Big Data Tool Sets: Dead on Arrival?

Books have been written in volumes about the value of our employees. Look at your income statement, and you can see your largest expense and measure your profit based upon the metrics you use daily to run your business. Our products, our services, and our profit all go back to and depend upon our employees. More books have been written about how to hire the best employees, and your business might even have a whole department for it. Your managers and executives spend a large part of their work lives building teams, tweaking them, empowering them, and enabling them to grow. That leads us to data – Big Data, and it is everywhere. You cannot open a periodical or peruse a website without finding some mention of Big Data and how it can help you do great things!

I recently read a great article by Matt Mullen, entitled “Big Data: Does Variety, Volume, and Velocity really deliver Veracity?” (12-Mar-2012). In it he characterizes Big Data as “… a clever tool set desperately looking for a purpose....” Like most of you, I am solicited daily by the latest tool set looking to solve my problem. Yet most of it is what we call “Dead Data.” Dead Data is a noun – a computing term meaning data that is no longer relevant. While data is easy to get – after all, we have spiders, trackers, and various databases to suck in other data bases –it is almost always all dead data. For instance, for a small investment made monthly, I can have all of the employment Big Data pulled from various sources. If you ask your IT guy nicely, he can download it all for you (and if they will not, just email me, and I will show you how) starting from this sample list:

U.S. Department of Commerce –
Bureau of Economic Analysis -
State Personal Income and Employment (SPI)
Local Area Personal Income and Employment (LPI)
National Income and Product Accounts (NIPA)
Annual Input-Output (I-O) Accounts
Benchmark Input-Output (I-O) Accounts
GDP by State
U.S. Census Bureau -
American Community Survey (ACS)
County Business Patterns (CBP)
ZIP Code Business Patterns (ZBP)
Nonemployer Statistics (NES)
Quarterly Workforce Indicators (QWI)
OnTheMap (OTM)
TIGER/Line File (with additions by DM Solutions Group)
Population Estimates
U.S. National and State Population Projections
Census 2000 & 2010 Summary Files
Census of Government -- State and Local Government Finances by State
Journey-to-Work (JtW)
Consumer Expenditures Survey (CEX)
U.S. Department of Labor –
Bureau of Labor Statistics -
Quarterly Census of Employment and Wages (QCEW)
Current Employment Statistics (CES)
Current Population Survey (CPS)
Local Area Unemployment Statistics (LAUS)
National Compensation Survey
National Industry-Occupation Employment Matrix (10-year, current/projected)
Occupational Employment Statistics (OES)
Occupational Education and Training Projections
Employment and Training Administration (ETA)
Characteristics of the Insured Unemployed
National O*NET Consortium, O*NET Production Database
U.S. Department of Education, National Center for Education Statistics
Integrated Postsecondary Education Data System (IPEDS)
Office of Educational Research and Improvement for the CIP, 2000 Standard Occupational Classification Crosswalk to 2000 Classification of Instructional Programs
U.S. Department of Health and Human Services, National Center for Health Statistics
Health, United States
U.S. Postal Service
Address Information Systems (AIS) Products, Delivery Statistics
AIS Products, 5-Digit ZIP Product
AIS Products, City State Product
Internal Revenue Service
Statistics of Income Division, County-to-County Migration Data
U.S. Railroad Retirement Board
Annual Railroad Retirement Act and Railroad Unemployment Insurance Act Statistical Tables
Oak Ridge National Laboratory
County-to-County Distance Matrix (Skim Tree)
Private sources job-posting search engine
Equifax business-level data

This Big Data will tell you everything that has already happened. Oh, and most of it is posted on a quarterly, bi-yearly, or yearly basis only. So yes, sure, it is really good data, if you are interested in learning about what happened in the past. There are also the services that will tell me where all the jobs like mine our posted – but be warned, those cost a pretty penny. Of course, then I realized that I have access to Monster, CareerBuilder, Dice, and the free aggregators, like Sure, I can search for my needed skill sets on those and form my own list (it took 15 minutes; my time isn't worth that much). However, if 136 other companies in the Greater Chicago area are looking for Java developers, and I am too (being realistic, I am probably not going to find many of those candidates willing to take my job given all those other opportunities), then these sites are great at telling me where not to go for talent. All of this data might be easy to find with time or money, but it is dead data when you get it.

So, what to do? Well, I can always go to the Executive Recruiters. If I engage the so-called industry experts, I get their whole rolodex! By rolodex, I mean a nifty paper card database, for those of you who do not remember those lumbering card holders we all had on our desks back in the day. How accurate is that data, though? In today’s market, studies tell me that people change jobs frequently, meaning there will be changes in email addresses, phone numbers, and skill sets. So how “live” can their data really be? Then I think back to how an executive, retained, or even a contingent recruiter works – I get the first three candidates to fit my minimum qualifications followed by a barrage of phone calls demanding that I pick one!

Even LinkedIn has its disadvantages. Through some clever Boolean searches I was taught, I can see everyone on LinkedIn with a certain set of keywords on their profile. Plus, I know how to find phone numbers; therefore, with enough time I can get to the candidates I want. Heck, I found my sister’s high school friend twenty years later, and I got her to marry me! Still, how do I know that everyone I need is on LinkedIn? Some simple math tells me that is just not the case.

This is what has led us to “Avancos Advanced Insight Intelligence” ( Data is easy to get. Synthesizing data to attain knowledge is a lot more difficult. In the case of talent management, knowledge for us means knowing where to find the people you need – where have they been, where are they going, and what inspires them to make a move? It means gathering and accessing “Live” data to help companies make better decisions, with “Live” being the operative term here. “Live” Talent Intelligence is invaluable. Using the Avancos proprietary process that produces the “Market Analysis Profile, ” or MAP for short, 70% of a given skill set can be identified within 48 hours. Within a week, that number jumps to 90%. Customers have said that after utilizing the MAP, they simply cannot see paying a “recruiting fee” ever again.

Is your data “Live?” Or is it dead on arrival?

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