Selecting a Data Storage Platform in an Era of AI and Analytics
Making Data Analytics accessible across an organization
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As we look ahead to the remainder of 2020 and the new decade, what will be the conversations around data and cloud storage? Artificial Intelligence (AI) and Machine Learning (ML) continue to become mainstream technologies and more companies are incorporating the technology in their analytics solutions, making data that much more valuable. Data currently stored in archives will become more valuable in calculations, trend analysis, and so forth. Data will find itself residing in geo-dispersed hot storage and being used more so than previously.
As AI and ML become bigger drivers for hot storage, the conversations will turn to storage performance and storage characteristics. High-performance storage will become more the norm and the high-speed fabric connecting individual storage elements and points of presence will increase in importance.
How much data are we talking about?
Examining data in aggregate, it is important to note that more than 90% of the world’s data has been generated in just the past two years and the accumulation of new data will continue to accelerate as new sources emerge every day.
Looking beyond the traditional business sources of this data explosion, IoT has now become a major contributor to data and even back in 2016, the Internet of Things was already connecting 5.5 million new devices each day. As the number of autonomous vehicles, health monitoring devices, and remote sensors grows, that number will explode. According to Gartner, the total number of IoT devices will hit 20.8 billion this year, 2020, which is equivalent to 2.5 devices per person on the planet. More statistics.
Who will be able to use all of this data?
Making all of this data available to a broad set of people is extremely important if we want to realize the benefits from the potential insights to be gleaned from such a massive trove of information.
Artificial intelligence can make massive volumes of data accessible to everyone in an organization, helping them to not only achieve better outcomes with fewer resources, but also make stronger recommendations to the C-suite, recommendations that are supported by data-based findings.
Here are five ways to get started and make the most of the data explosion
- Clean and prepare your archived data.
In order to make the best use of data, companies are taking archived data cleansing the data and the metadata and preparing it for useful analysis for the long haul. Do not assume that your archived data follows standard data formats or that proper metadata tags are in place.
- Establish an entry point for data exploration.
An AI platform will churn through all the available data and recommend entry points to begin your data exploration journey. Once the entry point is identified, managers can perform key-driver analysis, which looks more closely at a particular relationship to find additional insights related to the variables that are driving that outcome.
- Remove confirmation bias through automation.
AI-driven analytics is an iterative process finding connections in the data relevant to your target, rather than answering specific questions. AI removes bias inherent in the way a question is phrased, prompts further research, and can drive additional insights.
- Democratize your data
Incorporate natural-language queries to make data analysis accessible to anyone and everyone. AI enables the iterative process through queries that are made using conversational, natural language. Using conversational language will bridge the skill diversity within an organization. Statistically speaking, 60% of people at an organization are data consumers, 30% are data explorers, 8% are data analysts and only 2% are genuine data scientists. Such NLQ tools make the 98% as capable as the 2%.
- Find patterns to highlight previously undetected data drivers and create visual representations.
One of the great powers of AI is its ability to find patterns among disparate sets of data that would ordinarily go unnoticed.
By running data through a decision tree, an AI platform can segment and stratify data by common characteristics. Segmentation may be used to identify factors driving a target and offer some predictive value. Tools may then be used to produce diagrams to show associations between various data drivers.
Craft data into a narrative that executive leadership can understand.
It is far more effective to lay out a narrative to present to senior executives or a board of directors than it is to inundate them with endless stats and charts.
Your next step is to pin down the things you want to act on and put that information into a story to create a narrative that your entire organization can readily absorb. Curate your insights into a storyboard, stepping viewers through the data and its meaning.
Data and analytics have always been integral to businesses but until the advent of AI they have traditionally been left in the hands of focused specialists. Applying artificial intelligence coupled with a high-performance storage platform is what companies need in order to generate insights faster and in a format that is operational.
Selecting the right storage platform coupled with a high-speed network fabric is a critical step in creating a highly scalable foundation for analytics. With the volume of data projected to grow by a factor of 10 in the next two years, a system should be able to ingest all of your data and continue functioning at a high level. Having a scalable structure will be critical to the success of any data analytics program.
See it in action.
RStor’s fast access and lightning-quick geo-dispersed distribution provide faster workflows and disaster recovery at some of the lowest price points in the industry.