How to Choose the Right Analytics Tool for Your Business

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Credit: forbes.com

Data is one of the most integral assets for any company, but sometimes, the best way to leverage that information can be unclear. AI provides constant data at extremely large volumes. It’s all incredibly beneficial for companies, but many don’t know how to interpret or analyze such large amounts of information, according to management consulting firm Aspirant.

A survey carried out by Oxford Economics and NTT Data found that the effective use and management of data and analytics is crucial to keeping businesses alive throughout the next five years. Most of the five hundred executives who participated in the survey were in agreement that data is necessary for an organization’s growth, customer experience, employee experience, financial performance, and overall industry competitiveness.

Figuring out which analytical tools to use can often be one of the biggest challenges in data analytics. And, as an increasing amount of new analytical tools are released, businesses often have a tougher time determining which one is the best fit for their needs.

Today, more than ever before, data is being generated at huge volumes by businesses of all sizes. While that’s unquestionably valuable, it adds even more value when you have the means to fully take advantage of the data and monetize it in a way that gives your business a competitive advantage.

Using analytical tools is the best way to do that, and there’s certainly no shortage of options to choose from. We’ve put together four steps to choosing the right data analytics tools for your business.

Understand Your Business

The first and most important step is understanding your business; this will ensure that the tool you choose is the right one to help you meet your goals and objectives. What questions are you seeking to answer? What do you hope to accomplish with the results? What problems do you want to solve, and how are they important to your organization?

Writing out a few explicit, exact, quantifiable questions can help. Consider the nature of the queries that those questions will require. For example:

  • Will they need continuous updates to architecture?
  • Will the queries run a standard way?
  • Will they pull on a mostly static data model?
  • Will they pull on a data model that’s often changing?

Where is the Data?

Get to grips with the data that you want to analyze.

  • Is it in a single, consolidated source?
  • It is in disparate sources?
  • What about query complexity?
  • How much data do you have?
  • Where is it located?
  • How will you migrate the data?
  • Who handles the data?
  • How much do you expect different users to collaborate?
  • How will you protect the data?
  • How do you expect your data needs to increase in the future?

Accounting for future needs is especially important; failing to do so can lead to additional costs, timed-out queries, or even serious database problems that can hold you back and cause your business to lose money. This article on analytics from Emerson College Online might be useful.

Research Currently Available Analytical Tools

Secondly, professionals need to take an inventory of the current analytical tools on the market and separate them into different classes, including:

  • Semantic layer reporting tools
  • Report writing tools
  • Data discovery tools
  • MDX/Cube query tools
  • Visualization tools
  • Data science and modeling tools
  • Embedded BI and reporting tools
  • AI and machine learning case driven tools

This is important to figure out what the landscape is like in terms of the various vendors of analytical tools and what they are offering. In addition, this step makes it possible for you to sort through what’s available and how they fit into any holes that you found during the first step.

Once You’ve Narrowed Down Your Options

Once you have some suitable options using the steps listed above, it’s time to look deeper into the individual tools available. There is no shortage of data analytics tools available today and there are many offering similar features, so it’s important to do your research to determine the exact best choice for your company.

  • Make a list of mandatory features: Write a list of the important features that you need an analytics tool to have, based on the information that you have determined above. Be 100% clear on the must-haves and nice-to-haves as this will allow you to accurately assess the options.
  • Consider mobiles: Are you mobile-first? If so, bear in mind that some analytics tools have been on the market for a comparatively long time, and their products were built for the web. Their mobile versions are an afterthought; when smartphones came along, they adapted the existing product to accommodate native apps, unlike some of the newer tools which were built with smartphones in mind. It’s something to be aware of if mobile is a huge focus for your company.
  • What do you want to track? While there’s no need to put together a complete tracking plan as any tool you choose will have the capability of doing this for you, it’s a wise idea to have a clear idea of what you are going to want to track. You should have determined this using the steps mentioned above. It will provide you with the necessary context for how you are going to use the potential data analytics tool or tools that you choose.
  • How technical are you? How tech-savvy are you and the people who’re going to be using the tool on a regular basis? What technical requirements do you have?
  • How much support will you need? Analytics tools offer various levels of support. It’s important to know whether you have the in-house experience to reliably manage the full implementation of a new data analytics tool, or whether you’re going to need additional support to get started.
  • Is there a demo available? After you’ve determined which exact tools are the best choice for your company, it’s worth test driving the tool before you make an investment. Do all the groundwork above before you request a demo as this will put you in a position to ask the right questions and apply what you see to your day-to-day business requirements.

With so many data analytics tools out there, choosing the right one for your business needs is paramount.

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