Data is growing at an unprecedented rate, coming from all kinds of sources. The Internet of Things, artificial intelligence, and any other new technologies where people interact with the internet, social media, or apps create data at a breakneck speed.
This huge influx of data creation is what we call big data, which is both a solution and a problem. It's a solution for areas where organizations didn't have data or insight before. But it's a problem for traditional data centers since big data is only helpful if you can use and manage it.
So many IBM i (AS/400, iSeries) data centers run on legacy software, like Query/400, which can't handle the complex queries required to use big data. Information needs to come from multiple places in a big data environment, so IT departments have to build several queries. This makes processing tasks more resource intensive and creates demand for new storage hardware.
Today, solutions are available for the sole purpose of managing big data. From marketing departments to IT administrators, employees throughout many organizations are digging in to make decisions based on big data.
However, all big data initiatives need to address the core issues associated with the technology:
VOLUME - Volume of information
VARIETY - Wide range of data types and sources
VELOCITY - Speed at which it must be accessed
Big Interest in Big Data Analytics
Your organization can gain significant value by using big data technology. Adopting processes and solutions that streamline data collection and management can help you achieve that goal.
Both IT users and business users alike are pushing for these investments. They want the insight that big data provides.
But, before investing in new solutions, you need to understand:
- The scope of information growth
- The challenges associated with big data access and analysis
- How to overcome those issues
Big Data Challenges: The Three Vs
The defining attributes of big data—volume, variety, and velocity—make it challenging to deal with. The amount of data entering business technology ecosystems is more than what traditional technology solutions were meant to handle, and big data is only getting bigger.
Domo’s “Data Never Sleeps 5.0” report for 2017 indicates that 90% of all data today was created in the last two years—breaking down to 2.5 quintillion bytes of data… per day. That’s a lot of data to manage.
The exploding data volume issue is further complicated by the fact that information is coming in from a greater variety of sources. For IT managers, this means data must be collected from numerous systems on different platforms.
There's another layer of complexity to the big data problem. When it comes to data variety, a large portion of data in enterprise IT systems today is considered unstructured. This type of information does not have a defined data model and does not fit well into traditional relational databases. Considering that IDC estimates that 90 percent of big data is unstructured, it’s imperative that organizations can effectively categorize and search this data.
The final ‘V” of big data—velocity—means that companies must be able to find and use critical information quickly.
The value of any given data diminishes over time. Knowing which scheduled jobs failed is important for an IT employee to know as soon as possible. The same concept applies to data analytics initiatives, whether they deal with large or small amounts of information.
Many businesses lack the proper tools and expertise to leverage big data environments. And while moving big data initiatives into public cloud environments can help lighten your IT team’s load, analyzing and using data from multiple sources can be difficult, particularly combining data from in-house sources and cloud instances.
And, as we mentioned earlier, big data is only helpful if you can use it. You need to be able to access and analyze data quickly.
Big Data Solutions: Features to Look For
Changing the game when it comes to big data has a few different components. Your organization should build a business culture around analytics, temper idealism with use cases, and use data to improve processes. This is easier said than done. Often, projects like this do not progress past the pilot stage. Take stock of your current data management practices to most effectively choose and implement a solution. These two questions should help your team build a strategy for big data management.
1. What needs to change in our operations for us to be able to leverage analytics?
2. What technology do we need to access and process big data?
Once you define action items from these questions, address the technical roadblocks that often emerge in big data deployments. Although the focus is on making things easy for business users, a big data solution deployment will also impact IT. While there may be other areas to consider, the top four are automation, usability, accessibility, and adoptability.
Many organizations launching IBM i analytics initiatives use manual processes to merge data from different platforms. This is time consuming for IT teams and frustrating for business units that do not get their data quickly enough.
Success depends on the ability to bring together and analyze data in real time. Manual collection and analysis could extend delivery time to days, making the information far less valuable. Consider solutions that automatically migrate data between different partitions or even different platforms.
The value of any big data investment depends on how usable the data is. This means it’s important to consider visualization tools and how information is displayed. C-level executives aren't likely to have the time to delve into a spreadsheet of financial records for insight. And the ability to create charts to see growth or change at a glance is beneficial for both IT and business users.
Looking at a chart instead of a spreadsheet helps users process information better. Visuals also show the relationships between data sets and trends in large volumes of data much more quickly. And faster analysis means being able to act on the information faster.
When choosing a solution, it's important to consider the needs of users across departments or levels of expertise.
Some employees may only need access to a graphical interface without any querying capability. Some more technical users might need access to build their own queries and reports.
Even in the rush to find a solution to make big data usable, remember the basics. How do users access reports and insights? Today's employees are increasingly mobile. The solution needs to adapt to different devices. Drag-and-drop functionality is convenient when working on a desktop but essential on a touchscreen device. Solutions that offer secure, platform-agnostic remote access are the best option for many modern companies.
Tools for big data should enable employees to get insight faster. Technology with a high learning curve can prove frustrating for users. And if IT needs to spend copious time training, it can also prove more costly than beneficial.
Selecting a Big Data Solution for IBM i
While many of the challenges associated with big data relate to business strategy, they also require technology to solve. Selecting the right big data solution determines the success or failure of any data access initiative. An effective solution should give access as well as make it easier to interpret results, with features such as:
- Access and accessibility for all levels of users
- Web interface for anywhere access
- Custom visualization tools to display objects in charts and graphs
- Support for multi-platform databases
- Unstructured and structured data management
Most business decision makers recognize the potential value of big data from others' success stories, but are hesitant to adopt solutions. Fears over data privacy, security, and the time involved with managing large volumes of data can be daunting. However, selecting the right solution can meet the core technical challenges, while empowering users with secure, reliable data access.