Connect with us

Business

How Turning Your Core Data into a Product Drives Business Impact

By Venki Subramanian, SVP of Product Management at Reltio

Data drives efficiencies, improves customer experience, enables companies to identify and manage risks, and helps everyone from human resources to sales make informed decisions. It is the lifeblood of most organisations today. Sometime during the last few years, however, organisations turned a corner from embracing data to fearing it as the volume spiralled out of control. By 2025, for example, it is estimated that the world will produce 463 exabytes of data daily compared to 3 exabytes a decade ago.

Too much enterprise data is locked up, inaccessible, and tucked away inside monolithic, centralised data lakes, lake houses, and warehouses. Since almost every aspect of a business relies on data to make decisions, accessing high-quality data promptly and consistently is crucial for success. But finding it and putting it to use is often easier said than done.

That’s why many organisations are turning to “distributed data” and creating “data products” to solve these challenges, especially for core data, which is any business’s most valuable data asset. Core data or master data refers to the foundational datasets that are used by most business processes and fall into four major categories – organisations, people (individuals), locations, and products. A data product is a reusable dataset used by analysts or business users for specific needs. Most organisations are undergoing massive digital and cloud transformations. Putting high-quality core data at the centre of these transformations—and treating it as a product can yield a significant return on investment.

The Inefficiency of Monolithic Data Architectures

Customer data is one example of core or master data that firms rely on to generate outstanding customer experiences and accelerate growth by providing better products and services to consumers. However, leveraging core customer data becomes extremely challenging without timely, efficient access. The data is often trapped inside monolithic, centralised data storage systems. This can result in incomplete, inaccurate, or duplicative information. Once hailed as the saviour to the data storage and management challenge, monolithic systems escalate these problems as the volume of data expands and the urgent need for making data-driven decisions rises.

The traditional approaches for addressing data challenges entail extracting the data from the system of records and moving it to different data platforms, such as operational data stores, data lakes, or data warehouses, before generating use case-specific views or data sets. In addition, because of the creation of use case-specific data sets that are subsequently exploited by use case-specific technologies, the overall inefficiency of this process increases.

One inefficiency arises from the complexity of such a landscape, which involves the movement of data from many sources to various data platforms, the creation of use case-specific data sets, and the use of multiple technologies for consumption. Core data for each domain, such as customer, is duplicated and reworked or repackaged for almost every use case instead of producing a consistent representation of the data used across various use cases and consumption models – analytical, operational, and real-time.

There’s also a disconnect between data ownership and the subject matter experts that need it for decision-making. Data stewards and scientists understand how to access data, move it around and create models. But they’re often unfamiliar with the specific use cases in the business. In other words, they’re experts in data modelling, not finance, human resources, sales, product management, or marketing. They’re not domain experts and may not understand the information needed for specific use cases, leading to frustration and data going unused. It’s estimated, for example, that 20% or fewer of data models created by data scientists are deployed.

Distributed Data Architecture – An Elegant Solution to a Messy Problem

The broken promises of monolithic, centralised data storage have led to the emergence of a new approach called “distributed” data architectures, such as data fabric and data mesh. A data mesh can create a pipeline of domain-specific data sets, including core data, and deliver it promptly from its source to consuming systems, subject matter experts, and end users.

These data architectures have arisen as a viable solution for the issues created by inaccessible data locked away in siloed systems or rigid monolithic data architectures of the past. Data fabric decentralises the management and governance of data sets. It follows four core principles – domain ownership of data, treating data as a product and applying product principles to data, enabling a self-serve data infrastructure, and ensuring federated governance. These help data product owners create data products based on the needs of various data consumers and for data consumers to learn what data products are available and how to access and use these. Data quality, observability, and self-service capabilities for discovering data and metadata are built into these data products.

The rise of the concept of data products is helpful for analytics/artificial intelligence, and general business uses. The concept for either case is the same – the dataset can be reused without a major investment in time or resources. It can dramatically reduce the amount of time spent finding and fixing data. Data products can also be updated regularly, keeping them fresh and relevant. Some legacy companies have reported increased revenues or cost savings of over $100 million.

