In this post, we will look at how observability can help reduce IT costs by optimizing engineering time, reducing the volume of downstream tools, lessening data storage costs, optimizing data sent to downstream tools, building an agile development culture, and decreasing incidents and outages.

What is Observability?

Observability refers to gaining insight into a system's internal state by observing its external outputs or behaviors. In simpler terms, it's the ability to see what's happening inside a system from an outside perspective.

Observability is a primary concept in modern IT operations because it allows teams to understand the behavior of complex systems and processes. IT teams collect data from various sources, such as logs, metrics, traces, and events, to monitor the health and performance of the system in real-time.

Observability aims to make it easy for IT teams to diagnose and fix issues quickly. It can help them identify the root cause of problems and take the required corrective actions before they impact end-users by providing a complete picture of how a system behaves.

With the help of observability, IT teams can optimize resource utilization and plan for future capacity needs. By analyzing the data collected from observability tools, teams can gain insights into how resources are being used and where they can be optimized. That can reduce costs, avoid negligent security, and improve the overall efficiency of IT operations.

How to Reduce IT Costs With Observability

Let's see how observability can help reduce IT costs.

1. Reduce Engineering Time

Remember that evaluating cost savings regarding data volumes and saving time to optimize data flow to your teams is essential. You will see significant increases in the productivity of your teams since you deliver the data you need downstream, and you're sure that all of it is useful and actionable.

Mean Time to Detection (MTTD) and Mean Time to Resolution (MTTR) are the most common metrics that see decreases. Observability pipelines can be used to optimize engineer productivity and have impactful knock-on effects. You can optimize the time that your employers expend in identifying and fixing different problems and give them more time to concentrate on significant tasks, such as developing a new big product or improving current features.

In addition, the ability to reveal and eliminate issues quickly can save you millions as your Ops and IT teams keep a close eye on cyberattacks and system breakdowns, resulting in cumbersome data loss and leaks.

2. Optimize Volume to Downstream Tools

When using traditional observability data tools, one of the major hurdles is the high cost of ingesting and storing vast amounts of data. Historically, teams sent all their data to a SIEM or observability platform to guarantee quick access, query ability, and insights. However, with modern environments generating an explosion of data, these legacy methods contradict budgetary constraints.

Additionally, these tools offer a more complex way to filter or route data to other destinations, leading to potential overspending to access a limited amount of data. That puts teams in an unfavorable position, forcing them to choose between staying within budget or having sufficient observable surface area.

By implementing an observability pipeline, teams can evaluate each piece of data before sending it to downstream destinations, filtering or deduplicating data to eliminate useless data from their stream. That ensures that teams only pay for the required data and can derive value from it rather than incurring storage costs for all the data.

3. Lessen Data Storage Costs

In the current zero downtime era, monitoring system performance and ensuring it meets SLAs (service-level agreements) is critical. However, handling, storing, and reading the data involved presents a significant challenge in terms of cost. While having observability data is essential and considered a best practice, keeping only the necessary data can substantially reduce costs.

4. Reduce Data Sent to DownStream Tools

Starting to save by reducing data volumes sent to downstream tools is an easy first step, but it gets better with observability pipelines. You can choose where your data flows, so you don't have to pay for the same data in multiple destinations.

However, you still need to retain some data types for compliance purposes. Previously, this meant routing all data to expensive legacy systems. But with an observability pipeline, you can segment compliance data and route it directly into cost-effective object storage, such as Amazon S3, bypassing more expensive platforms while ensuring historical data accessibility. This way, your team stays compliant, and you save your budget.

5. Build an Agile Development Culture

Observability tools enable you to swiftly detect and mitigate unnecessary resource usage, such as high CPU utilization before it negatively impacts users or applications. If an application on a server uses 100% of its CPU but only requires 50%, this can result from suboptimal code or algorithms. Identifying these issues can optimize your code and prevent future performance problems.

For software companies, responding to changing business requirements is essential. Application failures can cause significant losses and workplace accidents for your organization and its clients and partners. By using observability tools, you can monitor your system's behavior from start to finish without writing code or restarting the app after an unexpected crash. Furthermore, you can track the duration of each request, allowing you to pinpoint the root cause of issues related to endpoints or microservices.

6. Decrease Incidents, Unplanned Work, and Outages

An IT department typically spends 30% of its time on non-value-added activities. However, observability tools can help to mitigate this by providing crucial visibility into the performance and availability of your applications.

With increased visibility, you can detect anomalies in your production systems (physical or virtual) before they turn into significant problems, reducing the number of incidents. That helps you quickly resolve issues before they impact your users or customers. By spotting patterns across your entire infrastructure, you can identify problems at the source rather than merely reacting to symptoms, helping your team find root causes faster and reducing unplanned work.

By detecting problems early on, you can take action before they impact your customers or users, reducing outages and ensuring that your systems are always up and running.

Conclusion

Observability is crucial in modern IT management, allowing organizations to understand complex systems and detect issues. By analyzing data generated by these systems, IT teams can improve performance, optimize resources, automate processes, identify cost savings, and enhance collaboration. Observability is essential in DevOps and SRE practices, emphasizing automation, collaboration, and continuous improvement.

The post 6 Ways to Reduce IT Costs Through Observability appeared first on Datafloq.

Cost optimization is a strategy that adjusts computing infrastructure to minimize cloud spending-in other words, matching the cost to the actual requirements. For example, a company may discover that the Azure VMs hosting its web applications are not fully utilized. In response, they can reduce the scale of the VMs to avoid paying for unused capacity.

Azure is a cloud computing platform with services based on virtualized infrastructure and the global Microsoft network. It offers businesses granular control over cloud resource deployments. As a cloud hosting platform, it differs from more rigid environments that require businesses to predict resource usage or deploy redundant infrastructure to support spikes in demand.

Azure offers various services tailored to specific use cases, making it much more flexible than traditional server-based hosting. However, this level of complexity and flexibility has its downsides.

To effectively optimize their Azure costs, customers must manage their resource consumption to match business needs accurately. It can be challenging to do so without strong knowledge of the Azure platform and its various pricing models. It is also important to understand how to monitor performance comprehensively and respond by changing organizational processes to enable optimization.

Azure Cost Optimization Best Practices

Here are some best practices to help you perform cost analysis in Azure:

Calculate the Cost of Your Azure Deployment

Evaluate the cost of the solution before deploying your infrastructure. Assessments help organizations prepare a workload budget in advance. This budget is useful for measuring the accuracy of the first cost estimate over time compared to the actual cost of the solution after deployment.

Azure offers tools to help calculate the cloud costs:

• Pricing Calculator-a tool that provides cost estimates for different possible Azure service combinations. There are various ways to implement a solution in Azure, and each combination affects the overall spending. It is important to consider all the infrastructure requirements of the cloud deployment early to increase the accuracy of the cost estimates. The more data the tool has, the more reliably it can estimate the projected costs of the Azure solution.
• Azure Migrate-a service for assessing an organization's existing on-premise workloads and provides insights into the possible requirements of an alternative Azure solution. It starts by analyzing the local computer to see if migration is possible. Next, it provides VM sizing recommendations for maximum performance in Azure. Lastly, it generates an estimated cost for the Azure solution.

Leverage Cost Analysis Tools

Organizations use cost analysis to understand their costs in depth by splitting and assessing costs based on resource attributes. The following considerations are a good starting point for cost analysis-periodically addressing them provides the information needed to make accurate cost-related decisions:

Current estimated monthly expenses-how much has the company spent this month? Is it within the budget?

• Anomaly investigation-regularly check that costs are within reasonable limits for normal use. What are the usage trends? Are there anomalies?
• Invoice verification-is the latest invoice higher than the previous one? How have spending habits changed each month?
• Charge analysis-once the bill has arrived, it is important to understand its breakdown by classifying the charges.

