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RELEASING THE ENTERPRISE DATA GOLDMINE: HOW TO IMPLEMENT A SUCCESSFUL MDM STRATEGY

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Releasing The Enterprise Data Goldmine: How To Implement A Successful MDM Strategy

By Tony Smith, Continental Europe Head of Enterprise Application Services Cognizant

Master data management (MDM) is not always regarded as the IT department’s best friend. While some businesses acknowledge the value it can offer, others consider it something of an enigma as it can be difficult to demonstrate the ROI that it delivers. However, MDM presents a hugely strategic opportunity for any business willing to take the plunge; they just need to go about it in the right way.

Barriers to MDM success

In many cases, technical complexities are blamed for the slow uptake of MDM projects, but a more common reason is lack of understanding of the concept and, in some cases, a lack of business support. The IT department has the opportunity to educate their colleagues and the Board on the benefits that MDM projects provide and guide them to implement these projects successfully.  To do this, it is critical for data and IT experts to make sure they can calculate and measure ROI to secure support from executive sponsorship and the wider business.

Releasing The Enterprise Data Goldmine: How To Implement A Successful MDM Strategy

Releasing The Enterprise Data Goldmine: How To Implement A Successful MDM Strategy

On the flipside, some businesses roll out MDM projects before they are fully prepared for them. This approach can yield low success rates, as the process needs to be adequately planned and evaluated before implementation. Moreover, there are also issues around data governance and stewardship that can negatively affect the successful implementation of MDM projects. With careful planning and a clear strategy in place, MDM success can be easily achieved.

More specifically, a data governance framework helps create proper policies and stewardship structures, which help to increase the project’s ROI. It also aids in creating a culture that encourages the creation and maintenance of high-quality data. For data governance to be clear, companies need to align the business processes with which MDM will interact. This will also help to ensure the on-going operational sustainability of the project.

Overcoming the challenges

To ensure an MDM project is successful, businesses need to determine and prioritise the scope of each programme. Expectations need to be set around what can and cannot be done through MDM and a timetable of activity needs to be established before the project commences. Understanding how to prioritise the scope — based on factors such as business urgency, ease of technology implementation and specific project requirements — becomes a critical success driver for the programme.

When it comes to budget allocation and ROI estimation, it is essential to evaluate the organisation’s information management vision; looking at data governance and data quality will help to establish how much information management is required.

Defining the strategy and roadmap of each MDM project is the perfect way to manage its scope and determine what an organisation hopes to achieve, as well as how its requirements are prioritised.

The next step is to divide the requirements into logical categories, such as data acquisition, data distribution, data management, authentication and control, these steps help map the requirements with the relevant MDM architecture and data quality needs.

One of our clients in the Nordics is taking a pragmatic approach to ensure business buy-in. Whilst the IT team is driving the MDM programme, they have incorporated key business personnel into both the Governance process and the overall Steering Committee. By doing so they are not only sowing the seeds for a successful programme but also ensuring some of the typical stumbling blocks around funding are circumnavigated.

Finally, any organisation looking to implement a successful MDM strategy should produce a detailed plan outlining activities, deliverables and milestones for each phase of the MDM initiative. It is also advisable to account for the level of dependency among stakeholders, and indicate a governance approach for the project. This ensures that the MDM initiative is rolled out based on a feasible and measurable plan, with a specific timeline attached to it.

Why adopt MDM?

When carried out in the right way, MDM projects achieve many benefits ranging from business agility and improved business performance to increased revenue and lower IT costs. MDM helps businesses run better and run differently; allowing them to improve efficiencies in a cost-effective way, which is a huge priority in the current climate, along with becoming more innovative to stay ahead of the competition.

In addition, MDM helps to improve data usability and compliance with legislation such as FATCA and SEPA as well as providing visibility into customer behaviour. Customer insight data is hugely useful for analysing feedback and making changes to the business to provide better service and enhance loyalty. It can also help companies remain competitive by getting on top of data and understanding what customers want and need.

Finally, it eliminates data inconsistency and improves data sharing, which is of immeasurable benefit to all businesses. By undertaking a thorough planning process and building a clear strategy around MDM projects businesses will be able to achieve a successful MDM project that delivers the highest return on investment.

