The evolution of business intelligence (BI) looks a little something like this. In the beginning, BI was really about the data warehouse – the building of schemas, data marts, and the like – with data analysis involving OLAP (online analytical processing) tools to look at information in multiple dimensions. This model of development and deployment meant that only a select group of employees could take advantage of the analytics developed by IT. Consequently, BI’s bottom line was one of high costs, large IT integration requirements, and limited business benefits.
On the positive side, the types of analytics gleaned were new to the so-called super users within the organization. Detailed analytics and trends based information that was elusive beforehand became more broadly available to employees with strong backgrounds in analysis-related business roles. The information provided through OLAP tools gave these end users broader insights into organizational performance and general trends occurring within the company.
With the advancements in technology, decreasing prices, and a broader focus on ease of use, BI is slowly becoming more accessible to companies and people within the organizations that are adopting it. This means that analytics can now be used more broadly across the organization.
This article takes a closer look at the mainstream adoption of data analysis, why there is debate surrounding broad statistical analysis, and what businesses should consider when looking at the implications of adoption across the organization.
Why make Data Analysis Mainstream?
Aside from the benefits of providing broader information access to more people within the organization, financial and direct business benefits exist. Whether called “BI for the masses”, “self-service BI”, “Operational BI”, etc. the reality is that having more people interact with business intelligence within an organization adds value to that organization. For instance, large expenditures and solutions that require additional hardware and software are evaluated closely. The more people that utilize these solutions and gain benefits from them, the more successful these solutions are. Whether success is measured by evaluating overall costs versus benefits, by time savings, or by performance management activities that drive an increase in profits, the fact remains that the more access end users have to analytics, the more likely the organization will gain better visibility into their operations.
The General Debate about Data Analysis
There are discussions surrounding the benefits of widely deploying advanced and statistical data analysis. Traditional BI keeps advanced analytics within the hands of super users who are familiar with the types of analytics that are available and the business rules behind the data they are interacting with. The concept behind deploying data analytics for the masses is to extend this access to a broader range of business decision makers. General availability of analytics (whether using predictive models or applying a series of algorithms to address specific business rules) results in a large degree of IT involvement. The issue for debate, however, does not center on IT involvement, but on the need for expertise when developing advanced analytics.
The development of advanced analytics requires detailed knowledge of statistics combined with an understanding of the business. When business users drive the process by using a pre-developed solution or through in-house development, concerns exist in relation to the validity of the data. Questions surrounding whether or not business rules were accurately transformed into analytics tools that can drive performance management encompass the main issue involving the broad deployment of advanced analytics. Therefore, some solutions retain their super user focus, whereas others are starting to develop business user offerings with pre-developed statistical analysis.
Pros of Self-Service Data Analysis
For organizations looking to expand access to analytics, the ability to develop more autonomous environments can help expand the value achieved through deployment. Sometimes the right questions are not asked because decision makers do not know what information can be accessed. Alternatively, if requests need to be sent to IT, time sensitivity may be an issue. To get the most out of analytics, it is important to put information in the hands of end users. The expansion of solution offerings provides greater access to information and enables broader analytics on the whole.
Additional benefits include better decision-making, broader visibility, and lower total cost of ownership due to self-service deployment models and expansive analytics use. Consequently, organizations with mature BI environments are beginning to look at ways to extend the way in which analytics are applied within the organization. One way is through self-service analytics that let end users manage how they interact with data.
Cons of Self-Service Data Analysis
On the negative side, argument about the benefits of providing advanced data analytics to a wide range of business users has merit. The development of effective analyses requires an understanding of the data behind the business and the affect of calculations on that data. For instance, one set of metrics may only be applicable to a certain set of scenarios. Without detailed knowledge, employees might analyze invalid data sets or put together results that only apply in certain situations. Pre-packaged advanced analytics solutions may help limit these concerns but the level of the concern will be a combination of solution choice and end user expertise.
General Data Analysis Recommendations
BI that provides advanced data analysis for the masses will become a reality for more organizations as vendors move towards developing more self-service, advanced data analysis solutions, and this broader availability will result in more business intelligence value to organizations as a whole. Early adopters of easy-to-access data analytics will lead the way for other companies to follow suit.
About the Author
Lyndsay Wise is an industry analyst for business intelligence. For over seven years, she has assisted clients in business systems analysis, software selection and implementation of enterprise applications. Lyndsay is the channel expert for BI for the Mid-Market at B-eye-Network and conducts research of leading technologies, products and vendors in business intelligence, marketing performance management, master data management, and unstructured data. She can be reached at email@example.com. And please visit Lyndsay's blog at myblog.wiseanalytics.com.
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