Tagged: data

Great Lists to Follow

There are some great lists out there to follow for Data Science and Technology – Check out a few I have been honored to be listed on – Please feel free to check out everyone on these lists, some really great folks out there!


50 Data Science Gurus (and 10 Organizations) You Must Follow On Twitter ->


PRESENTING: The 100 Most Influential Tech Women On Twitter ->


10 Big Data Pros to Follow on Twitter  ->


4 Women Leading the Way in Business Intelligence ->

Top 200 Thought Leaders in Big Data & Analytics


The Women Behind The Data


Top 5 Data Science Gals


Big Data: Experts to Follow on Twitter



I appreciate all the mentions and inclusions to these lists and WOW, I’m in great company!


Lastly, this is not a list but some advice ->  “An understanding of math is important,” he says, “but equally important is understanding the research. Understanding why you are using a particular type of math is more important than understanding the math itself.”


Have fun with DATA 🙂



You don’t know how to hire a REAL Data Scientist!


I was recently contacted by a recruiter concerning a Director of Analytics position at a Private University in my home state; they wanted to know if I could recommend someone for the position since I had a background in Education and Data Science. I dug through my contacts and supplied them with 4 names of experts that would be more than qualified for what they were looking for, all of them Data Scientist with at least 15 years of experience. Well, finally I heard from the last of those I had recommended, and each, for various reasons, were disqualified for the position, HUH?? I called the recruiter and asked what was going on, she said…. “her client, was looking for someone with more experience in fundraising” so, let me get this right….. you disqualified some of the greatest minds in Data Science because of lack of fundraising experience, really.

This is not the first time I have seen “truly amazing” people overlooked for a position due to “them” NOT having some little missing detail on their resume. It makes me question the people doing the hiring; do they even know what they want? Can they recognize an experience candidate or are they going on gut feelings and preconceptions of what “they” think the position needs.

Would you recognize a true Data Scientist if you met one? If you wanted to add data science or analytics to your University or Corporation where would you go, to a head hunter or a recruiter? Probably, but what makes you think they are qualified to find you the best Data Scientist out there when most of them are still trying to figure out what Data Science is!

If you really want to hire the best, I recommend you research the position first, how can you find the perfect candidate if you don’t? Buzzword like “Data Science” and “Big Data,” are added to everyone’s resume in analytics, this DOES NOT make them qualified, stop searching for the obvious and look for words that REAL Data Scientist would use – probability, models, machine learning, statistics, data engineering, pattern recognition, learning, visualization, data warehousing, are some examples.

In conclusion of my rant, I’d like to make one point…… If you really want to hire an expert in Data Science don’t go for the one with the biggest blog or the one that writes the most books, honestly a great data person doesn’t have the time or desire to write blogs and books, we’d rather be doing what we love; playing with data. If I had my choice on hiring the best, I would check out LinkedIn, find all the candidates I wanted and then call and verify reference before I even set up the first interview. Too many people out there pad and just flat out lie about their skills so verify everything! Sometimes I wonder if anyone follows up on anything anymore! A 10 minute phone call will stop you from figuring out how to get rid of someone that sucks, believe me, I see it happen frequently. There are some truly gifted individuals out there, don’t overlook them because you are mesmerized by your own agenda.


Data Science ROCKS!