We are consistently finding that businesses view MDM programmes as critical and are implementing projects in an increasingly sophisticated way. MDM is no longer seen as an IT project but as a way to release the data goldmine in their enterprise.

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How AI and ML are changing insurance for good

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By Alan O’Loughlin, Director of Analytics and Statistical Modelling, International and John Beal, Senior Vice President of Analytics at LexisNexis® Risk Solutions

The Insurance industry has been dealing with vast volumes of data for years, but analytics, Artificial Intelligence (AI) and Machine Learning (ML) techniques are increasingly being used to help insurance providers make faster data driven decisions.  Given the exponential level of data available today with AI/ML, insurance providers can now efficiently extract new insights into their customer’s needs and create stronger long-term value.

Personalising Insurance Pricing

Starting with how the market calculates premiums, the insurance sector now has access to thousands of data points to help them calculate premiums. Machine learning algorithms expedite the identification of the most predictive attributes driving claims losses – the most recent data points being historical cancellation data and gaps in cover.

This helps insurers become more competitive, match their risks to the most appropriate pricing strategies and write the risks that meet their underwriting appetite. In turn, customers get more personalised quotes based on their unique risk characteristics across any line of business

Achieving a single customer view

Personalisation within any sector works best of course when you really know who you are dealing with. Today, an explosive amount of data is collected, but it is vastly under-utilised as many organisations do not have the expertise to bring data together from different parts of the business to create a single customer view.  Add to this, the amount of mergers and acquisitions in the insurance market over the past few years and the challenge of managing multiple customer databases.  Linking and matching technology using policy history data to find common threads helps overcome this problem to create one consolidated view of the customer. Optimised matching algorithms are also the most accurate and relevant data is reviewed, reducing consumer friction during the quoting process.

Normalisation makes sense of masses of data

In the same vein, as organisations aggregate massive volumes of data, the value of cleansing and normalisation can’t be overlooked. One example, as usage-based insurance develops, whether through aftermarket telematics devices, smartphone apps, connected vehicles, even in the future from smart home data, all that data needs to be gathered, normalised, standardised. That way, any consumer can enjoy an improved shopping experience based on their needs and preferences, no matter the device brand and insurers have consistent quality standards and outcome decisions for all consumers.

Making Vehicle Data work for insurance

Data normalisation is already helping insurance providers understand the presence of Advanced Driver Assistance Systems (ADAS) on a vehicle at the quotation stage.  An ADAS classification system has been created using machine learning to scan millions of lines of car manufacturer vehicle data to logically sequence and classify vehicle safety features and component’s intended operation or purpose.  Extraction and proper classification of this type of data is extremely difficult, time consuming and error prone without the use of AI/ML

Thinking big, starting small in motor claims

At the claims stage in motor insurance, image recognition technology is being used to capture damage or invoices, run a system audit, and if the claim meets the approved criteria, it is automatically paid without human involvement.  This kind of virtual or ‘touchless’ claims handling is speeding up claim settlement times, cutting costs and improving the customer experience.  The ability to quickly analyse years of historical policy and quote history at the consumer level will add an additional level of security prior to a carrier releasing any claim payments.

Alan O’Loughlin

Alan O’Loughlin

Building context through AI and ML

Staying with motor insurance, telematics data can be used much more broadly than originally intended through AI and ML.  From the point of impact through to claim resolution, telematics data can allow insurance providers to get on the front foot at first notification of loss (FNOL), helping to deliver a better consumer experience post-accident, whilst providing invaluable insights regarding the circumstances of the collision.

AI/ML techniques communicate the conditions before, during and after the time of the accident.  Data points like air bag deployment impact sensor activation and g-force metrics can be analysed to understand claim severity and bodily injury potential.  In addition, by combining vehicle build data, carriers can understand the repair cost and potential impact to expensive ADAS features.  Insurance providers can instantly also help their customers with emergency services, vehicle rentals and repairs through instant analysis.

Taking the pain from home insurance applications

Moving into home insurance, we know that conversion rates of people shopping for home insurance is quite low due to a number of hard to answer questions along the customer journey. Rebuild costs is a classic example.  Prefill and data validation solutions are now helping to solve that problem but they are only possible through a huge amount of modelling, linking and AI-ML techniques to pull all the data together to return accurate and up-to-date information on the person and property.

