Modern enterprises prioritize s caling analytics and AI . However, t hey often face challenges in managing costs . Platforms like Databricks offer a unified environment . Companies can manage data pipelines, analytics, & machine learning workloads with Databricks . Yet, as usage grows across teams and use cases, costs can increase quickly without clear visibility or control.

Databricks cost optimization focuses on reducing unnecessary compute spend. This goes parallel with maintaining performance, reliability, and scalability. In most cases, costs rise from architectural decisions, workload design, & operational practices that evolve over time. Without guardrails, even well-designed platforms can become expensive to operate.

In this blog, we highlight seven common mistakes that inflate Databricks bills. We also explain how business leaders can avoid them while scaling analytics.

Mistake 1: Treating All Workloads the Same

Organizations use the same compute setup for multiple jobs. This helps them simplify the initial setup. However, each workload has very different performance and resource requirements.

Shared environments quickly become a bottleneck as usage grows. This enforces teams to overprovision compute to maintain optimal performance. ultimately, this leads to inefficient resource utilization.

It becomes difficult to understand which workloads are driving costs. Clusters scale more often than necessary over time. It masks inefficiencies and inflates Databricks bills without delivering measurable business value.

How to manage this effectively:

• Separate compute for ingestion, transformation, analytics, and ML workloads.
• Align cluster configurations with workload characteristics.
• Isolate production, development, and experimentation environments.
• Define clear workload ownership and usage boundaries.

Mistake 2: Leaving Databricks Clusters Running on Idle

Idle clusters drive most of the costs in Databricks environments. They quietly consume compute even when no jobs are executing. This problem grows as more teams and projects are added.

Auto-scaling alone is not the right solution as clusters can remain active without any meaningful workloads. This results in inefficiency, meaning that a cluster with minimal workloads is not contributing much.

How to manage this effectively:

• Configure auto-termination policies based on inactivity.
• Schedule cluster start and stop times for non-production workloads.
• Enforce stricter lifecycle rules for development environments.
• Periodically audit running clusters and usage patterns.

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Mistake 3: Inefficient Compute Usage (Sizing, Provisioning, and Code Usage)

Many cost issues rise from disorganized compute management. Teams often overestimate resource needs, choose oversized clusters, or rely on default configurations that are never revisited. At the same time, inefficient code and long-running workloads silently increase compute consumption. Together, these factors lead to persistent cost overruns even when infrastructure appears “correctly set up.”

This problem usually develops gradually. Clusters are initially sized conservatively to avoid performance issues. Workloads evolve over time. Inefficient jobs accumulate. As a result, organizations pay for more capacity and runtime than they actually need. They gain no clear visibility into the root cause. Inefficient compute usage multiplies cost in three ways:

• Oversized clusters consume more resources per minute
• Overprovisioned capacity remains underutilized
• Slow or inefficient code keeps clusters running longer

How to manage this effectively:

• Right-size clusters based on actual workload behavior rather than assumptions.
• Review worker count, node size, and scaling limits periodically.
• Avoid overprovisioning compute “just in case”.
• Use cost-efficient compute options for non-critical/fault-tolerant workloads.
• Optimize slow queries, joins, and transformations.
• Reduce unnecessary scans and redundant processing.
• Monitor utilization trends to detect underused or overloaded clusters.

Mistake 4: Ignoring Ownership and Accountability for Usage

Shared Databricks environments often make cost ownership unclear. Without defined accountability, teams may create clusters or jobs without visibility into their financial impact.

This lack of ownership makes it difficult to trace cost spikes, prioritize optimization work, or hold meaningful cost discussions across engineering and finance.

How to manage this effectively:

• Define owners for workspaces, jobs, or pipelines.
• Introduce cost attribution through tagging or labeling.
• Align engineering, platform, and finance stakeholders.
• Review ownership regularly as teams and workloads evolve.

Mistake 5: Poor Data Layout and Storage Optimization

Data layout impacts how effectively Databricks processes workloads. Excessive small files cause unnecessary data scans, increasing runtime and compute consumption. These issues compound as datasets grow and make even simple queries more expensive than expected.

How to manage this effectively:

• Design partitioning strategies aligned with access patterns.
• Reduce small files through consolidation approaches.
• Periodically review table structure and layout.
• Align storage design with downstream workloads.

Mistake 6: Treating Databricks Cost Optimization as a One-Time Exercise

Companies treat Databricks cost optimization as a one-time cleanup after noticing a spike in spend. This may deliver short-term savings. However, costs often rise again as new workloads and teams are added. Databricks environments evolve continuously, making cost optimization an ongoing responsibility rather than a one-off task.

How to manage this effectively:

• Establish recurring cost and usage reviews.
• Monitor trends rather than isolated spikes.
• Assign long-term ownership for cost governance.
• Embed cost awareness into platform operations.

Mistake 7: Lack of Visibility into Usage and Cost Drivers

Many organizations are struggling to optimize Databricks costs. This is due to the lack of visibility into the drivers of costs. Information about usage might be scattered, delayed, or hard to decode.

Without visibility, cost growth is seen only when the invoices arrive. Teams must respond to cost overruns rather than control their consumption. It is difficult to identify inefficient workloads, unnecessary clusters, and cost-driving resources early on.

Without centralized visibility, cost spikes appear only after invoices arrive. Teams are left reacting to overruns instead of proactively managing usage. This makes it difficult to identify inefficient workloads, unused clusters, or fast-growing cost contributors early.

When visibility is low, optimization becomes more guesswork than decision making. Management can understand costs increasing but not the reason behind it.

How to manage this effectively:

• Build dashboards to track usage trends, spend patterns, and growth over time.
• Monitor cost by workspace, team, or workload.
• Set alerts for abnormal usage or sudden spikes.
• Review consumption trends regularly instead of only at billing time.
• Share visibility across engineering, platform, and finance teams.

Calculate Your Databricks Cost

Understanding how Databricks costs are calculated is essential before attempting any optimization. While multiple factors influence total spend, the core pricing model is straightforward and based on compute consumption.

At a high level, your Databricks cost depends on how many Databricks Units (DBUs) your workloads consume and the rate applied to those units. This makes visibility into usage and runtime behavior especially important.

Databricks cost formula:

Databricks DBU consumed × Databricks DBU rate = Total Cost

The number of DBUs consumed depends on several factors, including:

Even small inefficiencies can significantly increase DBU consumption over time. Teams can apply the strategies mentioned above and implement Databricks cost optimization best practices to optimize costs effectively.

How Credencys Can Help with Databricks Cost Optimization

Databricks cost optimization requires more than isolated fixes. It demands a structured approach that combines architecture, governance, & ongoing operational discipline. This is where the right implementation and consulting partner can help.

At Credencys, our certified Databricks engineers help you take control of Databricks spend. We enable you to identify hidden inefficiencies, optimize compute usage, & design scalable, cost-aware data platforms.

We work closely with various teams to:

• Assess current Databricks usage & cost drivers
• Identify quick wins & long-term optimization opportunities
• Right-size clusters & improve compute efficiency
• Optimize workload design, data layout, & execution patterns
• Establish governance, visibility, & ownership models
• Build a repeatable framework for ongoing cost optimization

Whether you’re just getting started or running Databricks at scale, we help make cost optimization an ongoing practice. The result is better performance, stronger cost control, and a Databricks environment that scales with confidence.