Facilitate Remote Work to Boost Productivity

Windows Virtual Desktop lets organizations quickly launch and scale virtual desktops, giving users access to work desktops and applications from anywhere. There is no need to configure new hardware. Windows Virtual Desktop provides the best Office 365 and Windows 10 virtual desktop experience with multi-session support. Companies can save money by paying only for the necessary infrastructure and turning off unused machines.

Aside from virtual desktops, employees must also access on-premises and cloud-based resources. Azure allows companies to quickly scale their on-prem virtual private network (VPN) using Azure VPN Gateway. It is possible to configure the solution to expand and shrink easily. Azure's network can withstand rapid shifts in resource utilization, making it easy to manage spikes in utilization.

Optimize the Costs of Azure SQL Database

When building an SQL database in Azure, choosing a server and determining the performance tier are important. These tiers provide different levels of performance expressed in virtual cores (vCores) or database transaction units (DTUs).

Optimizing for a stable database load is easy by choosing a tier size that matches its performance requirements. However, some databases have unpredictable bursts of activity. Using an elastic pool can save money if the workloads are unpredictable.

Elastic pools in Azure SQL Database are a cost-effective way to manage and scale multiple databases with varied and unpredictable usage requirements. Databases in an elastic pool reside on a single SQL Database server and share a fixed number of resources at a fixed price. Pools are suitable for large numbers of databases with specific usage patterns. This pattern has low average and relatively low peak utilization for some databases.

The more databases added to an elastic pool, the more cost-effective they become. Elastic pools spread the usage costs across several databases, which can significantly impact the overall Azure database costs.

Prepay with Azure Reservations

Azure Reservations offer some Azure services at discounted prices. They can save up to 72% compared to a pay-as-you-go pricing model. Companies can book services and resources in advance to receive a discount.

For example, organizations can pay upfront for one or three years of Azure resources like VM usage, database throughput, and database compute power. Here is an example showing the estimated savings for VMs. In this scenario, the organization would save about 72% by committing to three years.

Image Source: Azure

Customers with a pay-as-you-go subscription or an enterprise agreement can use Azure Reservations.

Leverage Spot VMs for Low-Priority Workloads

Spot instances allow customers to leverage Azure's unused computing capacity and achieve significant savings. However, Azure ejects spot VMs whenever it needs to restore the capacity. Spot instances are ideal for non-critical workloads that can tolerate disruptions, such as batch jobs, dev/test environments, or large compute workloads

Conclusion

In this article, I explained the basics of Azure cost optimization and provided several techniques you can use to reduce your Azure costs:

• Calculate the cost of your Azure deployment using cost calculator tools
• Leverage cost analysis tools to identify waste and opportunities in existing deployments
• Facilitate remote work to reduce the number of machines required
• Optimize the costs of Azure SQL Database
• Prepay with Azure Reservations to gain discounts for long-running workloads
• Leverage Spot VMs for low-priority or fault tolerant workloads

I hope this will be useful as you improve the cost efficiency of your Azure workloads.

The post Complete Guide to Azure Cost Optimization appeared first on Datafloq.

• The cost of data analytics depends on multiple factors, including the amount and quality of your corporate data, your data analytics needs, the choice of data analytics tools, customization efforts, and organizational agility.
• To reduce data analytics cost and get a faster payback, your organization needs to define clear data objectives, develop a data management strategy, select primary use cases, and gradually scale your analytics efforts while gathering stakeholder feedback.

Data is the fuel that keeps the wheels of your business turning.

Most companies only realized that during the pandemic, so data analytics spending soared in the past two years. A recent study by Ernst & Young revealed that 93% of CEOs consider data and analytics their top IT investment priority for the time being.

What goals do businesses pursue when tapping into custom Big Data application development or procuring off-the-shelf analytics tools, anyway?

According to the BI & Analytics survey conducted by MicroStrategy in 2020, 63% of businesses splurge on data analytics tools to become more efficient, while 51% of the respondents seek to improve their financial performance.

The benefits of becoming a data-driven company are huge. But how much does data analytics cost? Source: MicroStrategy

One does not simply adopt data analytics solutions on a whim.

Data analytics newbies might struggle to aggregate the right information across a company's IT infrastructure and prepare it for further analysis. SaaS analytics tools can be hard to customize and scale across multiple use cases. And empowering every employee, regardless of their technical background, to use analytics insights in their daily work is a task few businesses succeed in.

These technological and organizational challenges, coupled with the presumably high cost of data analytics, slow down analytics adoption in enterprises.

We've already told you about the obstacles companies face when attempting to boil Big Data down to meaningful insights, so let's talk about data analytics cost today.

Key factors affecting the cost of data analytics

1. Amount, nature, and quality of your data
2. Your data analytics objectives – and tools that best meet them
3. Data analytics vendor fees
4. Data analytics software customization and development efforts
5. Organizational agility and willingness to change

Factor 1: Amount, nature, and quality of your data

Back in 2016, the average company was managing 162.9TB of data; in enterprises, the data volumes typically exceeded 347TB.

Six years on, companies see their data grow by 63-100% EVERY MONTH. The median number of data sources per company has already reached 400, while 20% of companies surveyed by IDG and Matillion last year claimed to be pulling information from over 1,000 sources to meet their business intelligence (BI) and data analytics needs. These sources often span eCommerce platforms, customer databases, project management software, social networks, and IoT systems, among others.

Analysts estimate that 80-90% of business data is unstructured. This data comprises images, videos, audio files, uneditable documents, and sensor readings, so you cannot store it in traditional relational databases.

Unlike structured data, which resides in on-premises or cloud-based data warehouses and is ripe for BI tools and dashboards, unstructured data belongs to data lakes and data lakehouses. Since data lakes do not possess computing capabilities by default, companies should meticulously prepare unstructured information for streaming analytics, data warehouse-assisted analysis, or AI-driven algorithmic processing.

That's why the cost of data analytics includes the expenses associated with devising an end-to-end data management strategy, setting up a complete data storage infrastructure for company-wide data aggregation, and bringing unstructured data to a unified format. Overall, these expenses comprise one-third of the total data analytics cost.

Factor 2: Your data analytics needs – and tools that best meet them

Depending on your company's digital maturity and stage of development, you might need tools for:

• Descriptive data analytics. When you dig into historical data to determine what has happened, when it's happened, and why, you're tapping into descriptive analytics. Essentially, it's the backbone of all business intelligence and reporting tools, which can provide ad hoc or canned reports. The former give answers to particular business questions – for instance, how many users clicked on your banner ad. The latter can be a monthly report produced by your social media or PPC specialist.
• Diagnostic data analytics. This type of analysis involves matching historical data against other information to figure out why something has happened and what you can do about it. If your sales managers closed abnormally few deals last month, you can turn to diagnostic analytics to boost their performance in the future. For this, two techniques can be applied:
  • The first approach is called query and drill downs. Such analysis identifies the root cause of an event. Going back to our sales department example, the reason for your managers' lackluster results could be long-term sick leaves.
  • Discovery and alerts is the second approach. This technique allows you to catch early signs of a problem and take action before the disaster strikes. For instance, a data analytics platform could notify you about sales managers' prolonged absence from work in the middle of the month so that you could double your business development efforts.

• Predictive data analytics. With predictive analytics, your company could spot correlations between certain events and identify trends – for example, anticipate sales volumes based on the target region and audience demographics. To that end, data specialists turn to statistical and predictive modeling:
  • Statistical modeling helps find dependencies between different parameters – say, geography and revenue.
  • With this data, it is possible to further calculate sales for various target audiences in a particular region – and that's where predictive modeling comes in useful.

• Prescriptive data analytics. Powered by Big Data and artificial intelligence algorithms, prescriptive analytics tools not only notice recurring patterns and identify root causes of events but also recommend the best course of action to avoid problems, boost employee productivity, and reduce your company's operating costs. To make intelligent predictions, your data analytics platform will implement the optimization and random testing techniques.

Subsequently, we need different tools to perform those types of analyses – and data analytics cost will increase proportionally with the platforms' feature set, complexity, and integration capabilities.

