Author: Justin Hesser

In this space, we've discussed how Big Data principles can help you make better hiring decisions. Now, with the right database and some shrewd analysis, it can help you determine which employees deserve to be promoted — and which ones should be shown the door. 

It's difficult, bordering on impossible, for any manager to be completely objective about his or her staff. You get to know certain people better than others. Some employees are great at appearing to put in a lot of effort without actually trying very hard, and others have a difficult time promoting the good things they are able to do. 

In a big company, with a huge staff, these sort of imbalances even themselves out over the course of time. Even if the promotion/firing process isn't as efficient as it could be, the sheer size of the business serves as some insulation against a few less-than-productive members. For a small or medium sized business, however, every employee is a vital cog in the machinery that keeps things moving forward. If one isn't working properly, it's time to make a switch. 

That's where FileMaker support can help. 

Instead of being forced to simply use the eye test and determine which employees seem to be the most capable, you can actually create a database that will identify the metrics most accurately correlated with success. The results could end up surprising you: criteria that seemed negligible may in the end wind up being big drivers of sales. In addition, a more data-driven promotion process will more accurately reward the best employees, and allow you to cut yourself free of ones that are hampering productivity. 

The thing about Big Data is that, well, it's big. Not only are thousands of years of information collated and stored online, there are the millions upon millions of interactions that are constantly happening every day. From online transactions to social media posts, managing the sheer amount of data is a daunting task even for the most specialized custom database software. For many companies, this volume is tantalizing, but also intimidating.

The challenge, of course, is to corral all of these disparate pieces into a format where they're usable — it's why spending on Big Data IT was somewhere in the range of $34 billion last year. Companies are interested in not only collecting the information, but also parsing it and deriving actionable takeaways. There's precious little good in having a tool if you're having trouble properly wielding it. 

That's part of why there's such a big buzz about IBM's Watson. Especially after it handily dispatched Jeopardy champions, proponents have thought of it as the next big thing in analytics. There are now 2,000 sharp minds behind it, who are bolstered by $1 billion in funding and the freedom that comes with being a standalone entity. Never before has there been such a concentrated expenditure by a single company on the implications of Big Data, so companies that are interested in using these techniques are keeping a close eye.

Ultimately, some of their findings will become part of common practice. It's this endless capacity for growth that makes the analytics industry one that  is so important to invest in. 

Retail and Big Data form a natural partnership. Companies want to be able to best serve their customers, consumers want the best possible experience, and the widespread availability of information has proven to be an efficient way to bridge that gap. It's no surprise, then, that this relationship was all the buzz at this year's National Retail Federation convention.

Hundreds of vendors gathered to show off and discuss their wares, which is good news for related industries like software and analytics. These ancillary businesses found that retailers were much more likely to spend on their services, indicating that there was a definite shift towards respect for the utility of Big Data.

Even in a slowly improving economy, many retailers are feeling the squeeze and have had to slash prices to drive sales numbers. It's no wonder that they're coming to the conclusion that a FileMaker developer could be the key to their concerns: the better they can discover and target their intended market, the more readily they will be able to convert consumers.

Ginni Rommetty, chairman and chief executive of IBM, addressed a fascinated crowd on Monday.

"This is a new era of man-machine collaboration," she said.

She went on to explain that the very same sort of technology that allowed her company's Watson computer to handily defeat Jeopardy champions could be applied to learning more about what people like to buy, and why. In fact, she went on to say that this sort of data gathering will be as germane to business as steam in the 1700s and electricity in the 1800s. 

And if the buzz around the convention floor is any indication, few, if any, of these companies want to be left out in the cold. 

As more and more companies start to use custom database software to conduct their everyday business, they are in turn getting a greater understanding of how it works. This increased awareness is resulting in expansions on the traditional analytical framework, which could ultimately result in progress far beyond what we've traditionally come to understand as the limits of Big Data.

One advancement that appears imminent is the debut of the Big Data "stack". 

A slew of businesses have already made use of the first level of analytical capability, some 42 percent of organizations per a CompTIA study. These groups are now looking into what else they can do with the technology, and developing interfaces that are more layered, with a wider range of options available at each interaction. 

