FlashPoint Newsletter

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		February 9, 2012	ANNOUNCEMENTS Submissions for the next Business Intelligence Journal are due February 24. Submission guidelines CONTENTS Three Steps for Transitioning from a Data Management Career to an Analytics Career Origins of Your Next Generation Data Warehouse Platform Mistake: Not Securing the Proper Expertise See what's current in TDWI Education, Research, Webinars, and Marketplace
	Three Steps for Transitioning from a Data Management Career to an Analytics Career Piyanka Jain Aryng
Topic: Business Analytics Many people think analytics is about gathering data using software tools and creating dashboards and reports. However, analytics is much more. Analytics goes beyond just the data to enable business decisions based on that data. This involves working with stakeholders to understand the gaps in the business and using this knowledge as a guide to manipulate data, derive actionable insights, and make recommendations. Do you have a data management, data warehousing, or business intelligence role and wonder how to move into a more impactful analytics career? On the surface, “analytics career” can be quite broadly defined. However, the structured approach we describe in this article will make it easier. My first question to someone looking at an analytics career is usually, “Why do you want to change careers?” Once you understand your motivation to change and how well this career will fit your personality, you can consider your next moves. Step 1: Align Your Disposition to Your Career What are the telltale sign of a good analyst? Ask yourself these questions to see if you have what it takes: Are you a problem solver? Do you like puzzles and other games involving logical thinking? Are you generally curious? Do you like working with people and helping them solve their problems? Are you driven toward making an impact through your work? If you answered “yes” to most of these questions, you will likely enjoy being an analyst. Step 2: Get Trained Once you know you will enjoy this career, it is time to get trained. To do that, you’ll need to decide what kind of analytics career you are looking for. Do you want a career in business analytics driving decisions in the business world, or would you prefer a career as a data scientist doing advanced analytics? McKinsey Global Institute's report on big data predicts that by 2018, there will be a shortage of 1.5 million analysts/managers who can make data-driven decisions versus 140,000–190,000 positions open for data scientists. There are several key differences between the two tracks. Data scientists need advanced analytics skills. They need an advanced degree and at least two years of education. Data scientists spend more time on computer algorithms than they do working with people. If you love working on data, software, and systems, this is a good fit. Your education options depend on your situation: For full-time or part-time courses in analytics with core topics in statistics, algorithms, quantitative methods, data mining, and predictive analytics--along with tools training in SAS, R, etc.--consider North Carolina State University and Northwestern University, two well-known universities, that offer master’s degrees in analytics. Stanford and many other universities offer professional development courses through their statistics, data mining, or other departments. Training on software tools is widely available through vendors such as SAS, SPSS, and Angoss. Short, hands-on courses in advanced topics such as logistic regression, decision trees, and data mining are offered by analytics consulting companies including Aryng, Elder Research, and Abbott Analytics. A business analytics professional/manager will need a basic understanding of analytical techniques that most data professionals can learn quickly. Analysts/managers spend more time interfacing with people than computers. This track requires less time for transitioning, especially if you already have experience working with data. Data professionals already equipped with SQL skills to manipulate data will need training in data analysis and soft skills to start tackling business analytics challenges. Although 80 percent of business problems can be solved via business analytics techniques and don’t require advanced data analysis, historically there has not been formal business analytics training offered, and most people have learned it on the job. Even today there isn’t much in the marketplace. You may find books and occasionally courses at conferences. At Aryng, we recognized this gap and created business analytics training classes. Our five-step analytics framework course marries years of practical business operations experience with technical data analysis techniques to quickly enable business and data professionals in data-driven, decision-making processes. Step 3: Find a Job Analytics is a hot field with many jobs available. To land a great analytics job, consider networking via LinkedIn. Use LinkedIn Jobs as well as LinkedIn analytics groups and highlight your analytics skills using tags. Also consider key job portals, such as craigslist, icrunchdata, Indeed, Dice, and Monster. Things to Avoid Here are my recommendations for things to avoid. Don’t expect to learn analytics from blogs and social chatter. There is a lot of information published online. Do your own due diligence. Don’t view conferences as a solution for training. Be choosy; attend only the top notch vendor-neutral conferences to get the real scoop on analytics: Predictive Analytics World, Strata, and TDWI conferences are the top few I would recommend. If you are using a specific tool such as SAS or R, attending their annual conference may be a good idea as well. In summary, know your strengths, get the right training, and go get the job--and don’t forget to have tons of fun! Piyanka Jain, CEO of Aryng, former head of business analytics at PayPal NA, is an established analytics thought leader and acclaimed keynote speaker at business and analytics conferences. For more information about Aryng, go to www.aryng.com. Highlight of key findings from TDWI's wide variety of research Origins of Your Next Generation Data Warehouse Platform Remember that almost half of survey respondents say they’ll replace their current data warehouse platform by 2012. If they do, what strategies will they follow in assembling their next generation data warehouse platform? (See Figure 4.) Today, most DW platforms are assembled by in-house personnel. According to our survey, most data warehouse platforms are custom solutions created internally by members of IT or the data warehouse team (55%). Even so, the system integration required of custom solutions is a time-consuming distraction, which is why some user organizations offload it to consultants or system integrators (28%). Still other users turn to pre-integrated hardware/software bundles from vendors (8%), and a few early adopters are using data warehouse appliances (6%). Users are open to DW appliances and similar hardware/software bundles. According to the survey results in Figure 4, if given the chance to replace a data warehouse platform, more respondents would go with an appliance (20%) than with consultants or a system integrator (16%). Users are likewise open to a vendor’s pre-integrated hardware/software bundle (15%) for their next generation DW platform. However, a custom in-house design (44%) would still be the preference for most users choosing a new platform. (Click for larger image) Source: Next Generation Data Warehouse Platforms (TDWI Best Practices Report, Q4 2009). Access the report here. FlashPoint Rx prescribes a "Mistake to Avoid" for business intelligence and data warehousing professionals. Mistake: Not Securing the Proper Expertise By David Loshin Developing an MDM program is a strategic undertaking and its success depends on both business and technical expertise. Maintaining an enterprisewide, strategic focus requires both vision and hands-on experience. It takes many complex tools and methods to ensure success, so experienced advisors must help marshal the team through the stages of MDM program development. Make sure you engage the right experts to help launch the program: Hire professionals with experience in data governance, data quality, metadata, and MDM programs from day one. These individuals will identify opportunities for tactical successes that contribute to the program’s strategic success. Engage external experts to help jump-start the assessment process and business case development. This will reassure your team that your problems are not unique while allowing you to learn from the best practices of others. Use internal procedures to attach responsibility and accountability for enterprise data stewardship and data governance. Train staff in policies and procedures, especially in the use of acquired tools. Learn from experienced practitioners, even if it means hiring consultants from outside your organization. Their advice will expedite the initial stages and will guide the road map over the long term. Internally, choose partners who will transfer best practices and ideal operational procedures to establish patterns for success. Source: Ten Mistakes to Avoid When Developing an MDM Program (Q1 2009). Access the publication here.