According to Vitali Likhadzed, ITRex Group CEO and Co-Founder, we could segment modern data solutions into three categories:

• Standalone tools and utilities. This category includes open-source products like Apache Kafka and RabbitMQ and SaaS offerings like Tableau and Power BI. These tools solve a particular business or technical task – e.g., visualizing operational data, ensuring data exchange between IT systems, preventing transaction fraud, or locating misplaced inventory using RFID tags. But from a company-wide data analytics implementation perspective, such tools constitute only one building block of a more complex system.
• Industrial SaaS platforms like SAP, Snowflake, Salesforce, and TIBCO Spotfire. These systems provide a single platform for managing operational data. Thanks to a wide selection of modules and settings for specific scenarios, use cases, and entire industries, industrial platforms can cover most of your company's data analytics needs. Salesforce's Einstein, for example, even makes intelligent predictions based on historical and real-time information it scavenges across your company's IT infrastructure. The problem is, such systems might be too tricky to customize – or lack out-of-the-box modules for your industry. When it comes to data analytics cost, implementing SaaS platforms may also become too expensive over time as your business grows and starts generating more data – after all, most SaaS vendors' plans are tied to storage and computing resources.
• Integrated enterprise-wide data ecosystems that incorporate industrial SaaS, open-source, and bespoke data analytics tools. Such systems aggregate data across your IT infrastructure and are well-suited for company-wide analytics. You could also spice up your data ecosystem with artificial intelligence capabilities to spot recurring patterns in both structured and unstructured data, anticipate events and scenarios that could affect your business, and automate tedious tasks. Custom data systems are vendor-agnostic, extendible, and tailored to your processes, business functions, and usage scenarios. This helps unlock new opportunities and revenue streams, thus maximizing return on your data analytics investments.

This cohort of tools offers comparable data analytics capabilities. Implementation costs aside, the key drawback of standalone and industrial SaaS offerings is their lack of flexibility.

To be precise, Tableau and Power BI boast reasonable flexibility within one department or set of tasks. And data analytics solutions powered by industrial SaaS platforms are vendor-locked; should you decide to switch to another platform in the future, you'll have to rewrite the whole thing from the ground up and invest millions of dollars in employee training.

Integrated data ecosystems, on the other hand, can be costly to implement, but offer utmost flexibility, meet industry standards, and best suit your needs.

Factor 3: Data analytics software pricing

If you opt for a ready-made data analytics solution, which may need either little or extensive customization, your data analytics costs will incur vendor licensing fees.

Let's see how much popular data analytics tools cost – and what they offer functionality-wise:

Let's do the math, shall we?

If your organization opts for an advanced solution like SAP, your data analytics initiative could cost you at least $14,000 monthly – and that's not to mention software configuration and employee training expenses.

According to a survey conducted by 1PATH, 46% of small and medium-sized businesses spend anything between $10,000 and $25,000 to purchase a data analytics tool, while 41% of the respondents pay an equal amount in maintenance fees annually.

Extended implementation timelines, high data analytics software costs, and failure to ensure company-wide access to intelligent insights are haunting data projects across small and medium-sized companies. Source: 1PATH

Even though most vendors are well aware of the data analytics cost problem and attempt to democratize their products, it still takes companies around four months to implement a standard BI solution and start benefiting from it.

Factor 4: Data analytics software customization and development efforts

Data analytics tools are not created equal.

Some plug-and-play solutions, such as Power BI and Tableau, can produce quick insights without extensive customization. Their intelligence, however, seldom stretches beyond descriptive and diagnostic analytics and cannot be scaled company-wide.

More advanced tools like SAP and Salesforce could empower you to source, aggregate, process, and analyze data across your entire organization – but it's easier said than done.

Let's take Salesforce as an example. Although the platform offers a rich selection of add-ons and tools to sync it with your IT infrastructure and third-party services, you could easily spend $10,000-100,000 to tweak the tool to best suit your unique business needs.

That's why some companies choose to create bespoke data analytics tools around their data, processes, and goals – even if the approach will involve higher upfront investments.

How much would a custom data analytics platform cost your company should you choose this path?

Case study: ITRex creates an AI-powered self-service BI solution for a leading retailer

ITRex Group partnered with a retail juggernaut operating over 10,000 grocery stores, discounters, and hypermarkets in 20+ countries. The customer wanted to create an AI-based self-service BI platform providing on-demand, hassle-free access to critical information for three million employees scattered across the globe.

Following a discovery phase, we outlined the project scope:

• Break down the silos between the company's disparate technology systems to enable uninterrupted data sourcing and aggregation
• Detect, modify, and delete inaccurate, incomplete, or irrelevant data across the company's IT infrastructure
• Create a Master Data Repository serving as a single source of truth for all organizational data
• Develop a web portal providing a 360-degree view of all the company's data sources and information available in different formats – PDF files, documents, Excel spreadsheets, emails, images, etc.
• Build a self-service BI platform to empower users, regardless of their technology background, to interpret data insights and create ad hoc reports
• Implement advanced security and role-based access control mechanisms

We created a data ecosystem spanning several innovative features and technology components:

• Node and edge-driven graph data structure that supports complex queries and simplifies algorithmic data processing
• Effective search through massive volumes of data with the Hashtag Search and Hashtag Autocomplete functionality
• Integration with third-party systems via a custom API
• Integration with different applications and systems comprising the customer's IT infrastructure: Office 365, SAP, Atlassian products, Zoom, Slack, and enterprise data lake, among others
• Option to create and share detailed reports by querying numerous data sources
• Built-in collaboration tools
• Role-based security mechanisms restricting access to sensitive information stored in graph databases

The data analytics platform can handle up to eight million queries per day, serving the needs of non-technical employees who previously had to request reports from the client's internal IT teams.

The platform's core advantage is its flexibility and scalability across use cases. Whether our client needs to produce a financial report, gain an insight into consumer behavior, or adjust their pricing strategy, the system can do it all.

For instance, the data ecosystem helped the company reduce operating costs by advising on whether they should repair or replace pieces of equipment and other assets – and that's just one of its possible applications.

Vitali Likhadzed estimates the cost of developing and implementing a data analytics solution like this at $150,000-200,000 – and we're talking about a very basic version of the system here. It's possible to roll out a minimum viable product (MVP) in three months, while full-scale implementation would take six to nine months on average.

Factor 5: Organizational agility and willingness to change

Broadly defined as a company's ability to quickly respond and adapt to changing market needs, organizational agility plays a pivotal role in data analytics implementation.

While not directly related to data analytics cost, organizational agility (or lack thereof!) will affect your data solution deployment timeline and, subsequently, time to value.

A recent survey by Exasol indicates that 65% of organizations have experienced employee resistance when adopting data-driven methods in their work – even though 73% of the respondents initially believed they would not face such obstacles. Among the reasons for employees' reluctance to use data analytics systems are a lack of understanding of a company's overall data strategy (42%) and limited knowledge of data analytics benefits (40%).

If your supply chain management company has been using Excel spreadsheets for 25 years, you can't force your employees to start using Tableau, SAP, or a custom self-service BI system without proper onboarding – and learning takes time. Employee training costs aside, you will also have to part with some of your employees and hire tech-savvy ones.

How to get started with data analytics, reduce costs, and achieve payback faster

Considering the high data analytics cost and extended project cycles, you're probably wondering whether you should tap into Big Data analytics in the first place.

Let's get something straight: nowadays, becoming a data-driven company is no longer a compelling, albeit elusive, advantage – it's a key to survival in the digital economy, where the divide between data leaders and data laggards will become unbridgeable in just three years.

