The Progress of AI Data Analysis, and Why You Still Need An Agency

  ●   September 19, 2023 | Analytics
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September 19, 2023 | Analytics

It’s getting increasingly difficult to stay up-to-date with the advances in the world of AI, with different tools being released each week. Just looking at the volume of tools released from one aggregator shows the speed of progress is astonishing:

Source: https://theresanaiforthat.com

With the release of ChatGPT back in November 2022, you can see the huge spike in tools coming to market month-on-month (although it appears things have now started to slow down). 

With Large Language Models (LLMs) now “out in the wild”, there are thousands of developers around the world pushing change in every industry. There are some superbly useful tools coming to market, which are drastically improving productivity and outputs, whilst equally as many with seemingly unknown reasons for existence

One area that we at Semetrical have been particularly interested in is Data Analytics. It’s an area that forms a large part of work for our existing clients and something we are truly passionate about as a team. Yet, it’s another area that, apparently, AI will supposedly swallow up as a job. 

But just as our take on AI and Content, whilst it can seriously speed up productivity of individuals and support our creative efforts, it should never completely replace humans. (Our little mantra being: “Humans will not be replaced by AI. Humans who know how to use AI will.”

That being said, there are some great tools out there to help smaller marketing teams. Here’s a run down of some of the better offerings on the market.

Data analysis AI tools review: 

DataSquirrel.ai

DataSquirrel.ai is an AI-powered data analytics tool that allows users to generate auto insights and instantly see patterns and trends in their data.

Pros

  • Easy to use: Many users have praised DataSquirrel.ai for its ease of use.
  • Auto insights: The tool generates auto insights, which can save users time and effort.
  • Quick analysis: Users can quickly analyse their data and see patterns and trends.
  • Affordable: The pricing is flexible and affordable.

Cons

Most useful use case

DataSquirrel.ai can be useful for a variety of use cases, including identifying pricing information for hotels, predictions for churn prevention, and extracting data from academic websites. The tool is best suited for users who need to quickly analyse their data and generate insights without spending a lot of time on manual analysis. It is also a good option for users who are looking for an affordable data analytics tool with flexible pricing options.

answerrocket.com

AnswerRocket is an AI-powered business analytics platform that leverages natural language processing and automation to provide in-depth insights and visualisations in seconds.

Pros

  • User-friendly interface: Users have praised AnswerRocket for its friendly and intuitive interface.
  • Fast analytics: The tool provides lightning-fast advanced analytics and business intelligence.
  • Natural language queries: Users can ask questions in plain language and receive relevant insights.
  • Custom models: AnswerRocket allows users to publish custom models and make them accessible to business teams.

Cons

Most useful use case

AnswerRocket is a powerful tool that can be useful for a variety of use cases. It enables businesses to ask natural language questions about their enterprise data and obtain actionable insights. This makes it particularly valuable for users who want to quickly analyse their data and make data-driven decisions without the need for extensive technical knowledge. AnswerRocket’s ability to generate visualisations and provide fast analytics can be beneficial for businesses across various industries.

IBM Watson

IBM Watson Analytics is an AI-powered business analytics tool that enables users to quickly and easily analyse their data and generate insights:

Pros

  • Easy to use: Many users have praised IBM Watson Analytics for its ease of use and intuitive interface.
  • AI-powered insights: The tool generates AI-powered insights, which can save users time and effort.
  • Customisable dashboards: Users can create customisable dashboards to easily visualise their data and insights.
  • Integration with other IBM tools: IBM Watson Analytics can be integrated with other IBM tools, such as IBM Watson Studio and IBM Watson Discovery.

Cons

Most useful use case

IBM Watson Analytics can be useful for a variety of use cases, including identifying trends and patterns in customer data, predicting sales, and optimising marketing campaigns. The tool is best suited for users who need to quickly analyse their data and generate insights without spending a lot of time on manual analysis. It is also a good option for users who are looking for a powerful data analytics tool that can be integrated with other IBM tools.

rapidminer.com

RapidMiner is a data science and data mining platform that offers a range of features and capabilities for analytics and machine learning.

Pros

Cons

Most useful use case

RapidMiner can be useful for a variety of use cases, including clustering, churn prevention, and predictive modeling. The tool is particularly valuable for users who want to leverage machine learning and analytics to gain insights from their data. RapidMiner’s automation capabilities and data extraction features make it a versatile tool for users across different industries.

alteryx.com

Alteryx is an analytics and data science platform that offers a range of capabilities for data analysis, machine learning, and automation.

Pros

  • Comprehensive analytics: Alteryx provides a wide range of analytics capabilities, including data integration, data preparation, and machine learning.
  • Automation: The platform offers automation features that allow users to streamline their analytics processes and workflows.
  • User-friendly interface: Alteryx is praised for its user-friendly interface and drag-and-drop functionality.
  • Community support: Alteryx has a strong community of users who share knowledge and resources.