Push verses Pull – are you marketing or spamming

There have been a lot of articles over the years; going into great depth about “Push verses Pull Marketing” therefore I am not going to over kill the subject by regurgitating what has already been written. What I will add is that marketing has changed with the introduction of social media into our daily regimen, analysis and reporting. We try various things when we, break into the world of social media, i.e. “push verses pull” marketing, do we blast our message to the world or do we provide great content and let them come to us? In my time on social media, I have seen links stolen and articles cloned, “how many likes for this” (what?) Facebook post, pins redirected to unintended sites and auto-posting gone mad with old articles from 2010 passed around like “hot news” because no one even bothered to read the article they just wanted to re-tweet someone with high Klout. So, push marketing has definitely taken on a new aspect, it is not just about spitting out your product news to anyone out there, it about misdirection and un-professional behavior. Followers and fans figure this out pretty fast so any sales they make are a single purchase and not about building a loyal clientele. I guess if that is your purpose, you have succeeded but businesses are built on longevity not tricks. I have seen great social media campaigns which made me think, giggle, scratch my head and lean into with anticipation of their next tweet so I know there are many companies that are doing it right. Hats off to the social media teams out there that create great content that goes viral and circles the world, you inspire us to turn it up a notch! Companies like Kellogg’s want to be part of your family, they don’t blast their message to unexpected recipients or beg you to “like” their page, they drip, drip, drip, with their messages, until one days, boom, you need cereal and without even thinking, you are buying corn pops.
It takes time to build a following, good marketers know this and slowly build a loyal following through likes, re-tweets, pins, discussion on LinkedIn groups and providing thought provoking content and ads. Occasionally ads go viral but the norm is to engage and be the 1st product or service that pops into someone minds. Take me for example, I am not unique by any means, they are lots of great people out there to follow. I started my Twitter account in 2010 but didn’t bring in the business aspect until March 2011, since then I have added over 5,000 followers, not because I have a high Klout score, beg for followers or talk about my business every 5 minutes. I spend hours a day searching for relevant content to share with my fans and friend on social media, to educate, inform, entertain and spread the word about analysis, data mining, mathematics as well as good habits in analytics and social media marketing. Not because I want to just give away good content but to form a relationship, to imprint my name, whether it be @data_nerd, Carla Gentry or Analytical Solution, into your mind so when you need research, sentiment or text analysis, social media campaign and analytical marketing you’ll head over to my site and give me a call.
In conclusion, when you are creating your social media marketing plan, keep this in mind, do you want to be a household name or a spammer who “gets and loses” followers on a daily basis, the choice is yours. Blast your message to anyone who will listen or target your message, create relevant content, respect your followers, and follow up with leads in a dignified manner. Engage and show potential clients why they should do business with you, if you have expert knowledge, share with others and promote your field. No matter what strategy you use (push verse pull), showing you and your businesses character might not make you a millionaire but I guarantee it will lengthen your business career and lifetime earning potential. Thanks for taking the time to read this article and have a great day of marketing!

Big Data – reduced to a buzz word



A “buzz word”, that is what data has been reduced too. “Big Data” is now a common phrase used to describe numerous counts of different types of data, social media data, point of sale data, financial data, digital and visual data…. Arg, make it stop. But what is it “really” and what makes it useful versus noise?

Over the course of my career, I have worked for companies of all sizes, with some handling data better than others; the best was actually one of the smallest (go figure). Most companies struggled to figure out what to do with the data they have versus how to get more. Retail and CPG companies that can afford all the latest and greatest BI and data mining tools usually collect and use their data very well since it’s REALLY their bread and butter and without it the competition would eat them alive. Unfortunately they aren’t usually able to “house” the data, making “real time” almost impossible. Smaller companies that have jumped into the data pool (per sae) purchase large amounts of data or gather their own live data but rarely have the insight to know what “is” or “isn’t” important. Example, I sell a 250 dollar yard trimmer, now 250 bucks is a bit steep so I know the average person is not going to buy. So, I would need someone who owned (the norm) or rented and really cared (the outliner), and someone who made above average income (the norm) or someone who saved to make the purchase (since it’s a yard trimmer, we’ll say that this is an outliner) but I only have name and email address, what can I do? Honestly, not a whole lot really, except maybe a mailing list. Say I have name, complete address and email, a little better… you could use the addresses to overlay with federal, state and local data or census data from that neighbor. That would tell you median income, average home price, etc. but without more demographic and financial data, it would still not be sufficient to deduce too much insight. So the kind of data you collect becomes more important than ever, if you want to target your customers think about what it would really take for you to get the best insight.

Next issue, when working with data, one needs to think about its quality, what do I mean by that? Is it accurate and clean data? Take a look at the number of duplicate rows of information and incomplete or N/A data fields, these are very important to note and take action on. Next, how your data is labeled and defined, the “metadata” or data dictionary of your database, it tells you if the data field is a character or numeric, the length (max 255 so watch out for those “NOTE” sections), and if applicable a short description of what the variable actually is. A unique quantifier is preferred, when working with FICA/FICO, we used SSN# but in other cases usually a client ID or purchase id, which may not be unique is used. If multiple purchases or visits, with a non-unique way of labeling, occurs this can be a headache especially when working with live data and adding into the master database. Updates in a data warehouse involve data dumps or extraction, transformation and load to merge new data in with existing data (segmentation is based on some type of quantifier, a hopefully unique variable), sounds easy (not) but it gets worse, the bigger the data the longer this process takes and we haven’t even started talking about unstructured data yet, whew. How are incomplete rows beneficial, if you are looking at web data or basket sales, it can show you were someone abandoned their shopping carts, if it’s a loan application, it can tell you where they stopped, see where I’m headed? Data entry is VERY important, a few fat fingered data sets add up fast when you are talking terabytes of data, especially when they are keys in but a multitude of people.

