5 Top data tools application use cases for business

Data scientists and their teams engage in a wide range of data tools. They do it to structure and analyze data, identify patterns, and enable companies to make informed critical business decisions. In fact, over 90% of businesses name unstructured data as the number one problem.

Data has the capacity to transform businesses and impact all aspects of their growth. But, without the proper tools for the job, data remains an unused resource. Since more and more businesses have started to use these tools, it’s easy to spot some patterns. Here are the top data tools application uses for modern businesses.

What are data tools

Data tools is a humongous software category that includes tools for data discovery, collection, cleansing, validation, transformation, enriching, and analysis tools. Yes, the list is quite long, and one of these subcategories encompasses hundreds of different data tools. These data tools come with their own set of features, user interfaces, and pros and cons.

The only thing they have in common is that they have something to do with data. Depending on the features they ship out with, businesses use them for various purposes. Here are the most common use cases you can see across industries.

Data tools application for lead generation and market insights

It’s borderline impossible to find a company using one software tool. They often built custom-tailored technological stacks to help them achieve specific goals. Unfortunately, no tools can help a brand increase lead generation and get actionable market insights but data tools.

Businesses can use these tools to see what the competition is doing, which products and services their target customers prefer, and align their offers better to meet the demand in the current market. As a result, these companies can experience growth, as data-driven decision-making can help boost sales and increase profits.

Sentiment and behavioral analysis

Thanks to deep learning systems, machine learning, and artificial intelligence, every person leaves a trail of digital breadcrumbs one can follow. A company can discover what their potential customers do online, what they think about the brand, how they use similar products, and how they prefer to do their shopping.

This is where data tools for behavioral and sentiment analysis come in. This is one of the most common data science use cases in the business world. It helps companies identify usage and buying patterns, see how their customers’ opinions change over time, and identify factors that lead to changes.

Thanks to the actionable data, companies can react in time, make relevant changes, and strengthen their position in the market. Most importantly, companies can maintain a good brand image by quickly identifying negative mentions and addressing them accordingly.

Detection of anomalies and fraudulent entries

Imagine having peta, exa, or even zettabytes of data to validate, transform, and analyze? Now imagine that, plus having to deal with a live stream of petabytes of data. It’s literally impossible to make use of huge data sets without data tools. Errors and anomalies can easily sneak past you, and some of them can be quite costly for your business.

Yes, you can end up making important business decisions on false data, but data anomalies such as fraudulent entries can literally cost you money. Enter data tools for anomalies detection. These tools are commonly used in the financial sector to process transaction data and detect fraudulent spending behavior.

These tools can also help prevent cyber-attacks which are increasing year over year. They can alert administrators of critical changes and help them prevent destructive and often quite costly cyber attacks.

Accurate forecasting

It would be nice to have a tool that can analyze the complete set of business data and spit out accurate predictions. These tools do exist on the market. Have you ever heard about predictive modeling? It refers to leveraging the existing data to predict the developments in the future with great accuracy. Data tools used in this instance are quite sophisticated as they employ both ML and AI.

Businesses can now benefit from models able to predict all sorts of developments, including market demand, risks, equipment malfunction, and customer behavior. You can see this specific data tools application across verticals.

Manufacturers use it to predict downtimes for their critical equipment; transportation giants use it to forecast maintenance needs; energy brands use it to forecast equipment reliability. All business forecasting is powered by data tools. In fact, big data analytics even helped manage and control the Covid-19 pandemic.

Sophisticated personalization systems

Another common data tools application use is personalization. As more and more industries adopt a customer-centric paradigm, the need for personalized offers increases. We are not talking about products and services here but the entire customer experience. It becomes increasingly important to know how to approach every customer, what product or service to offer, and when to do it.

This is a task that no one can do manually. Well, it can be done manually, but it would be resource-heavy and time-consuming, making it simply not feasible in the long run. There are simply too many customers, and each one of them has a unique personality, needs, and preferences.

With data tools, this process becomes streamlined. Data tools help companies collect, record, store, and analyze data on their customers/users. It all happens almost instantly, and customers receive personalized offers in real-time. This initiative delights customers and can save a lot of money for companies.

The future of data tools applications looks very bright. Organizations across industries see a huge value in big data, but to harness its power, they need a proper toolset. Given the potential of big data and the capabilities of data tools, it’s safe to say that we will see more data tools implementations in the future.