The Role of Personalization in Data Management

The Role of Personalization in Data Management

Visualize this:

You work in a finance company where thousands of gigabytes of data are generated each minute. From purchase orders, contractual agreements, and transactions to emails, call records, and social media streams, you receive an overwhelming amount of data each day, most of which is unstructured. Such vast volumes of data make it challenging to sift through it, identify relevant details, and process it. Apart from adding to the time you spend on each data set, it will also increase the pile of redundant data that your system stores.

There is no denying that new issues necessitate new solutions. Therefore, today's data-driven enterprises need to look out for technologically advanced, digital solutions to deal with the tons of unstructured and fragmented data, thereby reducing overall organizational silos.

One dependable solution to address this concern is data-driven personalization. It is not only a nice-to-have feature in the current hyper-competitive, data-run financial business ecosystem but has now become a must-have. Moreover, with data personalization, companies can foster a culture of trust, transparency, and loyalty, which in turn, helps them engage customers and offer them better services.

Data Personalization: A Boon For Data-Driven Enterprises

Before understanding how personalization can transform data processing, it is only fair to understand what a data stream is. In the most basic form, a data stream is a feed of information collected via voluntary integration from dedicated platforms like a collaboration tool and contains raw data that has been extracted from the user's browser behavior, social media platforms, recently read webpages, emails, etc. This data can be used as good source material for targeting, predictive research, and Big Data analysis.

One of the fundamental use cases of a data stream is adjusting its structure according to your business needs and data management goals. And this is possible because data streams are elastic data services.

True personalization necessitates a comprehensive combination of business capabilities, underlying technology, and relevant data. Financial services firms may correctly and more precisely communicate with clients, provide value for them, and eventually gain new revenue streams once this entire set of skills is in place.

Personalized Data Streams For Enhanced Experience

A personalized data stream is the need of the hour to:

  • Classify the content using machine learning
  • Analyze the context in the task a data worker performs to recognize associated topics
  • Identify spam, unsafe, erroneous, redundant, or irrelevant data and take appropriate actions
  • Employ personalization engines to quickly pick up on a user's interests and offer solutions as per their needs and requirements

A personalized data stream creates a feed full of sophisticated, compelling, innovative user experiences without exhausting resources in building the technology. By introducing a personalization platform to handle the development of these feeds, companies can focus on serving users' unique needs, streamlining the process to save time, energy, and resources.

Introducing personalization in data management can help professionals observe users' behavior, compare characteristics, understand intentions, and conduct efficient sentiment analysis. With their data experts, companies, big or small, can gain from raw data the most. Additionally, a personalized data stream can enable them to meet data needs and build their data tools, including personalized experience, collaboration and machine learning algorithms.

Companies today need to implement a comprehensive and slick activity feed into their apps to create a unified, personalized, single data repository. The aim is to make these feeds as simple, familiar, and easy to use as possible so that each user is empowered to focus on processing relevant data instead of trying to figure out how to navigate through the massive data pile. Moreover, threading and multi-level group analysis are necessary to integrate and fill each data worker's feeds with relevant content in real-time.

Implementing Data Personalization with Needl.AI

The question is not if companies need to integrate an activity feed into their apps; it is how. A considerable benefit of partnering with Needl.AI is that with technology smart enough to collect the right data efficiently, it offers templates for customizable feeds, enabling users to personalize their feeds. In addition, we make it possible to develop feature-rich feeds for data workers so their experience feels comfortable and valuable. From extracting data for personalized manipulation to removing data or organizational silos by stitching data together, Needl.AI helps you improve productivity, increases return on investment, equating personalization to leveraging data.

Our deep personalization algorithms, including social and knowledge graphs curated from users' prior behaviour, relationships, and communication patterns, operate and process on scaled data, in turn, helping to reduce noise. This is due to the algorithm's hybrid relevancy factors used to rank and filter datasets. Additionally, we make more advanced correlation and clustering improvements to monitor how users engage with their data and our platform.

So, to witness seamless intermeshing of data from various sources into a single data repository or personalized data stream, Needl.AI can be your be one-stop-solution.

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