There is more than meets the eye to data, everyone wants it but if you want it just for the sake of having data, make sure it’s not just noise, what do I mean by noise. Data experts usually take different stances on this one; I’m the, make a mental note but remove for the sake of immediate insight, (null data does not make a pretty spreadsheet) kind of person. I take special note at the end of the evaluation or data analysis but don’t freak out trying to figure out why I have 87 records that indicate the person was over 90 years old or they made 123 dollars a year, mis-entries, errors, fat fingers… no time for them now but will contact IT to correct records later (this part is very important as well, if not corrected that is 87 wasted records and they keep coming up with each analysis).
Unstructured data, what do I mean by unstructured, all data has some type of structure… yes, but take Twitter and Facebook data, it doesn’t fit into a tabular form or model but if you manipulated it (using whatever method you choose) you can still infer insight but it’s messy and sometimes a lot of useless information i.e. Joe ate a sandwich and boy was it good, giggle. Lots to think about, tools for collection of data, tools for extraction and updating data, tools for converting unstructured data into usable information, talent to glean insight out of data. Storage used to be a big deal, but now a terabyte is 50 dollars but a data warehouse or data mart will require multiple servers or a mainframe, now there’s some money. But this is enough for you to think about for now, do you still want to build that database or start a data warehouse, if so please don’t shrug it off as begin a piece of cake, to gain insight the corrects steps are to think first, collect second. Happy Mining!


Social Media ROI

60 years ago some thought TV commercials were a waste of time and money. Is history repeating itself with social media?

I know comparing commercials to social media is not apples to apples but its close enough for my purposes. Flash forward to 2011: with over 300 million Twitter users, it offers an even bigger audience than TV did back in the day.

So why isn’t everyone advertising on social media? The reasons range from lack of staff or time to lack of analysis or return on investment (ROI).

1. Let’s tackle a commercial’s ROI
As humans, we are generally skeptical of new things. No one believed that advertising on television would be as successful as it is now, nor did the Internet grow by leaps and bounds when it first started. Advertising on social media (e.g., Twitter, Facebook, etc.) is relatively new, but it is no more an instantaneous lift than a burger commercial: no one runs out to buy the burger right then.

Like planting a seed, one must wait for social media advertising to grow and mature. Unless your product, service or advertising sucks, you will realize traffic, and in turn sales. Do not become a slave to the peaks and trends of your Twitter analytics. Instead, use your energy to establish yourself.

2. Have a strategy
Gather a strong group of followers by posting for a few weeks before you start selling yourself or your product, and repost subject matter that interests you. You now have a foothold to build on, and should learn from mom-and-pop stores:

a) Treat each customer with respect and you can count on their return business.
b) Show you have nothing to hide and your honesty by settling complaints immediately and publicly.

Track your social media efforts with tools like Google Analytics Social or Twitalyzer. Paid versions are available, but if you are a small business why spend money when it’s not necessary?

Tools like Crowdbooster will tell you the best times to post or tweet to increase engagement. What you post is up to you, but ensure it is not garbage that leaves readers wanting more (you know what I mean). If you present yourself in a professional manner, respect others, engage and share great content, you won’t have any problems attracting followers and making sales.

If you follow thousands, have thousands of followers without sales, you made some errors in judgment on who to follow; you need to find your target audience.

3. Did you do your research?
No way, research!?
Yes, way. Topsy, Kurrently and even Twitter have sentiment search capabilities: see who comes up in a search for your service and/or product, and follow or engage them.
Snow shovels don’t sell in Florida, so don’t follow ‘robots’ and expect to run up your sales. Be logical and market smart – if you have a niche, follow it. Analytics, search sentiment, good curation of relevant stories to share and timing may be the difference between open for, or out of business.

Bottom line
Consider what was going through Joseph Bulova’s mind when he signed a contract for the first-ever TV commercial. Did people think he was wasting his time? Bulova went on to become a household name and the man himself was considered a great business mind…

Be a pioneer – do not let lack of ROI stop you when you see potential value, but always do your homework. The great ones always had a well thought out business plan even when the value was unknown.

Think of those who, at the time, reached millions through little-known channels like billboards, magazines and direct mail. Look at Jay Baer and Peter Cashmore, who make great livings from advertising on relative newcomer social media. I am sure they both would say they treated social media as a business capable of great profits because they saw value.