Trusted, Mastered Data as a Product

Data product owners have to create data products for core data to enable its activation for key initiatives and support various consumption models in a self-serve manner. The typical pattern that all these data pipelines enable can be summarised into the following three stages – collect, unify, and activate.

The process starts with identifying the core data sets – data domains like customer or product – and defining a unified data model for these. Then, data product owners need to identify the first-party data sources and the critical third-party data sets used to enrich the data. This data is assembled, unified, enriched, and provided to various consumers via APIs so that the data can be activated for various initiatives. Product principles such as the ability to consume these data products in a self-service manner, customise the base product for various usage scenarios, and deliver regular enhancements to the data are built into such data products.

Data product owners can use this framework to map out key company initiatives, identify the most critical data domains, identify the features (data attributes, relationships, etc.) and the sources of data – first and third party that needs to be assembled – to create a roadmap of data products and align them to business impact and value delivered.

With data coming from potentially hundreds of applications and the constantly evolving requirements of data consumers, poor quality data and slow and rigid architecture can cost companies in many ways, from lost business opportunities to regulatory fines to reputational risk from poor customer experience. That’s why organisations of all sizes and types need a modern, cloud-based master data management approach that can enable the creation of core data as products. A cloud-based MDM can reconcile data from hundreds of first and third-party sources and create a single trusted source of truth for an entire organisation. Treating core data as a product can help businesses drive value by treating it as a strategic asset and unlocking its immense potential to drive business impact.

Continue Reading
Click to comment

Leave a Reply

Your email address will not be published. Required fields are marked *

Business

How 5G and AI shaping the future of eHealth

Global Director for AI/ML Solutions, Mona Nia Tecnotree

The digital transformation of the healthcare industry continues to gain momentum. This shift can be attributed to the rapid advancement of widely applied technologies such as 5G networks, cloud computing, artificial intelligence (AI), and big data.

Moreover, integrating 5G networks with cloud-based healthcare platforms and AI is driving the emergence of intelligent eHealth technology, projected to reach $208 billion by 2030, according to recent reports. Recent research by Grand View Research emphasises that the synergy between 5G and AI is pivotal in transforming healthcare by enabling faster data exchange, reducing latency, and improving the reliability of health solutions. This collaboration aims to revolutionise the healthcare sector by facilitating hyper-personalisation, optimised care, enhanced sales and services, and streamlined operations. Leading venture firms actively invest in healthcare start-ups using AI, fostering a rapidly growing ecosystem of innovative advancements.

As AI and 5G continue to make waves through all industries, healthcare needs to adapt to changes quickly. However, with operational, security, and data privacy concerns, healthcare organisations remain wary. As such, they must analyse their current and future needs to understand how AI and 5G technologies can help fulfil them and establish a comprehensive plan to guarantee its efficient and secure implementation in their practices.

Recent research by the International Data Corporation (IDC) emphasises that the synergy between 5G and AI could potentially reduce operational costs by up to 20% and improve patient outcomes by enabling more accurate diagnostics and personalised treatments.

5G Integration in eHealth

5G technology stands at the forefront of healthcare reform with its superior data speed and dramatically reduced latency. Tailored to concurrently accommodate multiple connected devices such as sensors, wearables and medical equipment, 5G is truly indispensable in healthcare, allowing IoT devices to seamlessly transmit accurate data for healthcare providers.

It empowers healthcare professionals to handle large, high-definition files like clinical visuals, videos, and real-time patient insights. 5G’s capability for network slicing—dedicating specific network segments for certain uses—simplifies the management of such files. In addition, it optimises the performance of each application, thereby removing the strain on medical staff.

However, the implementation of 5G technology shouldn’t be oversimplified. It’s essential to analyse the potential risks and challenges thoroughly. A principal component to consider is regulatory cybersecurity and data privacy. Given that 5G networks are susceptible to cyber attacks, it falls upon healthcare providers to protect data such as patient information.

