In the last two decades, the Internet has transformed the way we read, work, and collaborate. From a situation of scarcity of data, we have moved to a situation where knowledge workers are witnessing a proliferation of data coming in from a variety of sources. This has resulted in huge efforts to save, organize, retrieve, and try to make sense of the data. It is also one of the most important factors of productivity loss on a daily basis.
Knowledge workers today are experiencing data chaos, multiple platforms creep, critical data siloed in platforms with limited processing capabilities that do not have seamless access to data for frictionless processing. In our conversations with knowledge workers across industries, we learned that everyone deals with this one common problem on a daily basis – what do I do with my data scattered across platform silos and devices and, where do I find it, and how do I use it best?
Three issues of critical importance to the user are:
Customers today have vast amounts of unstructured data, much of it incredibly useful if it can be discovered as it stored in many formats, and spread across different data sources (e.g. searching for a tweet embedded in an image in a chat platform or a file directory of the desktop).
There is a need for a platform that offers a powerful single search engine that returns results from all your sources for every single query in a single search removing the need for search across multiple platforms, curated sources of information, and data silos.
The holy grail of data management is to build and experience the convenience of a single data repository aka a single source of truth for both structured and unstructured data across private and public sources. Such a data repository needs to be live, secure, backed up, and accessible across devices to be meaningfully useful to the user.
Data is meant to be converted into insights and knowledge and that requires the processing of a wide variety.
Search is just one of several processing functions along with commonly used including
Cloud computing can now relieve users from several repetitive and routine but time-consuming tasks. Machine Learning and Artificial algorithms (ML/AI) have a process for creating auto and user correctable tagging of user data eliminating the need for data folders and directories.
It is not just the volume and variety of data; the velocity of data is getting higher and higher. That is why the user requires to have intelligent filters and automated workflows to get back scarce attention to the important task at hand requiring undivided attention. Ability to mute data streams, ignore certain messages, respond later, etc tag and filters built-in assisting customer deal with what is important as against what is at the top in the inbox or messaging feeds.
Prioritization of data on multiple parameters and the ability to filter valuable signal noise from a sea of noise is the best use case for needl.ai The vast computing power is now available at a very reasonable cost. Technology ought to work to make users more productive and efficient rather than drowning them in overtime work resulting from data overload.
Users should be able to port and share their data in whole or in parts seamlessly with their co-workers within the same organization or peers outside the organization. Once data is in a single repository and processable it’s possible to build context-based subset feeds based on an event, individual, topic, or interest and create collaborative space to work on the same in real-time.
Modern-day data platforms need to enable users to access the best possible content of once choice by enabling frictionless access to personal data and frequently used external websites across apps and devices, enable processing that most customers need most of the time, and share the same to build a community of likeminded people they know and want to work with. The value of such platforms to users is dependent on context-specific functionality, the scale of users, ways of collaborating, and ease of use.