Arvensys Dynamic CRM team was working onsite with client and deployed the Dynamic CRM successfully .
Here is the celebration Cake. Cheers and you deserve it.
Microsoft Dynamics CRM connections are an excellent way to establish relationships between almost any records in CRM without having to create custom relationships between the entities. The big question is whether or not connections are a better option than creating multiple lookups on a CRM form. It’s a classic battle of connections vs lookups. We generally recommend using connections, and in today’s blog, we’ll talk about why connections are often more beneficial than lookups. Let’s start the debate!
Out-of-the-box Connections are handy in situations where one entity record looks up multiple records from another entity or when there is a need to represent a many-to-many relationship between records. When designing your CRM solution, sometimes it’s necessary to think about how NOT to create new lookups to fulfill the business need while still representing the same data.
For example, say you need relate four contacts (AuthorizedRep1, AuthorizedRep2, AuthorizedRep3, and AuthorizedRep4) to a custom “project” record. Instead of creating four lookups to the contact entity on the project form, it is a better idea to create one or more connection roles and then connect each of the four contacts to the project record using appropriate connection roles.
What are the Benefits of using Connections over Lookups?
1. Connections help keep your forms clean. For example, instead of putting four lookups to a contact on a form, you can instead create four connections and use a sub grid to display the contacts on the form.
2. Multiple records can be connected using the same role. Lookups do not allow more than one record to associate with one lookup, nor do they allow you to define how the records are related.
3. Connections eliminate the need to establish custom relationships between entities. Connections are an out-of-the-box feature and can connect almost any entity using a connection role. Lookup fields do create entity relationships behind the scenes.
4. Representing many-to-many relationships using lookups creates intersecting tables in the background. Using connections avoids intersecting tables, still enabling many-to-many connections.
5. When there are multiple departments or business units using the same entity for unique purposes, a business-specific custom lookup might mean nothing (or may be ambiguous) to other businesses units using that entity. Using connections eliminates the confusion caused by creating department specific lookups for an entity.
6. Connection roles can be categorized by specific categories and business. Doing this helps with data retrieval and reporting.
7. With connections, no custom SQL index is required since it is an out-of-the-box feature and the fields are already indexed.
As you can see, there are a lot more advantages to using connections over custom lookups in terms of data representation and the data model. However, one disadvantage of using connections over lookups is that with connections, data retrieval is a little less intuitive. Regardless, the advantages still make connections a CRM designer’s choice over lookups.
That’s all for the blog today! Hopefully you found this information useful when determining whether to use connections or lookups when designing your CRM solution. For more information about connections, check out these helpfu
Customer relationship management (CRM) is a term that refers to practices, strategies and technologies that companies use to manage and analyze customer interactions and data throughout the customer lifecycle, with the goal of improving business relationships with customers, assisting in customer retention and driving sales growth. CRM systems are designed to compile information on customers across different channels — or points of contact between the customer and the company — which could include the company’s website, telephone, live chat, direct mail, marketing materials and social media. CRM systems can also give customer-facing staff detailed information on customers’ personal information, purchase history, buying preferences and concerns.
CRM software consolidates customer information and documents into a single CRM database so business users can more easily access and manage it. The other main functions of this software include recording various customer interactions (over email, phone calls, social media or other channels, depending on system capabilities), automating various workflow processes such as tasks, calendars and alerts, and giving managers the ability to track performance and productivity based on information logged within the system.
Common features of CRM software include:
Marketing automation: CRM tools with marketing automation capabilities can automate repetitive tasks to enhance marketing efforts to customers at different points in the lifecycle. For example, as sales prospects come into the system, the system might automatically send them marketing materials, typically via email or social media, with the goal of turning a sales lead into a full-fledged customer.
Sales force automation: Also known as sales force management, sales force automation is meant to prevent duplicate efforts between a salesperson and a customer. A CRM system can help achieve this by automatically tracking all contact and follow-ups between both sides.