Organisations should also consider the financial implications of implementing 5G technology, as it involves a considerable investment in infrastructure and equipment. Therefore, they must balance the potential gains against the costs to ensure the viability of the investment.

Recent discussions at Mobile World Congress 2024 highlighted the critical role of regulatory frameworks in ensuring the secure deployment of 5G in healthcare. Experts advocated for robust cybersecurity measures and collaborative efforts between technology providers and healthcare institutions to mitigate potential risks.

Marrying 5G and AI for Improved eHealth Solutions

Despite the challenges, integrating 5G and AI will pave the way for unprecedented growth within the internal medical ecosystem, enhancing healthcare quality and patient results. For example, deploying data to carry out descriptive-predictive-prescriptive analytics and transmitting the acquired insights using 5G can drastically improve the user experience while helping make informed decisions. Such an approach can assist healthcare organisations in identifying promising healthcare use cases like remote patient monitoring, surgical robotics, and telemedicine.

Moreover, AI-facilitated hyper-personalisation, driven by the profusion of data accessible through 5G networks, can evaluate patient histories, genetic profiles, and lifestyle elements alongside real-time vitals to prescribe tailored advice and treatments. AI can also automate scheduling appointments, streamline supply chain management, and enhance transactions such as claims and prior authorisations. AI-powered chatbots and virtual assistants can deliver real-life support, while patient and customer service applications can provide an enriched experience through increased data accessibility.

AI can also streamline healthcare services by predicting and managing disease outbreaks. Supported by 5G’s capacity for real-time operability, AI systems can instantly analyse patient data, oversee bed availability, and notify medical personnel of potential complications—promoting efficient, effective care delivery.

Finally, AI-empowered fraud detection algorithms operating on 5G networks can analyse copious amounts of data in real time to detect suspicious activities and alert responsible security teams. This can also be applied to security cameras that can detect anomalies in patients’ and visitors’ behaviour and notify appropriate staff members.

A study published in the Journal of Medical Internet Research (JMIR) in 2023 demonstrated that combining AI and 5G in telemedicine significantly improved patient satisfaction and reduced consultation times by 30%.

Shaping an AI Blueprint for 5G eHealth

Integrating AI and 5G technologies can revolutionise disease assessment and surveillance, facilitating more precise diagnostics and tailored treatments. In return, it will drastically improve the standard of care, curbing expenses and boosting efficiency.

Over the next few years, healthcare providers should focus on specific areas where 5G and AI can deliver the most impact. For example, developing telehealth platforms that excel in security, accessibility, and user-friendly interfaces will be paramount. This design aspect is set to thrive, particularly with 5G paving the way for high-definition video consultations, remote patient monitoring, and instant data sharing between patients and healthcare

providers.

The precision and availability of diagnostic applications powered by AI and tele diagnostic services will notably increase in tandem with the widespread adoption of 5G. The strategic emphasis should be on enriching its capabilities, ensuring compatibility with existing systems, and seamlessly integrating the tech into existing healthcare processes.

AI-guided care management systems will also play an integral role in eHealth. There is a need to structure these systems to constantly monitor patient progress, suggest highly personalised treatments, and coordinate care across multiple providers while prioritising patient privacy and data protection.

Finally, when it comes to home health monitoring, emphasis should be placed on creating IoT devices that can integrate seamlessly with AI-driven health platforms and securely transmit data; this will be a critical development within the field.

The synergy between 5G technology and AI will continue revolutionising the healthcare industry, offering more customised, efficient, and cost-friendly solutions. By developing a precise AI blueprint for critical eHealth applications and capitalising on the capabilities of 5G, the benefits will drastically outweigh the challenges.

Continue Reading

Business

Driving business success in today’s data-driven world through data governance

Source: Finance derivative

Andrew Abraham, Global Managing Director, Data Quality, Experian

It’s a well-known fact that we are living through a period of digital transformation, where new technology is revolutionising how we live, learn, and work. However, what this has also led to is a significant increase in data. This data holds immense value, yet many businesses across all sectors struggle to manage it effectively. They often face challenges such as fragmented data silos or lack the expertise and resources to leverage their datasets to the fullest.

