In order to run their operations, businesses always needed to collect certain amounts of data like customer feedback, market research, industry figures, etc. One of the biggest obstacles the bygone era of analog communication laid in front of them was assembling resources to collect a sufficient amount of these scattered chunks of information.
Today, in the wake of the digital age can make you, the problem has become completely opposite. Namely, the sources of information have become so abundant that the companies need to invest significant resources into managing and processing these vast pools of data.
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Let us try to discover just how deep your data needs to be and see how the gathering process can be successfully streamlined to accommodate your business's infrastructure.
Before we proceed to further discussion, we need to set some things straight. The real problem is not truly the depth of your data – it is the volume. Namely, big data represents the amalgamation of all data your company gathers across available channels. On the other hand, the term "deep data" stands for high-quality, actionable information relevant for your business. So essentially, as long as you are able to successfully store and efficiently manage these data streams, no data is too deep.
Keeping this in mind, deep data still presents some significant obstacles in front of the business owners.
All these goals are based on specific hypotheses and use data collection either to confirm or refute them.
Finally, we have to glance over some issues concerning data warehouses. These repositories have, by now, become an industry standard for storing figures relevant for strategic analytics. They are, however, often expansive, require people with high expertise to manage them, and are terribly inept at storing enormous volumes of still unstructured data.
If you are, in spite of all of your best efforts, still unable to reduce the volume of your data without reducing its depth, you should try implementing the data lakes that are more optimized to be fed by many different data flows.
So, to conclude – as long as you are gathering relevant information that can be used for quality analytics, no data you gather will be too deep. However, even deep data presents some storage and management obstacles that can be successfully mitigated with collection optimization. We hope the advice we provided you will prove to be helpful in this regard.
Today, in the wake of the digital age can make you, the problem has become completely opposite. Namely, the sources of information have become so abundant that the companies need to invest significant resources into managing and processing these vast pools of data.
Related post: Best Proxy Services 2019
Let us try to discover just how deep your data needs to be and see how the gathering process can be successfully streamlined to accommodate your business's infrastructure.
The differences between big data and deep data
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The challenges of deep data
- Data inconsistency – To put it simply, due to a number of different reasons (the time spent on the website, VPN protection, etc.) not every user leaves the same data trail. Such inconsistencies can make analytics considerably harder.
- Infrastructure inconsistencies – If you are storing data across multiple platforms and mediums, the conversion between different data formats can slow down the analytics and potentially cause quality problems.
- The volume – Even though deep data cuts out a lot of redundant bits, if not carefully selected, it can still amass volumes of data that can pose problems for business with less developed infrastructures.
Adjusting the volume of data to your company’s needs
Although all these three problems are very different in nature, they can all be addressed if you make your data collection more focused and qualitative, instead of quantitative. Now we will take a look at some of the ways you can achieve this level of efficiency and adjust the "depth" of your data to your company's assets.
Select your issues carefully
As we have already mentioned, your company is constantly collecting vast amounts of data that are either redundant or unnecessary. In order to eliminate this problem, you will first need to discover the priority issues your company is currently concerned with, and then identify the opportunities for appropriate data collection. For instance, if you are lacking the information on how your company is perceived by Australian customers, this issue should get a higher priority than some other channels that are producing superfluous data.Set clear goals
If the previous step was aimed at steering your efforts in the right direction, your next move should be concerned with identifying specific goals you want to achieve. In the case of social media, that would be identifying specific complaints, measuring the success of your previous campaigns, testing the mood of specific demographics, and so on.All these goals are based on specific hypotheses and use data collection either to confirm or refute them.
Select data collecting methods
Finally, you need to select the methods that will provide the best quality of information without amassing unnecessary residues. Following the previous example, you should probably consult experts in social media monitoring with great knowledge of local issues rather than use some all-encompassing tools that are not designed around your specific problems. This way, you will be able to drastically optimize the size of the gathered information and invested resources.
Data warehouses vs. data lakes
Finally, we have to glance over some issues concerning data warehouses. These repositories have, by now, become an industry standard for storing figures relevant for strategic analytics. They are, however, often expansive, require people with high expertise to manage them, and are terribly inept at storing enormous volumes of still unstructured data.If you are, in spite of all of your best efforts, still unable to reduce the volume of your data without reducing its depth, you should try implementing the data lakes that are more optimized to be fed by many different data flows.
So, to conclude – as long as you are gathering relevant information that can be used for quality analytics, no data you gather will be too deep. However, even deep data presents some storage and management obstacles that can be successfully mitigated with collection optimization. We hope the advice we provided you will prove to be helpful in this regard.
