Artificial Intelligence (AI) and its intrinsic disciplines, including Machine Learning (ML), Natural Language Processing (NLP), and so forth, help to acquire the learning and decision-making abilities in an RPA task. Basically, RPA is for doing. Artificial intelligence is for contemplating ‘what should be done’. Artificial intelligence makes RPA intelligent. Together, these advances offer ascent to Cognitive Automation, which automates many use cases, which were just inconceivable before. The most recent transformation was the point when the virtualized platforms permitted the expansion and expulsion of assets required for processes dependent on the workloads. This permitted the organizations to investigate opportunities to characterize their processes based on automated rules. This was the development of Robotic process automation. RPA goes above and beyond making the monotonous process automated so human intercession is lost. A straightforward application for this could be rule-based reactions you need to accommodate certain work processes. When you code in the Rules once they don’t need any kind of intervention and the RPA deals with everything. Organizations have profited by executing RPA based solutions and processes to reduce expenses multiple times.
The GDPR states the Data Protection Officer must be capable of performing their duties independently, and may not be “penalized or dismissed” for performing those duties. (The DPO’s loyalties are to the general public, not the business. The DPO’s salary can be considered a tax for doing business on the internet.) Philip Yannella, a Philadelphia attorney with Ballard Spahr, said: “A Data Protection Officer can’t be fired because of the decisions he or she makes in that role. That spooks some U.S. companies, which are used to employment at will. If a Data Protection Officer is someone within an organization, he or she should be an expert on GDPR and data privacy.” Not having a Data Protection Officer could get quite expensive, resulting in stiff fines on data processors and controllers for noncompliance. Fines are administered by member state supervisory authorities who have received a complaint. Yannella went on to say, “No one yet knows what kind of behavior would trigger a big fine. A lot of companies are waiting to see how this all shakes out and are standing by to see what kinds of companies and activities the EU regulators focus on with early enforcement actions.”
EA is an enterprise-wide, business-driven, holistic practice to help steer companies towards their desired longer-term state, to respond to planned and unplanned business and technology change. Embracing EA Principles is a central part of EA, though rarely adopted. The focus in those early days was reducing complexity by addressing duplication, overlap, and legacy technology. With the line between technology and applications blurring, and application sprawl happening almost everywhere, a focus on rationalizing the application portfolio soon emerged. I would love to say that EA adoption was smooth, but there were many distractions and competing industry trends, everything from ERP to ITIL to innovation. The focus was on delivery and operations, and there was little mindshare for strategic, big-picture, and longer-term thinking. Practitioners were rewarded only for supporting project delivery. Many left the practice. And frankly, a lot of people who didn’t have EA-skills were thrust into the role. That further exacerbated adoption challenges and defined the delivery-oriented technology-focused path EA would follow. It is still dominant today.
Artificial intelligence and machine learning technologies have proven to accelerate the ability for banks, insurance companies, and retail brokerages to successfully combat fraud, manage risk, cross sell and upsell, and provide tailored services to existing clients. To harness the power and potential of these solutions, financial institutions will look to leverage external data from third-party vendors and partners and in-house data to mine for the best answers and recommendations. Today’s cloud-native and cloud-first solutions offer financial institutions the ability to capture, process, analyze, and leverage the intelligence from this data much faster, more efficiently, and more effectively than trying to do it internally. Improving Customer Experience Through Digital Modernization: Banks and insurance companies have been modernizing and/or replacing legacy core systems, many of which have been around for decades, with cloud-native and cloud-first solutions. These include offerings from organizations like FIS Global, nCino, and my former employer EllieMae in the banking industry, and offerings from Guidewire and DuckCreek for cloud-native policy administration, claims, and underwriting solutions in the insurance sector.