Originally published at https://www.credencys.com on January 6, 2026.

Every industry leader w an t s to become data driven . Yet most enterprises still struggle to turn raw data into decisions before the window of opportunity closes. Data promised clarity. Instead, it left many busines ses drowning. What these leaders require is efficient data warehouse solutions.

The more data businesses collect, the less control they seem to have. Until Snowflake rewrote what a data platform could do. Its cloud-native architecture unlocked on-demand performance, unified governance, and real-time insights.

Many organizations rely on expert Snowflake consulting services in order to speed adoption & optimization. With the right partner, businesses modernize their data foundation faster and achieve Snowflake ROI more effectively.

Let’s explore data management and data warehouse capabilities of Snowflake for three key industries.

Data-Driven Manufacturing with Snowflake Data Warehouse

Production lines generate more data in an hour than many organizations once stored in a month. However, manufacturers still struggle to connect insights across plants, machines, and suppliers. Siloed systems delay decisions; quality issues surface only after defects; and maintenance is reactive instead of predictive.

These pain points represent opportunity to transform operations with live, governed, and scalable data. This is why modern manufacturers turn to Snowflake. It offers the ability to unify sensor, ERP, and supply chain data into a single, analytics-ready platform. Moreover, it also helps strengthen end-to-end data management . Manufacturers can amplify their data warehouse efficiency. This helps drive measurable performance gains.

Why implementing data warehouse with Snowflake works for manufacturing

• Integrates data models that bring operational, financial as well as quality data together
• AI-ready architecture for predictive maintenance & anomaly detection
• Secures collaboration across partners & suppliers

Case Study: A leading US manufacturer accelerates new product rollouts with Golden Product Records in Snowflake

A mid-sized US manufacturer struggled with inconsistent data management. This impacted their forecasting accuracy and supply chain operations. We implemented Semarchy xDM directly on the Snowflake data platform. This enabled the client to match, cleanse, and merge product and supplier data into a trusted “Golden Product Record.”

Snowflake use cases for the manufacturing industry

Customer Experience Management for Retailers with Snowflake Data Warehouse

Retailers deal with torrents of data. Their data sources include POS transactions, customer interactions, supply metrics, and online behavior. These must be analyzed quickly to stay competitive.

Snowflake’s cloud-native architecture integrates disparate retail data into a single analytics-ready platform. Moreover, it unifies data from disparate sources into a single analytics-ready platform. Additionally, the platform helps companies with efficient data management. Retailers can significantly amplify Snowflake data warehouse performance and power decisions that reflect real-time demand signals. They can turn omnichannel visibility into measurable revenue and customer experience gains.

Why implementing data warehouse with Snowflake works for retail

• Demand forecasting that integrates web, mobile, and in-store datasets
• Automated reporting for merchandising and inventory teams

Snowflake use cases for the retail industry

Modernizing FMCG Data Pipelines with Snowflake Data Warehouse

FMCG brands operate in one of the fastest-moving sectors. Consumer preferences shift overnight, retail shelves change weekly, and margins depend on forecasting demand accurately. However, many companies still suffer from fragmented and delayed data pipelines. This leads to unnoticed stockouts, overproduction, and poor promotional ROI.

Modern FMCG leaders choose Snowflake consulting services to integrate distributor, POS, loyalty, & demand data into a scalable, governed analytics platform. Snowflake amplifies the performance of existing data warehouses and enables proactive forecasting. Additionaly, it also helps businesses with promotion optimization and supply chain responsiveness.

Why Snowflake works for FMCG:

• Enables rapid response to shifting demand signals.
• Powers AI-driven promotion and assortment optimization.
• Simplifies integration beyond legacy data pipelines.

Snowflake use cases for the FMCG industry

Snowflake Data Warehouse for insights-driven growth

For too long, enterprises have been dealing with voluminous data but mining only the surface. Snowflake allows industries to finally utilize their data efficiently. It helps turn scattered datasets into insights that drive deliberate action.

The companies that adopt Snowflake achieve data excellence with value-driven analytics. With the right Snowflake consulting company, business leaders can turn growing data challenges into catalysts for sustainable growth.

Originally published at https://www.credencys.com on December 24, 2025.

The insurance industry being very traditional in its way of functioning is faced by the increasing modern demands on the rising customer expectations, fraudulent activities, claims processing inefficiencies, and outdated risk models. It requires innovative solutions that can benefit not only in streamlining operations but also improve customers’ experience.

Do you often ask such questions to yourself as an insurance provider:

• How can I streamline these workflows to serve my customers faster while reducing mistakes?
• With the increases in operating costs, how can I remain profitable without compromising on quality of service?
• How can I cut costs while still ensuring the quality service?
• How do I reduce my fraud-related financial and reputational damage without raising my administrative burden?

The answer lies in leveraging agentic AI services.

According to a report by McKinsey & Company, AI-enabled claims management can reduce claims-processing time by up to 70% and lower the cost of claims handling by 30%.

Agentic AI represents a change force for insurers, enabling them to leverage automation, predictive analytics, and real-time data insights. It helps insurers become more competitive in a rapidly digitally transforming world, with changing customer expectations. It helps redefine the concept of operational resilience, improve customer loyalty, and ensure long-term growth.

In this blog, we will discuss how leveraging agentic AI services can transform the insurance industry by addressing key challenges and driving transformative outcomes. We will explore its use cases, look into the challenges that insurers face, how Agentic AI resolves them, and the tangible business impacts it delivers. Whether it’s increasing operational efficiency, mitigating risk, or improving customer satisfaction, this blog will explain why agentic AI is an important enabler for the future of insurance.

What is agentic AI?

Agentic AI is artificial intelligence with a decision-making ability that does not rely on human input. Agentic AI is typically defined as systems that operate with autonomous decision making, with reinforcement learning, natural language, and contextual analysis being prevalent. In contrast to traditional AI, which relies heavily on predefined rules, Agentic AI dynamically adapts to complex situations, learning and improving over time.

Agentic AI: Redefining the future artificial intelligence

Explore agentic AI in depth. Learn about the key differences between traditional and agentic AI. Understand its workflow and benefits of agentic AI.

Read more

Use cases of agentic AI for insurance

Automated claims settlement

Challenge: Manual handling of claims proves to be time-consuming and prone to mistakes. Moreover, settlements usually get delayed thereby affecting customer satisfaction.

Total losses combined with claims processing expenses account for up to 70% of the premium collected. — AWS

How agentic AI helps:

• Automates end-to-end claims lifecycle, including assessment, validation, and approval.
• Uses image recognition and natural language understanding to analyze claim documents and damaged photos instantly.
• Flags anomalies for potential fraud in real-time, which allows for faster resolution.

Business impact:

• Reduces claim processing times from weeks to days or even hours.
• Improves operational efficiency through reduction of manual touchpoints.
• Increases customer satisfaction and retention through fast settlement.

Risk assessment

Challenge: Classic models are based on static information and do not respond promptly to changing risk factors like climate change, economic unrest, or even customer’s behavior.

In a sector still defined by a high degree of manual processes and legacy systems, we expect a 10 to 30 percent increase in productivity across the risk and compliance function in insurance. — McKinsey & Company

How agentic AI helps:

• Real-time data feeds, such as IoT or geospatial data makes more accurate predictions.
• With predictive analytics, simulating possible risks, it enables policymakers to devise customized options.
• Adaptive underwriting processes allow updating risk levels in real time.