Contact center automation: Designed to reduce tedious aspects of a contact center agent’s job, contact center automation might include pre-recorded audio that assists in customer problem-solving and information dissemination. Various software tools that integrate with the agent’s desktop tools can handle customer requests in order to cut down the time of calls and simplify customer service processes.
Geolocation technology, or location-based services: Some CRM systems include technology that can create geographic marketing campaigns based on customers’ physical locations, sometimes integrating with popular location-based GPS apps. Geolocation technology can also be used as a networking or contact management tool in order to find sales prospects based on location.
The CRM technology market
The four main vendors of CRM systems are Salesforce.com, Microsoft, SAP and Oracle. Other providers are popular among small- to mid-market businesses, but these four tend to be the choice of large corporations.
On-premises CRM puts the onus of administration, control, security and maintenance of the database and information on the company itself. With this approach the company purchases licenses up front instead of buying yearly subscriptions. The software resides on the company’s own servers and the user assumes the cost of any upgrades and usually requires a prolonged installation process to fully integrate a company’s data. Companies with complex CRM needs might benefit more from an on-premises deployment.
With cloud-based CRM — also known as SaaS (software-as-a-service) or on-demand CRM — data is stored on an external, remote network that employees can access anytime, anywhere there is an Internet connection, sometimes with a third-party service provider overseeing installation and maintenance. The cloud’s quick, relatively easy deployment capabilities appeals to companies with limited technological expertise or resources.
Companies might consider cloud-based CRM as a more cost-effective option. Vendors such as Salesforce.com charge by the user on a subscription basis and give the option of monthly or yearly payments.
Data security is a primary concern for companies using a cloud-based system since the company doesn’t physically control the storage and maintenance of its data. If the cloud provider goes out of business or is acquired by another company, a company’s data can be compromised or lost. Compatibility issues can also arise when data is initially migrated from a company’s previous system to the cloud. Finally, cost may be a concern, since paying subscription fees for software can be more costly than on-premises-based models.
Open source CRM programs make source code available to the public, allowing companies to make alterations with no cost to the company employing it. Open source CRM systems also allow the addition and customization of data links to social media channels, assisting companies looking to improve social CRM practices. Vendors such as SugarCRM are popular choices in the open source market.
Adoption of any of these CRM deployment methods depends on a company’s business needs, resources and goals, since each has different costs associated with it.
Traditionally, data intake practices for CRM systems have been the responsibility of sales and marketing departments as well as contact center agents. Sales and marketing teams procure leads and update the system with information throughout the customer lifecycle and contact centers gather data and revise customer history records through service call and technical support interactions.
The advent of social media and the proliferation of mobile devices has caused CRM providers to upgrade their offerings to include new features that cater to customers who use these technologies.
Social CRM refers to businesses engaging customers directly through social media platforms such as Facebook, Twitter and LinkedIn. Social media presents an open forum for customers to share experiences with a brand, whether they’re airing grievances or promoting products.
To add value to customer interactions on social media, businesses use various tools that monitor social conversations, from specific mentions of a brand to the frequency of keywords used, to determine their target audience and which platforms they use. Other tools are designed to analyze social media feedback and address customer queries and issues. Companies are interested in capturing sentiments such as a customer’s likelihood of recommending their products and the customer’s overall satisfaction in order to develop marketing and service strategies. Companies try to integrate social CRM data with other customer data obtained from sales or marketing departments in order to get a single view of the customer.
Another way in which social CRM is adding value for companies and customers is customer communities, where customers post reviews of products and can engage with other customers to troubleshoot issues or research products in real time. Customer communities can provide low-level customer service for certain kinds of problems and reduce the number of contact center calls. Customer communities can also benefit companies by providing new product ideas or feedback without requiring companies to enlist feedback groups.
