced scorecard, Six Sigma etc.), scorecards are an effective way to keep tabs on key metrics. For example, a scorecard might establish a grade of “A+" to 40 percent year-over-year growth if the goal was set at 14 percent.
What type of buyer are you?
Before evaluating software, you must determine what type of buyer you are.
Business users and departmental buyers. These buyers favor small data-discovery vendors and BI tools over the big, traditional BI systems. Ease-of-use and fast deployment are more important than in-depth functionality and integration. They are usually business users rather than IT staff.
IT buyers. Traditional buyers are more focused on functionality and integration within their information infrastructure stacks or other ERP applications. Integration across different entities and departments is usually more important than ease of use.
Market trends to understand
As you begin your software comparison and evaluation, there are a couple trends to consider:
In-memory processing: OLAP systems of the past would pre-calculate every possible combination of data. These calculations would be stored in the “cube,” and users could retrieve them when they needed a certain analysis. Creating these cubes was very time-consuming—sometimes taking as long as a year—and required expertise. Today, computer processors and memory are faster, cheaper and more powerful overall. This same process can happen in-memory, rather than using a disk-based approach with cubes. Analytics software built on an in-memory architecture can retrieve data and perform calculations in real-time or on-the-fly.
Big Data: The Internet is rapidly creating vast amounts of data. This phenomenon is known as "big data" among IT and business leaders. Business analytics software companies are beefing up their data warehousing and analytics capabilities to keep up with demand.
However, according to Gartner , through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage. The right BI tools can help harness the power of so much data.
Companies dealing with large amounts of data may also want to consider investing in dedicated IT security suites to support their computer security needs.
Business users to outnumber IT staff: This is a major trend playing out in the market. More business users—rather than traditional IT staff—are evaluating and purchasing software. So usability is becoming more important than functionality during software evaluations. As a result, small data discovery vendors that develop really good interactive visualization tools are gaining market share. Meanwhile, traditional BI vendors are parroting new market entrants by promoting ease of use.
Software-as-a-Service (SaaS): A growing number of organizations are considering SaaS or “cloud” BI software instead of traditional, on-premise software that you install on-location. Cost is a major driver of this trend. The poorly performing economy is motivating companies to look at lower-cost BI software from SaaS and open source vendors. Of course, perceived ease of use, faster implementations and reduced IT needs are also driving this trend. On-premise BI vendors are responding by committing development resources to cloud technology.
Mobile BI applications: Proliferation of the iPhone, iPad and other mobile devices is pushing vendors (e.g., Microsoft and Oracle) to develop on-the-go business intelligence applications. Analysts think mobile BI could expand the population of BI users to a larger, mainstream audience.