It's safe to say that “big data" is no longer just a buzzword. Today it's a $200 billion industry addressing one of the most significant challenges facing organizations: analyzing vast amounts of disparate data to find patterns or trends that can potentially improve their business.
With the advent of social media and the internet, businesses from small startups to large enterprises are contending with more data than they know what to do with. Approximately 2.5 quintillion bytes of new data are created every single day, meaning the need for big data tools to do the heavy lifting on this analysis will only continue to grow. According to Gartner, almost half of all businesses are investing in big data now, and a quarter plan to invest in big data soon. (Full content available to Gartner clients.)
That's where we come in. In this Buyer's Guide, we'll explain everything you need to know about the big data landscape so you can choose the right tool that will best suit your needs and budget.
Here's what we'll cover:
Gartner defines big data as “high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing."
Big data tools include any business intelligence (BI) tool that does this all-important processing.
More specifically known as big data analytics tools, these software platforms are designed to parse through large, disparate datasets—be they product data, customer data, employee data etc.—to discover meaningful patterns that connect them and visualize the results in an easily interpretable way for stakeholders to make decisions.
Big data analytics in Tableau
Because big data is such a broad term, the functionality of big data tools can vary greatly. Some tools represent robust BI suites that can handle data collection, extraction, cleaning, visualization and more, while others are more stripped down, focusing solely on one aspect of big data analysis.
In general though, here is some of the most common functionality you can expect to find in big data tools:
|Data extraction||Pulls raw data from various integrated sources and reformats it all into a similar schema or format for easier analysis.|
|Data mining||Uses statistics and data modeling to analyze vast amounts of disparate data and identify trends or patterns that connect them. Learn more about data mining here.|
|Data visualization||Visualizes trends in an easy-to-understand graphical form. These visualizations are usually customizable and interactive, allowing users to change various scales and data sources to analyze trends.|
|Predictive analytics||Applies discovered trends from historical data sets to create models predicting what's likely to happen with that data moving forward.|
|Reporting||Allows users to create and distribute a preloaded set of customized reports or customize their own.|
Not sure which functions to prioritize in your search? Check out "5 Core Data Analytics Software Capabilities For SMBs" for guidance.
Big data tool buyers generally fall into two categories, each with vastly different needs and priorities:
Simply picking a tool based on good reviews and signing on the bottom line is a surefire way to end up with buyer's remorse. When evaluating different big data tools and talking to software vendors, here are a few things you should keep in mind:
If you're ready to find the right big data tool for your needs, call us at (844) 680-2046 for a no-obligation consultation. Our BI tool advisors will ask you a few questions about your business and your software needs and send you a shortlist of best fit products for free.
If you're not ready to take the plunge yet though, that's OK. This is a complicated software market for beginners. Here are a few more resources where you can learn more about big data:
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