Top 5 Big Data Issues Organisations Face
Organisations today have access to more data and it’s easier than ever to collect (and create) information. The advent of technology made it easier for everyone to acquire information with minimal cost and in a shorter amount of time.
IBM estimates we create 2.5 quintillion bytes of data every day. This is often referred to as “Big Data” – raw information about business, the clients, the market, and more.
However, technological advances in acquiring data present challenges in harnessing this data. Moreover, the understanding of what to capture and how to capture it is a labyrinth to even the most seasoned analysts.
Big Data refers to extremely large sets of data that are either structured or unstructured, which can then be used in making informed decisions. However, with the ease of access, minimal cost, and fast turnaround, why do many organisations still fail to make the right decisions and drive revenue?
Today, we’ll look at the common issues organisations face when dealing with not just Big Data, but harnessing data in general:
Knowing What Data Points to Collect
As mentioned earlier, it’s easier than ever to gather data, but the problem is, which data should you collect?
Having a lot of data points (an identifiable element in a data set) available invites challenges, one being the identification of the right data points and reducing the amount of irrelevant data or noise. Do you need to find your members gender? Their age? Their location? These are basic examples that may be found in most sets of data, but will not always be relevant. How about taking it a step further, knowing how often you interact with members or whether or not your member values their relationship with you? The data points for this are not defined, but it’d be valuable to have these answers.
Connecting the Dots
Let’s assume you managed to identify and collect the right data points that are relevant to your goals. The challenge now is identifying how each of these quantitative variables or data points correlates or interacts with one another to help you test your hypothesis.
Even after you’ve identified indications that several data points interact with each other, you need to still ensure these are robust otherwise, you’re running the risk of misdiagnosis on a large scale.
Bringing False Positives
With the enormous amount of data in front of us, often raw and unstructured, we might be enticed to think and act fast without us thoroughly examining the variables at play. When this happens, Big Data becomes Bad Data.
Going back to the previous point, we need to examine our data points and see how they correlate and interact with each other before we can form a hypothesis, come up with a conclusion, and make decisions. Failing to do this exposes us to false positives which not only leads to waste of resources but also confuses the customers we’re interacting with.
System Integration Issues
Data can be derived from almost unlimited sources and can be gathered using different systems. But when different systems used in gathering different data points and variables do not work together, it will be impossible to easily translate data into insights.
While there are dedicated systems and integration tools available to help you collect and interpret data from disparate sources, it is advantageous to know exactly what you need for your organisation, what is required in future and not over or under estimating on complexity and cost.
Painting the Bigger Picture
The end goal of acquiring data in any organisation is to make fact-based decisions that can be used to drive revenue.
To do that, we must understand that every piece of information we collect is only as valuable as our interpretation of it, and the course of action to be implemented based on this interpretation.
While data can present you with all the information you need and even data you don’t, to see the bigger picture, you need to know how to analyse trends, patterns, correlation and be able to form a hypothesis that can be translated into solid and accurate conclusions.
There is much to learn about data. It is continuously evolving and improving and will continue to do so in the years to come. We can’t be certain as to the extent of its growth and capability it will allow in the future. What we can be sure of today, is that no data will be useful without foundational structure, correct analysis and informed action.
Global Enablement can help turn your data into actionable insights to help bring you closer to your milestones. Contact us today to know more!
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