As a result, data governance has become an essential topic for executives and industry leaders. In a data-driven world, its importance cannot be overstated. Combine that with governments and regulatory bodies rightly stepping up oversight of the digital world to protect citizens’ private and personal data. This has resulted in businesses also having to comply e with several statutes more accurately and frequently.

We recently conducted some research to gauge businesses’ attitudes toward data governance in today’s economy. The findings are not surprising: 83% of those surveyed acknowledged that data governance should no longer be an afterthought and could give them a strategic advantage. This is especially true for gaining a competitive edge, improving service delivery, and ensuring robust compliance and security measures.

However, the research also showed that businesses face inherent obstacles, including difficulties in integration and scalability and poor data quality, when it comes to managing data effectively and responsibly throughout its lifecycle.

So, what are the three fundamental steps to ensure effective data governance?

Regularly reviewing Data Governance approaches and policies

Understanding your whole data estate, having clarity about who owns the data, and implementing rules to govern its use means being able to assess whether you can operate efficiently and identify where to drive operational improvements. To do that effectively, you need the right data governance framework. Implementing a robust data governance framework will allow businesses to ensure their data is fit for purpose, improves accuracy, and mitigates the detrimental impact of data silos.

The research also found that data governance approaches are typically reviewed annually (46%), with another 47% reviewing it more frequently. Whilst the specific timeframe differs for each business, they should review policies more frequently than annually. Interestingly, 6% of companies surveyed in our research have it under continual review.

Assembling the right team

A strong team is crucial for effective cross-departmental data governance.  

The research identified that almost three-quarters of organisations, particularly in the healthcare industry, are managing data governance in-house. Nearly half of the businesses surveyed had already established dedicated data governance teams to oversee daily operations and mitigate potential security risks.

This strategic investment highlights the proactive approach to enhancing data practices to achieve a competitive edge and improve their financial performance. The emphasis on organisational focus highlights the pivotal role of dedicated teams in upholding data integrity and compliance standards.

Choose data governance investments wisely

With AI changing how businesses are run and being seen as a critical differentiator, nearly three-quarters of our research said data governance is the cornerstone to better AI. Why? Effective data governance is essential for optimising AI capabilities, improving data quality, automated access control, metadata management, data security, and integration.

In addition, almost every business surveyed said it will invest in its data governance approaches in the next two years. This includes investing in high-quality technologies and tools and improving data literacy and skills internally.  

Regarding automation, the research showed that under half currently use automated tools or technologies for data governance; 48% are exploring options, and 15% said they have no plans.

This shows us a clear appetite for data governance investment, particularly in automated tools and new technologies. These investments also reflect a proactive stance in adapting to technological changes and ensuring robust data management practices that support innovation and sustainable growth.

Looking ahead

Ultimately, the research showed that 86% of businesses recognised the growing importance of data governance over the next five years. This indicates that effective data governance will only increase its importance in navigating digital transformation and regulatory demands.

This means businesses must address challenges like integrating governance into operations, improving data quality, ensuring scalability, and keeping pace with evolving technology to mitigate risks such as compliance failures, security breaches, and data integrity issues.

Embracing automation will also streamline data governance processes, allowing organisations to enhance compliance, strengthen security measures, and boost operational efficiency. By investing strategically in these areas, businesses can gain a competitive advantage, thrive in a data-driven landscape, and effectively manage emerging risks.

Continue Reading

Auto

The Benefits of EV Salary Sacrifice: A Guide for Employers and Employees

As the UK government continues to push for greener initiatives, electric cars have become increasingly popular. The main attraction for both employers and employees is the EV salary sacrifice scheme.

By participating in an EV salary sacrifice scheme, both employers and employees can enjoy cost savings and contribute to environmental sustainability along the way! This article will delve into the specifics of how these schemes operate, the financial advantages they offer, and the broader positive impacts on sustainability.