The first layer is where the data resides, an arena that is quickly becoming more scalable. In the next layer, companies will be able to integrate from other sources, which will help prep, clean and integrate the information so that it is even more useful. After this comes the analytical section, where conclusions can be drawn and action plans formulated. Finally, the predictive layer closes out the process, allowing companies to look forward and anticipate changes before they even occur. 

Richard Daley, one of the founders and chief strategy officer of analytics and business intelligence specialist Pentaho, explains why this approach will help to increase the value of the collection process. 

"In the last 12 months, we've seen more and more people doing big data for gain," he says. "There is much more to gain from analyzing and utilizing this big data than just storing it."

This represents another in a long line of exciting improvements in database technology. 

Attached are the zip files for the FileMaker 13 certification Exam Review. These files were used in both the CFDG and BAFDA meetings. Please feel free to study these as needed when getting ready for the FileMaker certification test. If their are any further questions please feel free to contact Tim Neudecker at tim@kyologic.com

Files: Certification Part 1_0

A strong company starts with the right employees. If the people carrying out your strategies aren't talented and capable, it won't matter how worthwhile your vision is. Thus, being able to hire well should be a priority for any business. By identifying and retaining the best possible candidates, you can put your company into a position to thrive. 

It's a process that could benefit from FileMaker help. 

Hiring is by its nature an inexact science. Not only do you need to evaluate a person's skills in a short period of time, usually on the basis of nothing more than a resume and a handful of brief interviews, you also need to envision how they fit in with your corporate culture. Somebody who is perfectly suited for the role might not fit in with the rest of the staff. If this is the case, you've wasted your time and prevented yourself from having the best possible team.

This is where analytics come in. Parsing data on your current employees, as well as on the candidates you are looking for, can help you decide what metrics are actually important for a particular job. The answers could very well be surprising. Building the right database can even give you a way to incorporate information that would otherwise be tricky to mesh. In a recent article on Wired, author Michael Morrell discussed these sorts of criteria.

"Remember that personal interaction and communication provide perhaps more important data than massive amounts of publicly available data. Did a candidate respond to an email? Show up for an interview? And of course there are the personal referrals and references, which should carry a lot of weight in a matching algorithm," writes Morrell.

In addition, a stronger adherence to big data processes in hiring will benefit job seekers. Potentially talented people who have had issues showing it will be evaluated more holistically, which can take some of the pressure off of resumes and cover letters, both inexact proxies for actual talent. If hired, they will also be much more likely to fit in well with the company, since they would have been selected for traits that make that a likelihood. 

It's a win-win: businesses get the best possible staff, and candidates find the best possible working situation. 

The internet is in a state of constant flux. Growth in data hasn't just been fast — it's been exponential. Statistics suggest that 1.3 zettabytes will be whirring around the web by 2016, an acceleration of information that was virtually unfathomable just a decade ago. It's nearly impossible to predict what the next decade will hold for network growth, but there is one constant: having a business strategy that is able to adapt to changes is the only way to remain relevant.

For the foreseeable future, that involves a comprehensive Big Data policy. Rich Spitzer of TrendPointers, Inc., a company that specializes in the industry, sums up how necessary it is to stay current.

"With big data, it's all about identifying what's coming up, as it is coming up," explains Spitzer. 

The actual storage of this information is becoming easier than ever, thanks to the rise of cloud computing. However, with this ability to collect data comes an attendant burden of actually sorting it and figuring out how to translate it into sales. For many companies, this process is outside of their comfort zone, as they don't have the available staff or infrastructure. 

This is where a product like FileMaker can play such a pivotal role. FileMaker developers can help a business new to Big Data devise an easy and comprehensive strategy that will transform a jumble of unconnected numbers into an actionable plan that will translate to sales. These days, companies simply can't afford to fall behind when it comes to collecting information. 

More than many other industries, insurers have to deal with information. How likely is it that an accident will occur? What is the proper amount to pay out if it does? Based on the answers to the previous two questions, what is the appropriate monthly premium to charge the consumer? Because these calculations are iterated across so many people, a small increase in their accuracy could seriously boost revenues.

