Month: January 2014

Retail is one of the most natural possible frontiers for the implementation of Big Data. There are hundreds of individual transactions that occur every day, and the margins businesses work under are narrow enough that any advantage, regardless of its size, could be crucial. Here's how you can get the most out of using analytics:

Use every available resource.

While the data that you collect might be a good start if you're a small shop, it probably pales in comparison to the amount that's actually out there. Being able to incorporate freely available information into your proprietary custom database software can be a cheap and effective way of realizing valuable insights. 

Consider the sales you didn't make.

Too often, companies just think about their current customers, and break them down into particular categories. It can be equally important to consider the conversions that didn't happen, and examine what factors were impediments. You should also be keenly aware of your potential market, and look to demographic information that can better help you to address segments that you're not reaching properly. 

Create a context. 

The proper framework is a critical part of getting the most out of Big Data. Matt Felton of Datastory spoke about the value of this process. 

"There's plenty of patterns to be discovered in big data alone, but you miss the really good ones if you analyze this data out of context. If you're simply looking at the patterns in the data you collect, you're limited to discoveries about the things 'you know you don't know,'" he says. 

Make it manageable

The amount of data is getting ever more vast, and it's difficult to completely parse it all. Consulting with a FileMaker developer is a good way to create workflows that you can translate a lot of noise into valuable signals. Instead of trying to do everything, focus on one particular facet of the experience: try improving your customer service funnel, or your welcoming channels or your loyalty programs individually, before making a lot of wholesale changes at once. 

Look to the cloud

There are a ton of services now that will help you manage information virtually, so it's easy to access whenever you need it. This can help you make decisions in close to real time, without having to wait for tedious loading times. 

In the past, health care was completely responsive. A patient would come in with a particular malady, a doctor would diagnose it as best as possible, and then they would set out on a course of treatment. Now, physicians are making a greater attempt to anticipate and react to health related issues, before they even rise to the level of requiring a clinical visit. 

For many doctors, Big Data can be an important part of those efforts. One of the primary issues in modern health administration is the sheer amount of infrastructure and scheduling necessary. Unlike the past, when a house visit could be made on a particular day, modern medicine necessitates a level of administration that can often be cumbersome. That is where analytics can come in. 

One of the problems that doctors face is coordinating records across multiple systems. Many things are done on paper and have to be scanned or filed by hand. By using the FileMaker service, however, they can better organize data and treat patients more effectively. 

There are ancillary benefits as well. By parsing the information and identifying trends, a physician can see which patients are likely to come back and how often, knowledge which can speed up their care process. In addition, they can pinpoint factors that are leading to recurrences, and note national health patterns before they reach the level of crises. It could even reduce the incidence of chronic diseases like diabetes and cancer, which tend to occur relatively more frequently in particular populations. 

As care gets more specialized, it will be fascinating to watch how doctors are able to implement new database technology into the age-old process of making people feel better. 

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.