Business impact:

• Improves risk profiling accuracy. Thus, less mispriced or over-priced policy risk gets incurred.
• Attracts high-quality customers by providing personalized policy offers.
• Reduces claims payouts through proactive identification and mitigation of risks.

Fraud detection

Challenge: Insurance fraud continues to be a persistent problem, costing billions of dollars annually and affecting brand trust.

Fraudulent claims account for 5–10% of all claims and losses at a cost of ~$34 billion every year. — WNS DecisionPoint

How agentic AI helps:

• Analyzes big data to identify patterns of fraudulent behavior.
• Monitors claims submissions in real-time through anomaly detection and behavioral analytics.
• Collaborates with blockchain technology to validate transactions and the authenticity of claims.

Business impact:

• Reduces financial losses resulting from fraud.
• Strengthens regulatory compliance and internal audit processes.
• Improves the reputation of the insurer by maintaining transparency and trust.

Suggested: Role of AI in fraud prevention

Customer engagement and retention

Challenge: Standardized insurance products rarely cater to the diversity of modern customers, causing dissatisfaction.

According to Accenture, less than 29% of insurance customers are satisfied with their current providers.

How agentic AI helps:

• Utilizes customer data to provide hyper-personalized policies and recommendations.
• Engages policyholders proactively through intelligent chatbots, ensuring continuous communication.
• Uses sentiment analysis to understand customer concerns and refine offerings.

Business impact:

• Increases customer retention by creating meaningful and personalized experiences.
• Improves cross-sell and upsell opportunities with data-driven insights.
• Cultivates loyalty, since the insurer is basically a reliable partner in a customer’s financial journey.

Reduces operational costs

Challenge: The costs of manual workflows, overheads, and regulatory compliances keep piling for insurance companies.

G&A expenses typically account for approximately 20% of total operating costs in a property and casualty insurance company, and 30% in a life insurance company. — McKinsey & Company

How agentic AI helps:

• Automates administrative routine functions like updating of policies, approving claims, and reporting compliances.
• Makes resource management optimal to prevent wastages and optimize productivity.
• Improves continuous learning that lessens errors and decreases cost of reworks.

Business impact:

• Saves significantly in operational expenditure.
• Improves profitability through reduction in cost of internal operations.
• Permits scaling without corresponding hikes in cost.

Proactive policy adjustments

Challenge: Insurers are unable to keep policies relevant to policyholders’ changing circumstances, which leads to missed opportunities for customer engagement and revenue growth.

Roughly 40 percent of insurance customers who considered canceling their policy were considering doing so because they believed the policy was not necessary or did not provide sufficient value. — McKinsey & Company

How agentic AI helps:

• Continuously monitors policyholder behavior and external events, such as relocations, vehicle usage changes, or driving behavior, through IoT data and AI analytics.
• Automatically recalibrates coverage or pricing to match real-time circumstances, ensuring policies stay aligned with customer needs.
• Uses predictive modeling to anticipate future changes, offering timely recommendations.

Business impact:

• Enhances customer retention through adaptive and relevant coverage.
• Improves revenues through cross-selling and up-selling based on the proper adjustments in policy.

Dynamic underwriting

Challenge: Traditional underwriting depends on static, historic data, and therefore creates a generic policy structure. This leads to inefficient risk evaluation for many customer groups and results in incorrect pricing.

According to a report by McKinsey, real-time underwriting processes can improve operational efficiency by 30–50%.

How agentic AI helps:

• Analyzes real-time data streams about applicant behavior, market trends, and environmental factors.
• Leverages advanced machine learning algorithms to predict and classify risks dynamically.
• Automates the generation of personalized policy recommendations suited to individual needs.

Business impact:

• Risk evaluation accuracy is improved in terms of underwriting loss.
• Attracts a broad section of customers with highly tailored products.
• Raises profitability by linking the premiums to the actual profile of risks.

Personalized insurance products

Challenge: Standardized insurance products fail to address the needs of the customer, thus creating dissatisfaction and loss of revenue opportunities.

Personalization most often drives 10 to 15 percent revenue lift (with company-specific lift spanning 5 to 25 percent, driven by sector and ability to execute). — McKinsey & Company

How agentic AI helps:

• Builds a detailed profile of the customer using data from multiple sources such as social media, IoT devices, and purchase history.
• AI-driven insights help predict individual preferences and risk tolerance.
• Dynamically creates customized policies, such as pay-as-you-go car insurance or health plans based on lifestyle and fitness data.
• Continuously updates product recommendations based on changes in customer circumstances.

Business impact:

• Personalized policies encourage loyalty by catering to specific customer needs.
• Increase in revenue through cross-selling and upselling.
• Demonstrate customer-first approach that gives the insurer an edge above all competitors.
• Operational efficiency with automatic product customization, decreasing effort and speeding up time-to-market.

Key benefits of agentic AI in insurance: Why you should leverage agentic AI services

• Personalized experience for customers: Tailored policies and real-time adjustments enhance customer satisfaction and retention.
• Efficiency gains: Automates underwriting and claims processing tasks to help reduce costs and speed up workflows.
• Fraud detection: Detects fraudulent transactions through sophisticated pattern recognition to prevent huge financial losses.
• Risk management: Analyzes real-time data for accurate risk assessment and dynamic pricing.
• Revenue growth: Promotes cross-selling and upselling with data-driven recommendations.
• Scalability: Adapts quickly to market changes and handles high data volumes seamlessly.

Adoption strategy for agentic AI in insurance

Assess organization readiness

Before embarking on implementation, it is important to assess the existing infrastructure, workforce skills, and data ecosystem. This will ensure that the company is ready to utilize AI effectively.

Pilot small-scale projects

Start small to avoid risks and create strong insights on how the scalability of AI can go forward in the organization.

• Target use cases: Select a high-impact yet manageable region, such as claims processing, or fraud detection for ease of starting.
• Measure success KPIs such as:
• Processing times
• Cost saving
• Accuracy

Set data foundation

Agentic AI feeds off quality data. Insurers must focus on building an efficient data infrastructure that underpins the smooth running of AI operations.

Partner with experts

Partnering with AI consulting companies like Softweb Solutions can make the process simpler and ensure best practices.

Emphasize on change management

Change in AI implementation can be met with resistance from employees or customers. A good change management plan helps alleviate such fears and increases adoption.

Ensure compliance and ethics

AI systems must align with regulatory standards and ethical guidelines to avoid legal or reputational risks.

Scale and optimize

Once a pilot project is proven, roll out AI across a range of business functions and continually evolve the processes.

AI system monitoring and maintenance

AI systems must be constantly monitored to continue working effectively and meeting evolving business requirements.

Embrace the future of insurance with agentic AI

The insurance industry is on the brink of a revolutionary shift, and agentic AI is spearheading the change. With the potential to automate risk evaluation, streamline claims, and offer customized policies, this technology will eliminate inefficiencies and create growth. As competition increases and customer expectations grow, leveraging agentic AI services is no longer an option-it’s a necessity. Now is the moment to redefine how insurance operates; the future of this business will be determined by people who take on this technology.

Softweb Solutions can help insurance firms unlock the full potential of agentic AI by offering customized solutions for dynamic underwriting, automated claims processing, and personalized policy management. Our expertise in AI consulting services enables insurers to streamline processes, enhance customer experiences, and drive cost-effective growth. Connect with our experts to know more.

