7 Data Science Applications and 6 Data Science Tools to Drive Your Data Journey

Data science has developed and produced a variety of new applications that provide better insight and value to small businesses. Data science applications, methodologies, tools, and technologies can give organizations the ability to extract valuable information from increasing amounts of highly variable data. Data science is an applied science that helps businesses get more out of the information they own.

Artificial intelligence and big data tools provide the ability to analyze large amounts of data and decide using applications such as predictive modeling, pattern detection, anomaly detection, and personalization. Data science and the data scientists who perform it are now a key part of business operations.

Although there are many types of organizations that implement analytics applications driven by data science, many focus on areas that have been proven to be valuable over the past decade.

Businesses can get competitive advantages over their competitors, better service to citizens, patients, and users, and the ability to adapt to changing business environments that require continuous adaptation.

Let’s inspect seven data science applications that are common:

1. Anomaly Detection

A powerful tool for data science is statistical analysis, which can spot anomalies within large data sets. It might seem easy to group data and identify outliers with small data sets, but this task is much more challenging for organizations that have to analyze petabytes to exabytes of data.

Financial services companies are increasingly being challenged to spot fraudulent spending in transaction data, which continues to grow in volume and variety.

American Express was a pioneer in the application of data science techniques to big data in real-time for fraud detection and other purposes.

This enabled them to respond quickly to changes and events. Also, anomaly detection can eliminate outlier values from data sets in order to improve analytics accuracy.

2. Recognition of Patterns

Identifying patterns within data sets is a fundamental project in data science. Pattern recognition is a tool that retailers and e-commerce businesses use to spot patterns in customer buying behavior.

Organizations that want their customers to be happy and keep them coming back for more are going to need to make sure they have reliable supply chains.

Data Science Pattern Recognition

Amazon and Walmart use data science to identify purchasing patterns. Both companies seek unexpected correlations that can lead to more efficient purchasing, inventory management, and marketing strategies.

There are many other data science applications that we can use pattern recognition for. It can aid in stock trading and risk management, as well as the diagnosis and treatment of medical conditions.

3. Predictive Modeling

Data science is not only about spotting patterns but also about outliers. It aims to improve predictive modeling accuracy.

Data science extends to predictive analytics, a form of business analytics. It applies machine learning and other algorithmic approaches to large data sets to improve decision-making capabilities.

This includes creating models that can better predict customer behavior, market trends, and financial risks.

Data Science Predictive Modeling

A wide variety of industries use data analytics and predictive analytics applications, such as financial services, government, manufacturing, healthcare, and travel. To reduce equipment downtime and increase production, manufacturers employ predictive maintenance systems.

Airbus and Boeing also use predictive maintenance to increase their fleet availability. Chevron and BP, as well as other companies in this sector, use predictive modeling to increase equipment reliability in environments where maintenance can be expensive.

Organizations are also using data science’s predictive power to improve their business forecasting. Faced with sudden changes in consumer spending and retailer purchasing patterns, formulaic buying strategies by retailers and manufacturers failed.

These brittle systems are now being replaced by data-driven forecasting apps that can better respond to changing customer behavior in forward-thinking companies.

4. Personalization and Recommendation Engines

Customer and user satisfaction are usually highest when products or services are customized to meet their needs and interests. A great learning is that this is especially true if the customer can receive the right product at just the right time through the right channel with the right offer, communicated with the right message, and the right level of attention. Customers are far more likely to return if they are satisfied and engaged.

In the past, it was difficult and expensive to customize products and services to individual needs. Thus, systems that recommended products or personalized offers grouped people into buckets that were generalized to their characteristics.

This approach was far better than no customization at all, but it was not optimal.

Data Science Personalization Engine

Thanks to the use of machine learning, statistical learning, data science, and big data, organizations can now create detailed customer profiles. Over time, their systems can learn people’s preferences and match them with others who have similar preferences - an approach known as hyper-personalization.

Companies such as Home Depot, Lowe’s, and Netflix use hyper-personalization techniques driven by data science to better focus their offerings on customers through recommendation engines and personalized marketing.

Many financial service firms also offer hyper-personalized services to customers. Healthcare organizations use the approach to deliver treatments and care to patients, and educational institutions provide highly customized, adaptive learning for students.

5. Categorization and Classification

Data science tools can sort large amounts of data and classify or categorize it based on learned characteristics. This is useful for unstructured data. Structured data can be searched and accessed through a schema.