The good news is, you can reduce the cost of implementing data analytics and reap its benefits faster by following the steps below:

• Define the mission and goals of your organization with regard to digital transformation. When doing so, leverage the research and planning frameworks like PESTEL/TEMPLES, VRIO, Porter's Five Forces, and SWOT. Make sure your objectives are specific, measurable, achievable, relevant, and time-bound (SMART).
• Identify the business problems you're aiming to solve with data analytics. For this, you can conduct an IT infrastructure audit, talk to employees, run customer surveys, benchmark your performance indicators against those of your competitors, and, should the need arise, hire external technology consultants. The experts could help you assess the digital maturity and agility of your organization, giving you realistic data analytics cost and effort estimates.
• Win the C-suite's support for project execution. It is essential to show how the selected data analytics use cases will help the organization reach the SMART goals listed in step 1. Once you get the green light, you could prioritize the approved use cases using frameworks like MoSCoW, RICE, or Kano.
• Get to know your data. Prior to data analytics development and implementation, we recommend that you sift through your data to identify the information that can deliver immediate insights (i.e., hot data) and the information that does not need active management (i.e., cold data). You can move cold data to a lower-cost storage, reducing your data storage and backup costs by up to 75%.
• Architect your vision. Design a blueprint of your data analytics solution architecture – and validate that it is technology and infrastructure-agnostic, vendor-neutral, and built for scale.
• Create an end-to-end data management strategy. Here you should pay special heed to core data governance and democratization principles. The former would cover the management, quality, and security of your corporate data, as well as user roles and access permissions. The latter would allow any employee, regardless of their background, to access data, ask questions, and produce different types of reports.
• Get down to data analytics implementation. Together with your in-house data team or external developers, you need to create data components for the priority use cases, leaving an option to scale and expand innovative capabilities horizontally to support other scenarios. The ultimate result will be an MVP of your data platform, which would cover the essential use cases and contain enough features to get value from day one while optimizing data analytics costs.
• Iterate your way to perfection. Following the MVP implementation, you need to collect feedback from business stakeholders, make the necessary adjustments, and scale your data analytics efforts across other use cases. That's how you get an end-to-end data ecosystem that eradicates the silos between your teams, departments, and components of the IT infrastructure, paving the way for company-wide data analytics.

Last but not least, brace yourself for the change. Besides promoting data literacy within your organization and clearly communicating the benefits of becoming a data-driven company, you should promote the development of cross-functional teams, minimize hierarchy, and encourage decision-making at all levels of the organization.

And should you need help customizing a standalone or platform-based data analytics product (or developing a custom data ecosystem from scratch!), we're happy to accompany you on your transformation journey! Just drop ITRex team a line to discuss your data analytics needs.

The post Top 5 Factors Behind Data Analytics Costs appeared first on Datafloq.

Here's what the majority of IoT cost estimates do not take into account

How much does the Internet of Things solutions cost? Go on, type that phrase in the Google search bar! You'll see dozens of articles featuring ambiguous cost estimates of mobile applications working in tandem with connected devices and not a single word about the devices in question! Meanwhile, the Internet of Things term refers to cyber-physical systems where electronic and non-electronic objects collect environmental data using sensors and exchange it over a network.

These networks are often wireless; companies implement wired IoT solutions in industrial settings and other environments with a high risk of electromagnetic interference. IoT devices further relay the information to the cloud for analysis and visualize insights via a user interface typically, a cloud dashboard or mobile application (although voice interfaces have also been trending up lately). In edge IoT deployments, devices possess enough computing power to process sensor data locally and orchestrate other IoT nodes.

Good old ITU-T Y.4000/Y.2060 recommendations document , which has been around for almost a decade, still provides the most accurate reference model for IoT products and is a must-read for all companies eyeing the Internet of Things market. As you can see from the scheme above, IoT systems operate at four different layers:

• Devices. IoT devices, or things, may range in complexity from printed tags attached to inventory items to AI-powered security cameras that store and process data locally. What various IoT devices have in common, though, is the ability to collect environmental data via sensors and connect to the Internet. To perform these tasks, IoT devices must be equipped with firmware, middleware, or proper embedded systems, which interconnect the hardware components of electronic devices, interface gadgets with each other and a central hub, and facilitate data collection.

• Network. The networking layer comprises wireless protocols that allow devices to communicate with each other, transfer sensor data to the cloud, and encrypt all communications.
• Service support and application support. Here we talk about back-end infrastructures driving the business logic of IoT products. Composed of cloud or on-premises servers and services, such infrastructures aggregate, store, and process sensor readings.
• Applications. An IoT app is an umbrella term describing all kinds of applications that allow end-users to interpret sensor data, interact with connected devices, and adjust device settings.

So when we talk about the Internet of Things cost, it is key to understand how much every one of these functional components is going to cost you.

What a realistic IoT cost estimate may look like

Deciphering the cost of IoT devices

If you're looking to create a custom IoT solution, there are two routes you can take: design a device from the ground up or enhance non-electronic/analog objects with sensors. How much does it cost to build custom IoT hardware? The price of building a custom device depends on the type, functionality, and complexity of your IoT solution and may amount to 70 ‘80% of the total Internet of Things project costs.

The custom hardware/firmware design process spans several stages:

• Analysis. During this phase, a team of hardware experts collaborates with software system engineers, business analysts, and company stakeholders to elicit technical requirements for a custom device, polish the concept, and optimize the development budget.
• Design. Based on the requirements defined in the Analysis phase, engineers and industrial designers create printed circuit board (PCB) layout schemes and visualize the gadget's enclosure in 3D CAD. Hardware design should also meet all the software requirements gathered in the previous step.
• Prototyping. A hardware manufacturer creates up to ten PCBs, debugs them, and makes corresponding changes to the requirements document. At the same time, the firmware team implements the basic features and modules and tests them on DevKits or the prototype itself.
• Testing. Successful prototypes are transformed into pre-production models that use different materials for the device case. Various types of tests are then conducted, including thermal, signal integrity, and power integrity analyses, as well as user tests. During this phase, critical errors might be detected, and the prototyping process starts all over again. Typically, a novel IoT device goes through three to five iterations until the desired performance is achieved.
• Mass production. The technical documentation for PCB, electronic components, and enclosure production are handed over to a factory. The manufacturer produces the required quantity of devices, installs firmware, and performs testing to validate that the gadgets function as intended.

Overall, connected devices spend anything between six months and two years in the development stages listed above. How much would it cost your company to build IoT hardware given that you need to analyze technical requirements, prototype and test your idea, and manufacture devices en masse?

The honest answer is, It depends

A self-learning smart home system with facial recognition capabilities may cost up to $5 million (hardware and software costs included). The price of building a custom ECG tracker that analyzes the electrical signals of a human body and measures how well your heart works could reach $ 300,000 but there are additional IoT development costs you should be aware of. Hardware certification Certification is often considered one of the major factors behind the cost of IoT hardware. Although the Internet of Things regulations differs from country to country, they usually encompass the following categories:

• Environment and electrical safety. These regulations include the Restriction of Hazardous Substances (ROHS) and Energy Star compliance, as well as standards tests for issues like overheating and electric shock, which are part of the International Electrotechnical Commission (IEC) and Underwriters Laboratories (UL) certifications.
• Communication protocols. Before you label your gadget as Bluetooth-compatible, for example, you have to test it against the Bluetooth SIG Qualification program. The same goes for Zigbee, Z-Wave, LoRaWAN, and other popular connectivity technologies.
• Electromagnetic and radio-frequency interference. Before your product hits the shelves, you need to validate that the performance of your device won't be affected by other connected devices within a Wi-Fi/BLE/cellular network range, while the device itself conforms to the electromagnetic radiation exposure standards.
• Product and industry-specific standards. For example, a wearable device company has to perform chemical tests to ensure that their gadgets' enclosures do not contain skin allergens. On top of that, there's compulsory Food and Drug Administration (FDA) and National Institute of Standards and Technology (NIST) security certification for medical IoT devices, which not only affects time to market, but also involves significant expenses.

