"We have to go into the year 2021 absolutely hating the word average," Lovelock said. "As soon as you say 'the average is', the only thing you are going to know for sure is that absolutely nobody is going to do that." Some CIOs spent on devices to get their workforces equipped to work from home. Others didn't. That's because executives looking to preserve cash in a crisis cut back where they could, according to Lovelock. "In 2020 devices is one of those first areas where you can save cash," he said. "When CIOs are faced with cash flow restrictions like they were in March and April, the first thing you save on or the first thing you defer is that deferable spending. That's mobile phones, laptops, all those hard things you can buy and pay cash up front form. You can sweat these assets." Meanwhile, categories that saw huge growth included desktop as a service and cloud-based video conferencing, according to Lovelock. These extremes in spending are part of what makes the 2020 recession different from the Great Recession of 2009 and 2010. That earlier economic downturn hit everyone across the board. "The decline in IT spending was more evenly spread," Lovelock said.
While we still use COBOL and other older programming languages, we also keep inventing new languages, each with its own advantages and disadvantages. For example, we have Rust and C++ for low-level, performance-sensitive systems programming (with Rust adding the benefit of safety); Python and R for machine learning, data manipulation, and more; and so on. Different tools for different needs. But as we move into this Everything-as-Code world, why can't we just keep using the same programming languages? After all, wouldn't it be better to use the Ruby you know (with all its built-in tooling) rather than starting from scratch? The answer is "no," as Graham Neray, cofounder and CEO of oso, told me. Why? Because there is often a "mismatch between the language and the purpose." These general-purpose, imperative languages "were designed for people to build apps and scripts from the ground up, as opposed to defining configurations, policies, etc." Further, mixing declarative tools with an imperative language doesn't make things any easier to debug. Consider Pulumi, which bills itself as an "open source infrastructure-as-code SDK [that] enables you to create, deploy, and manage infrastructure on any cloud, using your favorite languages." Sounds awesome, right?
The first new thing was caching data in the Apache Arrow format. The company employs the creators of Arrow, the in-memory data format, and it uses Arrow for in the computation engine. But Dremio was not using Arrow to accelerate queries. Instead, it used the Apache Parquet file format to build caches. However, because it’s an on-disk format, Parquet is much slower than Arrow. ... The second new thing that Dremio had to build was scale-out query planning. This advance enabled the massive concurrency that the biggest enterprise BI shops demand of their data warehouses. “Traditionally in the world of big data, people had lots of nodes to support big data sets, but they didn’t have lots of nodes to support concurrency,” Shiran says. “We now scale out our query planning and execution separately.”By enabling an arbitrary number of query planning coordinators in the Dremio cluster to go along with an arbitrary number of query executors, the software can now support deployments involving thousands of concurrent users. The third new element Dremio is bringing to the data lake is runtime filtering. By being smart about what database tables queries actually end up accessing during the course of execution, Dremio can eliminate the need to perform massive table scans on data that has no bearing on the results of the query.
The good news is that 71% of participants said that they view cybersecurity professionals as smart, technically skilled individuals, 51% view them as “good guys fighting cybercrime,” and 35% said cybersecurity professionals “keep us safe, like police and firefighters.” The bad news is that even though most view cybersecurity as a good career path, they don’t think it’s the right path for them. In fact, only 8% of respondents have considered working in the field at some point. “One of the most unexpected findings in the study is that respondents from the youngest generation of workers – Generation Z (Zoomers), which consist of those up to age 24 – have a less positive perception of cybersecurity professionals than any other generation surveyed. This issue in particular merits close attention by the cybersecurity industry at a time when employers are struggling to overcome the talent gap,” (ISC)² noted. The analysts posited that Generation Z’s perceptions of the cybersecurity field are shaped negatively by social media exposure, as social media platforms “tend to focus on the negative – arguments and venting.”
For businesses, progressive data governance encourages fluid implementation using scalable tools and programs. The first step is to identify both a dataset and the relevant function. Using the same example as before, this could be the data in a reporting system the accounts department uses. That data could then be used during data literacy training hosted by a data governance software tool. Sticking with data literacy, after establishing one use case, an organization may decide to progress by expanding existing programs to other departments and then moving on to another function of data governance, such as identifying the roles and responsibilities of various data users or developing an internal compliance program. Businesses can scale the scope of the data they include in a governance program gradually, which gives them the chance to learn important lessons along the way. As an organization grows in confidence, it may widen its data scope and source it from other departments and locations. Progressive data governance can be described as a three-step process that incorporates the three C’s: catalog, collaborate and comply. Cataloging data assets makes data discoverable.