Originally published at https://www.softwebsolutions.com on December 17, 2024.

As we enter 2025, data, previously unimaginable to one’s mind, is now being utilized through emerging technologies. The future of organizations is for those who will unlock data’s worth. This year is not merely about keeping up with new trends; it’s about remodeling strategies to thrive in a world where data is the most valuable asset.

From being a necessity for operational purposes, data analytics services have evolved into a force that shapes industries and their economies. Simply put, it’s not about whether the data makes any difference or not but which way organizations are using this information to establish a competitive edge in a fast world. Now, let’s look at some trends in data which will emerge in 2025.

1. Data-centric AI and machine learning

Data-centric AI and machine learning show an emphasis on the role of high-quality data as a foundation to build robust AI systems. This approach focuses on curating, labeling, and structuring data rather than solely optimizing algorithms.

To maintain the accuracy of their data, 48% of businesses use machine learning (ML), data analysis, and AI tools. — O’Reilly

2. Data fabric architecture

Data fabric integrates diverse data sources across cloud, on-premises, and edge environments, allowing businesses to extract insights without traditional silos.

Source: Gartner

3. Quantum computing

Quantum computing is revolutionizing data analytics, offering unprecedented processing speeds in solving complex problems that traditional systems cannot handle.

Quantum computing is revolutionizing data analytics, offering unprecedented processing speeds in solving the complex problems that traditional systems cannot handle.Quantum computing is at the forefront of finance, with a substantial 28% adoption rate. — Market.us Scoop

4. Edge computing for data processing

AI on the edge provides the ability to process data closer to its origins-for example, in IoT sensors or smart devices-and thus obtain insights much more quickly, with fewer latency issues.

The global edge computing market size is calculated at USD 432.94 billion in 2024 and is predicted to reach around USD 5,132.29 billion by 2034. — Precedence Research

• Local data processing: Immediate insights from devices like smart meters or industrial sensors.
• Reduced costs: Lower reliance on centralized cloud infrastructures.
• Enhanced security: Sensitive data stays local, reducing risks of breaches during transit.

5. Augmented analytics services

Augmented analytics is an innovative data analysis approach, that incorporates AI and ML, automatically enriching and enhancing every single stage of the analytics lifecycle. This makes it easier for users at all levels of technical-organizational functions to make smarter, faster decisions.

The global augmented analytics market size is expected to reach USD 97.87 billion by 2030. — GlobalNewsWire

• Democratized data use: Empowers non-technical users with easy data access and exploration.
• Faster insights: Automates tasks like data cleaning and visualization, reducing dependency on data scientists.
• Improved decisions: AI-generated recommendations drive accurate, proactive strategies.
• Adaptability: Adapts seamlessly to diverse business needs and industries.

6. Hybrid cloud solutions

Hybrid cloud is the combination of the flexibility of public clouds and control of private systems, bringing businesses the best of both worlds.

More than 77% of respondents have adopted a hybrid cloud approach which can help drive digital transformation. — IBM

• Scalability: Handle workloads dynamically without overhauling infrastructure.
• Cost savings: Pay-as-you-go models reduce capital expenses.
• Regulatory compliance: Sensitive data remains within private clouds, utilizing public cloud scalability for analytics.

7. Data as a Service (DaaS)

DaaS democratizes data by transforming raw data into real-time actionable insights through accessible solutions provided in the cloud. This allows organizations to tap into advanced analytics and monetization frameworks without heavy infrastructure investments, as data is essentially transformed into a key revenue generating asset.

The global data as a service market size was estimated at USD 14.36 billion in 2023 and is projected to grow at a CAGR of 28.1% from 2024 to 2030. — Grand View Research

• Real-time updates: Data is continuously refreshed, ensuring relevance.
• Collaboration enablement: Teams across geographies access unified datasets without redundancy.
• Simplified integration: APIs make it easier to connect datasets with business applications.

8. Data democratization

Data democratization is how organizations unlock value from their data assets. Through cloud-based platforms that securely share data, capture real-time analytics, and make use of subscription models, this transforms previously dormant datasets into relevant business opportunities.

The global data monetization market is projected to be worth USD 3.47 billion in 2024 and reach USD 12.62 billion by 2032. — Fortune Business Insights

• Enables subscription-based access to proprietary datasets.
• Improves partnerships through data-sharing ecosystems.

9. Generative AI

Generative AI creates new possibilities by producing text, images, and even synthetic data, revolutionizing content creation and simulations.

67% of IT leaders surveyed said they have prioritized generative AI for their business within the next 18 months. — Salesforce

• Synthetic data training improves AI accuracy while maintaining privacy.
• Revolutionizes design with AI-generated prototypes in manufacturing.
• Enhances personalization in marketing campaigns with tailored content.

10. Data mesh and decentralized architecture

This is a paradigm shift in the data architecture trend by decentralizing the ownership and governance of the data, which enables all organizations to abandon monolithic data warehouses and lakes. It encourages the treatment of data as a product that’s owned and maintained by cross-functional teams, which provides better scalability, greater accessibility, and flexibility to achieve business needs.

Data mesh market size was valued at USD 868.59 million in 2023. The market is anticipated to grow from USD 1,010.00 million in 2024 to USD 3,375.87 million by 2032. — Polaris Market Research

• Improved scalability: Distributed ownership allows businesses to scale data management without burdening a single central system.
• Faster time to insights: Domain teams can directly work with their data, reducing dependencies and expediting decision-making processes.
• Enhanced collaboration: Encourages cross-functional teams to work together using shared data products, fostering innovation.

The data-driven tomorrow: Preparing for 2025 and beyond

The 2025 trends talk about a data-centric future built on advanced technologies. Businesses that adapt to these innovative technologies will not only streamline but unlock transformative opportunities for monetization, customer engagement, and decision making. These trends are now an imperative to stay ahead in this increasingly and rapidly changing digital landscape.

Softweb Solutions brings you expertise in data management, analytics, and AI-driven solutions to help you embrace these transformative trends. We empower businesses with tailored services and innovative tools. Our data consultants help you unlock the potential of your data to succeed in the digital age. Talk to our experts today to discover how you can apply these trends in your business.

Originally published at https://www.softwebsolutions.com on December 3, 2024.

Custom applications and websites are essential to meet both internal and external demands. However, building these solutions often requires significant development resources, time, and technical expertise.

Microsoft’s Power Platform offers businesses tools to create tailored solutions through low-code, no-code development. Among these tools, Power Apps and Power Pages enable organizations to build applications and websites with minimal development efforts. While both tools simplify digital development, they serve distinct purposes.

This blog explores the key differences between Power Pages and Power Apps and discusses respective use cases.

Power Pages vs Power Apps: Key differences

Power Apps is a low-code/no-code app development platform. It is used for custom business applications, primarily for internal employee-facing use cases. The tool enables non-developers to build apps for automating workflows, capturing data, and optimizing daily operations without extensive coding knowledge.

Power Pages was introduced as an evolution of the Power Apps Portals. It is a solution for building secure, external-facing websites. It allows organizations to create portals for customers, partners, and vendors. The tool ensures secure access to information and personalized experiences without needing an in-depth coding background.

Microsoft Power Pages: Capabilities, use cases, and benefits

Want to understand what Power Pages is and what are its key capabilities? Read our comprehensive blog to learn more about business benefits of Power Pages.