Putting customers in the picture

AI is also at work in the commercial property insurance arena.  It can provide valuable insights regarding a potential location for a new branch or business relocation – footfall, crime rate, exposure to perils or other local circumstances that increase risk. This insight when provided to the customer enables them to take preventative measures if they do go ahead in that location, decreasing risk and loss costs, whilst helping to improve customer experience and retention.

AI and ML can help in the democratisation of data

Finally, AI and ML techniques are helping consumers take advantage of their individual data points which in turn provide the most accurate and updated view data to the insurance providers they choose to interact with on their own schedule.

A good example is the way driving behaviour data from aftermarket devices, or in the future, direct from the connected car gives a clearer picture of someone’s driving risk on the road.  Drivers then benefit from being judged based on their individual behaviours, rather than paying premiums based on average driving habits.

This requires transparency. Each time a consumer applies for insurance they consent to their data being used to provide the insurer with the best information possible, so they can set an appropriate premium based on the risk. Within insurance, we are focusing more than ever on educating consumers about how their data can be used and evaluated in a way they control and understand.  AI and ML automate and process the data consumers are happy to share – supporting greater choice, improved fairness and reduced friction with more personalised insurance protection.

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How Assistive Learning Technology Is Making Online Learning Inclusive

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By Sandra Goger is Learning Technology Analyst at Iflexion, Denver-based software development company.

The global online learning market is expected to reach a total size of $319.2 billion in 2025. As education is evolving from the teacher-centric, traditional chalk-and-talk delivery methods into highly adaptive learner-focused hybrid learning.

We at Iflexion are seeing an intensified interest in e-learning solutions. Online education services have gained a distinctive boost, especially in the past few months, as a result of the global pandemic. The forced lockdown has spurred a sudden shift away from the physical classroom, but it has also brutally exposed the inefficiencies in delivering remote learning to struggling students.

The Strife to Accommodate Underprivileged Students

Students and teachers alike find it hard to get their minds around the new normal, what with the connectivity issues, lack of face-to-face interaction, limited access to class resources, and the uphill battle to keep their focus, to name just a few hurdles. But adapting to the remote mode is even more difficult for those who had already struggled with learning before the outbreak.

For financially underprivileged students just accessing an online course may be a problem. Those less well-off learners often lack the basic equipment or sufficient internet connection simply because their families can’t afford it. This, in turn, leads to increased absence from online classes — for some teachers working in poor neighborhood schools, it’s not uncommon to see less than half of the group attend online meetings on a regular basis.

Another barrier is the technical aspect of online classes, which usually need to be additionally adapted to accommodate the needs of the challenged students. Without subtitling or sign-language rendition, it’s almost impossible for those with hearing difficulties to participate in Zoom calls, and the speech recognition systems that are built in the most popular video call apps are usually far from perfect. “Such captioning is generally subpar and would be a disservice to those who rely on accurate captioning to understand and follow their college classes,” says Howard Rosenblum, CEO of the National Association of the Deaf.

The sight-impaired are unable to work with text-based tasks that are incompatible with screen reader devices or otherwise inaccessible, and those with other disorders often struggle with operating e-learning platforms. “I have challenges with coordinating and I find using the online portals unfriendly,” admits a Kingston University student with dyslexia and dyspraxia.

Sandra Goger

Sandra Goger

And it doesn’t end just there. Even if software and resources are tailored for the underprivileged learners and easy-to-use, it’s the new learning environment itself that can be the problem. Autistic students, for example, often find themselves overwhelmed by the change. And no matter in what way the students are challenged, all of them face the anxiety and fear that they may not be able to keep up with their peers. “It’s stressful because you feel like you’re falling behind,” says Tiffany Anderson, a blind student at Bowling Green State University. It goes without saying that such pressure is anything but encouraging.

Can struggling students be spared many of those limitations and the resulting stress? The answer is yes — and that’s where assistive technology steps in.

