Proper data management fosters an environment where information can benefit the entire organization. By handling data effectively, issues caused by poor-quality data, like unnecessary hurdles, inaccurate forecasts, or difficulties in accessing information, can be addressed proactively, reducing their impact before they arise.

Managing data is a demanding process that requires cleaning, extracting, integrating, cataloging, labeling, and organizing data and defining and executing various data activities. These responsibilities often lead to challenges for data specialists and other team members whose roles aren’t directly tied to data.

Artificial intelligence has proven its value in countless areas, though one of its less flashy yet highly impactful uses is enhancing data management. AI significantly contributes to five key aspects of data management:

  • Classification: AI aids in gathering, extracting, and organizing data from various sources like documents, images, handwriting, and more.
  • Cataloging: It simplifies the process of discovering and accessing data.
  • Quality: By minimizing errors, AI improves the reliability of data.
  • Security: AI protects data from malicious actors and supports compliance with laws, policies, and cultural practices.
  • Data Integration: It facilitates the creation of unified, comprehensive data sets by combining and managing disparate lists.

The sections below explore each of these areas in detail. Additionally, we’ll examine the landscape of AI vendors and highlight humans’ indispensable role in data management.

Classification

Data classification and extraction cover a wide range of tasks, and this area has expanded significantly with the rise of digitized media and the dominance of images and videos on social media platforms. Content moderation to detect inappropriate material at scale would not be feasible without AI. Key processes in this domain include classification, identity or entity resolution, matching, and data extraction.

For decades, basic AI technologies have supported tasks like optical character recognition (OCR), enabling the extraction of important details from documents such as bank checks or addressed envelopes.

Today, OCR is so commonplace it’s rarely even seen as AI anymore. However, advancements in AI, particularly through deep learning models, have expanded these capabilities, making it possible to accurately interpret human handwriting.

Much critical data resides in rigid formats like PDFs, faxes, and lengthy word-processing files. It first needs to be extracted to use this data, whether for analysis, answering questions, or deeper insights. For example, in healthcare, information is still commonly sent via faxes, and retrieving it has traditionally consumed extensive human effort.

One example is a program developed by an electronic health records company, which uses AI to automatically extract data from faxes and integrate it directly into EHR systems, significantly reducing time burdens.

Similarly, AI applications are adept at extracting important clauses from contracts, making them invaluable tools for professionals such as lawyers and auditors. For a better understanding, enroll for a doctor business administration online degree and see the impact of AI on data management firsthand.

Cataloging

For years, businesses have struggled to pinpoint where critical data is stored across their systems and records. The rise of data cataloging in recent years has provided a significant tool for managing this challenge. However, building and maintaining these catalogs has traditionally required a significant manual effort.

AI offers a solution that automates the scanning of various data repositories and generates catalogs. It can extract metadata from system documentation and map the data lineage, tracking its origin, creators, modifications, and current location.

Even with the simplicity AI brings to catalog creation and data lineage tracking, businesses still face the complexity of their existing data landscapes. Many have hesitated to create catalogs using traditional methods, either to avoid exposing the chaotic state of their systems or to postpone the effort until their data was better organized and higher in quality. With AI, however, cataloging becomes more efficient and less daunting, enabling businesses to enhance data access while integrating ongoing improvements into their data management practices.

Quality

Data quality tools use controls, often through business rules, to specify the range of acceptable data values. For example, a date that includes a day and month has only 366 possible valid combinations, meaning “Jebruary” isn’t a month, “35” isn’t a valid day, and “February 31” isn’t a proper combination. Setting up, coding, and maintaining these business rules can be a complex and time-consuming task, an area where machine learning-based AI provides notable benefits.

AI tools can examine data to detect invalid entries, automatically correcting some while flagging others for manual review. Several providers advertise that their tools leverage machine learning for these purposes. Beyond this, AI can improve data quality by enriching it with information from external or internal databases, predicting values for missing data, and eliminating duplicates or rarely used records.

Vendors should consider shifting towards a more proactive approach to data quality management to further advance these capabilities. This involves preventing data errors at their source rather than focusing exclusively on fixing them later. Controls should be implemented as close to data creation as possible. Additionally, tools should tightly link data quality metrics to business outcomes and support methods like statistical process control and ongoing quality improvement.

Security

Ensuring data security and privacy remains a vital concern for every organization. Traditionally, tasks such as preventing hacks, breaches, or service disruptions have relied heavily on human expertise in data protection. Artificial intelligence (AI) now offers valuable support in many areas. For instance, AI excels in threat intelligence by:

  • Monitoring external activities
  • Analyzing threat signals
  • Identifying potential actors
  • Predicting harmful actions

This AI-driven approach addresses key challenges cybersecurity professionals face. These include the overwhelming number of threat actors, abundant fragmented data, and a persistent shortage of skilled personnel.

Advanced solutions leverage machine learning to streamline gathering security data from multiple internal and external sources. These tools transform unstructured data into usable formats and prioritize the most credible threats. AI can analyze historical attack patterns to predict possible attack paths and assess whether new threats originate from known or unfamiliar actors.

By combining rule-based systems with machine learning models, AI reduces the high rate of false positives generated by disparate security systems. This allows threats to be prioritized for further human review.