Explore more

Though Power Apps and Power Pages both leverage Microsoft’s Power Platform, they have significant differences in capabilities and application areas.

Practical applications of Power Apps and Power Pages

Creating solutions within the Power Platform is as much about choosing the right tool as it is about understanding each tool’s best-fit scenarios:

Power Apps use cases:

• Internal task management: Streamlines daily tasks, helping employees manage workflows, track tasks, and update data.
• Approval and review applications: Enables quick internal approvals, reviews, and other workflows directly through the app.
• Data collection and entry: Gathers and updates information on the go, suitable for field agents or data collection scenarios

Suggested: Top 9 Microsoft Power Apps use cases

Power Pages use cases:

• Customer portals:

Provides secure login portals where customers can access their account details, order history, and other personalized information.

• Event registration sites:

Helps organizations create websites for events, allowing attendees to register and access event details.

• Partner and vendor portals:

Facilitates secure data exchange and communication with external partners, vendors, or collaborators.

Pricing and licensing

Microsoft provides flexible licensing options for both Power Apps and Power Pages, allowing organizations to scale their use of each tool based on needs.

Power Apps licensing

Power Apps offers two main pricing models:

• Per-app plan: Licenses individual apps for specific users, ideal for organizations that need limited applications for designated tasks or projects.
• Per-user plan: Provides unlimited apps for each licensed user, allowing for broad application across the organization without per-app limits.

This pricing flexibility helps organizations start small and scale as demand grows.

Power Pages licensing

Power Pages has a unique pricing model that considers both — the number of authenticated users (those logging in) and anonymous page views (where no login is required). This pricing structure is beneficial for customer or partner portals with varied user volumes and types.

Power Pages licensing is structured to support scalability for websites with high external engagement, providing options to fit various levels of interaction and complexity.

Best fit scenarios for Power Apps and Power Pages

When to use Power Apps

• Optimizing internal processes: For internal workflows, task management, or routine approvals. Power Apps provides a powerful solution to automate and manage these processes efficiently.
• Data collection on the go: For field teams needing quick data entry or updates, such as site inspections or surveys. Power Apps offers mobile-friendly applications to collect and upload data seamlessly.
• Managing inventory and assets: Inventory management applications can help keep track of assets within an organization, offering real-time visibility and control to administrators.

Softweb Solutions developed a Power Apps application for a leading supplier of construction materials. With the app, the company can improve efficiency, minimize downtime, and streamline fleet management.

Explore more

When to use Power Pages

• Customer-facing portals: Ideal for customer service portals or membership sites where users can securely log in to view personalized information, submit requests, or update account details.
• Partner collaboration: Partner or vendor portals provide a controlled access point for external partners to collaborate on specific projects, share documents, or access limited data.
• Event management and registration: Power Pages provides an interactive site where attendees can register, browse schedules, and receive updates.
• Self-service solutions: Power Pages can host self-service portals where users can troubleshoot issues, access FAQ sections, or explore product documentation, reducing the demand on support teams.

Real-life uses of Power Apps and Power Pages

Choice Aviation

A US-based international airline cargo company utilized Power Apps, Power Automate, and Power BI. Choice Aviation replaced manual, paper-based processes with automated workflows. It streamlined cargo tracking and improved communication. The app enabled staff to input data directly into digital forms. This eliminated paperwork, enhanced data accuracy, and allowed real-time tracking and updates.

G&J Pepsi

G&J Pepsi-Cola Bottlers streamlined its operations using Microsoft Power Platform. The company significantly reduced manual tasks across distribution, finance, and HR. They implemented Power Apps, Power Automate, and Power BI. The company gained real-time data access, automated workflows, and improved reporting. This transformation allowed employees to focus on higher-value activities, optimizing efficiency and enhancing decision-making processes across departments.

• Store Audit app with Power Apps saved the company an estimated $100,000 in outside development costs.
• Instantly cut data overages by at least $5,000 a month.

Call2Recycle

Call2Recycle is a battery recycling nonprofit organization. By using Power Pages to create a self-service portal, Call2Recycle streamlined customer engagement. This enabled users to easily access recycling information, request battery recycling kits, and schedule pickups. The platform also allowed them to save:

• 10 hours per month on invoicing
• 8 hours on banking activities like vendor payment uploads
• 12 hours on processing vendor invoice approvals

Leverage low-code/no-code app development platforms for faster time-to-market

Microsoft’s Power Platform tools, Power Apps and Power Pages, empower organizations with solutions tailored to internal and external needs. By understanding their unique capabilities and use cases, organizations can select the right tool to optimize operations and enhance user interactions.

Power Apps is best suited for internal application needs, while Power Pages provides a user-friendly solution for external engagements. Power Apps drives efficiency within teams, while Power Pages delivers secure and engaging online experiences for customers, partners, and other external stakeholders.

Softweb Solutions is one of the leading Power Platform consulting companies. We can help you leverage Power Apps and Power Pages to enhance productivity and drive customer engagement. Our team of certified Microsoft consultants provide end-to-end support-from design and development to integration and deployment. Discuss your unique business case with our experts.

Originally published at https://www.softwebsolutions.com on November 29, 2024.

• Exchange ideas and strategies among teams
• Share knowledge, expertise, and capabilities with stakeholders
• Gain more visibility
• Improve brand awareness

Businesses that promote collaborative working are 5 times more likely to be high performing. — Gitnux

Despite such advantages, often companies find it difficult to choose from tools and technologies that help them collaborate effectively. Organizations that don’t prioritize collaboration face challenge, such as: wasted time, low productivity, and disengaged workforce. Microsoft 365 addresses such issues by providing workplace efficiency, productivity and connectivity.

Many companies have started realizing the benefits of M365 collaborative tools. However, some organizations find it difficult to migrate their existing system to Microsoft 365. Let’s explore some of the key challenges businesses face with migrating to M365 and address them one at a time.

Planning and preparation

Inadequate planning may lead to project failures. Hence, businesses must emphasize on careful consideration and investment in cloud.

According to an IDG study, many companies are still in the cloud migration planning phase, and only 39% of companies surveyed have carried out at least one cloud migration project thus far.

How Softweb Solutions can help

Our certified Microsoft consultants ensure a successful Microsoft 365 (O365) migration with meticulous planning. This involves thorough assessment of your current infrastructure, identification of potential roadblocks, and creation of a comprehensive migration strategy that addresses every aspect of the process.

Maintaining hybrid environment

This IT infrastructure model involves a mix of public and private cloud or on-premises infrastructure. Here are some reasons why organizations might choose this model:

• Enhanced security of private cloud
• Regulatory compliance requirements that necessitate having critical data on premises.
• Insufficient internet bandwidth

Managing a hybrid environment can be cumbersome. Directory synchronization, ensuring seamless access to both cloud-based and on-premises data, and maintaining consistent security policies across both environments are all challenges that require careful consideration during the planning phase of your migration.

How Softweb Solutions can help

Being one of the leading Microsoft consulting services providers, we leverage the following services to ensure security:

Directory synchronization

Implement Azure Active Directory Connect (Azure AD Connect) to synchronize identities and access controls between your on-premises Active Directory and Azure Active Directory. This ensures users can sign in to both cloud-based and on-premises applications with a single set of credentials.