According to Entrepreneur, the price of a certificate for a simple electronic gadget (including devices that rely on wireless connectivity) starts from $10,000. Estimating the cost of non-electronic IoT solutions The Internet of Things concept revolves around continuous data acquisition and exchange and you don't always need an electronic device with custom-written embedded software running on it to collect sensor data and send it to a cloud-based or on-premises server. Here are several examples of non-electronic and pseudo-connected IoT devices that cost little to nothing:

• A typical smart farming solution is a set of temperature and soil moisture sensors paired to a microcontroller or microcomputer; in the meantime, sensor prices dropped by over 200% between 2004 and 2018, reaching historic low of $0.4 .
• Brick-and-mortar retailers track inventory levels and foot traffic using RFID tags and beacons, which cost around $20 on Amazon.
• And now it's totally possible to create detergent containers that scavenge energy from their surroundings to communicate with smartphones and Wi-Fi devices, alerting homeowners when they're running low on cleaning supplies.

While there are many ways to save on hardware components, cyber-physical systems still need a place to collect and process sensor data and that's where your IoT cost estimate might go awry.

Assessing IoT infrastructure costs

For your convenience, we've combined the network and service/application support components under a single moniker while providing approximate IoT cost estimates for each tier:

• Network. IoT connectivity is usually enabled by means of short-range wireless (Wi-Fi, Bluetooth, NFC), low-power wide-area network (LPWAN), and cellular solutions. In case your IoT product comprises a system of connected gadgets communicating over a cellular network, your connectivity expenses might fluctuate around $0.04 per megabyte. Some telecom companies offer narrowband IoT pricing plans for enterprises, charging $6 per device annually.
• Embedded software. Custom IoT devices even dumb sensors that merely gather environmental data use embedded software with pre-programmed logic. Ranging in form and complexity from bare-metal firmware to customized Android, this software may add up to $20,000 to your IoT cost estimate. If you're planning to incorporate a third-party gadget into your IoT ecosystem, you might also need middleware i.e., drivers, APIs, and SDKs connecting devices and applications that fail to communicate otherwise. Custom firmware for an IoT solution may cost you $10,000 ‘30,000.
• Data storage and analytics solutions. Here we talk about data lakes and warehouses where sensor data is kept and organized and software that boils down gigabytes of raw data to meaningful insights. IoT adopters typically leverage smart gadget connectivity and data analysis through PaaS solutions built by Google, Amazon, or Microsoft. While cloud IoT platform pricing could be fairly reasonable (Azure IoT Hub fees, for example, start from $10 per month per an IoT hub unit, with 400,000 messages sent daily), you still need to develop the business logic of your IoT solution from the ground up. A UK-based national park has recently turned to ITRex Group to create a robust digital twin solution for effective land, water, and other natural capital asset management. The solution comprises a network of sensors that aggregate environmental data and send it to the cloud for further analysis. Technology-wise, the system will incorporate a data lake driven by AWS services (S3, DynamoDB, Redshift, Glue, etc.), streaming services facilitating sensor data collection without an intermediary hub, and dashboards for data visualization. A realistic IoT cost estimate for this project would start from $250,000.

Hidden IoT infrastructure costs

Many IoT solutions require a complex support system, which is also called infrastructure . For instance, your company specializes in predictive maintenance and sells transformers enhanced with smart sensors. You need a mobile app to generate alert notifications if sensors register abnormal behavior.

Your IoT solution may also incorporate a web application for managing field technicians and ordering equipment parts that need replacement. To automate these processes, you have to contract equipment manufacturers, network providers, and maintenance, repair, and operations (MRO) companies and integrate your custom software with their business apps, which is bound to incur additional IoT development costs. Another example is IoT-based remote patient monitoring systems, which (besides hardware, applications, and connectivity!) require a dedicated customer support team ready to help users whose condition is worsening.

Calculating the cost of IoT applications

How much does it cost to develop an IoT app? Just like with hardware, it depends on the size and complexity of your undertaking. These examples of IoT projects from the ITRex portfolio will give you an idea of what you should expect.

IoT software projects and their cost estimates

• We collaborated with a digital health startup looking to bring a custom heart rate monitoring device to the market. Our team created an iOS application that visualizes sensor data and alerts hospital staff in emergency cases. To render ECG sensor data in real-time, we leveraged the OpenGL API and partially shifted the workload to mobile GPU. A similar IoT application costs $10,000 ‘15,000.
• ITRex created an intelligent indoor navigation system for an exhibition center. Powered by BLE beacons, which track users' location via a mobile app, the solution helps guests find their way around the facility and receive real-time information about exhibition participants. An IoT system like this would cost your company $40,000 ‘60,000.
• To help healthcare facilities reduce hospital-associated infection (HAI) rates, a US-based digital health company created an IoT platform improving hand hygiene among medical personnel. The company turned to ITRex Group to create a complete software ecosystem for their solution, including a cloud-based back end, a desktop application, and an Android app. The intelligent hand hygiene compliance platform tracks handwashing events using BLE-powered wearables, aggregates and processes sensor data in the cloud, and reminds hospital staff to wash their hands regularly via the Android application. Hospital administrators, meanwhile, can monitor hand hygiene compliance using the desktop app and generate reports for specified time periods. An IoT system like this costs approximately $90,000.

How to reduce IoT development costs and avoid failure

If you add up the costs of IoT components cited in the previous sections, you won't arrive at a sum smaller than $50,000 thousand. That's how much a minimum viable product (MVP) version of an IoT solution costs. Considering the fact 75% of IoT projects never materialize into market-ready products, this is a significant investment. According to the Microsoft IoT Signals survey, some of the common reasons why IoT projects fail include technology roadblocks, insignificant budgets, and a lack of a clearly established use case.

Below you will find several tips that would help you sail through IoT development and avoid making costly mistakes in the process.

• Start your project with a discovery phase to establish a business case for your IoT solution and validate that there are no technical limitations on your way. Although your vendor will inevitably bill you for the discovery phase hours, $10,000 ‘12,000 is nothing in comparison with the losses you'll suffer should you scrap an entire project halfway through.
• Leverage off-the-shelf prototyping tools such as systems on a chip (SoCs), microcontrollers, and microcomputers to create a proof-of-concept version of your device. You can buy a powerful microcontroller like the ATmega328P-based Arduino Uno for less than $30, have it shipped in 24 hours, develop a prototype within a fairly short time frame, and focus on embedded software and mobile/web application development while simultaneously negotiating the deal with a hardware manufacturer.
• Take an iterative approach to IoT development to avoid feature creep and scale your system flexibly along with your business. The problem with IoT products (especially within the consumer electronics segment) is that companies are overstuffing their gadgets with features to cater to a larger audience. This usually extends the development and testing cycle, pushing release dates into an indefinite future and sucking the budgets dry. To avoid this scenario, it is recommended that you launch an MVP as fast as you can, test the IoT solution with real users and on real-world tasks, and gradually expand its feature set to support new use cases.

That's all for now. Drop ITRex team a line if you have any questions about IoT costs or need help building a connected solution.

The post How Much Does the Internet of Things Cost? appeared first on Datafloq.

The breach also gave the hacker a long list of names, credit scores, addresses, credit limits, and other personal information.

According to CNBC, the hack will mean that the bank will pay around $100 million to $150 million this year.

Not the first

Capital One is not the first company to have suffered significant monetary losses from a data breach. Adultery website Ashley Madison paid $11.2 million to roughly 37 million users who were affected by a data breach on the site in July 2015.

Anthem paid $115 million to settle with complainants when they suffered from a data breach that same year. That's not all. These companies also have to pay fines and penalties for the breach. They also have to endure several regulatory bills.

For instance, Anthem was fined around $16 million for the breach by the US Department of Health and Human Services.

The data breach at Equifax involved close to 150 million people, and it cost the company $575 million. A similar incident cost Uber $148 million, but the total cost was substantially higher because the company also paid the hacker money to keep mum about the attack.

These incidents happened before Europe implemented the General Data Privacy Regulation (GDPR). Companies dreaded the GDPR coming into full effect because of the potentially huge fines.

British Airways became the face of hefty GDPR fines when it fell victim to a card skimming script that harvested the data of around 500,000 customers over two weeks. The airline paid $230 million for its lax security.

