What Is In-Memory Analytics?

Today, data lives everywhere. Its volume, velocity, and variety are increasing beyond all expectations. Harnessing data analytics has already helped many leading brands move beyond traditional business intelligence to real-time analytics for greater efficiencies, risk avoidance, and enhanced revenue through customer-tailored offerings. Businesses that are slow to tap value from data with analytics solutions can put themselves at a significant competitive disadvantage.

Speed is the key requirement for an IT infrastructure that can support analytics-driven decision-making. The business value of decision-support solutions often depends on being able to deliver results at least thousands of times faster than conventional solutions. Achieving this lofty goal requires taking a new approach to processing: In-Memory computing.

The concept of In-Memory computing is simple. In the conventional approach to processing data, the data resides on a hard disk in the system or attached by a network. When needed, it’s called into the local system memory (today known as RAM), and from there moves to the CPU. The long seek times for data residing on disks often can become a bottleneck.

With In-Memory computing, data is stored directly in system memory. This architectural approach dramatically reduces latency by eliminating the time spent seeking data on the disk and then shuttling it closer to the CPU. In-Memory computing has the potential to be significantly faster than the conventional approach.

In-Memory analytics often has two other important technical components that increase the performance of the software.

• Columnar data storage. Instead of the traditional two-dimensional structuring of data (rows and columns), In-Memory analytics data has a one-dimensional, linear structure.
• Massively parallel processing. In-Memory analytics makes full use of multi-core, multi-thread processor capabilities, which are freed to operate on the data given the reduced access latencies.

Business Value of Analytics

The sources of data with business value are endless: Data from factory sensors, from multiple retail channels, from social media, even from weather satellites and other third-party feeds. New developments like smart cities and the internet of things will only add to the load. Companies can't ignore this data if they want to remain competitive. Properly analyzed, it can increase sales by predicting the up sell most likely to succeed, cut distribution costs with smarter routing and inventory management, reduce manufacturing costs and improve quality with sophisticated root cause analysis—again, the list is virtually endless.

Sometimes, the path to actionable information derived from this flood of data is simply finding patterns in what already happened. In other instances, real-time results are needed to improve the customer experience, stop a malware exploit, or prevent fraudulent use of a credit card, to cite a couple of examples.

The barriers to its adoption are falling. All the major IT vendors offer analytics solutions, and there are numerous vertical solutions as well. The number of data scientists with the requisite skill sets to both use and support sophisticated analytics is growing. Also, many companies are working to “democratize” the use of analytics through simpler interfaces and built-in algorithms. The publicity surrounding analytics (along with its solid business case) have made funding easier to obtain.

The bottom line is that there is clear business value in analytics. Numerous brands are already using In-Memory analytics to boost revenue and cut costs. Those who don’t pursue these operational advantages are at risk of competitive disadvantage.

Step-by-Step Guide to Getting Started

Here is a five-step process for getting started with In-Memory analytics.

1. Identify pain points. Consult with business unit leaders to create a list of pain points that would be difficult or impossible to solve with existing systems. This list should be prioritized based on those items that align with existing strategies, hold promise for new insights, are within the skill capabilities of the IT organization, and have a solid business case. With some iteration, the end result should be a clear list of objectives and the resources to achieve them.
2. Research and familiarize yourself with the available analytics solutions on the market. (Intel is an excellent resource for this information.) In light of that knowledge, evaluate your current infrastructure. It’s important to understand where the data to be analyzed will be coming from, who owns it, and what measures will be necessary to ensure data quality and security.
3. Identify and cultivate the skills your team will need. Hire new talent or plan to outsource some tasks if necessary. In many cases, new employees will be coming with skill sets that match your needs.
4. Establish technology requirements above and beyond what’s currently in place. In-Memory analytics requires modern hardware, including compute, storage, and networking infrastructure. You will also need to determine which analytical queries and algorithms you’ll need to generate to achieve the desired outputs, and then decide how those outputs can be presented in engaging ways. Look to both proprietary and open source solutions for your software, as a wealth of options exist.
5. Create the final use cases for the project. Determine what data will be used, and map out data flows. Then develop a test environment for a production version.

If this sounds like a process you already do for your other projects, it should. Advanced analytics does not require a new approach to how you manage your IT portfolio. It merely requires you to think outside the box for new solutions.