Whether you’re a high-tech startup or a decades-old retailer, and whether you like it or not, you’re a data company. As enterprises everywhere begin to integrate deeper insights into their operations, those who focus on maximizing their data and analytics capabilities will be best placed to succeed today and tomorrow. According to a recent Gartner survey, across industries, business intelligence and analytics are now ranked as the number one CIO investment priority1. It’s a sign that data has become the gravitational center of business—the point around which everything else revolves.
Data and analytics are, of course, intrinsically linked. Data on its own is not interesting, but the insight that can be extracted from it—using analytics—is now being used by many as a competitive advantage. It can help reduce costs, improve understanding of customers, and drive innovation. And it can help organizations react more quickly to industry or market changes or evolving customer needs—a must-have capability in today’s digital economy.
So analytics is important for everyone, not just high-tech, blue-chip companies, and there’s no time to lose. The world’s data is expected to grow ten-fold in the next ten years2, but, even today, less than one percent of it is analyzed and used3. As these volumes continue to grow, we’ll have to boost our ability to turn more of it into valuable insights, otherwise, what’s the point of collecting it?
Create a Data Foundation
Some of the most common factors hindering our ability to better tame our rapidly expanding data assets include:
- Disconnected silos of data across the company that are difficult to access and blend
- Data that has been archived and is no longer retrievable
- Older data infrastructures that aren’t designed for ingesting or blending data from multiple sources
- Disparate governance rules and inconsistent metadata and formatting
- The growing expense of storing data (especially the most timely ‘hot’ and ‘warm’ data)
- And, of course, working out what to retain and what to analyze
Overcoming these challenges means ensuring the data layer of your IT infrastructure is carefully assessed and prepared for the age of advanced analytics and artificial intelligence (AI). For many organizations, this means transitioning from traditional data warehousing to a more integrated model based on a data ‘hub’ or ‘lake’. These new solutions, often supported by existing hardware and storage infrastructures, are designed to enable real-time analytics across the full spectrum of data types and sources. More details about how to develop such an analytics-ready data infrastructure is available in the new Intel® whitepaper Tame the Data Deluge.
Keep Accelerating Insights
While a strong, modern data strategy is an essential first step on the journey to advanced analytics and AI, it is just that: A first step. It’s not enough to make data available to analytics applications, it must also be done at an increasingly rapid pace that keeps up with business demand. Enterprise executives responding to a PwC survey indicated that they expect their analytics to be 75-percent faster and two-times more sophisticated by 20204, so IT has some big expectations to fulfill.
In order to shrink time to insight, it’s essential to have a holistic approach across hardware, software, and solutions. Some of the key things to consider, include:
- Hardware: The choice of processors, memory capacity, storage media, network technologies, and cluster architecture can all determine improvements in speed.
- Software: Adding the right library into your software stack can drive big performance improvements. In addition, optimized versions of industry frameworks and operating systems can make a major impact on speed and efficiency.
- Solutions: Using a fully tuned solution stack from a blueprint, such as the Intel® Select Solutions, can help accelerate analytics.
Enable Advanced Analytics Use Cases
While there’s a lot of talk about AI, enterprise adoption is still in its infancy. In a recent Forrester survey, 58 percent of business and technology professionals said they were researching AI, but only 12 percent said they are actually using AI systems5. However, as more and more companies adopt increasingly mature advanced analytics techniques, AI will become a standard part of the business toolkit.
The potential use cases for advanced analytics and AI are numerous. One of the beauties of these technologies is that they can be adapted to address almost any business challenge or scenario. It’s useful to bear in mind these distinctions between some of the more common technologies discussed in this context:
- Artificial Intelligence: A program that can sense, reason, act, and adapt.
- Classic machine learning (ML): Algorithms whose performance improves as they are exposed to more data over time.
- Deep learning (DL): A subset of machine learning in which multi-layered neural networks learn from vast amounts of data. For example, image/speech recognition, natural language processing, and pattern recognition/detection.
- Reasoning: Suitable for large, diverse, complex, structured or unstructured data sets from multiple data sources.
- Emerging technologies: New and emerging AI techniques that don’t fit the characteristics above. Today, this would include solutions like sequence alignment in computational biology, or binary neural network-based inferencing.
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