Daily Tech Digest - June 11, 2023

Tips Every CFO Should Consider For Implementing Tech Solutions

Conduct a cost assessment to pinpoint areas where tech upgrades may be needed and determine if these upgrades will add value to your financial operations. Remember, newer doesn’t necessarily mean better. Therefore, you must invest in tech solutions and upgrades that improve efficiency across the board. By taking the initiative and identifying areas where tech solutions can solve specific pain points, CFOs can help ensure a seamless transition when implementing new technology. ... While many organizations today jump at the opportunity to implement updated solutions to replace legacy systems, an overhaul doesn’t have to be made just because new technologies become available. ... The key is fully understanding why you’re switching to and implementing new technology. Just because certain tasks and processes can be done using advanced tech tools doesn’t necessarily mean your company needs new software.

The power of data management in driving business growth

Effective data management means business leaders can stay abreast of the ever-surging tide of data, as well as deploying new services quickly, and scaling faster. It can deliver insights which lead to new business streams or even the reinvention of the entire company. Data management comes in multiple forms, encompassing both hardware and software. Solutions include unified storage, which enables organisations to run and manage files and applications from a single device, and storage-area networks (SANs), offering network access to storage devices. ... As well as data management, the Data Leaders thrive in two other key areas: data analytics and data security. These three elements are interdependent. Data management naturally works hand-in-hand with data analytics, and data security is increasingly important as business leaders hope to share data with partners securely. It’s impossible for leaders to thrive when it comes to data management if they haven’t harnessed data security, or to adopt data analytics without mastering data management. 

Zero Trust: Beyond the Smoke and Mirrors

Despite misleading marketing, a lack of transparency into the available technologies, the limited scope of the technologies themselves, mounting privacy concerns, as well as a complete question mark when it comes to price and deployment, trust in zero trust remains. Organizations know they need to embrace it– and preferably yesterday. ... Despite this enhanced savviness and market maturity around zero trust, major barriers to implementation remain. These include:Damn you, marketers. Some vendors may use misleading marketing tactics to promote their zero-trust solutions, overstating their capabilities or making false claims about their performance. See through the noise the best you can. Most tools let you test things out first. Take vendors up on that. What the hell does this cost? Implementing zero trust security solutions can be expensive, especially for organizations with large IT infrastructures. Chances are, the more devices, networking gear, locations, and compliance standards you need to adhere to…the more this will cost. Complexity is almost always guaranteed. Zero trust can also be complex to deploy, especially across distributed, multi-vendor networks.

Technical Debt is Inevitable. Here’s How to Manage It

Technical debt is a threat to innovation, so how can we mitigate it? Well, if you don’t already do so, it’s a good idea to build technical debt into your budgeting, planning and ongoing operations, said Orlandini. “You have to manage it, expect it and be responsible with your technical stacks in the same way you are responsible with your financial stacks,” he said. Here are a few other ways to manage the debt you have and avoid accumulating more. Consider using AI to refactor legacy code. Generative AI could be leveraged to reactor legacy code into more modern programming languages. This could help automatically convert PEARL code, for instance, into JavaScript. Today’s large language models (LLMs) could help solve many of today’s problems. However, since they are built on a pre-existing body of work, they will use less trendy languages and might cause some technical debt in the process, cautioned Orlandini. Don’t over-rely on new DevOps processes as a cure-all. DevOps can accelerate the time to release features, but it does not, by its nature, eliminate technology changes, said Orlandini.

Cloud repatriation and the death of cloud-only

IT analyst firm IDC told us that its surveys show repatriation as a steady trend ‘essentially as soon as the public cloud became mainstream,’ with around 70 to 80 percent of companies repatriating at least some data back from public cloud each year. “The cloud-first, cloud-only approach is still a thing, but I think it's becoming a less prevalent approach,” says Natalya Yezhkova, research vice president within IDC's Enterprise Infrastructure Practice. “Some organizations have this cloud-only approach, which is okay if you're a small company. If you're a startup and you don't have any IT professionals on your team it can be a great solution.” While it may be common to move some workloads back, it’s important to note a wholesale withdrawal from the cloud is incredibly rare. ... “They think about public cloud as an essential element of the IT strategy, but they don’t need to put all the eggs into one basket and then suffer when something happens. Instead, they have a more balanced approach; see the pros and cons of having workloads in the public cloud vs having workloads running in dedicated environments.”