Assistive Learning Technology: Working around Student Challenges

Assistive technology refers to any tool designed to help people offset their impairments. The term is broad and may be misleading, as it also incorporates a variety of low-tech devices — think pencil grips, printed graphic organizers, or even inflatable cushion seats for children with sensory processing disabilities. Assistive tech also includes software, such as text-to-speech (TTS) programs, apps, or built-in accessibility functions of mobile devices. There’s plenty to choose from, and many of these aids can be leveraged for free.

With some creativity, teachers can use assistive technology to bring their e-courses to the next level. If teachers want to simulate real-life events in the virtual classroom or feel that their students could use some healthy competition for extra motivation, incorporating game elements can be the way to go. Electronic worksheets allow teachers to present information in a clear and concise way, which is incredibly helpful for learners with reading and concentration disabilities. These e-worksheets can also be a great way to diversify assignments and keep the entire group engaged.

It can’t be stressed enough that all these aids require a degree of empathy and far-sighted planning on the part of the teacher in order to achieve the best possible effect. For example, instructors should refrain from including texts in any graphic form, such as screenshots, book scans, and photos in school materials in order to avoid text readers’ compatibility issues. Recording lectures and distributing them later will be particularly useful for those who struggle with multitasking and taking notes. Apps that facilitate the communication between the teacher and class members are perfect for note sharing, ensuring that no one is behind as well as making up for limited social interactions.

It’s also worth remembering that in some cases even the simplest solutions can do the job. Some may consider keeping classes on the short side to account for students who find it hard to concentrate for an extended period of time, while learners with reading disorders will greatly appreciate larger, easy-to-read fonts. Inclusiveness is the keyword here.

Conclusion

E-learning solutions continue to build up and transform. Choosing the best tool from the entire array of available software, and hardware may seem overwhelming. Moreover, it’s easy to generalize and assume that two people with the same challenges will equally benefit from one particular aid. That’s why, in the end, it all comes down to asking students what works best for them — after all, it’s them who know their own challenges and it’s them who will use the assistive technology. With that knowledge, picking the right teaching aid will be much easier. Think about what students really need, and that little effort will go a long way.

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Hybrid Cloud Application Delivery in Financial Services

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How are Financial Services Firms Addressing the Requirements of Digital Transformation, Security, and Compliance?

By Adrian Taylor, Regional VP of Sales for A10 Networks 

The financial services sector is experiencing significant commercial disruption coupled with rapid innovation as established institutions strive to become more agile and meet evolving customer demand. As a result, financial services organisations are undergoing rapid digital transformation to meet changing customer needs and preferences, and to compete with a new generation of digital-native competitors. Hybrid cloud environments play a key role in this strategy, allowing greater speed, flexibility, and visibility over application delivery than on-premises data centres while also reducing costs.

But the move to hybrid cloud introduces new challenges as well. So, as financial services organisations plot their strategy for transformation, firms must make critical technical decisions about the clouds and form factors best suited to host their hybrid environment. They also need to consider how they will secure web applications against evolving threats such as ransomware, data theft, and DDoS attacks through measures such as DDoS protection and using a Zero Trust model. At the same time, they must also maintain regulatory compliance, governance, and auditability across complex, fast-evolving infrastructures.

To understand more about these challenges, we recently conducted a survey with Gatepoint Research involving senior decision-makers to gain insight into the current state of financial services technology and the future direction for organisations in this sector. Here are some of the key findings:

Today’s Financial Services Technology Landscape

Although financial services businesses are making a steady move to the cloud for application delivery, on-premises data centres continue to play an important role.

While adoption of public cloud infrastructure is strong, with almost half of those surveyed hosting applications primarily in the cloud, most respondents (58 percent) continue to rely primarily on their private on-premises data centre for application delivery. 35 percent of organisations described their environment as hybrid cloud, though with an emphasis on their own private data centre. This shows that even as transformation continues, the traditional data centre remains prominent in the technology strategy of financial services organisations.

That said, the balance between on-premises and cloud infrastructure may well shift soon. When respondents were asked about their plans for the coming year, 57 percent of decision-makers reported that they intend to move more applications to the cloud.

