Let’s jump right in, hiring and interviewing takes on many forms with various philosophies in play, Tim Graettinger has developed a philosophy based on the needs of an outdated headhunter, reviewing an outdated HR manual. Perhaps, if today’s headhunters would consider today’s date, this is the digital age where a mobile philosophy focused on, who can use the tools of data mining, just might establish a positive interviewing process which gets better results. The past hiring structure is interfering with today’s high tech business needs.
On the other hand, to effectively communicate expectations and build confidence, there must be a mutual dependence between HR and the candidate. It would be more productive to open an interview with a real-life business requirements question, which challenges the candidate to create a data model, provided that there is a laptop loaded with RStudio, RapidMiner, KNIME, Microsoft BI and Excel on hand. To generate synergy, the excited candidate is given a time limit to produce the result by clustering data loaded from a csv file. There is no need for a stiff, shake down, criminal investigation, masquerading as an interview to find the right person for the job. Therefore, if the candidate can produce a result within the immediate laptop, data mining environment, then, that is all the proof which is needed for HR to make a hiring decision. Today’s industries are performance based, so the data mining interview must rely on hands-on performance criteria to meet the needs of employers.
From the article, it has been found that it is inconceivable to believe that a Graduate student, fresh out of college today, without any real world, data mining, project experience, will be chosen to interview by a Fortune 500 company for a data mining position. This power packed article reinforces the urgent need for all data mining prospects to develop hands on skills, using algorithms quickly as possible, if planning to compete in the data mining arena. Enterprises need people who can use open source data mining tools to cluster data, now! The information presented in the article, has turned a future problem into an opportunity.
Greater challenges are on the horizon, now is the time to master RStudio, RapidMiner, KNIME, Microsoft BI, Excel, and gain access to the online repositories for job security. Opportunity is waiting for the person who can see the light, digging through the dark tech tunnel, while running with a backpack full of the latest data mining tools towards success. Generally speaking, the personality of the data miner is of course, a major factor. The positive attitude, backed by confidence, will prevail!
Business intelligence is a field that is highly important for organizations across all industries. Over the past thirty days I had the opportunity to embark on an intense course of study with positive results. Let’s get started, although technology is transforming business activities, business intelligence technology is causing great consternation with the top level executives, reporting from the business community. I have discovered that, business intelligence platforms must be evaluated by looking at the positive and negative aspects before making the decision to invest in a solution.
It is a major requirement for each champion, within the organization, to be responsible for conducting a SWOT analysis to determine the Strengths, Weakness, Opportunities and Threat for each BI vendor. This step is necessary, because there is no one-size-fits-all tool available on the market. The business intelligence tool user is responsible for locating the appropriate platform which produces a positive cash flow for the company. The objective is to locate, through a selection process, the proper tool for the BI project. Generally speaking, failure to integrate business requirements into the selection process can result in project failure.
Although the there are many business intelligence platforms available, the success of BI depends to a considerable extent on the data available in the organization. Indeed, data quality is considered to be the most important technical factor. Therefore, the information contained in reports from a BI solution is directly related to the available data which is provided by the enterprise. Furthermore, the organization’s data, including the data being provided by the transactional systems, and the data that resides in the data warehouse, should be of the highest quality. In effort to clearly point out the need for clean data, an organization should consider several aspects of data quality, including: relevance, accuracy, consistency, and or course, whether the organization has the data.
With all things considered, the buyer should know the capabilities of the solution being provided by the vendor. Such as, the information delivery in the form of reporting, dashboards, Ad Hoc Query, and Microsoft Office integration. The platform of choice should include: BI infrastructure integration, metadata management, development tools, workflow and collaboration capability, and support for the business process, execution language. On the other hand, the solution would be useless without analysis capability, in the form of OLAP, visualizations, predictive modeling, data mining, and scorecard presentation. The overall concern for the enterprise, is to find a vendor with the perfect solution which produces a high ROI.
In conclusion, it has been found that the best practice is to follow the Kimball University guidelines, and the Gartner Magic Quadrant before investing time and resources in a vendor. But, who will rate the champion who is going to lead the project to success? The enterprise executive must choose the leader of the project with great care, because the champion is responsible for ensuring the success of the BI project. Till next time, keep your KPI’s in mind and invest intelligently.
ED MAC AUDIO, 436 MEDIA INC.