5 Essential Analytics Tools Every Business Should Use in 2019

As we’re creating 2.5 quintillion bytes of data a day on this planet, it’s important to have tools to comb through it all. If you’re running a business, you know just how important it is to analyze your data but you might not have the right analytics tools. When you have the right tools, you can predict your next moves, create new products, and branch out into new territory.

Here are five tools you’ve got to have ASAP.

1. Apache Spark and Storm

These two tools from Apache represent some of the most powerful tools in the world of data analytics from a company build on data expertise. When you’re interested in working on a database or trying to manage large amounts of data, Apache is the company you can rely on for high-quality tools.

Spark is an open source tool that’s built as a processing engine for handling massive volumes of data for analysis. It’s especially good at struggling with unstructured data or analyzing data that comes in large and unfiltered chunks.

In the last few years, larger enterprises have put Spark to good use and are finding it’s easy to integrate with what they’re already doing. Companies that use Hadoop find that the machine learning library that Spark contains is useful for getting work done quickly and efficiently. Over time, we’ll see increased adoption of this tool that’s already reached a fever pitch.

Storm is another tool that large enterprises are singing the praises of. It’s a fantastic tool for Big Data when you have to move data from one place to another. If you’re getting data coming in through a stream or in real time, use Storm instead of Spark, which is better at handling static data than live data.

Storm is the weapon of choice for companies that are handling real-time analytics and having to spit out results quickly. If you’re trying to handle stream processing, go with Storm over Spark.

2. HIVE and PIG

If you’re working in a system using Hadoop, you know the value of speed and data accuracy. In this system, you need to reduce the complexity of your queries. If you’re trying to write MapReduce queries, it quickly becomes exhausting with a tool to help you out.

If you’re comfortable with SQL, you’ll be fine working with HIVE or even PIG. Companies working with Big Data use these platforms a lot for speeding up their analysis. They write their queries a lot faster and get the responses they need quickly, allowing for work to get done faster with better accuracy than hand-coding queries.

3. SAS

If you’re okay paying for a commercial tool, you’ll find that more people working in the industry are using SAS than anything else. There’s a lot of flexibility in pricing, which means that research facilities, corporations, and small enterprises are all paying their fair share. People working with the SAS Institute are happy to support, knowing how hard the people there work.

They’ve created a versatile tool that’s easy to learn while offering space for robust engagement on an easy-to-learn platform. In recent years, we’ve seen a lot of new modules added to SAS, which has helped the tool to expand broadly.

If you’re working in the financial sector, you’ll find that SAS has what you need, with it’s new “Anti-Money Laundering” tools. They’ve even created a special version of their Analytics Pro tool built for especially for midsized businesses and new enterprises.

For companies starting to flirt with the world of IoT, they’ve got a recently created analytics tool just for IoT. It’s hard to keep up with all of the data that IoT products collect and generate but with the tools that SAS provides, it’s much easier.

4. Excel Is Still Popular

Across the world of analytics, you’ll find everyone using Excel. It’s still ranked as one of the top tools for that industry. If you’re looking for a data scientist’s best friend and worst enemy, they’re probably both named Excel.

Excel is a powerful tool for doing the grunt work that’s left out of a tool like Tableau or R. If you’re collaborating with people who don’t do analytics work, they might not have SAS or other powerful tools on their machine. However, if you need to work across a shared data set, Excel is found everywhere in just about every industry.

It’s likely that your analytics team will have to sit down with a business team at some point and they’re going to want to see real numbers. Excel is the best tool for bridging that gap between those two worlds.

5. Splunk

For a company that’s dealt with log files in the past, Splunk has probably been a name you’ve heard thrown around. However, it’s grown beyond its origins and now has the ability to create powerful visual representations of data.

Splunk was once the primary tool for processing machine log files but now it does so much more than that. With the help of its web interface, your greener staff will find it easy to use. However, it still packs the power that it used to have in the past, allowing your more seasoned staff members to use it as a data log tool.

While it represents the past for data logging, it’s part of a wave of tools that are set to move to the future in data analytics.

Analytics Tools Generate Serious Money

If you’re using the right analytics tools, you’ll find that you’re able to generate massive profits from your data. Your data is the key to learning about how your business could grow and where you’re wasting resources.

If you want to find ways to improve the other elements of your business, like the happiness of your data analysts, check out our guide for tips.

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