2. Strengths and weaknesses of each tool in general (priyanka)
SAS
Strengths:
Breadth of functionality and degree of integration: SAS’s capabilities address all the base functional capabilities required in this market, although the vendor also enables customers to purchase various capabilities individually.
Ease of use and breadth of applicability: The ability of ease of use enables both business and IT resources to work readily with the tools. Notable is the ability to gain insight into the state of data quality rapidly via the profiling functionality, and then to turn that insight quickly into rules deployed in data-cleansing routines.
Product road map and direction: SAS DataFlux's product road map calls for further development of its Data Management Platform, which combines data quality functionality with data integration and Master Data Management capabilities. This aligns well with trends in market demand that make pervasive and broadly applied data quality capabilities foundational to an organization's information infrastructure.
Weaknesses:
Limited adoption of Data Management Platform: Although the DataFlux Data Management Platform is well aligned with trends in demand, it is still young and only a minority of customers have migrated from earlier versions of SAS DataFlux technology. In current market, approximately 25% of the organizations in a recent sample of reference customers indicated they were running recent or current versions of the Data Management Platform.
Pricing model and price points: Customers' satisfaction with SAS pricing model and prices is somewhat low in comparison with most of its competitors. SAS will need to adapt its pricing to offer more attractive entry points for customers with more modest require...
... middle of paper ...
...s. Reference customers routinely report substantial issues with reliability as well as challenges in keeping up with the pace of point releases and the end-of-life of prior releases.
Support and documentation: Reference customers frequently express frustration with quality of Talend's product support and the weakness of its product documentation. These are common issues for vendors with offerings based on open-source technology, but they will still impair Talend's ability to capture and retain enterprise-level deployments.
Alternative delivery models: Though SaaS and cloud-based deployments of data quality capabilities still constitute only a small part of overall market demand, interest in them is growing. To date, Talend has shown minimal activity in this area. Talend's tools can be deployed in public cloud settings, but it does not provide its own cloud services.
In the past number of years data has grown exponentially. This growth in data has created problems that and a race to better monitor, monetize, and organize it. Oracle is in the forefront of helping companies from different industries better handle this growing concern with data. Oracle provides analytical platforms and an architectural platform to provide solutions to companies. Furthermore, Oracle has provided software such as Oracle Business Intelligence Suite and Oracle Exalytics that have been instrumental in organizing and analyzing the phenomenon known as Big Data.
...rstand the capabilities and limitations of key technologies, the solutions they request are more likely to meet relevant needs.
This strategy successfully persuaded older employees to adopt Box, a cloud-based storage platform for the company’s architectural drawings and financial documents. The organization’s adoption of the Box software grew out of a trial at one job site and just took off, caught fire, adoption-wise... And soon, what had started as a small group test grew intoalmost one hundred Box users within a few weeks. The reason for this growth was word-of-mouth testimonials that employees gave after using the software within the company. In addition to his cheerleader approach, Sarrubi also makes sure that working with the new technology is “as easy as using Amazon.” Cost, scalability, and return-on-investment are important factors the company considers when making IT decisions, but end-user preference is also a big factor in what technologies the company
The business intelligence (BI) marketplace is teaming with new innovations and struggles for market share. With new technologies and more companies entering the business intelligence landscape pricing for BI processes and tools are decreasing. “The relational database market is around 30 years old. It should be mature by now, but every year there seem to be new innovations in the relational database space. I’m always astounded that there continue to be new entrants” (Beckerle, p. 281, 2008).
The BrightGauge Software case study, in turn, shows that the company understood the importance of tracking customer behavior, but was not using the right tools. As Paine (2011) notes, “Even the most sophisticated measurement tool is worthless if it can’t measure progress toward your goal” (p. 46). HubSpot assisted the company with not just the tools in this specific case, but with determining how to create processes that were repeatable so they could be measured. According to Paine (2011), “Because you become what you measure, it is critical to carefully choose the metrics by which you will track your success” (p.
Some challenges are business related. Many software suppliers use non-standard software to "lock in” vendors - i.e. make it difficult to migrate their data to a competitor. Business models that are predicated on historical service delivery models, such as face-to-face, fee-for-service consultations often penalize remote service delivery.
Additionally, Siebel would like to win FleetBoston’s faith in its products. Although a large percentage of the deal on Q&R’s side has already been done by representatives and department heads, it is essential to convince the new acquirers at FleetBoston of the feasibility of using Siebel Systems over the already existing Scopus product.
software. Due to the amount of data this provides we decided to focus on the data given for the
Jack O’Brien, the head, was convinced that the tools would provide a robust means to communicate the project status to management and to identify critical issues.
Data quality concerns and controls are something that I have had an interest in for several years, and I have encountered continuously in my professional career, as well as my work in graduate school. In my first job as a Loan Officer for Reliance Capital in India, one of my duties was database management for our region. During my tenure in this capacity, we transitioned from a paper-based workflow to an to e-filing system for all pertinent information. I then went on to further refine my understanding of data quality issues while researching genetically modified organisms during my graduate school research at the University of Idaho.
In today’s society, technology has become more advanced than the human’s mind. Companies want to make sure that their information systems stay up-to-date with the rapidly growing technology. It is very important to senior-level executives and board of directions of companies that their systems can produce the right and best information for their company to result in a greater outcome and new organizational capabilities. Big data and data analytics are one of those important factors that contribute to a successful company and their updated software and information systems.
First of all, business intelligence analysis requires the capturing of information and storing in a single location for effective data analysis. Currently, data analysis is supported by transactional systems, business specific data marts, and other ad-hoc processes. Information is distributed making it difficult and time-consuming to access. Business teams have adapted to this environment by creating user maintained databases and manual “work-arounds” to support new types of reporting and analysis. This has resulted in inconsistent data, redundant data storage, significant resource use for maintenance, and inefficient response to changing business needs.
However even though information is critical to the process it might not be easy for companies to achieve access to the same. Some issues that plagued the same are format of the information that is available across networks and sources might not be the same and may not be compatible with the systems available or might not be easily integrated with systems available. Once these issues are addressed we need to find out the actual information that is needed by the company or firms.
Prior to the start of the Information Age in the late 20th century, businesses had to collect data from non-automated sources. Businesses then lacked the computing resources necessary to properly analyze the data, and as a result, companies often made business d...