Businesses, with rare exception, are bombarded with vast amounts of information. Some companies turn it into insights and thrive. Others struggle to make sense of the overwhelming flow. Why is that, you may wonder? Well, the former most likely have an effective big data strategy. The latter either don’t have it at all or haven’t managed to build a strong one.
How’s Big Data Different from Just Data?
To begin with, one question many businesses have is whether they are dealing with big data or just data. Some think that it all is about the quantity. That is, normal data is any information that can be processed and when there’s a lot of it, it’s qualified as big. It’s not exactly so. To be called big, data must possess three characteristics: variety, velocity, and veracity.
- Variety
It usually comes from multiple sources: social media, sensors, transactions, and more. It also encompasses different information types (e.g., structured like databases or unstructured like videos).
- Velocity
It is generated at a super fast speed. Businesses must process and analyze it in real-time or near-real-time.
- Veracity
One part of it is accurate and reliable. The other is misleading. Big data strategies need to take that into account and pay attention to the quality of insights.
Which Businesses Need a Big Data Strategy?
A huge and surprisingly common misconception is that big data is just for tech giants or multinational corporations. In reality, ANY business that generates or relies on large volumes of information needs big data services. These services would normally include consulting, management, and analytics plus more specific tasks:
- processing
- storage
- security
- visualization.
For example, retailers can use these services to understand what customers buy and when. Then, based on it, they can predict trends and adjust their strategies.
In healthcare, in turn, they can analyze medical records, treatment histories, and even genetic information. This should help hospitals make smarter decisions about patient care and streamline operations.
Other industries where such services are particularly helpful include but aren’t, of course, limited to
- Finance — to detect fraud, assess risks, and tailor financial products to individual customers.
- Manufacturing — to optimize production processes, predict equipment failures, and manage supply chains.
- Marketing — to understand customer preferences and behavior across multiple channels.
All in all, If your business depends on big data to make decisions, you do need a strategy for it. It doesn’t really depend on the size of your company.
How to Build a Strong Big Data Strategy: 4 Components
As was said above, it’s best to build your big data strategy together with a reliable big data service. They’d normally build the process around four core steps.
Identify
This means deciding what matters most to your business. You’d normally start by defining your business goals and key metrics. Are you aiming to boost customer retention? Enhance operational efficiency? The task is to focus on information that aligns directly with these goals.
For instance, if customer satisfaction is your priority, you’ll look at
- feedback
- purchase history
- interaction patterns.
Store
Once you’ve identified what you need, you figure out where to store it. Storage must be organized in such a manner that the data is accessible, secure, and scalable.
Most likely, big data experts will suggest you use cloud storage. That makes sense because you’ll easily scale up when needed but won’t invest heavily in physical infrastructure. You’ll just need to consult them regarding security.
Process
Now, this is perhaps the most exciting part of it. Storage is one thing, but processing is when you start reaping the benefits. At this stage, you (together with the big data experts, of course) will analyze your insights to see how they can actually help with your business decisions.
Now, we come to why it’s worth working with a good big data service. The thing is that these guys usually have advanced analytics tools, AI-driven platforms, and machine learning algorithms. They’ve already invested THEIR money in those and, most importantly, they know how to use those. You, in turn, don’t spend any resources at all.
Govern
When all is done, you’ll set policies and procedures that’ll help you to manage data throughout its lifecycle. Such policies are normally centered around quality, privacy, and compliance.
Final Thoughts
The key takeaway is perhaps as follows: if your business is dealing with big data (you now know what exactly it is), you do need a strategy for it. Otherwise, you’ll just miss tons of opportunities your competitors may be already using. Of course, it’s problematic to build a strong data strategy all by yourself but with a good big data service, it’s totally doable.
