With an extended crisis a real possibility, new habits must be adopted and embraced for the business to adapt, recover and operate successfully in the long-term. It’s important for CIOs to take the time to understand these habits, how they have formed and if they are here to stay. One of the more obvious habit changes we’ve all experienced is the shift from physical meetings, where cases were presented and decisions made in person, to virtual conferences. This has made people feel more exposed in decision making as the human interaction of reading body language has been lost. However, people have unknowingly started using data more and have shifted to making more data driven decisions. If new and initial habits are here to stay for the long-term, CIOs must embed them into the new DNA of the business. If they aren’t, however, it’s crucial to curb and manage these new habits before they become automatically ingrained and costly to reverse. This happened to a CIO I recently spoke with, who made a massive technology investment, changed vendors and even shortened office leases in the rush to shift their organisation to a remote working model.
The obvious approach to addressing these mistakes is to identify wasted resources and reallocate them to more productive uses of data. This is no small task. While there may be budget items and people assigned to support analytics, AI, architecture, monetization, and so on, there are no budgets and people assigned to waste time and money on bad data. Rather, this is hidden away in day-in, day-out work — the salesperson who corrects errors in data received from marketing, the data scientist who spends 80% of his or her time wrangling data, the finance team that spends three-quarters of its time reconciling reports, the decision maker who doesn’t believe the numbers and instructs his or her staff to validate them, and so forth. Indeed, almost all work is plagued by bad data. The secret to wasting less time and money involves changing one’s approach from the current “buyer/user beware” mentality, where everyone is left on their own to deal with bad data, to creating data correctly — at the source. This works because finding and eliminating a single root cause can prevent thousands of future errors and eliminate the need to correct them downstream. This saves time and money — lots of it! The cost of poor data is on the order of 20% of revenue, and much of that expense can be eliminated permanently.
Through data science automation, companies are not only able to fail faster (which is a good thing in the case of data science), but to improve their transparency efforts, deliver minimum value pipelines (MVPs), and continuously improve through iteration. Why is failing fast a positive? While perhaps counterintuitive, failing fast can provide a significant benefit. Data science automation allows technical and business teams to test hypotheses and carry out the entire data science workflow in days. Traditionally, this process is quite lengthy — typically taking months — and is extremely costly. Automation allows failing hypotheses to be tested and eliminated faster. Rapid failure of poor projects provides savings both financially as well as in increased productivity. This rapid try-fail-repeat process also allows businesses to discover useful hypotheses in a more timely manner. Why is white box modelling important? White-box models (WBMs) provide clear explanations of how they behave, how they produce predictions, and what variables influenced the model. WBMs are preferred in many enterprise use cases because of their transparent ‘inner-working’ modeling process and easily interpretable behavior.
Microsoft notes that, in the last two years, the company has sent out 13,000 notifications to customers who have been targeted by nation-states. The majority of these nation-state attacks originate in Russia, with Iran, China and North Korea also ranking high, according to Microsoft. The U.S. was the most frequent target of these nation-state campaigns, accounting for nearly 70% of the attacks Microsoft tracked, followed by the U.K., Canada, South Korea and Saudi Arabia. And while critical infrastructure remains a tempting target for sophisticated hacking groups backed by governments, Microsoft notes that organizations that are deemed noncritical are increasingly the focus of these campaigns. "In fact, 90% of our nation-state notifications in the past year have been to organizations that do not operate critical infrastructure," Tom Burt, corporate vice president of customer security and trust at Microsoft, writes in a blog post. "Common targets have included nongovernmental organizations, advocacy groups, human rights organizations and think tanks focused on public policy, international affairs or security. This trend may suggest nation-state actors have been targeting those involved in public policy and geopolitics, especially those who might help shape official government policies."
Everything’s an abstraction these days. How many “existential threats” are there? We need “universal” this and that, but let’s not forget that relativism – one of abstraction’s enforcers – is hovering around all the time making things better or worse, depending on the objective of the solution du jour. Take COVID-19, for example. Based upon the assumption that the US knows how to solve “enterprise” problems – the abstract principle at work – the US has done a great job. But relativism kills the abstraction: the US has roughly 4% of the world’s population and 25% of the world’s deaths. How many technology solutions sound good in the abstract, but are relatively ineffective? The Agile family is an abstract solution to an age-old problem: requirements management and timely cost-effective software applications design and development. But the relative context is way too frequent failure. We’ve been wrestling with requirements validation for decades, which is why the field constantly invented methods, tools and techniques to manage requirements and develop applications, like rapid application development (RAD), rapid prototyping, the Unified Process (UP) and extreme programming (XP), to name a few.