Data governance is responsible for ensuring data assets are of sufficient quality, and that access is managed appropriately to reduce the risk of misuse, theft, or loss. Data governance is also responsible for defining guidelines, policies, and standards for data acquisition, architecture, operations, and retention among other design topics. In the next blog post, we will discuss further the segregation of duties shown in figure 1; however, at this point it is important to note that modern data governance programs need to take a holistic view to guide the organization to bake quality and privacy controls into the design of products and services. Privacy by design is an important concept to understand and a requirement of modern privacy regulations. At the simplest level it means that processes and products that collect and or process personal information must be architected and managed in a way that provides appropriate protection, so that individuals are not harmed by the processing of their information nor by a privacy breach. Malice is not present in all privacy breaches. Organizations have experienced breaches related to how they managed physical records containing personal information, because staff were not trained to properly handle the information.
The DataOps solution to the hand-over problem is to allow every stakeholder full access to all process phases and tie their success to the overall success of the entire end-to-end process ... Value delivery is a sprint, not a relay. Treat the data-to-value process as a unified team sprint to the finish line (business value) rather than a relay race where specialists pass the baton to each other in order to get to the goal. It is best to have a unified team spanning multiple areas of expertise responsible for overall value delivery instead of single specialized groups responsible for a single process phase. ... A well architected data infrastructure accelerates delivery times, maximizes output, and empowers individuals. The DevOps movement played an influential role in decoupling and modularizing software infrastructure from single monolithic applications to multiple fail-safe microservices. DataOps aims to bring the same influence to data infrastructure and technology. ... At its core, DataOps aims to promote a culture of trust, empowerment, and efficiency in organizations. A successful DataOps transformation needs strategic buy-in starting from C-suite executives to individual contributors.
While not as critical a decision as marriage, most organizations today face a similar trust-based dilemma- which cloud service provider to trust with their data? There is no debate over the clear value drivers for cloud computing- performance, cost and scalability to name a few. However, the lack of control and oversight could make organizations hesitant to hand over their most valuable asset- information, to a third party, trusting they have adequate information protection controls in place. With any trust-based decision, external validation can play an important role. Arranged marriages rely on positive feedback and references, mostly attested by the matchmaker. It also relies on supporting evidence such as corroborations of relatives and more tangible factors such as education/career history of the potential bride/ groom. In case of cloud service providers, independent validation such as certifications, attestation or other information protection audits could make or break a deal. The notion of cloud computing may have existed as far back as the 1960s but cloud services took the form we know of today with the launch of services from big players such as Amazon, Google and Microsoft in 2006-2007.
If successfully executed, Morris explained that BMIL could serve as the basis for a wide range of DERs supporting both Germany’s wholesale and retail electricity markets: “This will make it easy, efficient and low cost for any DER in Germany to participate in the energy market. Grid operators and utility providers will also gain access to an untapped decarbonized Germany energy system.” However, technical challenges remain. Mamel from DENA noted that BMIL is a project built around the premise of interoperability — one of blockchain’s greatest challenges to date. While DENA is technology agnostic, Mamel explained that DENA aims to test a solution that will be applicable to the German energy sector, which already consists of a decentralized framework with many industry players using different standards. As such, DENA decided to take an interoperability approach to drive Germany’s energy economy, testing two blockchain development environments in BMIL. Both Ethereum and Substrate, the blockchain-building framework for Polkadot, will be applied, along with different concepts regarding decentralized identity protocols.
Within the data vault approach, there are certain layers of data. These range from the source systems where data originates, to a staging area where data arrives from the source system, modeled according to the original structure, to the core data warehouse, which contains the raw vault, a layer that allows tracing back to the original source system data, and the business vault, a semantic layer where business rules are implemented. Finally, there are data marts, which are structured based on the requirements of the business. For example, there could be a finance data mart or a marketing data mart, holding the relevant data for analysis purposes. Out of these layers, the staging area and the raw vault are best suited to automation. The data vault modeling technique brings ultimate flexibility by separating the business keys, which uniquely identify each business entity and do not change often, from their attributes. These results, as mentioned earlier, in many more data objects being in the model, but also provides a data model that can be highly responsive to changes, such as the integration of new data sources and business rules.
Quote for the day:
"The closer you get to excellence in your life, the more friends you'll lose. People love average and despise greatness." -- Tony Gaskins