5 Ways to Implement AI During Information Risk Assessments

The problem is that there is no such thing as a perfectly secure system; there will always be vulnerabilities that an IT team is unaware of. This is why IT teams perform regular penetration tests – simulated attacks to test a system’s security. ... By turning this task over to AI, companies can run automated penetration tests at any time. These AI models can work in the background and provide immediate alerts the moment a vulnerability is found. Better still, the AI can classify vulnerabilities based on the threat level, meaning if there’s a vulnerability that could allow for a system-wide infiltration, then that vulnerability will be prioritized above lesser threats. ... AI-powered predictive analytics can be an incredibly powerful tool that allows an organization to estimate the results of a marketing campaign, a customer’s lifetime value, or the impact of a looming recession. But predictive analytics can also be used to predict the likelihood of a future data breach.

13 Cloud Computing Risks & Challenges Businesses Are Facing In These Days

Starting with one of the major findings of this report, we can see that both enterprises and small businesses cite the ability to manage cloud spend as the biggest challenge, overtaking security concerns after a decade in place one. This can be the consequence of economic volatility, where organizations keep spending and innovating with multiple cloud services to keep up with the digital world in an unstable environment. ... Proper IT governance should ensure IT assets are implemented and used according to agreed-upon policies and procedures, ensure that these assets are properly controlled and maintained, and ensure that these assets are supporting your organization’s strategy and goals. In today’s cloud-based world, IT does not always have full control over the provisioning, de-provisioning, and operations of infrastructure. This has increased the difficulty for IT to provide the governance, compliance, risks, and data quality management required. To mitigate the various risks and uncertainties in transitioning to the cloud, IT must adapt its traditional IT control processes to include the cloud. 

When are containers or serverless a red flag?

Limited use cases mean that containers and serverless technologies are well-suited for certain types of applications, such as microservices or event-driven functions. But they do not apply to everything new. Legacy applications or other traditional systems may require significant modifications or restructuring to run effectively in containers or serverless environments. Of course, you can force-fit any technology to solve any problem, and with enough time and money, it will work. However, those “solutions” will be low-value and underoptimized, driving more spending and less business value. Complexity is a common downside of most new technology trends. Container and serverless platforms introduce additional complexity that the teams building and operating these cloud-based systems must deal with. Complexity usually means increased development and maintenance costs, less value, and perhaps unexpected security and performance problems. This is on top of the fact that they just cost more to build, deploy, and operate.

Vector Databases: What Devs Need to Know about How They Work

Unsurprisingly, a vector database deals with vector embeddings. We can already perceive that dealing with vectors is not going to be the same as just dealing with scalar quantities. The queries we deal with in traditional relational tables normally match values in a given row exactly. A vector database interrogates the same space as the model which generated the embeddings. The aim is usually to find similar vectors. So initially, we add the generated vector embeddings into the database. As the results are not exact matches, there is a natural trade-off between accuracy and speed. And this is where the individual vendors make their pitch. Like traditional databases, there is also some work to be done on indexing vectors for efficiency, and post-processing to impose an order on results. Indexing is a way to improve efficiency as well as to focus on properties that are relevant in the search, paring down large vectors. Trying to accurately represent something big with a much smaller key is a common strategy in computing; we saw this when looking at hashing.

Understanding Data Mesh Principles

When an organization embraces a data mesh architecture, it shifts its data usage and outcomes from bureaucracy to business activities. According to Dehghani, four data mesh principles explain this evolution: domain-driven data ownership, data as a product, self-service infrastructure, and federated computational governance. ... The self-service infrastructure as a platform supports the three data mesh principles above: domain-driven data ownership, data as a product, and federated computational governance. Consider this interface an operating system where consumers can access each domain’s APIs. Its infrastructure “codifies and automates governance concerns” across all the domains. According to Dehghani, such a system forms a multiplane data platform, a collection of related cross-functional capabilities, including data policy engines, storage, and computing. Dehghani thinks of the self-service infrastructure as a platform that enables autonomy for multiple domains and is supported by DataOps.

Quote for the day:

"The level of morale is a good barometer of how each of your people is experiencing your leadership." -- Danny Cox

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