Selective migration

Identify and prioritize the data that needs to reside in the cloud versus what needs to stay on-premises. Migrate workloads that will benefit most from the cloud’s scalability and accessibility, while keeping sensitive data or data with strict regulatory requirements on-premises.

Data governance

Establish clear policies and procedures for managing data in both the cloud and on-premises environments. This includes data classification, access controls, and retention schedules.

Network connectivity

Configure a secure and reliable connection between your on-premises network and Microsoft 365. This ensures optimal performance and minimizes latency when users access data in both locations.

Data security

Security is one of the biggest concerns for businesses thinking to migrate to the cloud. Here are some common data security challenges in cloud computing:

• Data breaches
• Lack of visibility
• Access control
• Data transmission
• Compliance
• DDoS attacks

How Softweb Solutions can help

We leverage a suite of migration tools offered by Microsoft to ensure security. For example, Azure Data Factory provides access controls to regulate who can access your data, and data loss prevention (DLP) helps prevent sensitive information from accidentally being leaked or shared. We follow security best practices, such as:

Data classification

Microsoft 365 integrates with Azure Purview Information Protection, which allows you to define labels and policies to automatically classify and protect your data.

User adoption

Microsoft 365 (Office 365) migration implies winning half the battle. The challenge, thereafter, lies in training users to maximize the benefits of the cloud platform. Often, users resist change, which leads to reduced productivity and increased frustration.

According to a 2023 Statista report, the biggest challenge to cloud adoption is the lack of qualified staff. — Fortinet

How Softweb Solutions can help

Our certified Microsoft consultants promote proactive communication and comprehensive training programs are essential. We explain the advantages of Microsoft 365 migration and provide ample resources to help users adapt to the new tools and workflows.

Managing costs

Migrating to the cloud can be expensive, especially when you have zero visibility on the expenditures and spendings. Keeping track of costs across various user licenses, storage space, and additional services can be complex.

How Softweb Solutions can help

We leverage Microsoft Cost Management tools within the Microsoft 365 admin center. This empowers you to efficiently monitor your spending, identify trends, and optimize your cloud resource allocation.
These tools provide detailed insights into your Microsoft 365 usage patterns, enabling you to make informed decisions about licenses and services to optimize your costs.

Leverage our Microsoft 365 migration services for better collaboration and productivity

Softweb Solutions, a trusted Microsoft partner, stands ready to navigate you through every aspect of your Microsoft 365 migration. Our team of experts possesses the technical expertise and experience to overcome these hurdles. Contact us to start your journey to the cloud.

Originally published at https://www.softwebsolutions.com on April 30, 2024.

Large language models (LLMs) are becoming increasingly popular due to their ability to generate text like humans do, translate languages, write different kinds of creative content, and answer your questions in an informative way. However, choosing the right LLM for your needs can be a challenge. That’s where Needle comes in. Needle is a platform that provides you with access to three different LLMs: Azure OpenAI, OpenAI, and Amazon Bedrock.

Addressing key challenges with dynamic model selection

One of the most common pain points for businesses using AI chat solutions is the inability to seamlessly adapt to changing requirements. With Needle’s dynamic model selection feature, you can easily switch between different LLMs and embedding models to address specific issues, such as:

· Limited accuracy: If your chat solution is struggling to provide accurate answers to complex or nuanced questions, you can switch to a more advanced LLM model, such as Azure Open AI or Amazon Bedrock.

· Inefficient performance: If your chat solution is experiencing slow response times or lagging performance, you can switch to a more lightweight embedding model.

· Lack of multilingual support: If your chat solution needs to support multiple languages, you can select an LLM model that is specifically trained for multilingual communication.

By dynamically selecting the most appropriate models for your specific needs, you can effectively address these pain points and optimize your AI chat experience for improved accuracy, efficiency, and overall performance.

Key benefits of Needle’s dynamic model selection

Empowering users with choice and flexibility

Needle’s dynamic model selection feature not only addresses pain points but also empowers users with the flexibility to choose the models that best suit their individual needs and preferences. Whether you require a highly specialized model for a specific task or a more versatile model for general use, Needle’s dynamic selection feature provides the freedom to choose the most suitable option.

Seamless integration with your workflow

Needle’s dynamic model selection seamlessly integrates with your existing workflow. Simply select the desired model from the provided options, and Needle will automatically adjust your chat experience accordingly. No need for complex configurations or technical expertise.

Unleashing the power of AI

The benefits of dynamic model selection extend beyond mere convenience. By selecting the most suitable models for your specific task, you can optimize your chat experience for accuracy, efficiency, and overall performance.

Tailored solutions for real-world needs

Whether you require high-precision question answering, creative text generation, or multilingual support, Needle’s dynamic model selection empowers you to craft the perfect AI chat solution for your real-world needs.

Embracing innovation with Needle

Needle’s commitment to innovation extends beyond its dynamic model selection feature. Our platform is constantly evolving to incorporate the latest advancements in generative AI, ensuring that you always have access to the most cutting-edge technology.

Getting started with Needle’s LLM configuration module

To get started with Needle’s LLM configuration module, follow these simple steps:

· Choose your LLM provider: Select the desired LLM provider from the available options, such as Azure OpenAI, OpenAI, or Amazon Bedrock.

· Specify LLM model: Choose the specific LLM model that best suits your needs.

· Select embedding model: Choose the embedding model that you want to use for your AI chat solution.

· Save your configuration: Once you have selected your LLM and embedding models, save your configuration to apply the changes.

With these steps, you can successfully configure Needle’s LLM configuration module to tailor your AI chat experience to your specific requirements.

Scale according to your requirements with Needle

Join the ranks of businesses that are already leveraging Needle’s dynamic model selection feature to enhance their AI chat experiences. Visit our website today to learn more and start your journey towards a more personalized and powerful AI chat experience.

In conclusion, navigating the vast landscape of LLMs can be a daunting task, but Needle simplifies the process by providing a comprehensive platform that caters to diverse business needs. With its dynamic model selection feature, Needle empowers you to effortlessly adapt to evolving requirements, overcome performance hurdles, and tailor your AI chat experience to specific needs. Whether you seek enhanced accuracy, multilingual support, or creative text generation, Needle’s dynamic selection ensures that you always have the right tools at your disposal. Embrace the power of AI and unleash the transformative potential of your business with Needle.

Xamarin is a cross-platform development platform that allows developers to build native mobile apps for Android, iOS, and Windows using C#. It was first released in 2011 and has been used by a wide range of developers to create apps for a variety of businesses and organizations.

Microsoft has announced that support for Xamarin will end on May 1, 2024. This means that after this date, Microsoft will no longer provide bug fixes, security updates, or new features for Xamarin. If you are still using Xamarin after May 1, 2024, your apps may not be able to run on the latest versions of Android and iOS.

If you are planning to continue using Xamarin, you should think of migrating to .NET MAUI (Xamarin.UI) as soon as possible. MAUI (Multi-platform App UI) is the successor to Xamarin.

Why should you migrate to .NET MAUI?

.NET MAUI is the future of cross-platform development. It is the latest and greatest cross-platform development platform from Microsoft. It is built on .NET 6, the latest version of the .NET framework, and it offers several advantages over Xamarin.

Microsoft is committed to supporting .NET MAUI for many years to come. This means that you can be confident that your apps will be supported and updated in the future.

