Big data is now omnipresent. It is embedded into our day-to-day lives as the hard-wired DNA that fuels our business processes and decisions. Data-driven cultures went mainstream in 2016, with organizations scrambling to establish data strategies that enabled all employees to make better informed decisions. Yet despite all of the hype, the majority of organizations still have a long way to go before realizing the potential that big data holds.
The main idea of predictive analysis is to use current and past data to predict future events. The goal of the statistical techniques used in predictive analysis is to determine market patterns, identify risks, and predict potential opportunities for growth. In addition, data relationships can be reordered to determine the most plausible outcome of possible solutions and patterns can be recognized that might have the power to alter the outcome of a probable event.
One of the most important aspects to reliable predictive analysis is data quality. The information provided by predictive analysis can only be as effective as the abundance and accuracy of data available. Data quality is absolutely necessary to the process of predictive analysis. In order to attain accurate business intelligence, companies must maintain quality data. Predictive analysis requires both past and current data about many different things including customers, businesses, products, and the economy. All of this information is used to draw relationships and patterns between sets of data. If the data is accurate and well maintained, then the business intelligence produced will be high quality as well.
In the past, predictive analysis was mainly used for newly emerging technologies. However, in recent years these practices have quickly started to become common for mainstream businesses. There are a few differences between the ways that these techniques are currently used and how they were used in the past. One of the main reasons for these differences is why companies use predictive analysis. In the past, these techniques were used for long-term analysis of market and consumer trends. However, in recent years, the mainstream implementation of predictive analysis techniques has tended to focus more on immediate, tactical uses. Because of the “real-time” nature of this business intelligence, more and more companies are using predictive analysis as standard in making predictions about particular industry markets and consumer trends.
Some of the industries that have started utilizing these business intelligence techniques include telecom, insurance, pharmaceutical, and financial industries. All of the companies in these different business sectors have been able to use predictive analysis to make the right decisions to move their businesses in a positive direction. These processes can help with economic predictions as well as predicting the behavior of businesses and consumers. This type of information, made available in an efficient manner by business intelligence, is understandably invaluable. It can turn a simple prediction into intelligence that is more precise than even the most educated guesses. Predictive analysis with appropriate attention paid to data quality has made it easier than ever for businesses to make accurate market and consumer predictions and thus smarter decisions for the growth of their company.
In recent weeks the export function of massive data sets has been my best friend and worst enemy. Seemingly endless mounds of exact match keyword bliss has been pouring into my ODBC and then through to AdWords Editor (as well as uploads into Yahoo! SM and MSN), nearly seamlessly. Little snips and CONCATENATE functions here and there, and, within a few hours a beast is unleashed on the market capable of tremendously valuable insights and high ROI potential.
Struggling to come up with good keyword fodder is nearly impossible. No migraine is so great as the one which bubbles up from trying to bend phrases around a niche websites primary traffic solution until SEO decides to chime in. And so, here is this problem: When a website has no traffic and no diversity, how and where do you get enough keywords to start building insights and crafting strategy. Well, I’ll tell you, its in the business.
Somewhere in that business, and I don’t care what it is, you can find some data set. Be it a product list, a list of cities you plan to market to, types of what you sell, directories or any number of other odd variables too obscure to find mentionhere; somewhere some once glimmering light of organized information found its way into a list, datasource, directory, database, table or output. Find it.
Now that you have your prize, begin playing with it. Squeeze it. Pet it. Take it for walks into the meadows ripe with the scent of mountain laurel and introduce it to SQL statements. Feed it good commands and teach it to obey and do your bidding and be loyal to your will. Within a few hours, even the most lilliputian list can dominate a campaign through initial commands like ‘DISTINCT’ and then ‘APPEND’. Make sure you apply its ‘match type’ as exact. This will produce, in itself, insights which can guide your relationship with that data for weeks and months to come.
Obviously not all keywords are created equal. Just ask iPod. But they can march and subdue like ants on lollipops if you let them. The faithful minions of the marketer bring back information on their appeal (impressions/clicks) and ultimately tale of the bounty which exists beyond the sea of user appraisal. Its is by this test of endurance that these exact match phrases and word pairings have proven themselves worthy of endorsement. By the time you’ve finished ceremoniously inviting hundreds of new quality keywords to spend their years with you beyond the ocean of poorly accounted marketing budget expenditure, their rewards will have begun aggregating and the equasion of lifetime value will begin taking on new meaning.
Why then, you say, worst enemy?
True it is that this data which I mention is of a very high value. Where, then, does the issue come in which creates opposition to this ideal? It can be paralyzing. To be struck by massive data sets can be both a blessing and a burden.
Slicing down to the useful and unique parts of a major data set is, at least sometimes, more than a typical configuration of MS Office Access can handle. This creates a migration logistics issue which can easily be overcome by moving and slicing data from MySQL sources. Honestly, this can probably be done clean in Excel too, with some modifications. But, the truly sweet stuff comes in massive blocks, and, that requires something geared more toward sophisticated data tools meant for exactly this.
Another Small Warning
Using data set uploads and editors never comes with this warning so its important that you understand it completely: Any data which is sent up as active is active and, in saying that, should be thoroughly checked by applying alerts and filters, and visual inspection if necessary, to ensure bid prices and configurations are as you want them to be. A slight slip in the decimal placement can be a costly error on many fronts. You don’t want to get caught spending $100 per click on something which should be $1.
This post was provided by Daniel Shields, Chief Analyst from Wicked Business Sciences.