Unstructured data, however, is more difficult to process and analyze. Unstructured data includes emails, documents, images, videos, text, and binary information. Mining that data to gain valuable insights was an arduous task until recently.

Deep learning has made it easier for organizations to perform unstructured data analysis. This includes image, object, and audio recognition tasks, as well as classification of data based upon document type. Data science teams can train deep-learning systems to identify invoices and contracts among stacks of documents.

Data science is also being used by government agencies to classify and categorize data. NASA uses image recognition to provide deeper insight into objects in space. The U.S. Bureau of Labor Statistics automates the classification of workplace injuries based upon an analysis of incident reports.

6. Analysis of Behavior and Sentiment

Data scientists use machine learning and deep learning to analyze data and find out what customers and users think and how they behave.

Data science allows organizations to identify buying patterns and use data to analyze behavioral and sentiment characteristics.

Data Science Sentiment Analysis

This helps them understand how customers feel about their products and services, and what they think of the experience. These applications can categorize and track customer behavior.

Travel and hospitality businesses have adopted this high-powered method of sentiment analysis to identify customers who have had negative or positive experiences.

Law enforcement agencies are also using sentiment and behavior analysis to identify incidents, situations, and trends as they arise and develop, for example, by analyzing social media posts.

7. Conversational Systems

Machine learning was first applied to chatbots that could communicate with humans in a way that was almost lifelike.

The Turing Test was created in 1950 by computing pioneer Alan Turing. It uses a conversational format for indicating if a system can imitate human intelligence.

It’s not surprising that many organizations are turning to chatbots to augment their existing workflows and to take over tasks that were previously performed by humans.

Data Science Conversational Systems

Data science is a great tool for making conversational systems more useful to businesses. Data scientists use machine learning algorithms to train these systems using large amounts of text in order to derive conversational patterns out of the data.

Chatbots, intelligent agents and voice assistants are popping up all over the place, from websites and phones to cars. They can interact with people via text and voice, such as to help them find information, process transactions, and provide customer support.

Data Science Applications are the Future

We have already applied data science in many areas. It combines big data management, statistics, machine learning, and data wrangling to produce significant results. The applications that data science tools and techniques enable will continue to grow in the enterprise.

Developments in neural networks, cloud computing, natural language processing, and AI models are speeding up the use of data science applications today.

While the CIO/CTO is the primary data scientist and the most important technology role in an organization, the increasing prominence of the chief information officer (CIO), often responsible for data science initiatives and other functions, shows the business value of having a solid grasp of data.

The ability to use data science applications and data analytics to uncover critical insights and business knowledge may be more important than the operations that generate it. It is data that fuels modern enterprises.

Data Science Applications for Small Businesses

Companies are competing to have greater success by using data-related technologies. There are many tools available to help a small business keep up with its competitors. The size of your company, the tasks being considered, ease of use, and budget will all play a role in determining the type of tools you choose.

Data Science Applications for Small Business

There are various commercial tools for many applications, including sales forecasting and sales management, website traffic analytics, data analytics, audience segmentation, and business intelligence.

Here’s a list of the top data science tools that small businesses can use. These tools are very useful for small business growth.

1. Google Analytics

Companies that want to improve their websites can use the analytical and statistical tools from Google. Google Analytics ‘ free analytics platform gives companies many insights into the user traffic on their websites. Google Analytics features include:

Information about traffic reports. This allows you to monitor user trends and understand them.

It allows you to monitor conversion points and see which referrals brought traffic to your site.

Google Analytics allows you to analyze keywords the user has used to search for your site.

Google Analytics embeds a tracking code on websites to allow you to know more about visitors to your site and to use statistical analysis to uncover important insights.

2. DataChat

DataChat is a built-from-scratch analytics platform that employs Guided English Language (GEL). The platform allows a variety of users to do everything needed in their data journey, including data wrangling and preparation, visualization, exploration, and predictive modeling.

DataChat’s use of common words and phrases to drive the platform makes it unique amongst other data science tools. It allows business users that are not data scientists to perform complex data functions and work.

For a small business that doesn’t have the budget for a data science team, this ease of use makes DataChat ideal for the smaller enterprise.

2. Qlik

Qlik Sense is a data analysis tool geared toward Business Intelligence. Using Qlik Sense, you can combine all your data sources into one place, and it gives you the ability to analyze it all in one go. Interactive visualizations and drag-and-drop properties are available.