.NET MAUI is an open-source project, which means that it is more accessible to developers and has a larger community of support. This can be helpful if you need help with your app or if you want to contribute to the project.

Microsoft has made it easy to migrate Xamarin apps to .NET MAUI. There are many tools and resources available to help you with the migration process.

Benefits of Xamarin to .NET MAUI migration

• Single project framework: .NET MAUI apps can be built using a single project, which can make the development process more streamlined. Xamarin apps, on the other hand, require separate projects for each platform.
• More modern features: MAUI includes new features that make it more modern and powerful, such as support for custom renderers and native controls.
• Reduced maintenance costs: .NET MAUI is a newer framework than Xamarin, so it is less likely to have bugs or security vulnerabilities. This can help reduce the cost of maintaining your apps.
• Improved performance: .NET MAUI is built on .NET 6, which is a more modern and efficient framework than Xamarin. This can lead to improved performance for your apps.
• Better developer experience: .NET MAUI offers several improvements over Xamarin, such as better tooling and documentation. This can make the development process more enjoyable and productive.

Key features of .NET MAUI

• Cross-platform UI: .NET MAUI uses a single UI framework for all platforms. This means that you can write your UI code once and have it work on all platforms.
• Native performance: .NET MAUI apps can achieve native performance on all platforms. This means that your apps will feel and behave like native apps, regardless of the platform they are running on.
• Multi-platform support: .NET MAUI supports building apps for Android, iOS, macOS, Windows and tvOS. This means that you can build a single app that can run on all these platforms.

How to upgrade Xamarin apps to .NET MAUI?

There are two ways to migrate from Xamarin to .NET MAUI:

Manual migration

This involves manually migrating your Xamarin code to .NET MAUI. This can be a time-consuming process, but it gives you the most control over the migration.

Automatic migration

There are several tools available that can help you automatically migrate your Xamarin code to .NET MAUI. These tools can save you time, but they may not be able to migrate all your code.

How does Softweb Solutions ensure a smooth Xamarin to MAUI migration?

Softweb Solutions has a team of experienced developers who can help you with the migration of your Xamarin apps to .NET MAUI.

• Identify the changes that need to be made to your code: Our team of experienced engineers is familiar with both Xamarin and .NET MAUI. We can help you identify the changes that need to be made to your code to migrate it to the new framework.
• Create a new .NET MAUI project: We help you create a new .NET MAUI project for your app. This can be a helpful step if you are not familiar with .NET MAUI or if you need help getting started.
• Migrate your code to the new project: We start migrating your code to the new .NET MAUI project. This can be a challenging task, and we can help you ensure that the migration is done correctly.
• Test your migrated code to make sure it works correctly: We test your migrated code to make sure it works correctly. This is a key step to ensure that your app works as expected after the migration.
• Guidance and support throughout the migration process: This can be helpful if you are not familiar with .NET MAUI or if you need help making decisions about the migration.

Migrate from Xamarin to MAUI now!

Migrating from Xamarin to .NET MAUI can be a daunting task, but it can be worth it in the long run. If you are considering migrating your Xamarin apps to .NET MAUI, we recommend that you start by creating a migration plan and testing your apps thoroughly after the migration.

If you are not familiar with .NET MAUI or if you are not sure how to migrate your apps, you may want to consider seeking help from a third party. Softweb Solutions can help you with all aspects of the migration process, from identifying the changes that need to be made to your code to testing your migrated code to make sure it works correctly. To learn more about .NET MAUI consulting services, please talk to our experts.

Originally published at https://www.softwebsolutions.com on September 13, 2023.

As the demand for data-driven decision-making increases, organizations are increasingly recognizing the value of data analytics. However, not all businesses are fully utilizing the potential of this powerful process. One of the key reasons for this is the lack of understanding of which data analytics techniques to choose.

In this blog, we have highlighted the importance and types of data analytics for you to understand which one is appropriate for your business case. We will also discuss the role of various data analytics services.

What is data analytics and why is it important?

Data analytics is the process of collecting, cleaning, analyzing and interpreting data to gain insights that can be used to improve decision-making. It is a powerful tool that can be used in a variety of industries, including business, healthcare and government.

Data-driven companies are 23 times more likely to acquire customers than their peers. — Forbes

Data analytics is important because it helps businesses to:

1. Descriptive analytics

Descriptive analytics is the most basic type of data analytics. It is used to describe what has happened in the past. It does not try to predict the future or explain why things happened. Instead, it simply summarizes the data and identifies trends and patterns.

Descriptive analytics can be used to answer questions such as:

Descriptive analytics can be used to identify areas where improvement is needed. For example, if the sales figures for the past month are down, descriptive analytics can be used to identify the reasons for the decline. This information can then be used to take corrective action.

Descriptive data analytics can also be used to identify opportunities. For example, if descriptive analytics shows that a particular product is becoming increasingly popular, this information can be used to increase marketing efforts for that product. It is a valuable technique for businesses and organizations of all sizes. It can be used to improve decision-making, identify areas for improvement, and identify opportunities.

How Netflix uses data analytics for better recommendations?

Netflix is a highly data-driven company that uses descriptive analytics to understand its users and their viewing habits. These insights are used to personalize recommendations, improve user experience and make better business decisions.

2. Diagnostic analytics

Diagnostic analytics helps businesses understand why things happened. It is used to identify causes of problems and opportunities. Diagnostic analytics is typically used after descriptive analytics has been used to identify trends and patterns in data.

Diagnostic analytics can be used to answer questions such as:

Diagnostic analytics can be used to identify the root causes of problems, which can then be addressed to improve performance. For example, if sales declined last quarter, companies could use diagnostic analytics to identify the factors that contributed to the decline, such as a change in the competitive landscape, a decline in customer satisfaction, or a problem with the product. Once the root cause of the problem has been identified, corrective action can be taken to improve performance.

This technique can also be used to identify opportunities. For example, diagnostic analytics can show that a particular customer segment is churning at a higher rate than others. This information can be used to target those customers with specific marketing campaigns.

How does Bank of America leverage diagnostic analytics?

Bank of America uses regression analysis, a part of diagnostic analytics, to spot fraudulent commercial card or wire payments in real-time. This also helps them identify the causes of fraud.

3. Predictive analytics

Predictive analytics can be used to make predictions about future sales, customer behavior and other events. It is typically used after descriptive analytics and diagnostic analytics have been used to understand the past and identify the causes of problems.

Predictive analytics can be used to answer questions such as:

Predictive modeling techniques use statistical models. These models are trained on historical data to identify patterns and trends. The models can then be used to predict future events by applying the patterns and trends to new data.

How Walmart uses predictive analytics to improve customer service?

Walmart collects a vast amount of data about its customers, including their purchase history, browsing behavior and location data. This data is used to train predictive models that can predict what customers are likely to buy, when they are likely to buy it, and where they are likely to buy it. The company uses this information to personalize its marketing campaigns and to improve the customer experience in its stores.

4. Prescriptive analytics

Prescriptive analytics is a type of data analytics that recommends actions that businesses should take. It is used to optimize business decisions and processes. Prescriptive analytics is the most advanced type of data analytics and is typically used after descriptive analytics, diagnostic analytics and predictive analytics have been used to understand the past, identify the causes of problems, and predict future events.

