The Data Revolution: How AI is Transforming Analytics to Increase Efficiencies and Productivity

Data is revolutionizing the world. It’s estimated that by 2025, 463 exabytes of unstructured data will be created each day globally. For context, that’s the equivalent of 212,765,957 DVDs per day.

Within that data hides invaluable insights into consumer and competitor behavior, motives, challenges, and needs—all of which can lead to more informed decision-making. In fact, companies that inject data analysis into their operations outperform their peers by 5% in productivity and 6% in profitability.

Accessing, integrating, and processing all of this raw data to unlock the right insights, however, can be a massive undertaking.

That’s why more and more companies are taking advantage of the data revolution by leveraging artificial intelligence (AI) in their data analytics to find predictive and valuable patterns more efficiently. AI has streamlined the time-consuming and manual processes traditionally associated with data analytics, helping businesses unlock deeper insights and make better decisions in real-time.

To understand the reciprocal relationship between data analytics and AI, we define each—and how they work together to turn reams of data into actionable rules, decisions, and predictions that help your business get ahead.

What is Data Analytics?

In simple terms, data analytics can be defined as the process of transforming raw information into conclusive outcomes that support strategic decision making.

Data analytics can also be termed as a subset of usable data gathered from data mining, which delivers large quantities of unstructured raw data. Data mining helps find inconsistencies and specific patterns and correlations within large data sets to predict outcomes. Using a broad range of mathematical techniques, this “processed” information can be used to cut costs, improve customer relationships, increase revenues, reduce risks, scale and more.

What is Artificial Intelligence?

AI is not a new concept—it has been around the IT landscape for several decades. In recent years, it has become even more prevalent with the advent of cloud-based services. In fact, Gartner states that by 2023, cloud-based AI will increase 5x from 2019, making AI one of the top cloud services.

But while most organizations are aware of AI, many are still unaware of its full range of benefits and capabilities. For example, one of AI’s unique attributes is its ability to continuously learn and improve. This improvement comes from the AI processor learning from its own performance and new data sets. This allows product managers and developers to create innovative and leading-edge products and services that are not included in an average IT budget. By drawing on new advances in AI and machine learning, technology firms are able to develop mature systems capable of processing data in real-time.

How is AI Revolutionizing Data Analytics?

Every second, large amounts of data are being produced and exchanged over the internet and IT environments, making it challenging to filter the noise from the valuable information. Even more difficult are the massive repositories required to store this data, which become redundant quickly. To stay competitive in today’s fast-paced, customer-centric environment, IT enterprises need to learn how to derive useful insights from all that data—and how to do it quickly.

That’s where AI-driven data analytics comes in. The main focus of data analytics with AI is to drive efficiency for data management and presentment. When paired together, it allows businesses to leverage algorithms that quickly identify and extract the actual business value from data, subsequently generating a better and more engaging human experience. This yields advanced analytics that will:

• Discover deeper insights
• Predict future outcomes
• Generate recommendations to improve business practices
• Drive changes
• Unify cross-platform data to accelerate innovation through data engineering and data management

How Is AI-Driven Data Analytics Used?

AI technology has advanced the way data is read and used. It renders precise and focal data faster than ever before. Here are a few areas where AI is commonly applied.

• Data Analytics Software – Think business intelligence gathered from data analytics software that identifies patterns and formulates data relationships. This paves the way for actionable alerts, smart data discovery and interactive dashboards by using a comprehensive set of data analytics software on an enterprise-grade analytics platform.
• Machine Learning and Predictive Analytics Platform – A machine learning platform lets you analyze structured and unstructured data stored in data management platforms and external sources. AI and open-source data analytics platforms combine open-source machine learning with self-service analytics and predictive analytics to achieve data intelligence.
• Natural Language Processing (NLP) and Text Mining – Unstructured data holds stories, sentiments, emotions of your customers, employees and stakeholders. NLP and text mining extracts terms and concepts from brochures, legal documents, emailers, social media messages, videos, audio files and web pages to unlock the value hidden in unstructured text to yield valuable business insights.
• Interactive Visualizations – Interactive data visualizations and rich interactive dashboards are the significant takeaways from intelligent analytics, which help enterprises know their data more personally.
• AI Solution for Sentiment Analysis – Intelligent data analytics helps an enterprise understand and highlight people’s perceptions about specific products and services over social networks and the web. Smart analytics is thus a blessing to enterprises for targeted customer servicing, customer engagement and retention.

How Do Different Industries Apply AI to Their Data Analytics?

By using AI, experts can mine and process data and then convert it into qualitative insights. Here are a few real-world use cases of AI technology combined with predictive data analysis, and how businesses use both to fuel growth and enhance the customer experience:

• Retail – AI systems calculate credit scoring and predict the outcomes of giving loans to automotive customers. Retailers can use AI to adjust the pricing of products in real-time using predictive model applications. AI offers car dealers with accurate data on predicting a car equipment failure so that they can provide after-sales service just when needed.
• Financial – The financial services enterprises are using AI capabilities to analyze and gain a better understanding of how account holders spend, invest and make financial decisions to customize the advice they give customers. AI utilizes data to suggest products to consumers based on a variety of criteria, including past purchases, demographic information and search history. AI systems provide helpful tools for navigating the decision-making process involved in investments and risk assessment.
• Healthcare – Google was one of the initial trendsetters of using AI analytics in the healthcare domain with its Google Flu Trends (GFT) analyses. The online search tool predicted flu patterns and provided real-time estimated data about influenza for a specific region. Online pharmacies are using AI data analytics to understand customers’ health issues, prescriptions, dosage, the amount of time before refilling medicine, preferred brands and more.
• Weather Forecasting – The accuracy levels needed in weather forecasting have increased two-fold compared to the last decade. Governments and agencies can now warn citizens and take necessary steps ahead of hurricanes, floods and other natural calamities by using AI data analytics information. Atmospheric and geographical data is collected via satellite and then added into weather forecasting models that predict the weather changes in the coming days.
• Marketing – Organizations can study social media data by tracking user comments. The information gathered enables marketers to gain immediate feedback and understand consumers’ perspectives about their brand, allowing brands to innovate and market their products more effectively.

The Future of AI and Data Analytics

In the coming decade, we will witness revolutionary changes in the way we manage data, as customer preferences evolve, devices become smarter and data repositories continue to grow.

AI-blended data analytics aims to make the enterprise more efficient and productive, thereby increasing its brand loyalty, driving revenues and eliminating the need for manual data processing mechanisms. With customized business insights that are accessible and relatable to the most critical objectives of the enterprise, it’s clear that intelligent data analytics is here to stay.

Learn how Navisite’s Data Analytics Services can help your business unlock powerful insights and uncover the strategic answers you need to get—and stay—ahead.