Mobile CRM — or the CRM applications built for smartphones and tablets — is becoming a must-have for sales representatives and marketing professionals who want to access customer information and perform tasks when they are not physically in their offices. Mobile CRM apps take advantage of features that are unique to mobile devices, such as GPS and voice-recognition capabilities, in order to better serve customers by giving employees access to this information on the go.
For all of the advancements in CRM technology, without the proper management, a CRM system can become little more than a glorified database where customer information is stored. Data sets need to be connected, distributed and organized so that users can easily access the information they need.
Maybe you’ve heard of the x86 central processing unit (CPU) architecture that powers most PCs and servers today. But once upon a time in PC land, Intel made a bundle of cash selling x87 math co-processor chips to accompany the x86 products. These chips excelled at, and accelerated, floating point math operations and helped make PCs much faster at performing certain tasks that were hot and relevant back then, like recalculating spreadsheets.
But spreadsheets are old hat now, and math co-processor functionality eventually got integrated into the CPU itself, forcing the math x87 line to dry up. But Artificial Intelligence (AI) has, in a way, brought math co-processors back in vogue, by utilizing graphics processing units (GPUs) in a similar supporting role. As it turns out, the kind of mathematical capabilities required to render high-resolution, high frame-rate graphics are also directly applicable to AI.
Specifically, the work required to train predictive machine learning models, especially those based on neural networks and so-called deep learning, involves analysing large volumes of data, looking for patterns and building statistically-based heuristics. The more training data used, the more accurate the predictive models become. GPUs are great for this type of work, despite the fact that it’s not really about graphics or video.
That’s why NVIDIA, a company originally focused on GPUs and chipsets for video adapter cards and game consoles is rapidly morphing into an AI company.
For example, NVIDIA is now working with the Center for Clinical Data Science (CCDS) in Cambridge, Massachusetts, to employ AI in the service of assisting radiologists in reading and interpreting x-rays, MRIs, CAT scans and the like. The company’s DGX systems, based on its Volta AI architecture, are being used by CCDS radiologists to speed up the process of analysing medical imagery and finding abnormalities and patterns in them.
CCDS just took delivery of the world’s first NVIDIA DGX-1 supercomputer in December of last year and has already successfully trained machine learning models to do work not only in the sphere of radiology but also in cardiology, ophthalmology, dermatology and psychiatry. CCDS will soon be using a DGX Station — an AI-specialized desktop workstation — for medical AI work as well.
NVIDIA’s DGX technology is being deployed not just in medicine, but in a variety of industrial contexts. For example, the company has teamed with Avitas Systems, a venture backed by General Electric, in the service of drone-assisted industrial inspection. This work involves the physical inspection of industrial infrastructure, including flare stacks and heated gas plumes.
Drones can perform inspections in conditions that would be lethal to humans; NVIDIA explains that flare stacks must be shut down for days before they become cool enough for a human inspector to approach. Such multi-day shut downs involve huge production costs and drone-based inspection saves on those costs.
But drone-based inspection requires real-time intelligent guidance based on readings picked up by the drones’ sensors (including temperatures encountered and what the drone “sees”). That intelligent guidance is only made possible by AI, and that’s where the DGX technology comes in. Interestingly, because of all this real-time processing and given the super-human nature of the work, there’s an element to drone inspection that parallels gaming. That’s a pretty cool connection of old and new.
Here’s another one: Because Avitas Systems is a GE venture, it uses GE Predix, which is a predictive analytics platform that integrates with GE Historian. I’ve written about GE historian technology before, but I did so more than five years ago, when its applications were mostly limited to preventive maintenance. That Predix can now support downstream drone-based inspection shows how useful AI is in its industrial applications…and how much value it’s adding to the more rote data collection that has been in place for quite some time.
Detour or destination?
So NVIDIA, a graphics- and video-focused company founded nearly 25 years ago, is reinventing itself as an AI company in the present tense. That’s a great way to stay relevant, but is it orthogonal? After some pondering, I’ve decided it’s not. Not only is math co-processing common to both disciplines in terms of underlying technology, but both offer future-facing technology that can be aimed at rendering immersive experiences and simulations.
