What do we do when we’re doing whole-enterprise architecture? How do we choose what to do, when, in what order? And how do we record what happens, the outcomes, the results? Perhaps the core to all of this is the ‘Start Anywhere’ principle, and the focus on overall effectiveness of the enterprise. Yes, the potential scope of whole-enterprise-architecture might at first seem impossibly huge: anything, anywhere, in any aspect or domain of the entire enterprise, and even beyond. Yet the crucial twist is that the enterprise is seen as an ecosystem, or ecosystem-of-ecosystems: whichever way we look at it, it’s always oneintegrated whole, deeply interdependent, deeply interwoven. In which case, it doesn’t matter where we start: if everything’s connected to everything else, then we connect with everywhere eventually.
When IT deals with big data, the primary arena for it is, once again, large servers that are parallel processing in a Hadoop environment. Thankfully for the company at large, IT also focuses on reliability, security, governance, failover, and performance of data and apps—because if it didn't, there would be nobody else internally to do the job that is required. Within this environment, IT's job is most heavily focused on the structured transactions that come in daily from order, manufacturing, purchasing, service, and administrative systems that keep the enterprise running. In this environment, analytics, unstructured data and smaller servers in end user departments are still secondary.
"Once inside a network, attackers can identify high-value files, databases, and backup systems and then encrypt all of the data at one time," the report suggested -- and pointed to malware families such as SamSa which can be deployed manually into an infected system. As ransomware becomes more dangerous, researchers fear that cybercriminals will use its increased power to extract higher ransom payments from victims. Currently, the majority of ransomware perpetrators demand between $200 and $500 -- usually in bitcoin -- before they release the victim's system. ... "If attackers are able to determine that they have compromised a system which stores valuable information, and that infected organization has a higher ability to pay, they will increase their ransoms accordingly," the researchers said.
A second practice to kill EA complexity is to take a more selective approach to recording and managing data. This approach is often referred to as, 'Just Enough' Enterprise Architecture. It seems obvious when working with tangible ‘things’ - the more things you own, the more difficult it is to control and maintain the ones you want. Yet with data, this logic and reasoning is often lost. To kill EA complexity, Enterprise Architects should adopt a more vigilant approach in managing their data. Additionally, what EA’s choose to record should be more deeply considered. A ‘Just Enough’ approach to Enterprise Architecture has been championed by leading analysts - including Gartner - for some time, and for this exact reason. Maintaining data that provide value to your initiative is in essence, choosing to increase your own workload, and decreasing your productivity.
Mike Germano is partially in charge of cultivating the corporate culture that's helped Carrot Creative secure the prestigious title two years in a row. When seeking candidates, Carrot Creative's hiring managers take care to do things differently. Germano says the company prefers to avoid recruiters, utilizes social media diligently, focuses on relationships with educational institutions, and puts candidates for tech positions through a variety of tests to ensure both cultural fit and technical expertise. ... "Candidates meet with not only technical managers, but also members throughout departments to discuss various aspects of the job and [the company itself]. We put a lot of emphasis on the candidate’s natural excitement and drive, not only for what they do, but also for trying and learning new things."
We all know how great it is when technology works — and how frustrating it is when it doesn’t. Even sophisticated technology companies haven’t eliminated their human customer support teams, because when something goes wrong, it is often a human who needs to fix it. There will always be a need for on-site, human labor and expertise when we deal with machines. Robots will have glitches, need updates and require new parts. As we rely more and more on mechanized systems and automation, we will require more people with technical skills to maintain, replace, update and fix these systems and hardware. We see this starting already. IT departments have sprung into existence because of digital technologies. Network administrator, field service technician and web developer are job titles that didn’t exist 30 years ago.
The spark.mllib package contains the original Spark machine learning API built on Resilient Distributed Datasets (RDDs). It offers machine learning techniques which include correlation, classification and regression, collaborative filtering, clustering, and dimensionality reduction. On the other hand, spark.ml package provides machine learning API built on the DataFrames which are becoming the core part of Spark SQL library. This package can be used for developing and managing the machine learning pipelines. It also provides Feature Extractors, Transformers, Selectors, and machine learning techniques like classification and regression, and clustering.
With the break of digital Transformation, discipline of Enterprise Architecture, EA, is shaken on its bases. A questioning is more than necessity. Large consulting firms, carriers of miracle solution, are reduced to simplistic recommendations (bimodal IT) attacked by competitor gurus (see the debate), without real proposal on the bottom. Confronted on the one hand with an immense IT heritage, and on the other hand with this multiform disruption, Enterprise, CIO, do not know by which end take the problem. One claims to see cleavages everywhere: between the IT into bimodal, between the SQL and NoSQL, between intern and external Information Systems… But, clearly, these dichotomies does not function, because the value chain do not divide thus.
In today’s business situation with its complexity, required to be responsive, the costs to an organization can be important to stay competitive and meet business initiatives and challenges. An organization might face challenges and business problems like Global competition, product development costs, regulatory compliance, new business opportunity, and lack of skilled staff. While addressing any of these issues, the organization must be sure that the value of the business internally and the value provided to its customers is maintained or improved. This influences the executives to focus on how they can grow, sustain, change, and manage the organization to meet these challenges pertaining to corporate policies, processes, and IT infrastructure and systems that are required.
Traditional governance strategies often prove to be both onerous and ineffective in practice due to the focus on artifact generation and review. For example, delivery teams will often produce required artifacts, such as requirements documents or architecture documents, solely to pass through the quality gate. ... The result is a governance façade that often injects risk, cost, and time into the team efforts: the exact opposite of what good governance should be about. Lean IT governance, on the other hand, is a lightweight approach to IT governance that is based on motivating and enabling IT professionals to do what is best for your organization. Lean IT governance strives to find lightweight, collaborative strategies to address governance areas.
Quote for the day:
"Once a new technology rolls over you, if you're not part of the steamroller, you're part of the road." -- Stewart Brand