Part 1 of this column identified trends within the data warehousing appliance space, the effect of consolidations on the market and how social media concepts are slowly making their way into general business intelligence (BI) use. Part 2 continues to address trends within BI and data management by exploring the convergence of unstructured data and BI. This column also discusses data integration and data governance concepts.


BI Convergence with Unstructured Data


The role of unstructured data and specifically text analytics within BI is becoming a popular topic of discussion. Because organizations are starting to understand the value of using more varied information to make decisions, the ability to merge analysis of textual information with data residing in databases expands opportunities to create better marketing initiatives, improve customer relationship management (CRM) and increase performance. The ability to look outside of the organization to identify industry trends and gather relevant information from suppliers and customer interactions is becoming more important.


Through acquisitions and partnerships, software providers are able to leverage best-of-breed offerings to expand analytics by including text analytics capabilities. Generally, this involves structuring the information found in documents and emails by identifying key words or sentiments and identifying trends based on that information. This enables in-depth analysis that can be combined within BI applications.


The use of search within BI is another way that unstructured data has become popular. Although search uses text strings to identify unstructured content, the truth is that this form of unstructured data was created using structured data, and it only identifies what has already been created within the BI applications being used. This limits what data is identified but provides easier access to end users and can extend BI’s use beyond traditional super users alone.


Data Integration


Integrating data from disparate systems due to corporate consolidations and BI projects is an example of how data integration is used within an organization. Beyond integration are the activities that enable accurate and relevant data to get to the appropriate place in a timely fashion. For instance, real-time data integration enabling operational BI and data quality activities are becoming more mainstream. A good example is within the retail industry where capturing transactions in real time enables organizations to save money within their supply chain, work more efficiently with suppliers, partners provide better customer service and manage customer experience programs.


Data Governance and Master Data Management


With the focus on expanding data volumes and the increasing importance placed on data within an organization, the role of master data management (MDM) and the creation of golden records for a single view and access point to data across the organization have never been so important. The ability to apply data governance strategies and maintain a standard of data quality is becoming easier. As data integration vendors increase their focus on data governance and data quality can design and implement strategies that enable better process efficiencies, improve decision-making and increase the tie between better decisions, higher profits and access to data.


MDM is becoming an important initiative as a result of data warehousing and reporting being used to manage and maintain MDM initiatives. The increasing focus on look at data as information to enable better decision-making puts data quality and business processes under the microscope. Unfortunately, the closer many organizations look, the more they realize their current data doesn’t meet their expectations. Consequently, more effort is assigned to the management of information across organizations to ensure data quality and that common data definitions exist.


Market Implications


Unfortunately, as with any trend, the end is unpredictable. In many cases with BI, trends become cyclical based on marketing hype and new uses for technologies. This means that despite well wishers and early adopters taking advantage of new technologies and practical applications, in many cases, the implementation of these technologies is small at best. In addition, because of a lag time between development and release of newer technologies and hype over what can be done and how BI and data management can be used within the organization, the actual use of these technologies is sidelined based on the next trend. However, the renewed focus on data management enables organizations to develop and maintain infrastructure that are conducive to BI, MDM and data governance initiatives.


The convergence between BI and unstructured data, expansion of data integration and focus on MDM and data governance initiatives all overlap and highlight the importance of using data to help drive business decisions. This means using information to enhance how a business is run, as opposed to having IT initiatives drive how data is disseminated throughout the organization. Within BI the focus remains on data, meaning the difference is the way in which data and the management of data is being used to drive BI initiatives and the way information is being used to increase positive interactions with customers, suppliers, partners and disparate business units.

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