Thank you for your interest, please keep in mind the above article is comparing analysis of social media now to television commercials 60 years ago, I AM NOT comparing it to our ability to run analysis on commercials today.


More information doesn’t mean correct information: Do you really know?

I had a gentleman tell me today that he read that companies were becoming more efficient, that more was being done by fewer employees. Wow, really, are we that naive? The truth is that 84% of employees polled said that they would leave their jobs if another position became available (CNN Money). Guess what employers, the economy is getting better and companies are starting to hire again. If you haven’t already lost some of your employees, you will. So, why do stories conflict each other, because no one checks their facts or they put too much faith in what they hear from friend without verification. Recently I was told that “Person X” was an expert at a particular subject, only to find out, not only did this person not know what he was doing, he also caused a rift in the department that caused others to quit. How did this happen, the person in charge of hiring was not qualified to understand the intricacies of the position but his biggest mistake was, he assumed that the resume was 100% correct. In fact a 2010 survey suggests that 53% of perspective employees lie on their resume (click here). So, would some follow up with previous employers or checking references have changed the above scenario? Probably not, suggestions are to mandate a 90 day review and it have completed by the person working closest to the new hire. Co-workers know faster than anyone what is “really” going on. Below is from A Checklist for Success in Hiring Employees (http://humanresources.about.com/od/recruiting/a/recruitingtips.htm) Companies that select new employees from the candidates who walk in their door or answer an ad in the paper or online are missing the best candidates. They’re usually working for someone else and they may not even be looking for a new position. Here are steps to take to improve your candidate pool. • Invest time in developing relationships with university placement offices, recruiters and executive search firms. • Enable current staff members to actively participate in industry professional associations and conferences where they are likely to meet candidates you may successfully woo. • Watch the online job boards for potential candidates who may have resumes online even if they’re not currently looking. • Use professional association Web sites and magazines to advertise for professional staff. The key is to build your candidate pool before you need it. Good luck and remember to always check your facts and follow up on employee complaints.

© 2011 Analytical Solution

Have your data warehouse dreams died?

Have you attempted a data warehouse and failed, let experience and expertise lead the way to successfully attaining your goals – Analytical Solution.

The talent pool is short for mathematicians and data miners, and people in general who understand what it take to successfully deal with large amounts of data. Analysis and segmenting large data sets is not for the faint of heart, it require skill and experience that is only gained from day to day handling of transactional data.

Small business owners often spend a large chuck of time planning their company’s operations, with thousands of dollars spent on buildings, equipment, employees and advertising but they forget the one asset they get for free, DATA. Without data, an organization loses its record of transactions and or its ability to deliver value to its customers.

Organizations need to focus on strategic matters on a more or less continuous basis in the modern business world. Business analysts, serving this need, are well-versed in analyzing the strategic profile of the organization and its environment. Analysts are “Subject matter experts” who understand the data and are able to translate the results of data mining into actionable business information.

Data mining has found support in many areas. It has been used by companies like Kellogg’s and Kraft to find their target audience for new products; It has been used by Real Estate agents to determine the probability that a new area of town will thrive; even Police departments have used data mining to identify new crime waves by what is filed in reports.

Let data mining and analysis take your company to the “next” level of success. Don’t let this opportunity to use your free resource “data” pass you by because you don’t have “in-house” talent, Analytical Solution is up for the task!
423-552-2062 for a free evaluation.

© 2011 Analytical Solution

What is Analytical Solution?


Market Research

At Analytical Solution, we offer expertise in the design and execution of market surveys and custom market research to meet the specific needs of our individual clients. Our expert researchers will assist you in obtaining the market insights you need to grow and expand your business. We offer expertise in conducting all phases of both qualitative and quantitative research methods.

Market Research Capabilities

Analytical Solution offers a suite of consulting and market research services that enable our customers to increase market share and profitability. We provide near real-time intelligence and strategic insights to assist in making confident business decisions. We have continuous networking with customers, suppliers and competitors – thus providing you with complete visibility of the whole value chain in composites and various verticals such as automotive, construction, consumer goods, medical, and financial institutes. We have proven interviewing and data collection technique and we provide unmatched data accuracy and integrity. Some of the services offered by us in Market Research Category are:

·     Convert business problems into analytic projects with well-defined deliverables. Let your data work for you not against you. Knowledge is POWER

Market Research

·         Capability

·         Competitive Assessment

·         Concept Testing

·         Customer Analysis

·         Customer Satisfaction Survey

·         Market Segmentation

© 2011 Analytical Solution