There is no question that Big Data is already making big steps into different fields and industries. Starting from sophisticated to predictive analytics, from intelligent risk analysis to deep insights on customer behavior, Big Data plays a large role in almost every industry sector today. We live in an age where almost all sectors are generating large amounts of data and harvesting data at the same time. The speed needed to handle this continuous flow of data has led us to the age of Big Data – where very large amounts of information are created, transmitted and stored. Every day, vast amounts of data are created for anyone who requires it and information is being harvested at great speeds.
Imagine our universe as a void being filled with every fact, bit and byte of information – information that never ends, never dies and will always expand. It’s a hard concept to fathom because in reality we can’t comprehend how big data actually is. Every piece of material and every network around the planet is just too large an amount to fit in our brains.
Where It All Began
Big Data began when computers were able to compile and calculate large amounts of data and digitize this data to be accessible on almost every kind of electronic device. The implications of Big Data are vast and some of them have left many critics of the technology flabbergasted. Other implications have given inspiration to a wealth of new discoveries that will eliminate decades of research, provide more information on climate change, the dangers of epidemics and even save the lives of patients, just to name a few examples.
We are fast approaching the stage where data can be truly infinite and we are only limited by the space in which we can store it and the speed in which we can retrieve it or the price of keeping these large amounts of data organized.
When Is Too Much Data Too Much?
When it comes to information and data, we can say that we have an infinite amount of data available but not all of it is useable. Even the biggest supporters of Big Data admit that the data itself is useless and its size does not equate to its usefulness. Only when this data is converted into information can it be put to work. Before conversion, it’s just 0s and 1s. According to Inc.com, businesses are spending up to $50 billion/year on too much data.
Storage and cost are the two biggest issues concerning Big Data. Although storage fees have gone down, it’s still a waste of money to pay for storage that you’re unlikely to use anyway. Warehousing large amounts of data will become really expensive if there’s no quality control. Data analytics need huge amounts of computing power to pull this off, most likely in the cloud. Cloud is a very useful and powerful tool specifically for small enterprises and businesses, but the extensive use of the cloud puts data in 3rd party control, increasing the risk of security challenges.
Some experts also believe that stored data can be hard to retrieve and there is no legal precedent for owning the data; there’s no way of clearly defining who owns the data that any individual entity creates or generates. Some of these experts also believe that all stored data is a direct result of immature processes. These 3 points will give us an idea of how much money is wasted on unused data:
- $1 for database record verification
- $10 for cleansing and duplicating records
- $100 for working on any unclean, decayed and inaccurate record
So, we can say that any enterprise or organization will see a reduction in cost if they only collect the data they really need. The more data that is collected, the less controlled and accessible it will be. It will also create more errors and it will be more costly to organize in the long run.
Finding A Solution
Too much data can result in paralysis by overanalysis. This happens when too much data is over-analyzed to the extent that an entity is created within themselves until they are completely buried with large amounts of data. This is happening more often due to the advanced research and analytics tools that are readily available. We all know that Big Data has provided us with great intelligence that can lead to great success, but it’s also important and very critical to know how to use it.
Most experts say having vast wealths of data is not a bad problem to have, but it can be a downside if you don’t know how to use and maximize its potential. Any organization, enterprise or entity should have solid processes in place when using Big Data in order to reach its intended goal. You can save money and attain success in Big Data by following these pointers:
1. Start small
You have to begin somewhere and the best advice is to start small. You don’t need to jump immediately into predictive analytics. You can start by studying any past performance and know exactly what events took place during that period, like promotions or PR campaigns. Track the changes in performance and start to identify where there are solid relationships.
2. Know what information to gather
Data is everywhere and you may be tempted to collect all the data that you think may be useful. Again, not all data can be useful. Focus on the metrics that will help you dig deeper into the goal you’re trying to reach or the requirements you need to make everything a success.
3. Take a step back and check
For a business enterprise, there might be some piece of data that is one or two steps away from providing sales performance. For most businesses, it is as simple as increased website traffic or increased search. You have to check if you can draw any correlations between marketing and communications activities and sales.
4. Deploy and use the right analytical tools
As data continues to grow, the time available to make actionable decisions shrinks. Marketing automations, CRM systems and business intelligence will help aggregate and analyze the data faster to determine relevant insights.
5. You need to visualize
Identifying relationships can be easier when visualization tools are used. This can be as simple as Excel charts or more sophisticated software like Tableau. Looking at massive spreadsheets with many data points will make you crazy. Let a visualizing software work for you.
6. Share the data
Data should be shared with anyone who can actually put it to good use. There is no point gathering data and leaving it to collect electronic dust. That would be a huge waste of your money.
7. Have a chief data scientist manage the data flow
A business should appoint a central person in charge of the organization’s data analysis. This person should have a deep understanding of what information is available and how to make sense of it all. The chief data scientist should educate the whole organization specifically the marketing and sales departments on how important the available data is.