Despite this, some insurers have been reticent to adopt Big Data principles. However, as its efficacy becomes proven across other industries, companies are taking note. To date, more than two thirds have implemented some sort of analytics program, across a variety of sectors.

Not only does it help them with claims adjustments and underwriting precision, it can also improve their sales and marketing funnels. In short, they can better target potential consumers, and then, once they've retained them as customers, can offer a higher level of service.

Managed IT services that incorporate programs like FileMaker have internal benefits as well. Using real-time personal metrics, underwriters can better manage the risk profile of their companies, and reduce exposure to financial catastrophe. Because they can better build and evaluate models that calculate exposure, the underwriters can see how a particular position affects their overall book. In some cases, this information might lead a business to take a more aggressive stance, which can lead to increased revenues and an opportunity to take advantage of subtle market shifts.

Insurance is a competitive business, and companies that are not taking the possibilities of analytics into account find themselves at a risk for falling behind.

Marketing, at its core, is about two things: figuring out what exactly people want, and then positioning your product as the one that can deliver that most effectively. For a long time, before the ready proliferation of information, marketers were forced to do that via instinct — they had to quickly size up consumers and then hope that their impressions were correct. It was difficult, if not impossible, to determine how effective their techniques were.

Big Data changed that process. Instead of making choices blindly, marketers were able to use data to effectively identify the needs of their customers and figure out how best to target those needs. However, despite that obvious value, there are some pitfalls that are important to avoid in order to get the most out of the process.

Identify your core brand. It's critical to be able to adapt, but it's also crucial to provide a sense of continuity. Even companies with histories as long as Coca-Cola aren't immune to this trap: In 1985, the soft drink giant introduced "New Coke", a brand that tasted similar to top competitor Pepsi. Management thought it was a slam dunk. Pepsi had been winning in blind taste tests, so people clearly would gravitate towards this novel drink, right?

Wrong.

It became one of the biggest disasters in marketing history. Eventually, Coca Cola brass came to a conclusion that they should have started with — instead of using data analysis to copy your rivals, you should use it to enhance what is positive about your own brand. Once they went back to that, they retained their market dominance, and are still the leader in the industry, nearly two decades later.

Another potential mistake that marketers make is becoming complacent. They set up their custom database software, and expect it to do all of the work, while they blindly implement whatever it says to do. The oversight here is two fold. Not only is it important to have systems in place for properly evaluating the results and being able to separate signal from noise, it's critical to continue to improve the way in which you input data. Getting help with FileMaker isn't just a good idea, it's one that could save your core business.

Like any powerful tool, Big Data is incredibly helpful with the right insight and guidance. To properly wield it, make sure you have a firm understanding of the implications.

Residents across the Northeastern part of the United States, from Ohio to Maine, are in the process of digging out from the first major snow storm of 2014. Some areas have seen as much as two feet of the white fluff pile up and keeping the roadways clear is becoming increasingly difficult. On top of that, the below-zero temperatures are making road treatment processes less than effective.

This scene is not unusual in many parts of the United States during the winter months, but there is a new innovation that is making snow removal and road work much easier.

A recent article from the Daily Camera, a Boulder Colorado newspaper, profiled the National Center for Atmospheric Research (NCAR) and the new "smart plow" it developed and is testing in Michigan, Minnesota and Nevada. The process works by analyzing information from several databases including satellite and radar observations, computer weather models and real-time data collected from special sensors outfitted to plows that are clearing the streets.

The goal is to continuously monitor weather conditions and keep up with trouble areas more effectively. This will reduce accidents and save states potentially millions of dollars in maintenance.

"Whereas in the past, drivers would have some new data maybe every 30, 40 or 50 miles, now you could just look up every mile, and hopefully pick up the more subtle small things that happen on the roadway," said Sheldon Drobot, the NCAR scientist who has overseen the system's design.

This is just another example of how custom database software can be used to harness big data and improve an aspect of daily life.