Accelerating a data analytics project is critical for storage vendors that sell storage systems designed for big data analytics, artificial intelligence (AI), and machine learning (ML). These vendors all thrive by helping their customer get answers to their questions faster thanks to faster storage IO processing. The problem is many of these projects don’t get to the point where they can leverage these speedier storage systems. The project stalls long before there is a need for extreme performance storage systems.
What Your Data Analytics Project Needs Before All-Flash
Before that extreme performance flash array is needed, the business stakeholders and IT have to work together to identify what data it should query and where that data is located. IT needs to ingest that data into a business intelligence application or data lake. Organizations also need to understand what type of questions the information they are gathering can answer. All of these steps need to occur before the organization invests in a high-performance flash system. The problem is that most data analytics projects stall during these steps and so the business never purchases the flash array.
What Makes Data Analytics Projects Stall?
Data Analytics projects stall for a variety of reasons. There is a high degree of technical knowledge required. SQL queries for data analytics insight often take hours to build and test. The whole process of locating data, ingesting data, and preparing that data for questions is manual. Finally, there is a lack of visibility into the data. Organizations may have petabytes of data, but without visibility, they don’t know what questions that data can answer.
Getting Your Data Analytics Project Back on Track
Again, all the data discovery has to (or at least should) occur before an extreme performance flash array is purchased. Organizations need solutions that enable them to accelerate their data analytics projects so it can deliver value sooner. The first requirement is the solution needs to connect to various data sources natively by using API connectors. It is too time-consuming if IT has to import and transform data manually and continually. Without an advanced solution, the import/transformation process becomes a full-time job.
The second requirement is to aggregate data via metadata so that IT doesn’t need to move data continually. Understanding metadata means that data transfers become more efficient, moving only the required data instead of all the data. A solution can analyze the metadata to figure out where the data is located, what type of questions the data can answer, and also understands the relationships between data, all without copying a single byte. Leveraging metadata enables the organization to reduce the time to prepare data from months to minutes. IT’s primary role is just pointing the solution at the datasets so the analysis can begin.
The final requirement is a natural language query (NLQ) capability. This query though, has to be more than just using NLQ to call on an existing report created initially by an SQL programmer. The NLQ should query the actual data, creating its own unique reports, not just drawing on pre-scripted ones. The key to making natural language query make sense is to map the questions to the data.
Understanding the data within a data analytics project is critical to enabling organizations to extract full data from it. It is also the most crucial prerequisite before investing in a high-performance storage system. The value of an advanced data analytics automation system is that it reduces the time to get to the point of answering questions from months to a few hours.
To learn more about getting to value faster, register for tomorrow’s live webinar “The Top Four Challenges of Data Analytics and How to Solve Them“, with Storage Switzerland and our special guest, Promethium Data. The live event starts at 1:00 pm ET / 10:00 pm PT. During the webinar, we will address the challenges facing data analytics projects and provide you with ways to overcome those challenges. We’ll also show a live demo of Promethium’s solution so you can see an advanced data analytics automation solution in action.