Cons

  • Learning curve: Users may need some time to familiarise themselves with the platform and its features.
  • Pricing: The pricing structure of Alteryx may not be suitable for all budgets.
  • Limited data sources: Some users have noted that Alteryx may have limitations when it comes to integrating with certain data sources.

Most useful use case

Alteryx can be useful for a variety of use cases, including data integration, data preparation, predictive modelling, and machine learning. The platform is particularly valuable for users who want to leverage advanced analytics and automate their data processes. Alteryx’s drag-and-drop interface and extensive library of tools make it a versatile tool for users across different industries.

So, should everyone be using AI Data Analysis?

AI data analytics tools offer numerous benefits for businesses, revolutionising the way data is managed, analysed, and interpreted. There’s definitely plenty of reasons why AI data analytics tools are a good thing for businesses:

  1. Efficiency: AI tools automate manual tasks and time-consuming processes, allowing businesses to analyse data more efficiently. For those of us who are less tech-savvy, being able to automate the mundane tasks so you can double-down on strategy and more creativity saves valuable time and resources, allowing teams to focus strategic decision-making.
  2. Insights: AI-powered analytics tools provide advanced pattern detection, natural language query and generation, and embedded analytics capabilities. For smaller marketing teams who may lack data analysis within their skill sets, yet desperately need it in order to be more efficient, these tools allow for deeper insights from their data, spotting trends that would otherwise take hours, and make data-driven decisions based on real-time insights.
  3. Collaboration: If teams can now run their own analysis, sharing these insights internally and to aid business decisions means collaboration is far easier. Business analysts and users with varying levels of technical expertise can now work together, sharing insights, hypothesise easier and run multiple tests, all based on data that would otherwise have been undiscovered.
  4. Advanced analysis and visualisation: Predictive analytics has been a game-changer thanks to the development of more LLMs, and it’s this type of functionality helping out teams everywhere. But being able to get your point across is crucial, so data visualisation is a great example of more AI advances. From interactive dashboards and reports, to smart visualisations of complex data, AI data analysis tools are really coming into their own.
  5. Streamlined data management: Whilst this is certainly something that can be viewed as a positive with the development of these analysis tools, formatting of data is still an issue for many off-the-shelf tools. However, businesses can access and analyse data from various sources, ensuring data consistency and accuracy at a far easier rate.
  6. Cost-effectiveness: AI data analytics tools can provide cost savings by automating processes and reducing the need for manual data analysis. They also offer flexible pricing options, allowing businesses to choose the most suitable plan based on their needs and budget.

These tools are intuitive to use (most of them), making it easy for anyone to access complex data sets and make strategic decisions from them. At least that’s the top level headline. But in order for any business to stay competitive in today’s data-driven business landscape, it’s imperative you can efficiently use these tools and be able to validate the analysis it’s spitting out.

We’re continually adapting our processes to provide our clients with the most appropriate analytics solutions. With the boom of AI technology, our specialists are constantly adopting new strategies and finding ways for our clients to activate their data.

Danny Smith, Senior Analytics Manager

Why you still need an agency to assist your data analysis

You could have the biggest AI tech stack ever, but if the people using it do not understand the basic principles of data analysis, then it’s likely your strategic decisions could be flawed before they are used. 

Despite the benefits of AI data analytics tools, there are some drawbacks that businesses should be aware of. Whilst not everything, these are some of the key areas to pay attention to when looking to select an AI data analysis tool:  

  1. Limited features: Some AI data analytics tools may have limited features compared to other data analytics tools. This is usually the case with the free versions of tools and should always be a consideration if you have more complex analytics requirements. 
  2. Integration issues: If you are working with a number of data sources, then it’s important to understand the integration requirements of your analysis needs. Some AI data analytics tools may have integration issues with other apps and data sources, which can be a challenge for businesses looking to combine CRM, website and product data, for example.
  3. Learning curve: This is the biggest point Semetrical would highlight. As analytics experts, we are familiar with hundreds of tools, but as with any new piece of kit, there will always be things to learn when first using a new tool. This can be a disadvantage for businesses that need to quickly analyse their data and generate insights without spending a lot of time on manual analysis (the requirement of many businesses).
  4. Pricing: The pricing structure of some AI data analytics tools may not be suitable for all budgets, as the more enterprise solutions are usually only viable for larger businesses (and budgets). With many of us operating on reduced budgets or needing to stretch what you have available, this could be a disadvantage for businesses that are looking for an affordable data analytics tool.

The team at Semetrical are using AI every day in order to speed up more manual basic tasks, but it will never replace the smarts of the team. That being said, however, we are utilising a wider variety of tools into our own internal processes whilst discussing more solutions with clients each day. 

As ever, our years of experience and human interaction with large data sets, being able to interpret context and understand seasonality will always bring value to our analytics offering for our clients. It’s just being supercharged with the integration of AI tools.


Have you got an analytics project you want some help with? Stuck with what tool to use? Or just want the technical support and strategic direction of a vastly experienced analytics team? Then we’d love to arrange a chat. Contact us today!

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