June 17, 2024

Corporate Nex Hub

Bringing business progress

Tech Leaders Share Leading-Edge Approaches

6 min read

It’s often said that knowledge is power, and that’s never been more true for businesses than in today’s digital age. Technology tools enable businesses to collect a wealth of information from customers, and each of those data points is a valuable asset. Getting the biggest ROI from that data, however, requires strategic, updated data management strategies.

From how data is processed to who has access to it to tapping into advanced technologies to power analysis, the data management landscape is changing quickly. Below, 20 members of Forbes Technology Council reveal some new and emerging trends in data management and why these practices are so impactful.

1. Analyzing Who’s Using (Or Not Using) Collected Data

Combining insights from not only the data itself, but also from who is using or not using it, offers an opportunity to prioritize the quality, integrity and usability of data that is most useful in an organization and to rebalance the security of data that isn’t being used at all. – Claude Mandy, Symmetry Systems Inc.

2. Taking A Platform Approach To Storage

A platform approach is one effective way to reshape data management practices. When the entirety of a company’s data resides in one central repository, visibility becomes possible across all business applications and user profiles. This has both security and productivity benefits, enabling IT to take stock of who has access to what and change or update those permissions on the fly. – John Milburn, Clear Skye


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3. Using GenAI To Standardize, Enrich And Clean Data

In healthcare, generative artificial intelligence and machine learning can now mimic subject matter experts’ reasoning to standardize, enrich and clean data. This has made it possible to solve persistent data challenges, such as maintaining the accuracy of health plan provider directories. This technology isn’t replacing humans. Instead, it allows teams to engage in higher-order thinking and strategy. – Amit Garg, HiLabs

4. Developing Private LLMs

One technology reshaping data management is private large language models. If an LLM is synced to a structured data repository, the quality of the data can be managed, and the LLM will be able to search and contextualize the data much, much faster than a human. And a private LLM can also augment the structured data with invaluable special knowledge captured from worker-to-worker conversations and engagement. – David Francis, Virtual Method

5. Leveraging RAG

One innovative approach that is reshaping data management practices is retrieval-augmented generation. This technology combines the power of large-scale data retrieval systems with advanced natural language generation models. RAG paves the way for more intelligent, efficient and effective data utilization by bridging the gap between massive data resources and generative AI capabilities. – Dave Albano, RestorePoint.ai

6. Following The Federated Learning Method

One cutting-edge approach revolutionizing data management is federated learning. This method enables machine learning models to be trained across various decentralized devices or servers holding local data without actually sharing the data itself. This helps uphold data privacy, minimize breach risks and reduce data transmission, which in turn conserves bandwidth and lowers latency. – Fabrizio Blanco, Viant

7. Using Decentralized Ledgers

An exciting shift in data management revolves around decentralized ledger technology. This approach ensures data integrity and security through transparent, tamper-proof records. Its effectiveness lies in fostering trust and streamlining workflows, bringing a new level of authenticity to data management practices across industries. – Geetha Kommepalli, Skillsoft

8. Working With Low-Code Platforms

Low-code platforms expedite data connectivity and drive faster, more effective decision-making. They enable non-IT staff to activate data governance, allowing data teams to refocus efforts on supporting business outcomes, thus resulting in increased efficiency. Using low-code to support data management helps companies launch data projects faster by streamlining the needed application development. – Ed Macosky, Boomi

9. Focusing On Data Security Throughout Transit

Today’s technology can be penetrated, which is why businesses need to do everything they can to secure their data during all modes of transit. Newer VPN-protected, secured and encrypted server technologies are at the forefront of resolving security issues that have plagued technology in recent years. Secured servers are at the forefront of effective data management. – Michael Gargiulo, VPN.com

10. Taking The Data Fabric Approach

Data fabric is an innovative approach to data management that provides a holistic view of an organization’s data across many locations, including on-premises centers, cloud platforms and devices. By integrating this data into a cohesive framework and leveraging AI and ML to automate discovery and integration, data fabric enables organizations to improve data accessibility, security and governance. – Raj Neervannan, AlphaSense, Inc.

11. Integrating Security And Governance Into The ETL Pipeline

One leading approach is integrating data security and data governance into the extract, transform and load pipeline for analytical workloads. Applying data security measures such as encryption, masking or auditing after data has arrived in the warehouse can be cumbersome. If you classify data in the pipeline, this triggers encryption or tokenization calls. After this data is classified, tagged and protected, governance is much easier at scale. – James Beecham, ALTR

12. Analyzing Data Via GenAI

Generative AI is revolutionizing data management by enabling users to submit data analysis queries directly, in natural language. Instead of navigating complex dashboards, users can ask questions and receive analyzed responses within seconds, significantly enhancing data accessibility and decision-making. – Shahar Chen, Aquant

13. Using New Tools To Refine Raw Data

Data products are reshaping data management by turning raw data into a structured, consumable format that adds value beyond its original state. Their effectiveness lies in enabling organizations to monetize, share and utilize data more strategically, transforming raw data into a valuable asset for decision-making, innovation and customer engagement. – Mike Capone, Qlik

14. Data Cataloging And Profiling

Data cataloging and profiling is the stepping stone to effective data management. It provides users with a way to navigate the complexities of an organization’s data architecture and storage while enabling a fast assessment of data quality for downstream applications. – Gonçalo Ribeiro, YData

15. Thinking Like An Engineer

I think most folks in this discipline would agree that data has always been a decade, or even decades, behind software engineering in terms of maturity. Adopting concepts such as continuous integration and continuous delivery, unit and integration testing, observability, and nonmonolithic system architecture is a huge lever for building faster while being more cost-effective and reliable. – Elliott Cordo, Data Futures

16. Hydrating Data Via An Orchestration Layer

Hydrating data into systems of record from an orchestration layer that unifies a company’s ecosystems in real time is critical, but it’s often overlooked. With this free flow of information, companies gain more expansive AI insights and service governance oversight, minimizing operational, reputational and regulatory risks. – Alfred Kahn, OvationCXM

17. Treating Data As A Product

One approach that’s reshaping data management practices is treating data as a product. Treating data as a product forces leaders within an organization to understand who is using it, how they are using it and why it is essential to them. They must also understand service-level agreements on data quality, freshness and availability and establish practices for managing the data life cycle and related incidents. – Ashish Gupta, Capital One

18. Using AI And Third-Party Data For Clarity And Completion

More organizations are using AI and third-party data to clean, gap-fill and enrich enterprise systems, including enterprise resource planning and product life cycle management systems. Digitalization and integration initiatives fail because 90% of the data needed to make product decisions sits in the value chain, not in enterprise systems. To accelerate product development and procurement, you must augment systems with data on supply chains, environmental impacts, costs and other parameters. – Neil D’Souza, Makersite

19. Using AI Discovery And Mapping Tools

Heuristic data mapping of both structured and unstructured data, leveraging AI and ML discovery and mapping tools, is significantly easing data management practices. The benefits include deduplication, storage cost reduction, partial automation of EU GDPR DSAR processes, the creation of a searchable data library, and the creation of manageable enterprise data architecture models. – Mark Brown, British Standards Institution (BSI)

20. Leveraging Data Clean Room Technology

Data clean room technology is a relatively new development designed for a privacy-driven era. It ensures the protection of confidential data, whether anonymized, pseudonymized or aggregated, by preventing any unauthorized access or misuse of information, thereby reducing the risk of privacy breaches and maintaining user trust. – Ivan Guzenko, SmartyAds Inc.

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