Ransomware and PII Lead Security Concerns

Today, financial services organisations face a broad spectrum of security threats, including many being targeted at sensitive customer data.  The survey highlighted that organisations’ biggest security concerns or consequences were ransomware (57 percent); personally identifiable information (PII) data theft (55 percent); and phishing or fake sites (49 percent).

While threats to customers and their data are seen as the highest risk, dangers to the company’s brand image and reputation were not far behind. 38 percent of leaders cited concerns about hacking and cyber defacement, tied with brand damage and loss of confidence. Nearly as many (37 percent) were concerned about DDoS attacks, which can undermine a firm’s perception among customers through impaired service quality and customer experience. Meanwhile, insider attacks remain an issue, named by 28 percent of respondents, if not quite at the same level as most external threats.

To address the changing security landscape, many organisations have started initiatives around the Zero Trust model, in which traditional concepts of secured zones, perimeters, and network segments are updated with a new understanding that a threat can come from anywhere or anyone inside or outside the organisation. As of June 2020, 41 percent of respondents had already established a timeline for their Zero Trust model initiative with 15 percent having projects currently underway. Still, nearly two-thirds have no current plans or initiatives around the Zero Trust model.

Moving to Improve Flexibility, Agility, Scalability and Security

Technologies and strategies planned for the coming year reflect a key focus on the competitive requirements of fast-paced digital markets. The top-two initiatives included moving from hardware appliances to more flexible software form factors and deploying hybrid cloud automation, management, and analytics to increase operational efficiency.

With DDoS attacks a prime concern, 29 percent of respondents planned to deploy or replace an existing web application firewall (WAF) or DDoS protection solution. Surprisingly, even several years after the introduction of modern Perfect Forward Secrecy (PFS) and Elliptical Curve Cryptography (ECC) encryption standards for enhanced security, 29 percent of organisations are only now working to upgrade their Transport Layer Security (TLS) capabilities to support these technologies.

Even as cloud adoption continues to be strong, five percent of decision makers intend to repatriate applications from private cloud environments to their private data centre. While not a high number, this is not entirely insignificant. Given the diversity of form factors, architectures, and deployment methods to choose from, it is important to make sure that the approach fits the organisation’s needs before proceeding.

Addressing the Requirements of Hybrid Cloud and Rising Demand

Moving forward, decision-makers view capabilities related to risk as especially important for their financial platforms. When it comes to the most important capabilities for financial platforms running in hybrid cloud environments, regulatory compliance, comprehensive application security and redundancy/disaster recovery are top must-haves.

In addition to the importance placed on redundancy/disaster recovery, many respondents (43 percent) named centralised management and analytics as important capabilities. Along with elastic scale for variable/seasonal demands (25 percent), this shows a recognition of the requirements to provide effective service through redundancy, scalability, and a sound infrastructure.

Compared with risk-related and operational priorities, cost saw considerably less emphasis in the survey. While 28 percent of respondents placed importance on automation for operational efficiency and reduced costs, just 18 percent prioritised flexible licensing and pricing.

Desired Benefits from New Technology Investments

As they plan new technology investments, decision-makers are motivated foremost by risk reduction—far outpacing business factors such as revenue, customer experience, and competitive advantage.

By a large majority, security was the most likely benefit to spur funding for new technology. Operational considerations followed, including operational improvements (65 percent) and cost savings (63 percent). Regulatory compliance, emphasised earlier in the survey as a priority for a hybrid cloud requirement, was not necessarily top-of-mind in the technology funding stage—but still of high importance (57 percent). Revenue generation was named as a highly important benefit by only 35 percent, followed by customer satisfaction at 32 percent. Even in an industry undergoing rapid digital transformation, just 32 percent of decision-makers cited business advantage from new technology as a prime factor—and only 17 percent were moved by the ability to accelerate development speed.

The results of the survey offer a snapshot of an industry in transition, as decision-makers seek to keep control over security and compliance and maintain operational consistency, as they look to tap into the agility and scalability of the cloud. It is clear that, while security is important for digital transformation initiatives, application delivery and managing multi-cloud environments are of equal importance.  Above all financial services organisations must maintain their good reputation and ensure customer trust. Firms must demonstrate that they are protecting customer assets, providing an ultra-reliable service, working with trustworthy partners and reducing risk to the business.

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