As you start to rely on more data sources, and more frequently need to blend your data, you’ll want to build out a Data Lake—a spot for all of your data to exist together in a unified, performant source. Especially when you need to work with data from applications like Salesforce, Hubspot, Jira, and Zendesk, you’ll want to create a single home for this data so you can access all of it together and with a single SQL syntax, rather than many different APIs. ... In the Lake stage, as you bring in more people to work with the data, you have to explain to them the oddities of each schema, what data is where, and what special criteria you need to filter by in each of the tables to get the proper results. This becomes a lot of work, and will leave you frequently fighting integrity issues. Eventually, you’ll want to start cleaning your data into a single, clean source of truth. ... When you have clean data and a good BI product on top of it, you should start noticing that many people within your company are able to answer their own questions, and more and more people are getting involved. This is great news: your company is getting increasingly informed, and the business and productivity results should be showing.
Most tools’ catalog interfaces provide many helpful features that together provide the context behind the data. The interface has many visual features that are certainly not vintage 1980’s. For example, many data catalog products have data quality metrics built in, which show dashboards of an asset’s quality on many of the “data quality dimensions.” These dashboards can be visible to the user and can help them determine if the data is suitable for their purposes. ... Data lineage is an extremely important feature of data catalogs; the products vary in how they perform it and how deep the lineage goes. One of my government sponsors felt data lineage was critical to their understanding, especially the visual depiction of the lineage. The data catalog’s data lineage diagrams tell the whole “back story” of the data: where it comes from, where it’s going, how “good” it is (based on whatever quality metrics are relevant), and some products even show the level of protection in the lineage diagram. The interface is important because it displays a visual diagram of the data flow along with descriptive metadata. See Figure 2 from Informatica which shows column-to-column mappings as data flows from one system to another, from source to warehouse or data lake. Notice that the actual transformations can also be shown for a given column.
This approach drastically reduces project delays and cost overruns due to miscommunication between frontend and backend teams leading to changes in APIs and backend systems. After designing the APIs, it can take some time to get the live backend systems up and running for the frontend teams to make API calls and test the system. To overcome this issue, frontend teams can set up dummy services, called mock backends, that mimic the designed APIs and return dummy data. You can read more about it in this API mocking guide. There can be instances where the requirements are vague or the development teams aren’t sure about the right approach to design the APIs upfront. In that case, we can design the API for a reduced scope and then implement it. We can do this for several iterations, using multiple sprints until the required scope is implemented. This way, we can identify a design flaw at an earlier stage and minimize the impact on project timelines. ... In software engineering, the façade design pattern is used to provide a more user-friendly interface for its users, hiding the complexity of a system. The idea behind the API façade is also the same; it provides a simplified API of its complex backend systems to the application programmers.
With favourable tech regulation, massive mobile adoption, and shifting expectations across the demographics, digital challengers are well positioned to advance and evolve the personalised services they offer. Fintechs have the advantage of starting from scratch, without having to build on legacy IT infrastructure bureaucratic decision-making processes. They are lean and innovative, led by entrepreneurs on a mission to change the world. Using the latest technologies such as Artificial Intelligence (AI), Blockchain, Biometrics Security and Cloud, the processes, compliance requirements, policies and technology differ from conventional banks, providing lower operating and resource costs. With these foundations, they are well-positioned to pursue a highly customer-centric approach and rapid product innovation. By contrast, for traditional banks it can be an arduous task to innovate and reinvent. They are highly bureaucratic and slow-moving, with high-cost structures and substantial legacy tech. These characteristics prevent them from flexibly adapting to fast-changing consumer expectations. Service providers unable to live up to the expectations of best-in-class digital experiences will see high switching rates. As a result, providers are actively investing in initiatives that boost customer experience in a bid to increase long-term customer retention.
Quote for the day:
"Rarely have I seen a situation where doing less than the other guy is a good strategy." -- Jimmy Spithill