But while the numbers are staggering, you might wonder how the company and regulators have been able to put a dollar value on these attacks.

Determining the cost of a cybersecurity event is not always easy

When your house gets robbed, it's easier to estimate the financial loss. For instance, if a burglar was able to cart away a television, pieces of jewelry, and cash, you can itemize what was taken and check their current market value.

With a cyber-incident, it is not so clear-cut. Yet news reports about a data breach, hacking, or any other form of cyberattack, there is always a dollar value attached to these incidents.

A Radware and Merrill Research study estimates a data breach will cost a company an average of $4.6 million in 2019. Have you ever wondered how they came up with these numbers?

Some interesting facts about the cost of a cybersecurity incident

Did you know that if you have an incident response team in place, you could significantly lower the penalties you will have to pay? On average, you can save around $360,000 if you invest in such a team.

The use of encryption will help you save around $360,000. On average, it takes companies 279 days to detect and contain a data breach. The hacker will have been inside your system for 35 days before you get alerted of the incident.

If you can detect a breach and contain it within 200 days or less, you can save $1.2 million.

Here's something that you might not realize. The expenses are not usually upfront. Only 67 percent of costs will happen within the first year. Around 22 percent will occur in the second year. Another 11 percent of the expected costs will occur beyond the second year.

Why would you want to calculate the cost of a cybersecurity incident anyway?

When it comes to your business, you should always have a good idea of the risks that you face. Knowing the cost of a cybersecurity incident can help you better prepare for it.

Imagine suffering from a data breach and not having the money for compensation, investigation, and regulatory penalties, says Sidd Gavirneni, Co-Founder and CEO at Zeguro. A thorough assessment of your business and IT risk will help you earmark some money in the budget to recover quickly from this incident. What's more, it will help you justify your IT budget. If the risks and costs are too high, as is the case for most small and midsize businesses, consider investing in a better cybersecurity infrastructure and cyber insurance or data breach insurance for added protection.

Or will the price of new equipment or software be more than what you will spend if your business gets hacked?

How do you calculate the cost of a cybersecurity incident?

There are three components involved in data breaches.

1. First are the direct costs. These are the expenses you incur in dealing with the breach. Examples include the cost of fines, investigation, and compensation to the affected users.

2. Then you have the indirect costs that include the lost time and effort in dealing with the attack. For instance, after the attack, you are required to communicate the breach to your customers. Or when you need to issue new credit cards, accounts, and credentials to your users. You also have to account for the lost productivity and system downtime as you make sure the damage from the breach or hack is kept to a minimum.

3. Lastly, you have the lost opportunity cost. Lost opportunity cost includes the potential customers that are now afraid to do business with you because of the breach. Your company's reputation will take a hit.

Factors to consider in your computations

There are several factors that you should consider when calculating the cost of a cybersecurity incident.

The fines and penalties associated with a cybersecurity incident will depend on what caused it. A hacking or a stolen device will get you a mild penalty. However, data leaked by an insider, whether it's done maliciously or by mistake, will net you a heftier fine.

Also, you can expect to pay more if it took you a long time to detect the breach. This is because the hackers had a wider window to steal more data from your systems. It will also be more difficult to investigate.

You can also add the number of people affected to your list of considerations. Large-scale attacks are naturally more expensive because you have to compensate every user affected by the breach. You will also need to spend more when you deal with subject matter experts, lawyers, regulators, and other professionals who can help you clean up the mess afterward.

Other considerations

Aside from these factors, there are also some situations that can increase or decrease the cost of a cybersecurity incident.

• For instance, if it is your first time, then you can expect lower costs. However, if you have suffered through a data breach within the past 24 months, you will need to pay for additional penalties for the second and subsequent breaches.
• Network complexity is also a factor. If you have a lot of resources, devices, and software connected to your network, then you will need more money to investigate the incident.
• Another expense that you should anticipate? The public relations and information campaigns that will help assure customers that their data is safe.
• The type of information that was leaked will also determine your cybersecurity incident's bill. Health information is costly when it leaks. You will also pay more for financial details and personal information.
• Location matters, too, as fines and regulations are different from one state to the other.
• Having an advanced security system in place will also help you negotiate a lower penalty for any data breaches.

Summing It All Up: The questions you need to answer

This checklist of questions will help you remember the factors to consider when you want to assess the expenses associated with a cybersecurity incident.

1. What records were breached? Does it include customer information only or employees as well?
2. How many records were breached?
3. What types of records were exposed? Were there personal information, credit card details, or health data?
4. How was the attack carried out? Is it a case of accidentally releasing the data, device theft, or hacking?
5. Did you have a breach within the past two years?
6. How complex are your network and other IT resources?
7. Will it hit the news? If yes, how big will the coverage be? Will it show on national or regional news broadcasts?
8. Do you follow the security best practices?

Let's Make It Easier: The Tools You Can Use

While all of these may seem daunting now, you will need to learn how to come up with accurate estimates of your business risks. Thankfully, there are tools.

Doeren Mayhew CPAs and Advisors offers a calculator that can help you come up with an estimate of how much a data breach will cost you based on the number of affected records, your industry, and other significant factors.

Similar calculators are found at eRiskHub, The Breach Level Index, and At-Bay.

* * *

Estimating the cost of a cybersecurity incident will help you fully appreciate the work that your IT guys are doing. It will force you to look at your network and other IT resources, as well as the records and information you are storing.

This exercise will help you fully understand the risks your business faces and how to mitigate these threats. At the very least, calculating this cost will help you justify IT-related investments.

The post After a Cybersecurity Incident: Calculating Your Potential Loss from a Data Breach appeared first on Datafloq.

Not just large organizations, but small and medium businesses are also moving their applications to the cloud. However, you shouldn't rush the decision to move your business to the cloud. You need to prepare your staff and clients as well to embrace this transition. You also need to be aware of the challenges that you are likely to face during the migration.

Read on to know more about the common pitfalls when moving your medium-sized business to the cloud.

1. Lack of Thorough Planning

Jumping into cloud migration without a well-planned strategy can lead to severe consequences. As the business owner, you should have a clear idea of why you want to move your business to the cloud, know your current application portfolio in detail, and review your entire system before the migration begins. This will help you understand how to proceed with the migration.

You will need to decide the following:

• What to Retain: You may not have to change some of your applications or infrastructure. You can use them without moving them to the cloud for the time being.

• What to Move: These include in-cloud and on-site applications that you need to move to the new cloud platform. You may want to move these applications for agility, flexibility, performance, cost, and digital transformation, among others.

• What to Restructure: You will need to re-design or re-structure some of your business-critical applications when moving them to the cloud. However, this is a complicated process as it often involves redeveloping these applications.

What to Discard –Cloud migration may render some of your current infrastructure and applications useless. However, this decision has to be rational, not emotional. It can help you save money, time, and effort.

2. Failure to Keep Key Personnel Involved

To make your cloud migration successful, you need to keep your key personnel involved. Change can be scary for most people. So, you must try to understand how they feel about this transition and address their concerns, if any. It will help you to mitigate any potential disruptions before they escalate.

Plus, moving your business applications to the cloud will require you to redefine the roles of your staff and business partners. For example, as your cloud service provider will take care of regular IT maintenance, your in-house tech guys can focus on your core business activities. Similarly, other departments will also need to streamline their processes and organizational structure to make them compatible with cloud computing, which brings us to the next point.

3. Ignoring the Importance of Training

You may need to train your employees to fit into their new roles in a cloud-based work environment. You might also need to provide your business partners and clients with a heads up. For employees who don't have any experience or cloud computing knowledge, taking on their new roles is not going to be easy.

Mishandling the cloud-based data or application may lead to legal and financial implications. This why training is important. If your in-house IT team has the knowledge and experience, they can provide the necessary training to your employees. Alternatively, you can ask your service provider to offer a crash course for your employees.

