Top 8 Data Analysis Companies
Data analysis companies provide invaluable insights for growth strategies, product improvement, and market research that businesses rely on for profitability and sustainability. In this article you will find some of the world’s top data analysis companies that can analyze data powerfully, are each brilliant in their space, and are apt for different businesses - growing, medium-sized, and large enterprises.
You will also see practical data analysis examples from popular brands to understand how a top machine learning (ML) platform seamlessly extracts information from a plethora of data sources and types to provide essential insights for critical decision-making.
What Are The Top 8 Data Analysis Companies?
There are several top data analysis companies, however here are 8 that stand out. **1. **
Amongst data analysis companies**,** Repustate provides one of the most accurate text analytics solutions on the market. It has a powerful, named entity recognition (NER) capability for high-precision entity extraction. Its pre-trained, industry-specific machine learning models easily provide topic detection and sentiment analysis for all types of data and functions. These include enterprise research, social media sentiment analysis, TikTok insights, news monitoring, etc. to give you trends, based on which you can draw conclusions.
Its advanced user-friendly dashboard, the Repustate IQ, allows you to create your own sentiment rules without coding. You can also set alerts and notifications for hashtags, keywords, or other criteria for brand monitoring. It offers support for 23 languages, which are enviably powered by their own native part-of-speech taggers. Unlike other NLP tools, Repustate analyzes data in the language the data is in. This is another reason for its peak performance in providing aspect-based, actionable insights.
It can be integrated with PowerBI or Tableau for more targeted and powerful sentiment insights. You can also integrate it with your CRM tools or even with SurveyMonkey for granular data mining.
2. Google Cloud NLP
Google’s data analysis solution, Google NLP, is a great choice for software developers and data analysts at large organizations. The solution needs technical expertise and can extract topics and sentiment from social media, chatbots, and other documents. It provides results from which you can get key insights from your data.
However, it does not have spelling correction or text extraction. This means that if, for example, you need to process social listening on Instagram, you will need to manually go into the comments to extract the key phrase for further analysis for accurate interpretation. You can customize labels using your own training data. This means that if you have your own team of technical analysts, you can alter the model based on your requirements. The solution has great support, which is expected from Google. It works best with the English language.
3. Amazon Comprehend
Amazon’s data analysis tool, Comprehend, can analyze data in English, German, Italian, French, Portuguese, and Spanish. It can provide excellent sentiment analysis for audience sentiment automatically, both in real-time as well from historical data. It offers entity extraction and is good for categorizing text based on topics. You can analyze emails, feeds, social media data, reviews, and other sources.
Like Repustate, Comprehend offers custom classification, syntax analysis, as well as the ability to analyze video content. However, it cannot analyze any captions or text overlays in the video, which means that it can miss out on crucial information.
4. Microsoft Azure Cognitive Services
Microsoft’s Azure Cognitive Services is another great choice among top data analysis companies. It analyzes unstructured data from various data sources and data types easily. It identifies and extracts numerical entities like percentiles, dates, etc. effortlessly and recognizes key phrases to provide insights on customers, market trends, and other business functions.
Its entity extraction capability is not very strong for classifying people, which can be a hindrance in tasks such as social media sentiment analysis where names are important for contextual reference. It’s slow compared to Google and Repustate but can be easily integrated with a number of industries such as aeronautics, healthcare, and others.
Dandelion’s text analytics solution, Dandelion API offers great data analysis. It can extract entities such as brands, persons, locations, and others, and conduct emotion detection to provide customer opinions for business strategies. It can analyze 7 languages including English and can identify and understand social media listening data and news articles with ease.
Its natural language processing (NLP) capabilities are respectable, although it cannot conduct deep scrubs for fine-grained analysis. For example, it cannot resolve Twitter usernames from their personal profiles and has certain issues with grammatical constructions. However, it is great for data analysis of other unstructured texts such as online publications or review sentiment analysis.
TextRazor allows you to evaluate data by inspecting, cleaning, and processing data to provide you with useful insights. It allows entity extraction, linking, automated topic tagging, and classification, and provides support in 17 languages. It can analyze several text data sources and provides speedy insights for intelligent decision-making.
