Data is big new right now, and experts from all over the world are weighing in on why they think it’s so important. That’s what’s so interesting about big data at the moment: nobody quite knows how it will eventually change business. With that said, nearly everybody has a theory on the impact that it will have.
Here, we’re going to unpack what the experts think and where they see big data headed. We live in exciting times.
Big Data Needs Democratisation
Most businesses approach big data from the perspective that it’s a specialist discipline for a few trained experts. In other words, one or two people on their team have the skills necessary to use and access big data effectively. But there’s a problem with that approach. If companies really want a good return on investment on their data, it needs to be more universally accessible. Top CEOs in the data industry are now calling for big data to be “democratized.” In other words, big data can’t be the preserve of a few individuals with the skills to interpret it. Businesses need tools that allow everybody in their organizations to use data productively. Anybody in business will be able to go from raw data to useful analytics in a few steps. Businesses using a streaming analytics platform will become more agile and better able to respond to market changes. This will help them make better decisions in real time and maximize value for their customers.
Going Forward, Governance Will Be The Main Problem For Big Data
Humanity now has some powerful tools to help it understand big data. Among those tools, machine learning is arguably the most powerful. Big data gets fed to machines, and those machines then use those data to make informed and arguably intelligent decisions. Thus, we’re moving away from the early era of big data where the main challenge was learning from data. We’re now entering a phase where what really counts is how the data are governed. Who should have access to the data and how it should be used is currently a hot topic? Take Google’s latest translator algorithm that uses deep learning. It learned how to translate from one language to another using millions of real world translations by human translators. Its goal is to make a universal translator that can compete with people. And by using deep learning, it is able to take all those individual translations by people and feed them into a computer. The computer then learns probabilistically which how language is structured and how to translate.
The problem for Google here is that it didn’t pay those translators for their translation services. Instead, it just sucked up all their data and fed it into its intelligent algorithm. That algorithm is now being used to put translators out of a job.
As we go forward, we can expect more ethical considerations around the use of big data. Companies may have to reimburse those who create the data if they want to use it to increase their profits.
Pragmatic Businesses Will Learn To Sample Their Data
When big data first arrived on the scene, everybody agreed that there was a lot of it. It’s called big data for a reason, after all. But relative to the amount of data generated today, it was actually quite a small amount. The problem that companies face right now is the exponential growth of data that can be analyzed. Data, like internet traffic, is doubling every year. In fact, the pace of data growth is faster than price performance increases in tools to manage those data.
This means that companies are increasingly having to opt taking samples or snippets of the data. Taking samples allows them to reduce the costs of processing, but still gain useful insights.
Of course, as soon as you begin sampling from a population you run into a few problems. Perhaps the greatest challenge facing businesses today is whether their sampling is representative. Companies will need to focus on clever ways to guarantee that their samples are truly random. Measurement error could be a big problem here.
Businesses Will Develop Tools To Answer “Why” Questions
Big data, like any form of data, is useful for finding correlations. In fact, the main reason why businesses use big data is to be able to make predictions about the future. It’s about following trend lines and looking for ways to avoid sudden surprises.
The problem with data, as has always been the case, is that it doesn’t answer why questions. Data might be telling you that you need to expand your production line. But it doesn’t provide you with any theory explaining what’s driving it. In other words, the signals are there, but you’re just flying blind.
Arnoldo Muller-Molina, CTO at a tech firm, explains why this is important. If you don’t know why your company is headed in a particular direction, you can’t control it. Your fate is out of your control. Companies, therefore, need to develop theories that explain their trajectory. Big data isn’t enough.
Big Data Will Become A Service In The Cloud
We already see a movement of big data services to the cloud. The cloud, of course, gives businesses access to more powerful tools to analyze data on a scale that just isn’t possible in-house. But it goes beyond that.
For instance, cloud computing gives access to big data services without the business having to specialize in them itself. In other words, big data will be something companies outsource. Just as we’ve seen elsewhere, this will lead to cost reductions and more reliable services. Businesses will deal with firms whose job it is to provide a high-quality cloud service.
It’s analogous to what happened with data storage. In the past, data storage was only available to a few companies with the resources to hire data experts and buy servers. But today, any old business can buy storage space, and at a fraction of the cost. Big data will end up being the same. Companies from all over the world will begin to compete using insights from their data.
Click Stream Data Will Personalize Experiences
The dream of big data is to help bring disparate customer relations systems together. Right now call centers, CRM platforms, and ecommerce sites are separate entities. But, as businesses know, these platforms are all about understanding the customer’s needs. Big data doesn’t just build links between these different platforms, it removes the separation.
Take somebody who is in the market for cricket stumps. They go to an ecommerce site that sells stumps and then that site takes that data and makes recommendations. They could be provided with alternative products, like cricket pads. Or they could be forwarded to marketing content for gaining another lead.
Big Data Will Be Used For Behavioral Analysis
When it comes to big data, often it’s not the data itself that provides answers, but the questions asked of it. Take behavioral analytics as an example. The idea here is to use big data to understand the behavior of consumers. Businesses need to start asking questions like; how do consumers react to different marketing methods? What sales formats are best for encouraging retention? What website fonts are best for conversions? These questions can be answered with big data. But data must be collected from many different sources.
A Single Platform May Emerge
One of the interesting things that some experts in their field are predicting is the emergence of a single platform. The problem right now is that big data is fragmented. The data themselves are coming from multiple sources simultaneously. And there is a proliferation of different products which deal with different use-cases. These two factors risk splintering the entire big data project.
Top experts are now recommending that everything be brought together onto a single platform. Data scientists should be able to do everything big data-related on this platform. It should run the gamut from building apps to sophisticated analytics.
The good thing about having a single platform is that it reduces the steepness of the learning curve. Rather than having to learn a dozen products, businesses need only be familiar with one.
Data Problems Will Become Easier To Define
The number of questions that can be asked of data right now is enormous. It’s something the experts call the “vast search effect.” It’s useful for mining the data for relationships you didn’t know existed. But it’s bad if you end up measuring a correlation that is spurious.
For instance, let’s say that you’re tracking sales over time in an emerging market. You notice that sales seem to rise the more you invest in marketing. And so you use those data to justify increased marketing spend. The problem, though, is that you haven’t ruled out a third variable – higher wages. It could be that the higher wages are driving, the higher sales and that the marketing is irrelevant.
New techniques in big data hypothesis testing will look for ways to separate out the spurious results.