Plus, NVIDIA has corroboration from its competitors in making this pivot. AMD’s in the game too with its Radeon Instinct product, and Intel’s Xeon Phi processors are relevant to machine learning and AI as well. Data, analytics and AI are providing the momentum for almost everything in the computing world. Why shouldn’t the semiconductor companies, who are critical to computing’s infrastructure, align with that trend? It’s just common sense.
CeBIT Australia 2017 was well under way and it was be able to attract an expert panel of business technology experts from across Australia and overseas to be part of the event. Today, we heard about the latest innovations in Mobility, eGovernment, and FinTech.
Some of the highlights included Senator The Hon Arthur Sinodinos AO of the Australian Government, the women in technology panel, Dennis Andrucyk from NASA and the CEO panel.
Arvensys Business Develop team joined the three-days CeBIT and we will join CeBIT 2018.
A future with autonomous vehicles (AVs) is often touted as a sort of utopia. Imagine it: no frustrating traffic jams, no driving around in circles trying to find a parking spot, more green cities – in both literal and environmental terms, as areas previously used for transport infrastructure are reclaimed for green spaces, while our carbon footprint shrinks. We’ll have more mobility, more time, and more money in our pockets.
This technology will also save lives. Currently, 1.2 million people die on the road each year – this is equivalent to a 737 airplane falling out of the sky every hour – and in 94% of cases, the cause is human error. Driverless cars promise to drastically cut down the number of road fatalities.
But is the future really as rosy as tech and auto corporations make it out to be? And with 10 million (partially and fully) self-driving cars predicted to be on the road by 2020, are we fully prepared for the repercussions that are right around the corner?
There are some tough questions that have yet to be fully answered.
Are they safe?
With cameras and radar that can scan 360 degrees, the capabilities of AVs already far outstrip that of humans. So if the question is, “Are self-driving cars safer than humans?”, the answer is an unequivocal “Yes”.
However, if the question is, “Are they safe?”, the answer becomes less clear. While AVs don’t drink alcohol, get distracted by phones or fall asleep at the wheel, as a human might, they have demonstrated a more limited ability to cope with novel situations than humans have. Volvo, for example, has admitted that their self-driving cars, which can recognise elk and caribou, are confused by kangaroos because of how they hop. There’s also some evidence that AVs perform poorly in adverse weather conditions, such as rain, which can obscure cameras, create confusing glare and reflections, and reduce the range and accuracy of sensors. Even graffiti on road signs can confound the visual recognition software on AVs, with researchers showing that simply by applying stickers to a stop sign, they could trick the machine into thinking it was a speed limit sign. And with all the technology and software required to operate an AV, they are also vulnerable to cyber security attacks and hacking.
As these issues get ironed out, it is inevitable that others will arise, causing injuries and even death. There has already been one death: in May 2016, Joshua Brown became the first known fatality in a self-driving car when his Tesla Model S collided with a semitrailer, because the car’s auto-pilot system was unable to detect the white truck against the brightly lit sky. After a federal investigation, auto-safety regulators said no defects were found in the system and that Tesla’s Autopilot-enabled cars did not need to be recalled.
The reality is that AVs will likely never be perfectly safe. However, when you weigh the benefits against the costs, it seems impossible to prevent AVs from taking over our roads. As Nikolaus Lang, of the Boston Consulting Group’s Centre for Digital in Automotive, says, “As with any new technology, there will be failures and even fatalities, but the overall benefits – in terms of [estimated] 90% fewer accidents, 40% less congestion, up to 80% less emissions, and 50% of parking space saved – are so substantial that the technological development will prevail.”
Are they ethical?
There will undoubtedly come a time when AVs will have to make difficult decisions about who lives and who dies – should it sacrifice the passenger to save the life of a pedestrian, or vice versa?
