In this article I wrote for SearchDataManagement.com, I discuss how business intelligence analytics tools can leverage data and convert it to actionable information that can benefit organizations.
There are four articles in this series: Understanding BI analytics tools and their benefits, Business use cases can determine the right BI analytics tool, How to evaluate and select the right BI analytics tool and Comparing BI analytic tools from the leading vendors.
Enterprises are awash in data about their customers, prospects, internal business processes, suppliers, partners and competitors. Often, they can’t leverage this flood of data and convert it to actionable information for growing revenue, increasing profitability and efficiently operating the business. Business intelligence (BI) tools are the technology that enables business people to transform data into information that will help their business.
Although BI tools have been around for decades and many consider the industry mature, the BI market is vibrant, constantly innovating and evolving to meet the ever-expanding needs of businesses of all sizes and industries. Over the years, many BI tool styles have emerged to match the varied ways that business people need to analyze data. An understanding of BI tool categories and styles is needed in order to match your analytical needs with the appropriate tools.
BI analytics tools can be grouped into three broad categories that each support various BI styles and capabilities:
Guided analysis and reporting. This category includes the traditional BI styles that businesspeople have been using for years to perform recurring analyses of specific data. Examples include a vice president of sales examining the sales pipeline, a marketing manager comparing the performance of various marketing campaigns or a chief financial officer analyzing an enterprise’s financial key performance indicators.
Years ago, this category was limited to predefined, static reports, but now business users can select, filter, compare, visualize and analyze data using a variety of tool types. The underlying assumption in this BI tool category is that the data set and the metrics used will be predefined, but the analysis itself may vary based on the immediate needs of the information consumer when performing that analysis.
The IT group or BI team creates most of the BI applications in the guided analysis and reporting category for end users. However, business analysts also produce many BI applications using the self-service BI tools discussed in the next section. Regardless of who creates the BI application, IT will be responsible for the underlying data and managing the BI applications used on a recurring basis.
The BI tool styles in this category include:
Self-service BI and analysis. This category includes the BI tools business users use to perform ad hoc analysis of data. This analysis will either be a one-time-only analysis or the formulation of a recurring analysis that will be shared with others.
The users of these tools have dual roles: information consumer and analytics producer, when they share or publish the BI application they create with the self-service BI tool. Users of these tools typically have the word analyst in their title (e.g., business, financial or human resources analyst). Management staff members may also use these tools when they’re doing the work of the business analyst (or analytical guru) for their manager or peers, even if their titles might not imply that.
Whereas guided discovery tools operate with a pre-set collection of data and metrics, the self-service BI tools enable business users to add data and define new metrics when performing their analysis without requiring IT intervention.
However, there are some considerations to the no IT involvement needed hype that some BI vendors will pitch. First, IT will manage data source access based on need, security and privacy rights, so business users performing their analyses will have to obtain proper privileges to add data sources.
Second, the data sources need to be consumable by the BI tool. Although most data sources can be easily accessed by BI tools, there may be specific sources that prohibit access. Third, the data source must be understandable by the business user, which often requires business people working with IT to get an explanation of the schema and definitions of the data they need to analyze. Finally, no matter how easy the BI tool is perceived to be, having IT help train and support the business in the effective use of these BI tools will improve business user’s productivity and increase the business return on investment of these tools.
The BI tool styles in this category include:
Advanced analytics encompasses the tools data scientists use to create predictive and prescriptive analytical models. This includes predictive analytics, statistical modeling, data mining and big data analytics software. Here, data scientists tend to spend a great deal of time doing data ingestion, integration and cleansing. This category is outside the scope of this article but is mentioned here in order to provide the entire spectrum of BI tool styles. Here’s a look at other BI tool categories and styles:
Each of the BI styles discussed here originated as standalone, specialized BI tools sold by emerging BI vendors. As enterprises recognized their value, the following occurred:
A key buying question an enterprise must ask is: Is it better to buy a BI suite from one BI vendor or to purchase separate products from multiple vendors? The answer is: It depends. Although other articles in this series will deal with this question in more depth, there are key concepts to consider. First, you need to buy what you need, not just acquire the BI product with the most features because your enterprise may not need all that’s offered. The selection process should be guided by business need and best fit.
Second, an enterprise needs to examine the cost and skills necessary to develop and manage BI applications, not just purchase or subscription cost. Sometimes, BI suites are more cost- and resource-effective than standalone BI tools; however, there other times in which they’re much more complex, resulting in higher costs, longer development lead times and the need for a greater pool of skills.
The investment in and use of BI analytics tools has experienced long-term growth, regardless of the economic cycle. It has accelerated in recent years as enterprises are craving data to not just grow and improve, but also to manage their businesses on a daily basis. Historically, BI has been the domain of large enterprises due to complexity, costs and the skills required — but during the past several years, those factors have changed dramatically, resulting in small and medium-sized businesses (SMBs) becoming significant BI buyers.
Many enterprises, regardless of size, initially leverage the reporting capabilities offered by their business application vendors — such as SAP, Oracle, Microsoft, Infor and Epicor — by also using spreadsheets to fill in the gaps, especially when their focus is on tactical operational reporting. But this approach often results in data silos, limiting the ability of an enterprise to leverage its BI efforts to grow revenues and operate more effectively. In addition, this approach wastes people’s time in comparing and reconciling data from these silos — time that could be better spent running the business. When the limitations and costs of this approach become apparent, then an enterprise is ready for BI technology that’s independent of their operational applications.
In the early days of BI, only industries with the most significant need for data used BI; today, enterprises in all industries have information-intensive processes that require BI tools. The scale of the information that needs to be analyzed will vary by industry and enterprise size, impacting what specific BI tools should be considered; however, that doesn’t impact the particular BI categories and styles needed.
It can be overwhelming to examine the BI vendor landscape for the first time, as there are currently more than 100 vendors. In addition, the BI market has experienced a significant amount of merger and acquisition activity, so even people in the industry are sometimes confused as to who sells what.
BI vendors can be split into three groups:
The two deployment considerations are how the business people will access the BI tools (front end) and where the BI application itself will operate (back end).
Although there are some BI analytics tools that exclusively use desktop client applications, almost all offer a browser-based client interface that works across all major Web browsers. BI vendors were slow to implement native-based mobile interfaces and instead relied on using a browser on a tablet or smartphone; however, with the expanded use of mobile devices for business, that’s changing.
Although most implementations deploy BI application servers on-premises in an enterprise’s data center, more applications are being deployed on private clouds hosted by companies such as Amazon, IBM and Rackspace. When the BI client interface is browser-based, the decision on whether the BI tool will be deployed on-premises or in the cloud can be made based on an enterprise’s data center strategy, rather than by limitations in the BI tool. There are emerging BI players that are exclusively providing cloud-based BI deployments, often in a multi-tenant software as a service environment with the cloud BI vendor ensuring security and privacy.
Now that you have a better understanding of the different tool categories, the vendor landscape and how BI analytics tools are deployed, the next step is to determine your needs by taking a closer look at some typical use cases for which these tools are optimized.