Tm1: Fill & Download for Free

It stands from Tables Manager. It is a OLAP tool used for building cubes which can later be consumed for developing Business Intelligence reports. One can perform slicing/dicing on the data in the cubes.

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I can think of two paths that will get you to $250,000 (even if you adjust for inflation) in eight years:1: Statistical analysis and predictive modeling (AKA predictive analytics).Learn SPSS, R, and SAS. Become familiar with big data tools. Learn to analyze large sets of structured and unstructured data to find patterns. Gain experience (work) in at least one or two industries so that you understand those businesses and what kind of information is important to them. Insurance, health care and finance (specifically lending) are all good industries to know as predictive modeling specialist.Put in your time as an employee somewhere that allows you to learn as much as you possibly can. Get exposure to as many tools and technologies a possible. Establish yourself as a subject matter expert. Be the "nuts and bolts" person that everyone goes to when they need to dig into data to get answers. Get yourself noticed by underwriters, actuaries, credit and claims managers, people in charge of marketing, and take on the tough assignments. Whatever you do, stay in touch with key players and managers when you change companies (and you should change companies*) so that you build up contacts.Once you have done this for 6 or 7 years, you can strike out on your own as a consultant or contractor (there is/can be a difference, but that's another discussion). If you had started doing this 7 years ago you could easily charge $150 an hour (or MUCH more - I know one person who charges his clients at least $300 an hour) for your services. Even in a full-time job someone with this background could make $200,000 a year (or more) just in base salary.2: Financial Planning and Analysis (FP&A) specialist.Master one of the tools that companies are using to automate their financial planning and budgeting. One of the most powerful and ubiquitous is IBM's Cognos TM1. Learn how to install, configure and customize TM1. Learn to design and model TM1 solutions - the real money is in design and development, not administration. Learn about ETL (TI processes), rules, feeders, skipchecks. Learn Cognos BI as well - you should understand dimensional modeling and be able to create complex reports and dashboards, and understand how the various tools within the Cognos suite interact with each other. Become a data expert. Get really good at SQL.The key is that while you are doing all of this (and yes, it's a lot to learn, but you want to make the big bucks) you must gain a significant amount of knowledge about how the finance and accounting functions within various companies work. Being a TM1 expert will make you valuable, and you will learn a lot about finance along the way, but you need to really understand this business function. That's what will make you really valuable.Again, you must cultivate contacts and establish a "fan base" of satisfied managers, end users, customers and peers along the way. Do this for 6 or 7 years and then strike out on your own. Had you started this 7 years ago you could be charging $130-$170 easily for your services right now.**Neither of these areas shows any sign of slowing, certainly not for the next decade or so. Should there be a new tool or technology that suddenly gains a large share of the market, you will have the base skills, and learning a new tool should be easy for you.*One way to do this without having to change companies is to work for a consulting firm, such as one of the Big 4, IBM, or a boutique consulting firm. This will actually give you the opportunity to work with many companies and make many contacts without frequent job changes. It can also be a resume enhancer in that if you were planning to specialize in either SPSS or TM1, which are both IBM products, having 5-plus years at IBM would actually enhance your value.**The amount a consultant or contractor can charge will vary widely depending on many factors, including geography, length of engagement, level of proficiency with particular technologies, industry knowledge, and whether they find their own jobs or work with a broker or recruiter.