Emerging Trends in Data Management
As data is generated at an exponential rate, companies are awash with data, struggling to unlock its true value. Data is not only increasing in volume but also in complexity. In terms of ‘Global Datasphere’, it is estimated that by the end of 2019 it stood at 4.4 Zeta Bytes (ZB), up from 2.7 ZB in 2017. In addition, the growth in connected IoT devices is expected to generate 79.4 ZB of data across 41.6 billion IoT devices, according to IDC Forecast.
As organizations find their way through analyzing the data, it has become critical to have a data management system that can help solve challenges of data integration, silos, manual data management and governance throughout the organization. This can lead to challenges such as increased costs, threat to data security, prone to error and subject to bias.
If the company wants to continually evolve and stay ahead in the competition, it must have a data and technology-centric approach to Data Management. According to the survey, only 25% of companies feel like they are exactly where they want to be with corporate data management. The new emerging trends democratizes an entire data management value chain. Some of these trends are:
1. AI-enabled Data Management
The combination of Data Management systems and AI are synergistic in nature. When AI becomes embedded throughout the data management system, it has potential to impact an entire data value chain. AI-enabled data management helps in automating repetitive and complex tasks. It improves the performance, accuracy, and productivity in an enterprise.
Data scientists spend around 80% of their time on manual data preparation, feature engineering, and model selection. So, Augmented Data Management utilizes AI/ML capabilities to automate the manual data management tasks allowing highly skilled technical resources to focus on high-value tasks where AI is less mature. According to Gartner, by 2022 data management manual tasks will be reduced by 45% through augmented data management.
Augmented Analytics democratizes AI across the whole data value chain to automate data preparation process, key aspects of data science and ML/AI modelling using ML (AutoML) techniques and narrate relevant insights using NLP and conversational analytics.
Even today, organizations rely upon historical databases instead of real-time data for analysis. So, there is a need to collect, index and analyze all data in real-time. Continuous Intelligence is a seamless AI-driven solution that allows the companies to automatically integrate continuous and insightful data from disparate sources. It completely changes the time-consuming data wrangling process performed in Big Data. Backed with AI, ML, and right training data it minimizes human intervention throughout the process. According to Gartner, by 2020, more than half of the major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.
2. Semantic Data Catalog
As data is integrated from disparate sources, it is difficult to make efficient use of siloed data sources to easily access, interpret, and track data and its history. Semantic Data Catalog leverages knowledge graph model encoding a Semantic layer that map relationships and describes the data in its business context while integrating data from disparate sources. When linked with self-service tools, it will help data stewards and business users to prepare datasets and curate the data. It is quite necessary in data management initiatives like improving governance, data lineage and data quality as well.
3. NLP and Conversational Analytics
Until recently, it has all been about visualization of data which requires highly skilled users, but with NLP/Conversational analytics, it will allow users to ask questions about the data using NLQ search, as well as receive visualization of data and an explanation of insights supported by NLG. Natural Language Query (NLQ) interact with the data reducing technical and analytical query expertise necessary by mainstream business users. By 2020, 50% of analytical queries will be generated via search, natural language processing or voice, or will be automatically generated, according to Gartner.
4. Data Fabric
Data is available in a variety of formats which is distributed across multiple on-premises locations as well as hybrid and multi-cloud. As organizations are using many applications, their data becomes increasingly siloed and inaccessible. To provide single view of all the data, data fabric is used. It provides a single environment for accessing, collecting, and analyzing the data making an enterprise extremely agile and eliminating silos. It enables data ingesting, integration, quality, governance and sharing, by eliminating multiple tools and providing faster access to data.
With the advancement in data collection and storage methods, the future of data management is autonomous. To stay ahead of the competition, companies are compelled to keep up with new and innovative technologies. By doing so, companies become more agile enabling greater access and self-service, more cost- efficient by 38%, gain faster insights, gain better understanding of the data, enables informed and accurate decisions by 33%.
How does your organization tackle data management challenges? What are the plans to deal with it? Did your organization adopt any of the new trends for managing data? Feel free to share your thoughts in the comments section.
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Payal is a Product Marketing Specialist at Subex, who covers Artificial Intelligence and its application around Generative AI. In her current role, she focuses on Telecom challenges with AI and its potential solutions to these challenges. She is a postgraduate in management from Symbiosis Institute of Digital and Telecom Management, with analytics as her majors, and has prior engineering experience in the Telecom industry. She enjoys reading and authoring content at the intersection of analytics and technology.
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