Unsupervised learning systems bring additional value by detecting anomalies within IT environments. These anomalies can include unusual access patterns or rare IP addresses connecting to the organization’s systems. Such methods benefit from operating without training on historical cybersecurity tactics, which are continually evolving.

AI also plays a significant role in identifying internal risks. These include fraud or regulatory noncompliance. This capability is particularly valuable in highly regulated sectors like banking and finance. By monitoring digital communications within the organization, AI can flag suspicious language or behaviors for closer examination. Of course, human oversight is essential to confirm any potential misconduct uncovered by these tools.

Data Integration

One of the most significant advancements AI has brought to data management lies in data integration, often called data mastering. This process involves creating a master or “golden” record, a single, most accurate version of a data element within an organization.

Businesses may need data integration for various reasons, such as dealing with multiple versions of critical data accumulated over time, repurposing transactional data for analytics, or consolidating databases after mergers or acquisitions. Historically, integrating and mastering data across large organizations has been an enormous undertaking, often requiring years of effort.

Traditional data integration relied heavily on master data management, which involved applying sets of business rules to determine, for example, whether different customer or supplier records should be merged into one because they represented the same entity. However, the complexity and high cost of creating and maintaining such extensive rules often led to projects being stalled or abandoned.

Today, machine learning-based mastering systems, like those developed by companies such as Tamr, have transformed this process. These systems use probabilistic matching techniques to identify whether records represent the same entity. If the system determines a high probability of a match, for example, 90% or greater, the records are automatically merged.

Human experts step in to review cases where the data cannot be confidently resolved. This approach dramatically reduces the effort and resources required for data integration, making it more efficient and attainable.

What is AI Predictive Analytics?

Artificial intelligence has become a standard tool for predictive analytics within business intelligence. Companies of all sizes leverage advancements in computer-based models, particularly AI-driven ones, to efficiently analyze data tied to sales, customer behavior, marketing strategies, and more.

Today, businesses have access to data in numerous formats, ready to be applied across various functions. While predictive analytics has been a part of business strategy for as long as data has been gathered, the integration of AI has significantly improved how quickly and effectively organizations can harness large volumes of information for actionable insights.

Distinction Between Artificial Intelligence and Predictive Analytics

While many people may not fully grasp AI, understanding predictive analytics and its relevance to your business is crucial. In data science, predictive analytics is about forecasting future outcomes. It involves examining past data to predict what lies ahead.

While predictive analytics existed before AI, traditional methods often required hours to process just a few hundred data points. Conversely, AI excels in predictive analytics by quickly gathering, organizing, and analyzing data. With AI models, solutions based on millions of data points can be generated in minutes. And predictive analytics is just one of the many areas where AI demonstrates its powerful capabilities.

Essential Elements of AI-driven Predictive Analytics

To integrate AI into marketing campaigns effectively, understand what AI is and what it can achieve. With this knowledge, questions like “how does predictive analytics work?” can be answered, opening the door to exploring AI’s capabilities further and crafting solutions tailored to a business’s specific needs. The elements used in AI for data analytics mirror those in other AI applications.

By understanding how these components interact, businesses can better identify ways to develop AI-driven solutions. Across all AI applications, the three essential elements are data, algorithms, and predictions.

Data

AI models rely on data for training. In business settings, this data might originate from customer records, sales logs, or information collected through a website. Regardless of its source, tracking data and understanding what a dataset includes are crucial for deciding how to use it effectively. For predictive models, whether AI-driven or not, businesses must access historical data to forecast future outcomes.

Algorithms

An algorithm is a set of steps designed to achieve a specific objective. Computers rely on algorithms, and artificial intelligence is no exception. Over the years, significant progress has been made in developing AI algorithms, including approaches like machine learning and deep learning networks. These algorithms are crucial for analyzing data and making predictions.

Without incorporating AI, predictive analytics relies on examining historical data to forecast future outcomes. However, when AI is introduced, the potential of predictive analytics expands significantly. 

AI enables the analysis of data from multiple angles and in ways that are beyond human capability. Various algorithms have been created to handle specialized tasks, allowing AI models to focus on interpreting and utilizing specific data types more effectively.

Predictions

The output of any AI algorithm depends on the data it processes, generating predictions that are particularly valuable in predictive analytics. These predictions focus on future outcomes, such as forecasting sales trends or identifying a business’s most successful marketing strategies. Businesses can refine the AI algorithm by comparing these predictions with actual results, enhancing its accuracy and reliability over time.

Advantages of Leveraging AI-driven Predictive Analytics

AI predictive analytics is a powerful tool for enhancing decision-making. It allows businesses to make well-informed choices. These choices are more likely to yield positive results. By rapidly analyzing vast amounts of data, AI boosts operational efficiency and reduces the workload on employees by automating the evaluation of historical trends. This technology empowers companies to deliver personalized solutions in real time, elevating the customer experience while crafting targeted campaigns that drive success. 

Additionally, leveraging AI predictive analytics can provide a significant competitive edge, enabling businesses to harness data insights effectively to mitigate risks and improve overall campaign performance.

Endnote

Artificial intelligence and predictive analytics are essential for fostering business growth and success. These technologies unlock critical insights that allow businesses to make informed, data-driven decisions, streamline operations, improve customer experiences, and uncover new expansion opportunities. Harnessing the potential of AI and predictive analytics enables companies to remain competitive, respond effectively to shifting market conditions, and drive lasting growth.