Researchers have already started to tackle these sticky moral dilemmas. MIT, for example, have decided to use crowdsourcing to gather human perspectives on moral decisions made by artificial intelligence, in an initiative called Moral Machine.
And there is evidence to suggest that human moral behaviour can be adopted by machines. One study by the University of Osnabrück found human moral behaviour follows a relatively simple ‘value of life’-based model that could theoretically be described by an algorithm, meaning the ways humans would react in such situations can be applied to technology such as AVs.
But while we perhaps can potentially make machines act like humans, the question remains, should we?
What are the legal implications?
Along with new technology, comes new legal issues. Who is deemed at fault, for example, when a driverless car gets a parking fine or commits a traffic violation, like running a stop sign? Who is liable if an AV is involved in accident, and damages property, or causes injury, or even death?
Some countries are already looking to the future in terms of their legislation. The UK have recently passed a billthat says insurers would be primarily responsible for paying out damages stemming from accidents caused by AVs in self-driving mode, where the vehicle is insured at the time of the accident. This in effect protects manufacturers from potential lawsuits, which might threaten to stifle the development of AVs.
California has also recently proposed regulations to allow fully AVs to drive on public roads without any people in the car (currently AVs have to have a backup driver at all times) – a move that has been labelled a “game-changer”. The new changes would also allow companies to self-certify their vehicles as safe to operate without a human, though some experts have misgiving about this. Ryan Calo, a law professor at University of Washington, called it “a very big leap”, adding, “I’m worried by the idea of a company saying, ‘We’re good.’”
While countries like the US, the UK and Sweden already have policies in place to facilitate the development and introduction of AVs onto public roads (for example, in 2015, the UK government published a Code of Practice for testing driverless cars and the Swedish government launched a Strategic Innovation Program called Drive Sweden), Australia still lags behind in terms of a federal regulatory framework.
Some state governments are starting to get on board, though – in September 2015, South Australia became the first state to introduce legislation to permit on-road testing of driverless cars. And just recently, the NSW government has passed similar legislation, allowing for the testing of AVs on both city and regional roads across the state. This comes as the first trial of AVs in NSW gets underway, with the government partnering with HMI Technologies, NRMA, Telstra, and IAG for a two-year trial of a driverless shuttle bus at Sydney Olympic Park.
Other organisations have also taken up the helm in a bid to prepare Australia for the inevitable future. The Australian Driverless Vehicle Initiative, for example, is an organisation that aims to “accelerate the safe and successful introduction of driverless vehicles onto Australian roads”.
What societal impact will they have?
While AVs promise us more free time, less accidents and markedly reduced healthcare costs, less is said about the disruptive effects they will have, particularly on the labour market. The mining industry may be among the first hit, according to a McKinsey report, as AVs are initially adopted in controlled environments such as mines.
Thousands of taxi, bus, van and truck drivers will eventually lose their jobs, and this will have a trickle-down effect, impacting on management and support roles, as well as on those businesses, particularly in regional areas, that depend on trucking routes, like motels, retail stores and restaurants.
The entire auto industry will also likely get a big shake-up, as car ownership drops and cars run on electricity rather than petrol. This potentially impacts car dealers, auto mechanics and petrol station attendants.
As these people grapple with job losses, it will be up to governments to balance the merits of introducing this technology over the challenges of supporting, reskilling and redistributing this significant proportion of the labour force. There will also be implications on taxation, particularly on those multinationals who stand to gain the most financially. Some have even suggested a universal basic income could be part of the solution.
A brave new world
Autonomous vehicles hold a lot of promise, but they also come with a swathe of challenges that will need to be carefully considered and regulated in order to get the most societal benefit out of the technology and ensure no citizen is left behind.
Government agencies are already using smart technology to tackle big issues, create transformative change and foster social inclusion. Want to know how? Then download our free ebook Smart technology, happy citizens: how governments can foster social inclusion now.
Posted by CeBIT Australia