4. Not Paying Attention to Data Security

Moving your business applications to the cloud is essentially moving tons of your business data to the cloud. It involves transferring sensitive personal and business information such as passwords, logins, credit card details, and other financial information to the cloud. In other words, you need to pay attention to keeping your data secure during the transition, and after it gets moved to the cloud.

Of course, your cloud service provider will offer the best security measures possible. However, it is better if you get third-party reports on the security standards your service provider follows when making the transition.

While making sure your data stays secure, you might also want to avoid vendor lock-in. If the peculiar safety measures restrict you from changing service providers in the future, you should look for other alternatives. Make sure to understand how public and private cloud safety works before jumping in.

5. Underestimating the Migration Costs

Most medium-sized businesses tend to opt for cloud migration to save operating costs. Most cloud computing service providers will charge you a fixed amount once a month or follow a pay-as-you-go pricing model.

However, moving enterprise data and applications to the cloud is a complicated venture. Depending on the cloud you use, the size and nature of your business, and your service provider, you may have to pay for upfront costs, hidden charges, training, and additional support. For instance, a private cloud provider may require you to pay some upfront infrastructure costs.

You don't want your project to get stuck halfway through the migration because you are out of money. So, you need to speak with your service provider and do some research of your own to get a clear idea of the migration costs.

Thankfully, cloud migration is not restricted by geographical boundaries. So, whether you need a cloud migration service, hiring a cost-effective service provider shouldn't be a problem. Just makes sure you verify the credentials and experience of the said service provider before signing the contract.

Parting Words

Cloud computing is the future. However, getting carried away with the hype can lead to problems in the long run. Avoiding the above common pitfalls should help expedite your cloud migration process. Have you decided to move your medium-sized business to the cloud yet? When do you plan on doing it? Tell us about your cloud computing plans in the comments section. Your experience may help others.

The post Mid-Sized Business Cloud Migration: 5 Pitfalls You Need to Avoid appeared first on Datafloq.

Here is a list of issues that companies without a proper data strategy may face. If your company is experiencing any of these problems, it is a tell-tale sign that you need to review your company's data strategy:

1. Storing data is starting to cost more

Even though the price of data storage has plummeted over the years, a poor data strategy will lead to high data storage costs. According to Experian, an information services company in the US, 77 percent of CIOs believe data is a valuable asset in their organisation that is not being fully exploited and 70 percent believe they have underutilised data in their business that is costing them money to store. With the price of cloud storage as low as $0.03 per gigabyte per month, many organisations may be tempted to store all the data that they can get their hands on. Initially, things may seem under control. But as your organisation acquires more data over the years, your database will soon to be filled with legacy data that has long past its usefulness along with redundant data that are merely duplicates of others. Backups of backups become increasingly expensive and the cost of maintaining your database threatens to make a considerable dent in your IT department's budget.

2. Restructuring your database is nearly impossible

Data restructuring involves changing the way your data is logically or physically stored. There are many advantages why data restructuring should be performed. For instance, data restructuring is done to improve performance and storage utilization or to facilitate data processing. When you collect data from different sources, there comes a point in time when restructuring your database becomes necessary to align data collection with business objectives. The problem arises when you have so much data that database restructuring becomes nearly impossible to perform. With entire departments relying on both current and legacy data, database downtime bears too much of a risk for data restructuring to even be considered as an option. Being unable to make the difficult yet necessary decision, your data is trapped in limbo and is unable to be utilized effectively.

3. Data analytics is becoming harder to implement

In order to perform data analytics, you need a good understanding of the data that is available to you. This includes knowing what type of data you have collected, where your data comes from and how it is stored. For a small database, a comprehensive audit can be performed fairly easily. But when you are dealing with terabytes of data, this process can become complicated. Due to the large volume of data involved and depending on how your organisation is structured, obtaining a complete picture of your data assets might not be possible. Without this knowledge, data analytics becomes harder to implement which often leads to conflicts between IT departments and managers who expect results without fully understanding the capabilities and limitations of Big Data.

4. Insights become muddled

Data that is generated through business activities or research can be used to gain invaluable insights into consumer preferences. While it is tempting for companies to believe that gathering more data and crunching even more numbers will lead to better results, this is far from the truth. When it comes to data analytics, more data doesn't mean better insights. In fact, the more data that is available, the higher the chances of using wrong or inappropriate datasets for analysis. Many organisations succumb to this base rate fallacy when they collect more data than necessary.

5. You experience analysis paralysis

Sampling your data is necessary to decrease the time and costs involved for data analysis. For a small dataset, sampling may be as easy as including all of the records in your database. The difficulty arises when you have large amounts of data at your disposal. With a large database, a multitude of sampling techniques become available to you. Many businesses find themselves spending a large amount of time weighing the advantages of each technique and second guessing their decision. As a result, management teams experience analysis paralysis, which stalls the discovery and implementation of insights.

6. You risk losing everything if your data is compromised

Businesses have long been a favourite target for hackers bent on pilfering private data for identity theft and exploitation. While breaches at big corporations such as Target and Yahoo make the headlines, small and medium enterprises are still very much targets for hackers. Smaller enterprises have digital assets that are equally valuable but have less security as compared to larger enterprises. Without a proper data strategy, you may risk losing more than just your data. If security measures are not in place, inadvertent data loss could be a highly likely result. If your organisation deals with sensitive or private information, your reputation and relationship with your customers could be on the line.

Summary

Implementing a proper data strategy for your business is critical to prevent potential costly problems from arising. Failing to do so may lead to serious consequences that would negatively impact your business. If your business faces any of the problems above, consider reviewing your existing data strategy today.

The post 6 Signs Your Company Needs a New Data Strategy appeared first on Datafloq.

Develop new strategies

By allowing you quick and easy access to a diverse library of data, Big Data allows you and your team to develop strategies you haven't considered before. This is because the data you will be using is centralized and any type of information is available at the press of a button. Use this advantage to brainstorm ideas and run simulations in order to maximize your productivity and drive your business forward.

Communicate with clients

Sometimes it seems impossible to actually communicate with your client. This is why Big Data allows you to cross-reference, calculate and determine what your clients want and what their next steps will be in your relationship. Relying on data and analysis to determine what your clients want has become easier than ever before with Big Data, because of the sheer amount of information stored in one place. You will make your clients happy by letting them know you care about their wishes while using the latest technology to serve their needs.

Perform relevant analysis

Remember all the times you wanted to research a certain data pattern or customer behavior but you couldn't manage the calculations? Big Data will allow you to perform a variety of different calculations and analysis based on multiple filters and set outcomes. Using dedicated professional platforms for writing analysis papers such as EssayServices.reviews is a viable option if you run into trouble. Whatever analysis trouble you had in the past will become irrelevant when you move your business to Big Data and centralize your database.

Visualize data

It's easier than ever before to parse through and visualize data you are looking for at a moment's notice. You and your team are always working on a schedule. Using Big Data to quickly and efficiently prepare for meetings and work sessions with visualized data is an invaluable tool that you should certainly think about.

Streamlined work management

If your business has several fronts and offices, managing them can sometimes prove difficult. Even more so when you need to keep tabs on inventory, client files, projects and ongoing campaigns. Big Data makes managing these factors easier than ever thanks to a central hub that everyone has access to. Sharing work and delegating tasks has never been so easy, thanks to the development of Big Data and its relevant technologies.

Adapt to changes

Changes are hitting the market on a daily basis. While your business might be small and stay relatively unaffected, why risk it when you can avoid trouble altogether? Big Data allows you to adapt your business strategy in a fast and efficient way, with very little downtime and loss of productivity. You and your team can develop new ways and opportunities to adapt to changes as they come along. Having a plan on how to utilize Big Data in the event that any major change should happen could prove essential in today's market.

Safety of data

By using Big Data, you are committing to a safety of data that is unprecedented in today's world. In this digital age, everyone is afraid about their data being stolen, but by centralizing your data structure you will keep all of it safe inside your company. You and your team can use this to your advantage and keep all the relevant data about your company's business on the company server. Don't make the mistake of moving them to a local unit and risk the exposure of important data to the public. This is one of the smartest ways you can use Big Data to your advantage.