We will provide a comprehensive overview of the mechanics behind EV salary sacrifice schemes and discuss the various ways in which they benefit both employees and employers, ultimately supporting the transition to a greener future in the UK.

What is an EV Salary Sacrifice Scheme?

An EV salary sacrifice scheme is a flexible financial arrangement that permits employees to lease an EV through their employer. The key feature of this scheme is that the leasing cost is deducted directly from the employee’s gross salary before tax and National Insurance contributions are applied. By reducing the taxable income, employees can benefit from substantial savings on both tax and National Insurance payments. This arrangement not only makes EVs more affordable for employees but also aligns with governmental incentives to reduce carbon emissions.

For employers, implementing an EV salary sacrifice scheme can lead to cost efficiencies as well. The reduction in National Insurance contributions on the employee’s reduced gross salary can offset some of the costs associated with administering the scheme. Additionally, such programmes can enhance the overall benefits package offered by the employer, making the company more attractive to prospective and current employees.

Benefits for Employees

1. Tax and National Insurance Savings

By opting for an EV salary sacrifice scheme, employees can benefit from reduced tax and National Insurance contributions. Since the lease payments are made from the gross salary, the taxable income decreases, resulting in substantial savings.

2. Access to Premium EVs

Leading salary sacrifice car schemes often provide access to high-end electric vehicles that might be otherwise unaffordable. Employees can enjoy the latest EV models with advanced features, contributing to a more enjoyable and environmentally friendly driving experience.

3. Lower Running Costs

Electric vehicles typically have lower running costs compared to traditional petrol or diesel cars. With savings on fuel, reduced maintenance costs, and exemptions from certain charges (such as London’s Congestion Charge), employees can enjoy significant long-term financial benefits.

4. Environmental Impact

Driving an electric vehicle reduces the carbon footprint and supports the UK’s goal of achieving net-zero emissions by 2050. Employees can take pride in contributing to a cleaner environment.

Benefits for Employers

1. Attract and Retain Talent

Offering an EV salary sacrifice scheme can enhance an employer’s benefits package, making it more attractive to potential recruits. It also helps in retaining current employees by providing them with valuable and cost-effective benefits.

2. Cost Neutrality

For employers, EV salary sacrifice schemes are often cost-neutral. The savings on National Insurance contributions can offset the administrative costs of running the scheme, making it an economically viable option.

3. Corporate Social Responsibility (CSR)

Implementing an EV salary sacrifice scheme demonstrates a commitment to sustainability and corporate social responsibility. This can improve the company’s public image and align with broader environmental goals.

4. Employee Well-being

Providing employees with a cost-effective means to drive electric vehicles can contribute to their overall well-being. With lower running costs and the convenience of driving a new EV, employees may experience reduced financial stress and increased job satisfaction.

How to Implement an EV Salary Sacrifice Scheme

1. Assess Feasibility

Evaluate whether an EV salary sacrifice scheme is feasible for your organisation. Consider the number of interested employees, potential cost savings, and administrative requirements.

2. Choose a Provider

Select a reputable provider that offers a range of electric vehicles and comprehensive support services. Ensure they can handle the administrative tasks and provide a seamless experience for both the employer and employees.

3. Communicate the Benefits

Educate your employees about the advantages of the scheme. Highlight the financial savings, environmental impact, and access to premium EV models. Provide clear guidance on how they can participate in the programme.

4. Monitor and Review

Regularly review the scheme’s performance to ensure it continues to meet the needs of your employees and the organisation. Gather feedback and make adjustments as necessary to enhance the programme’s effectiveness.

Conclusion

The EV salary sacrifice scheme offers a win-win situation for both employers and employees in the UK. With significant financial savings, access to premium vehicles, and a positive environmental impact, it’s an attractive option for forward-thinking organisations. By implementing such a scheme, employers can demonstrate their commitment to sustainability and employee well-being, while employees can enjoy the benefits of driving an electric vehicle at a reduced cost.

Adopting an EV salary sacrifice scheme is a step towards a greener, more sustainable future for everyone.

Continue Reading

Copyright © 2021 Futures Parity.