Top 5 challenges of data analytics
Data — is everywhere. To your grades, to stock prices. To your game highscores — to the percentages of cotton in your favorite Hype Beast brand. From the heat that we complain about, to the frequency of the sound at which Mom yells — everything is data.
Anything that has existed, anything is recorded, anything that can be used as information — is surely data in some way or the other. Today’s data is tomorrow’s history. Yesterday’s history is today’s data. Google defines data as — “facts and statistics collected together for reference and analysis”. This analysis is what keeps us going all the way all these years. Data analysis is to pick up your favorite data; the ones found in real time, and using it to the fullest after cleaning it up. I believe this is what we need to know and realize this at least this time.
So it’s just numbers and metrics controlling our lives. Hmm?
Besides all the simping that I did, you and I just should realize that, nothing comes easy like sweets on a platter. Here are the top 5 challenges that data analytics poses for all of us.
#1. Data Privacy — This is a part of the data protection area that deals with the proper handling of data and checking with data protection guidelines. Many organizations and individuals have not made enough recovery plans to tuck in data privacy as part of their professional lives. Every cookie that we accept, all our online accounts that have our data, what our presence of social media talks about us, such things may get vulnerable at some point. However, it is not possible for them to keep track of our progress without our data representations, it is up to us to secure it in the best way possible to avoid intrusion and further attacks.
#2. Data Validation — we need to talk about how accurate our data is. We cannot directly take up data that is present out there. We need to check how valid it is — we could have some outdated data pretending to be recent ones, some could be incomplete and a few statistical metrics and lingo thrown here and there might impress people, but… is it real? Can I trust this data? Analysts have to ask this question. Sad to say, this could be a very time — consuming process, especially if the validation and data cleaning have to be performed manually. If the data obtained is inaccurate in some way or the other, forthcoming course-of-actions could be extremely harmful to businesses or our lives as individuals as a whole.
#3. Data Scaling — ScAliNg? No, don’t be scared, my friend. It just means that you are transforming your data so that it fits within a specific scale. Data keeps growing and getting bigger. Now think about this, you have millions of numbers, millions of stats generated every second, and have different metrics and measures for the same. It is agreeable that all the data we have obtained is not gonna be similar. Imagine scaling all this data to one common range. A lot of them are pretty much unstructured and come from docs and other non-relational sources.
For this I would suggest data tiering — allotting data to different cloud access levels based on priority constraints.
#4. Lack of data professionals — A lot of companies need more skilled data professionals — data scientists, analysts and engineers who have a knack of working with several tools and handling huge data sources. Companies need to train existing professionals who are happily looking for a change in field or interest in such areas. I can see this issue as a temporary one as data science is actually finding more importance. We never know, we might find more people for this job.
#5. Cultural changes — We have an ever-persistent issue of data literacy as well as a decline in work culture. Employees know their organization is serious about corporate cultural change only when they see their leaders changing their own behavior. It is this very culture that needs to adopt data governance to optimize benefits of data sharing.
