According to IDC 2012, only 23% of organizations have an enterprise-wide Big Data strategy. This is because some view Big Data as a threat and some view it as a savior.
What is Big Data?
The vast zettabytes of data that are flowing from our computers, mobile devices, and machine sensors and that surround us every day is called Big Data. If the right Big Data tools are used, your organization will be able to store, manage, and analyze this data so as to gain valuable insights that were previously thought impossible.
In-memory data management, analytics, artificial intelligence (AI), and machine learning are some Big Data technologies that can help you transform your decision-making process and even your business.
Big Data Benefits
Through the infusion of Big Data, businesses get supplied with an intuitive method by which they can discover actionable insights from within their data sets that enable their business efforts to become more successful. When businesses tap into Big Data, they get a quick and simplified way to focus on data that is usable.
There are 3 key benefits that can be capitalized on by businesses when applying Big Data to e-commerce strategies.
1. Increased analysis and discovery of actionable insights
Big Data helps companies hone in on the key pieces of information that tell businesses what they need to know about demand spikes, customer preferences, changes in buyer behavior and more.
Using the key insights from analyzing Big Data, businesses can update their offering in order to better serve customer needs, make specific recommendations for a particular customer and to innovate new products based on customer needs and wants.
2. Improved customer experiences and buyer journeys
Due to poor customer service and experience, companies lose more than $62 billion every year (Source: Help Scout). In order to eliminate the friction that comes with online purchases, customers are looking for more positive experiences and unified buying journeys. When businesses prioritize this, their bottom line gets rewarded.
Big Data leverages predictive monitoring so as to supply businesses with more in-depth ways to track customer journeys and to improve customer experiences. This will also increase the personalization of a specific buyer’s journey.
3. Easier and more secure ways to discover and pay online
Big Data provides businesses an opportunity to implement a variety of improvements in the way customers make their purchase and to also keep transactions safe which includes real-time fraud detection and even money laundering scams.
Big Data Challenges
As Big Data is a new set of complex technologies, it comes with its own set of issues in spite of the amazing benefits of it as well. Not many people are trained to work with Big Data technologies which result in bigger problems. Here are some of the top challenges that enterprises face when it comes to implementing Big Data.
1. Handling large amounts of data
Since a few years back, there has been an exponential increase in the data that enterprises can access. This includes data about customer likes, their reactions, etc. The data gathered is so huge that it exceeds the amount of data that can be stored, computed and retrieved. The problem lays in data management not so much the data availability.
The rise of the variety in data formats (video, audio, smart device data, etc.) comes alongside the rise of unstructured data collection.
2. Generating insights in a timely manner
Apart from proper data management, businesses need data so as to achieve business goals. Some goals that are associated with Big Data projects:
- With the use of operational cost efficiencies, expenses can be minimized.
- Establish a data-driven culture
- Create new avenues for innovation and disruption
- Accelerate the speed of deployment for new capabilities and services
- Launch new product and service offerings
These goals help organizations become more competitive but only if insights are extracted from their Big Data and then acted upon in a quick manner.
3. Recruit and retain Big Data talent
Organizations need professionals with Big Data skills so that insight generating applications can be developed, managed and run. This increases the demand for Big Data experts and has also driven up Big Data salaries.
There are a couple of options organizations have when dealing with talent shortages.
- Budgets can be increased along with their recruitment and retention efforts
- Offer more training opportunities for current staff members
4. Integrate contrasting data sources
Big Data leads have a lot of variety and this proves to be a challenge when it comes down to data integration. Big Data comes from enterprise applications, social media streams, email systems, employee-created documents, etc. and combining all that data and merging it so that it can be used to create reports can be extremely difficult.
5. Data validation
With data integration, comes data validation. Organizations often get similar pieces of data from different systems and the data in each of those systems don’t agree with each other. Through data governance, the process of getting all the data records to be accurate, usable and secure becomes possible.
Big Data Security Tools
There are several methods by which enterprises can carry out security measures to protect their Big Data analytics tools and some of the most common ones are as below:
- Encryption – Data that is encrypted becomes useless to external factors like hackers. This applies to data that is both inputted and outputted.
- Firewall – Building a strong firewall filters traffic that both enters and leaves servers with incredible efficiency.
- Root access control – Through the development of a tiered access system to Business Intelligence tools, organizations can reduce the opportunities for an attack.
Big Data Security Steps
Due to the huge volumes of disparate information involved, Big Data platforms have unique security requirements and operate in a distributed architecture format in which only one server needs to be secured. There are 3 steps that a forward-thinking, big-data-minded organization can do in order to smartly secure Big Data projects.
Step 1: Include data security from the very beginning
It’s the responsibility of the security team to provide their expertise at every step in the development process. They must be approachable, open with their knowledge, and have a commitment to finding a custom solution for securing Big Data technologies. However, it’s necessary that the data analytics team invite the security team into the project at an early stage and keep them informed at every step.
There can be trouble when the data analytics team views the security team as “spoilsports” or when they deliberately cut security out of the early stages of the process based on a belief that innovation will be limited due to security implications.
Step 2: Begin with specific security objectives, not solutions
A Big Data environment can’t be secured with just one solution. Organizations must understand that only a customized combination of tactics has a chance at risk management.
When it comes to Big Data security efforts, security teams should adjust their thinking and begin by asking what specific security objectives the team is trying to achieve. They can work backward from there so as to find custom and alternative solutions to secure your environment.
Step 3: Customize your Big Data security solution
When it comes to Big Data security there is no one-size-fits-all solution as Big Data technologies are a cluster of open source frameworks stitched together to fill a specific need and this makes creating a security solution complicated. Organizations need to customize a stack of tactics that can address the security objectives that have been identified at the beginning of the process.
It is an uphill battle when it comes to security but those organizations that form a true partnership between their data and security teams and develop a commitment to a customized and objective-driven solution experience the most success.