Prescriptive analytics can be used to answer questions such as:

Prescriptive data analytics can be used to recommend actions by using mathematical models and algorithms. These models are trained on historical data to identify patterns and trends. The models can then be used for decision optimization which includes recommending actions that are likely to be successful.

How Netflix uses prescriptive analytics for better recommendations and pricing?

Netflix uses prescriptive analytics to recommend movies and TV shows to customers, optimize its content library and price its subscriptions. Netflix uses a variety of factors to make its recommendations, including:

5. Cognitive analytics

Cognitive analytics is a type of data analytics that uses artificial intelligence (AI) and machine learning to understand and interpret data. It is often described as a way of “thinking like a human” when analyzing data.

Cognitive computing can be used to answer questions such as:

Cognitive analytics is still a relatively new field, but it is quickly gaining popularity as businesses realize the potential benefits it can offer.

Which data analytics is suitable for your business?

The type of data analytics that you choose depends on your specific needs and goals. Here are some factors to consider when making this decision:

• The type of data your business generates: Some types of data are better suited to certain types of analytics. For example, descriptive analytics is often used with historical data, while predictive analytics is often used with current or dynamic data.
• Business goal: What do you want to achieve by using data analytics? Some common goals include making better decisions, identifying opportunities and reducing costs.
• The resources available to the business: Implementing data analytics solutions can be a complex and time-consuming process. Businesses need to consider the resources they have available, such as time, money and expertise, when choosing the right type of analytics.

Transform your business with the power of data analytics

Data analytics is a powerful tool that can be used to improve business operations in many ways. It allows you to understand the past, identify the causes of problems, predict the future and recommend actions. You can make better decisions, identify opportunities and prevent problems.

Softweb Solutions is a data services provider that offers a wide range of data analytics services. Our data scientists help you collect, cleanse, analyze, visualize, report and consult on your data analytics needs. We help you choose the right data analytics tools and techniques for your business and implement them successfully.

The future of business is data driven. Are you ready to join the future? Contact Softweb Solutions today to learn more about how we can help you harness the power of data analytics.

Originally published at https://www.softwebsolutions.com on September 11, 2023.

• Complexity of model development and deployment
• Need for scalability to handle large datasets
• Increased workloads
• Ensuring reproducibility for model validation and auditing
• Monitoring and maintaining model performance
• Fostering collaboration between data science teams

Nearly 50% of the participants believed that lack of data quality or precision is the top reason ML projects fail. — iMerit

MLOps aims to address these challenges and streamline the deployment and management of machine learning models. It brings together best practices from software development, data engineering and operations. Also, it helps in creating a framework that supports the end-to-end life cycle of machine learning projects. In this blog, we will learn about the benefits of MLOps for businesses along with best practices.

How MLOps helps in streamlining business processes?

MLOps offers a systematic and automated framework for managing the entire life cycle of machine learning models. This streamlined approach optimizes the deployment and management processes, leading to improved efficiency and reduced costs. By integrating MLOps practices into existing DevOps workflows, you can automate repetitive tasks such as data preprocessing, model training and deployment. This, in turn, helps you save time and effort.

Uber, the renowned ride-hailing company, leverages MLOps to optimize its dynamic pricing system, a crucial component of its service. With MLOps, Uber can analyze a wide range of real-time factors, including demand, traffic conditions and supply availability, to make data-driven pricing decisions.

Through MLOps, Uber integrates machine learning models into their pricing system. These models continuously process and analyze data from various sources. The sources include historical ride data, time of day, location and events happening in the area. Uber streamlines and automates model training, updates and deployment with MLOps.

How does MLOps help in making data-driven decisions?

Data-driven decision making is the cornerstone of successful businesses today. MLOps plays a pivotal role in enabling you to make informed decisions based on reliable and scalable machine learning models. With MLOps, you can ensure the interpretability and explainability of their models, instilling trust in the decision-making process. It allows you to seamlessly integrate machine learning models into your decision-making workflows.

PayPal utilizes inference graphs, a technique commonly used in MLOps, to optimize the deployment and serving of machine learning models. Inference graphs are graph representations of machine learning models that capture the dependencies between various components and operations within the model.

By implementing MLOps practices, PayPal continuously feeds transactional data into their machine learning models. Models analyze real-time data (transaction amounts, user locations, device information) to detect fraud. MLOps enables PayPal to seamlessly integrate these machine learning models into their decision-making workflows, ensuring a smooth and automated process.

How does MLOps enhance customer experience?

In the age of personalization, providing exceptional customer experiences is crucial. With MLOps consulting services, you can gain personalized interactions and tailored solutions. By analyzing customer data, sentiment analysis and recommendation systems, you can gain valuable insights and improve customer satisfaction.

Amazon leverages MLOps to power their recommendation system, one of the key drivers behind their success in providing personalized shopping experiences. Through MLOps, Amazon ensures the accuracy of their recommendation engine.

Amazon attributes its 35% of its revenue to its recommender system.

With customer data analysis, ML algorithms and real-time MLOps deployment, Amazon offers personalized shopping experiences. This fosters customer loyalty and drives sales.

Before we read further, here are top 5 MLOps use cases for business to better understand its working and benefits.

6 MLOps best practices

While MLOps offers immense potential for business transformation, it is not without its challenges. Organizations need to address potential roadblocks to a successful MLOps adoption.

Challenges may include data quality and availability, model interpretability and the need for cross-functional collaboration. However, by following best practices and strategies, companies can overcome these challenges and embrace MLOps effectively.

Here are some MLOps best practices to address the key challenges in MLOps implementation:

1. Clear communication channels:

• Establish open and frequent communication channels between data scientists, operations teams and other stakeholders.
• Foster collaboration and knowledge sharing to ensure a shared understanding of goals, requirements, and challenges.

2. Robust testing and monitoring processes:

• Implement comprehensive testing frameworks to validate models, including unit tests, integration tests and performance tests.
• Establish monitoring systems to continuously track model performance, detect anomalies and identify potential issues.

3. Data governance and quality assurance:

• Invest in robust data governance practices, like data lineage, data quality monitoring and data access controls.
• Implement quality assurance processes to ensure the accuracy, consistency and reliability of the data sets used for model training and inference.

4. Stay updated with evolving MLOps practices:

• Keep abreast of the latest developments and best practices in the field of MLOps through industry conferences, forums and publications.
• Engage with the MLOps community, participate in discussions, and share knowledge and experiences.

5. Leverage cloud-based solutions:

• Utilize cloud platforms and services that provide scalable infrastructure and resources for model training, deployment and management.
• Take advantage of managed services for MLOps, such as automated model deployment, version control, and resource optimization.

6. Foster a culture of continuous learning and improvement:

• Encourage a growth mindset within the organization, promoting continuous learning and experimentation.
• Provide opportunities for training, upskilling, and knowledge sharing to ensure teams stay updated with emerging technologies and industry trends.

Adopt MLOps now!

As businesses navigate the complexities of managing machine learning models, MLOps offers a systematic and automated framework to overcome these challenges. By adopting best practices in communication, testing, monitoring, data governance, and staying updated, organizations can succeed in implementing MLOps.

Integrate MLOps for optimized processes, informed decisions and exceptional customer experiences. You can future proof your operations and stay ahead of the curve.

Don’t wait — the future of business transformation is here with MLOps consulting. Talk to our ML experts to better understand the benefits and opportunities of MLOps implementation.

Originally published at https://www.softwebsolutions.com on August 28, 2023.