Increase revenue and decrease costs

Everyone is looking for ways to cut costs and increase their income, but with Big Data, this has become clearer than ever before. By using this technology, you will use a centralized hub for all of your data. Meaning, that the upkeep of said data will be much lower than if you paid a third-party provider for servers and security of multiple units.

In contrast, using Big Data will increase your productivity and fasten your everyday processes to ensure maximum workflow in every operation. This is the essential feature of Big Data and what distinguishes it from other means of data management.

In conclusion

As you can see, the benefits of using such a modern and professional data management service are numerous. These are only some of the examples of how you and your team can utilize this technology to your benefits. Keep in mind that there are always new venues to explore. It's up to you to adapt Big Data to suit your business as best as possible.

The post 8 Ways Your Team Can Take Advantage of Big Data appeared first on Datafloq.

Still, some people are skeptical. Even though cloud hosting is one of the earliest cloud technologies, there are still individuals and companies who arent making use of the cloud and are missing out on the many benefits of websites that are hosted on the cloud.

In this article, well take a look at the top 6 reasons your website should be running on the cloud, to give you an inside perspective on the benefits of cloud web hosting.

1. Lower Costs

Hosting a website on your own is expensive. You have to invest in server architecture, licensing, and operating costs for an on-premises server. Not only that, youll have to have IT staff who are able to service and maintain your equipment or youll have to outsource to another IT firm that is willing to do so.

Cloud hosting allows you to avoid all of this. By simply paying a flat fee per month, your website will be hosted on cutting-edge servers that are professionally maintained without requiring extensive downtime or the services of your own IT staff.

Because of this, costs for cloud hosting are extremely low compared to on-premise web hosting. You dont have to pay for power, infrastructure, maintenance, or staff to maintain your servers, which allows you to run your website more efficiently while still maintaining a lower overhead and lower costs.

2. Higher Availability, Stability And Performance

Cloud hosting services are built for high availability and stability, and the most high-quality cloud hosting services offer an uptime of over 99.9%. Uptime and high availability are crucial for a successful website, and cloud-based hosting service offers much better stability than privately-hosted websites.

In addition, cloud hosting offers better performance the best cloud hosting services use SSD cloud server devices that deliver incredible speed. While these devices may be cost-ineffective for a single company to purchase, contracting with a cloud hosting service allows you to use the best, most top-of-the-line server infrastructure without paying an arm and a leg for the privilege of doing so.

This all adds up to higher availability, and a faster, more stable cloud platform.

3. Better Scaling Potential

When you buy a new server, any infrastructure you dont use is wasted. Why pay for 100% of a new server when youre only using 25%? Thats just a waste of money.

Cloud hosting allows you to purchase only what you need and scale accordingly. If you need more bandwidth, speed, or storage, its as easy as clicking a button, or getting in touch with a customer support team.

This also allows you to get much better performance without investing capital into your own infrastructure, allowing you to grow your company quickly without worrying about spending too much money on advanced infrastructure.

Since you can always scale up at will, and youre not paying for space you dont need, you can focus on growing your business and youll know that your web hosting capabilities will always be able to keep up with you.

4. Easier Maintenance

Maintaining your own web infrastructure is expensive and difficult and not just because the servers themselves cost money. If you have your own servers hosting your website, youre going to need your IT staff to maintain them which can distract them from other tasks that may be more beneficial to your company.

Your staff are also going to have to maintain your servers periodically, and if youve put your system together yourself, this maintenance could lead to a lot of downtime and lost sales or business due to your website being out of commission.

Cloud hosting avoids all of these problems. When you contract with a cloud hosting service, dedicated staff ensure that all server infrastructure is running effectively, and that your servers are patched with the latest security updates and software packages that you need to run your website. Easier maintenance means less downtime and lower costs and thats a win-win.

5. Faster Page Loading

High-tech SSD cloud servers offer incredible page loading performance, especially compared to an on-premises server infrastructure that may use older, hard disk based storage.

Page loading time is one of the most important factors that determines whether or not a customer will continue to use your website. Research has shown that if any given webpage doesnt load within about a half-second, abandonment rates increase dramatically.

Because cloud hosting services only use top-of-the-line web hosting equipment, you can rest easy knowing that your webpages will load quickly, and that your customers wont abandon your website just because of a long loading time.

6. Your Data Is Safe

On-premises web hosting can be risky especially if youre not making use of private or public-cloud backups of your data. Natural disasters, security vulnerabilities, or total hardware failure can wipe out your critical data, and totally cripple your online infrastructure.

Of course, web hosting companies arent immune from these issues but their entire business model is based on providing safe, redundant web hosting. If you contract with a reputable cloud web hosting firm, your data is totally protected with backups and redundant servers, allowing you to host your website with confidence, knowing that youre protected from data loss. For example, ScalaHosting SSD Cloud service keeps three copies of your data on three separate drives and three separate storage servers thanks to the StorPool distributed storage system. That results in great reliability and security for your data and its almost impossible to suffer data loss. Traditional servers usually keep one copy of your data.

7. Instant deployment

Cloud hosting works with virtual machines. You can configure a server once and create a template from it. You can use it to deploy as many servers as you want in less than a minute. With a traditional server just installing the OS will take no less than 30 minutes. You can quickly deploy new servers when you need more resources and terminate them once they are not needed anymore.

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Pros

Training Tool

Virtual reality apps are used for training purposes in business, education, medicine, armed forces, and construction. The use of VR apps in the healthcare sector is particularly in the field of surgery. For instance, the robotic surgery has been adopted in medical schools for teaching future surgeons. The benefits of this technology for training purposes are listed below.

• VR apps provide realistic scenarios ideal for teaching.
• They present little or no risk to the students and instructors.
• These applications are safe to use and can be remotely controlled.
• Virtual reality applications simplify complex situations during training.
• They are innovative and ideal for different methods of teaching.
• These apps are engaging and fun to use.

Economical

VR apps are cost-effective. They reduce the cost of creating different prototypes for training purposes. Effective training is essential to ensure that trainers acquire excellent knowledge and are productive. The development of models is necessary for productive practice. Virtual reality replaces the use of a series of prototypes which are expensive. VR apps can provide a single model that can be utilized from time to time.

Accessibility

These applications can be accessed from any location. They do not require a special environment for their usage.

In business, VR apps are used to display products in showrooms with limited spaces. Some of these applications provide a 3D vision of the products. 3D enhances the features of a product such that it appeals to customers. Satisfaction of clients increases their loyalty and results in higher returns for companies. These apps are also useful in marketing and branding of products and services.

Cons

Cost

The computers optimized for the use of virtual reality applications are expensive. Currently, most people cannot afford to use these applications. Since the technology is out of reach of most people, it cannot be used regularly or efficiently. Also, VR devices such as headsets used with these apps are expensive. The video card recommended by Oculus costs about $280.

Health Effects

The adverse effects of this technology on health have not been established. People have suggested headache, blurred vision and queasiness, but researches are carried out to determine permanent effects. App development companies issue warnings to the users of their VR apps to limit liability. For instance, Oculus advised the users of its VR device to take 10-15 minutes every 30 minutes while using its headset. Still, most of the addicted users do not adhere to these warnings.

Also, the technology company listed some potential symptoms that may arise from the use of its products. Some these signs include dizziness, eye strain, excessive sweating, lightheadedness, and nausea.

Quality

Most VR apps have low-quality videos. The films look muffled and make viewing unstable. Improving the resolution of these videos can fix this problem.

Social Effects: The use of an immersive VR applications can be addictive. Regular users of these artificially simulated environments find it difficult to stay away from them. These users prefer the virtual world to reality.

Virtual reality is a vital technology that will become more accessible as the consumer costs decline. Its positive contributions to different fields cannot be overlooked.

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