The company is located in the UK but the product is on a completely self-sustained, pay-as-you-go model. The TextRazor API can integrate with any source and language, provided that it can send an HTTP request and parse the JSON response.
Aylien is among the top data analysis companies specializing in news research through computational analysis. It can aggregate and analyze news feeds from over 80,000 resources to provide valuable insights. It has an app, especially for news analysts and researchers, and another news data-as-a-service. It can recognize 5.6 million entities and more than 4,500 industry tags, ensuring that you can get as much coverage from news data sources as possible.
You can investigate the reports for time series and trend analysis and get instant insights from real-time news data. You can share the news data onto the apps and share reports with your clients for brand monitoring, market trends, and other functions.
spaCy, unlike the other data analysis companies in this list, is an open-source library. It provides entity linking, text extraction, text classification, and other features, to analyze sentiment and derive insights from text. Despite offering some of the best data analysis capabilities powered by impressive NLP, it remains accessible to the public for free.
It can identify important words from text data and tag them along the right path and parts of speech, and it does so for 20 languages. It can get a bit complicated to set it up if you’re not technically savvy or familiar with Python. Yet, despite this small glitch, it can be reasonably good for research and an asset as a teaching tool.
What Is The Importance Of Data Cleaning?
Incorrect data cleaning can have a detrimental effect on your insights. This is regardless of the type of data you are using, which includes text, audio, or video. The quality of data is critical to data mining. Data quality is not only equated to the data source but also to how properly it has been cleaned and prepared for data analysis. Incorrect insights for your marketing, employee analytics, or any other business needs can lead to the risk of faulty strategies that can cost a fortune to rectify.
That’s why it is important to make sure that the data is rigorously cleaned so that there is no redundant, incorrectly formatted, incomplete, corrupted, incorrect or outlier item in your data. It is only then that you will have accurate results, whether you are looking for TikTok insights or for news sentiment analysis, or data mining for any other purpose.
How Can You Select The Best Data Analysis Company?
All data analysis companies have a benchmark to follow in order to remain viable in the market. So how do you choose the one that is the best fit for you? Here are some key aspects that the best tool must have so you get the best results and the best return on your investment.
1. Topic & Entity Extraction
Topic and entity extraction is a very important part of data mining. It is the first step before analyzing sentiment. The more entities and topics the data analysis tool can recognize, the more in-depth your analysis will be. That’s why the data analysis tool you choose must have a very robust topic extraction and named entity recognition (NER) capability.
2. Aspect-based sentiment
Only aspect-based sentiment analysis can give you any real insights that you can use to improve your business. With a tool that can give you both, an aspect-based sentiment score, as well as overall sentiment from your data, you will have the ability to see which areas that need immediate attention and how you can deal with them.
3. Semantic clustering
Semantic clustering is important because the tool can differentiate between similar-sounding words and words with similar meanings so that there is a contextual frame of reference. This increases the accuracy of insights during data mining.
It is important that you get accurate insights regardless of the data source. Unlike Repustate, most companies cannot provide in-depth data analysis, even among those mentioned above. Most data analysis companies are great at some data sources but fall short in other categories. To get the most out of your investment, you need to make sure that the solution you choose covers all the data sources that are important to you.
5. Processing speed
Time is of utmost importance when it comes to data analysis. Your data analysis solution must be able to analyze terabytes of data in minutes and not lag due to overload. Similarly, like Repustate, it should be infinitely scalable so that you don’t have to splurge when your data requirements grow.
You should use a pre-trained, industry-specific model so that you get the best results from data analysis. Industry-based models have tags that are specially built for your business and therefore help in providing more accurate results.
7. Multilingual capability
Even top data analysis companies provide multilingual text analysis, but through translations. You should opt for a tool that has native natural language processing (NLP) capabilities with language-specific speech taggers so that your data does not get diluted with machine translations that are rife with inaccuracies.
8. Social media data analysis
You should be able to analyze TikTok social listening data as easily as you get customer feedback analysis insights so that you don’t have to use two APIs for data mining. Based on what your marketing strategy is, this is an aspect you must check carefully.