Connection pooling in the .NET framework is controlled by the ServicePointManager class and the most important fact to remember is that the pool, by default, is limited to 2 connections to a particular endpoint (host+port pair) in non-web applications, and to unlimited connection per endpoint in ASP.NET applications that have autoConfig enabled (without autoConfig the limit is set to 10). After the maximum number of connections is reached, HTTP requests will be queued until one of the existing connections becomes available again. Imagine writing a console application that uploads files to Azure Blob Storage. To speed up the process you decided to upload using using 20 parallel threads. The default connection pool limit means that even though you have 20 BlockBlobClient.UploadAsync calls running in parallel only 2 of them would be actually uploading data and the rest would be stuck in the queue. The connection pool is centrally managed on .NET Framework. Every ServiceEndpoint has one or more connection groups and the limit is applied to connections in a connection group.
Commenting on the survey, Ritam Gandhi, founder and director of Studio Graphene, said: "They say necessity is the mother of invention, and the pandemic is evidence of that. While COVID-19 has put unprecedented strain on businesses, it has also been key to fast-tracking digital innovation across the private sector. "The research shows that the crisis has prompted businesses to break down the cultural barriers which previously stood in the way of experimenting with new digital solutions. This accelerated digital transformation offers a positive outlook for the future -- armed with technology, businesses will now be much better-placed to adapt to any unforeseen challenges that may come their way." Digital transformation, whatever precise form it takes, is built on the internet and so, even in normal times, internet infrastructure needs to be robust. In abnormal times such as the current pandemic, with widespread remote working and increased reliance on online services generally, a resilient internet is vital. So how did it hold up in the first half of 2020?
Though the term “cloudlet” is still relatively new (and relatively obscure) the central concept of it is not. Even from the earliest days of cloud computing, it was recognized that sending large amounts of data to the cloud to be processed raises bandwidth issues. Over much of the past decade, this issue has been masked by the relatively small amounts of data that devices have shared with the cloud. Now, however, the limitations of the standard cloud model are becoming all too clear. There is a growing consensus that the growing volume of end-device data to the cloud for processing is too resource-intensive, time-consuming, and inefficient to be processed by large, monolithic clouds. Instead, say some analysts, these data are better processed locally. This processing will either need to take place in the device that is generating these data, or in a semi-local cloud that is interstitial between the device and an organization's central cloud storage. This is what is meant by a "cloudlet”: intelligent device, cloudlet, and cloud.
Data collected today impacts business direction and growth for tomorrow. The benefits to having and using data that align with strategic goals include the ability to make evidence-based decisions, which can provide insights on how to reduce costs and increase efficiency of other resource utilization. Data are only valuable when they correlate to a company’s working goals. That means available data should assist in making the most important decisions at the present time. Data-based decision-making also coincides with lower overall costs. Examples of data that should be considered in any data set include digital data, such as web traffic, customer relationship management (CRM) data, email marketing data, customer service data, and third-party data. ... For some data sets, there may not be a need (and therefore the associated costs) for big data processing. Collecting all data that exists, just because it is available, does not guarantee inherent value to the company. Furthermore, data from multiple sources may not be structured and may require heavy lifting on the processing side. Secondly, clearly defined data points, such as demographics, financial background and market trends, will add varying value to any organization and predict the volume of data and processing needed for meaningful optimization.
A personal experience involved the development of an initial data warehouse for global financial information. The initial effort was to build a new source of global information that would be more available and would allow senior management to monitor the current month’s progress toward budget goals for gross revenue and other profit and loss (P&L) items. The effort was to build the information from the source systems that feed the process used to develop the P&L statements. To deliver information that would be believable to the senior executives, a stated goal was to match the published P&L information. After a great deal of effort, the initial goal was changed to deliver the capability for gross revenue. This change was necessitated because there was no consistent source data for the other P&L items. Even the new goal proved elusive as the definition for gross revenue varied among the over 75 corporate subsidiaries. Initial attempts to aggregate sales for a subsidiary that matched reported amounts proved to be extremely challenging. The team had to develop a different process to aggregate sales for each subsidiary. Unfortunately, that process was not always successful in matching the published revenue amounts.
Quote for the day:
"Inspired leaders move a business beyond problems into opportunities." -- Dr. Abraham Zaleznik