Which Data is More Relevant Today - Decision Making Strategy Hints
There are very few of us who are still not aware of how the digital wave reshaped the face of the business world. Still, there are even fewer who realize just how profound these changes are. Let’s take the very process of decision making. In the past times, we needed to harness as much of the sometimes random data as we could to make informed decisions.
The introduction of big data forever changed this dynamic. To deal with these overwhelming amounts of information, industry leaders first need to have in mind an overreaching goal that will, in turn, help them make informed decisions about the data collection and analysis methods.
Let us then take a look at some of the most prominent data collection methods and try to see which of them offer the most relevance in 2019.
Mean is the arithmetic process also known "the average." It represents the sum of all entries on a list divided by the number of the entries. Although very popular, thanks to its simplicity and usefulness in determining overall trends, this method also features some considerable drawbacks. Namely, it offers only a “snapshot” of data and wipes away all the nuances that are producing the median numbers.
Standard deviation is often represented by the Greek letter sigma (Σ). Speaking broadly, it measures the spread of data around a certain mean. We can divide into two distinct groups:
- High standard deviation – The data is spread more widely from the mean
- Low standard deviation – The data is more aligned with the mean
Keeping this in mind, we can see that standard deviation can be very useful for determining the dispersion of different data points, a margin of error and the level of confidence. However, much like mean, standard deviation paints the data in broad strokes and represents a very arbitrary version of the objective reality.
Regression is a data analytics method that tries to model the relationships between dependent variables and use the said relationships to determine how variables affect each other. For instance, if you want to determine how your company is perceived in the Australian or New Zealand media, you would have to use social media analytics to abstract the data you need for creating the mentioned model.
Because it's based on deliberate market research and uses detailed and purposeful information, regression represents a much more nuanced approach to data analytics than the previous ones. Still, being modeled on the mutual relationship of two data points, regression runs the danger of neglecting many outliers that are on the fringes of this isolated system.
Sample size determination
Sample size determination is a method that's incredibly useful when working with large data sets. For instance, instead of gathering information from every member of some company's workforce or one nation's population, you would extract data from an aptly sized sample and use proportion to project the results to the rest of the interest group.
Obviously, as much as it is useful and, in some cases, irreplaceable, this method is largely based on assumption. As a result, all of your later mathematical extracts may end up being corrupt from the get-go.
Hypothesis testing (also known as t testing) represents a method in which we make a certain assumption and then try to assemble data sets that will confirm or challenge that assumption. The hypothesis is considered relevant if the results we get at the end could not occur by random chance. Being one of the most comprehensive ways of analyzing data, hypothesis testing sees the application in a large number of activities ranging from science to the everyday economy.
Unfortunately, the relevance of testing often falls victim to wishful thinking and what we like to call the "placebo effect." The results of this method are heavily dependent on the researcher's objectivity.
Devising an effective data strategy
As we can see all these five methods offer some distinct advantages and similarly, feature their unique sets of pitfalls. What’s most important to understand, though, is that they are not mutually exclusive or interchangeable.
All of the methods we covered above represent conscious and purposeful attempts at data collection and analytics which makes them countless times more relevant than trying to draw conclusions based on data sets that are randomly harvested from the web.
Keeping that in mind, let us quickly cover a couple of guidelines that can help you to devise an overall data strategy and harness either of these methods to your advantage.
Identify business goals
Essentially, in order to perform effective data collection, you need to identify the areas that can make a difference in your business's day-to-day operations. In other words, instead of considering what kind of data your business can or should collect, you first need to identify what your business is trying to achieve in the grand scheme of things.
Identify the unanswered questions
Now that you have workout through your overreaching goals, you need to identify which questions you need to ask in order to achieve those goals. By working out these questions you will be able to easily discover the more specific data-related objectives.
Identify what you already have
The next step in devising the data strategy you need to take is to take a look at the infrastructure and data channels you have already established. You may be sitting on the resources that are underutilized or that can be repurposed without ever knowing.
Identify the appropriate data methods to answer your questions
Finally, once you have thoroughly covered all the previous steps it is time to choose the most effective data methods to fill in the knowledge gaps. Following the previous analogies, that would be identifying customer satisfaction in specific regions and on specific channels. What we have to underline here is that no method is inherently better than the other. The only way to judge their value is based on how capable they are to solve specific problems.
We hope that these few considerations can help you to put the issue of contemporary data collection and analytics into perspective. The most important we can abstract from this short discussion is that we are living in the time when different data sources and data channels have become so abundant that devising some overreaching method that will address this variety is simply impossible. Instead, business owners should look for specific problems that are affecting the business's operations and make their data strategy an amalgamation of answers to these distinct issues.