9. Audio & video content analysis
Analyzing audio and social video content is an important part of a multi-pronged marketing strategy so that you can analyze brand videos, podcasts, user-generated social media videos, and other data sources for brand insights, find TikTok influencers, customer trends, and so on.
10. Data visualization
You must be able to see your insights on a user-friendly visualization dashboard so that you can derive conclusions from your data mining. If you already have a tool with a visualization screen like Power BI then your data analysis solution must be able to integrate with it.
What Are Some Practical Data Analysis Examples?
Here are some top real-life data analysis examples from across various industries and data sources.
1. Hospitality - Disney World Resorts
We analyzed data from social media and review platforms like Google and Reddit for Disney World Resorts to explore market sentiment. We noticed that there was a lot of dissatisfaction among people. They were unhappy with the services at Disney and yet many seemed to have been second or third time visitors at the resort.
On analysis, we saw a stark difference in the sentiment scores between users of Reddit, a more detailed, fan-based forum, and Google reviews, used mostly by casual holidayers at Disney World. Even though both sets of customers complained about expensive tickets, inefficient staff, and unclean bathrooms, Google users gave the resort a much higher rating than Reddit users. This was not surprising given the different kinds of audiences each platform has.
2. Influencer Marketing- Cosmetics Industry
Data analysis companies can help you transform your social media and influencer strategies. To find the right fit for your Influencer campaign, you need to know how well an influencer’s content matches your brand, how frequently they post videos, how long it takes for audience engagement, and so on. Keeping these factors in mind, we used Repustate IQ to analyze data through TikTok social listening. We used #beautyhacks and analyzed all the videos that popped up on the feed.
On close analysis, we were able to find several TikTok content creators who were using the hashtag. We measured all the parameters that were critical, including the ones mentioned above, and were able to narrow down a list of 5 TikTokers who had the most followers and engagement.
3. Market Research - Opinion Mining From Financial News
Top data analysis companies can mine candid public opinion for brand reputation and investment purposes. In fact, data analysis of news sources is critical in the financial sector. Using Repustate IQ, we researched public opinion on the metaverse, following Facebook’s announcement of changing its name formally to Meta, in tow with its billion-dollar investment in the Metaverse.
Repustate IQ made some very interesting discoveries, which were in tune with several news sources. These included the fact that while many were optimistic about investment in the Metaverse, several were skeptical of Facebook’s spend on the space, in its bid to mimic a giant like Roblox. These fears, we saw later, were found not unfounded.
4. Brand Monitoring - MAC Lipsticks
To conduct brand monitoring, we chose the MAC lipstick created by Jordan-based make-up artist Hindash who won MAC’s widely-covered international competition. Public sentiment, we found, was very positive towards the new lipstick itself but negative towards the MAC brand. This seemed strange.
On further investigation, we found that market sentiment was negative because customers were not able to find the product in stores as they were all sold out. The brand had obviously underestimated the popularity and demand of the product due to inefficient market research, and this has affected the supply chain and customer sentiment.
5. Sports & Entertainment - In-Stadium Experience @ Maple Leafs Game
We analyzed data to understand public sentiment about people’s in-stadium experience at a Maple Leaf’s game. We used the #leafsforever on TikTok and Twitter to see the difference, if any, in sentiment and also to get a more balanced analysis. The hashtag pulled up more than 4000 texts. A detailed analysis showed that the audience was generally happy with their in-stadium experience, which was compounded by their love for their favorite team.
However, parking issues, expensive ticket prices, food and beverage, and other factors were high in negative sentiment, which meant there were areas that needed immediate attention from the stadium management. These are important insights you can only get through aspect-based sentiment analysis of data to analyze customer experience.
Data analysis companies use machine learning to automate data collection, cleaning, and transformation so you can extract critical business insights for growth strategies. These intelligent business intelligence tools with top data analysis capabilities provide you with powerful ways to approach your needs to build a better, more efficient business.
Platforms like Repustate IQ use native NLP algorithms for multiple languages and have comprehensive neural networks and NER capabilities. That’s how they provide fine-grained, aspect-based sentiment to give you market trends, customer experience drivers, and other key insights that can boost your operations.