The Qlik Active Intelligence Platform uses a machine-learning algorithm that allows you to close the gaps between data, insights, and action. Qlik Cloud integrates data services, analytics services, and foundational services all into one platform.

Qlik is a cloud platform without cloud vendor lock-in. It allows you to benefit from an open SaaS platform with cloud-agnostic and hybrid deployment options that offer maximum choice and flexibility in how and where you deploy, store and analyze data, across one or multiple clouds.

3. Infor Birst

Infor describes its Birst product as democratized analytics infused with artificial intelligence. Its industry analytics deliver relevant and meaningful insights for users from the boardroom to the shop floor.

Infor Birst makes business intelligence and analytics easy to consume with pre-built industry and role-specific content and metrics embedded wherever business users need information.

Birst’s Networked BI is a fresh approach to delivering data-as-a-service (DaaS) because trusted and well-governed data is not at odds with speed and ease of use. Birst uses an adaptive user experience, supporting all styles of business intelligence and analytics.

Its user experience includes visual data discovery, interactive and responsive dashboards, pixel-perfect enterprise reporting, native and offline mobile, and other important features.

4. Tableau

Tableau is an interactive data visualization platform owned by Salesforce. It focuses primarily on business intelligence and can query relational databases, online analytical processing cubes, cloud databases, and spreadsheets to generate graph-type data visualizations. The software can also extract, store, and retrieve data from an in-memory data engine.

Tableau has a robust mapping functionality and can plot latitude and longitude coordinates and connect to spatial files like Esri Shapefiles, KML, and GeoJSON to display custom geography.

It is possible to deploy Tableau in the cloud, on-premise, or natively integrated with Salesforce. It is possible to connect all the data with fully integrated artificial intelligence and machine learning capabilities, governance and data management, visual storytelling, and collaboration.

5. TIBCO Spotfire

Another data science program, TIBCO Spotfire, is a complete analytics solution, enabling all users to explore and visualize new discoveries in data through immersive dashboards and advanced analytics. Spotfire analytics can deliver at scale, including predictive analytics, geolocation analytics, and streaming analytics.

The platform allows you to get richer insights with AI-infused visual analytics. Users can combine historic and streaming data to predict trends via data science and embedded analytics.

The Spotfire Mods framework enables users to build a machine learning algorithm and tailored analytics apps to get all the power of Spotfire software in custom fit-for-purpose analytics apps.

6. Microsoft Excel

No discussion of data science tools, data mining, or data engineering is complete without a mention of the workhorse spreadsheet platform known as Excel. It is the most widely used tool for small-scale analytics. It includes spreadsheet functions, calculations, data wrangling features, graphing, pivot tables, query builders, and BI functions.

In Conclusion

Our discussion of the seven common data science applications provides an insight into how data science is being employed by data scientists today. We discussed six good data science tools that businesses can use to guide the data science journey.

There is data everywhere these days. It is estimated at roughly 2.5 quintillion bytes of data are created every day. With the growing popularity of the Internet of Things (IoT), this data creation rate is bound to grow significantly.

And, just to add to this staggering number, it is estimated that there were approximately 44 zettabytes of data in the world in 2020. At the current rate of growth, this number will probably exceed 175 zettabytes by 2025.

Multiple-byte Units

To continue with our computer science data trivia, Google processes over 20 petabytes of data every day. This includes around 3.5 billion search queries.

If you are interested in a discussion about how your business can get the most from your data, how to find a data scientist or data analyst, or want to learn more about data science applications, the insights team at Asymmetric, a global growth agency, will be happy to help guide you on the path to success.

Asymmetric Applications Group has been recognized as one of the Top Advertising Agencies in Wisconsin by DesignRush

Use These Ideas in Your Business

Asymmetric, led by former Army Delta Force operator and corporate executive, Mark Hope, can help you implement these ideas in your business. You can contact Mark by email at mark.hope@asymmetric.pro, or by telephone at +1 866-389-4746, or you can schedule a complimentary strategy discussion by clicking here.  You can read all of his articles on Medium.

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Mark Hope - Asymmetric

Mark Hope

Mark A. Hope is the founder and CEO of Asymmetric Marketing – a unique agency specializing in building high-performing sales and marketing systems, campaigns, processes, and strategies for small businesses. Asymmetric has extensive experience with organizations across many industry segments. If you would like some help in implementing ideas like these in this article, feel free to give Mark a call at 866-607-3593 or by email at mark.hope@asymmetric.pro.

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