Tag Archives: Business Intelligence

What Do You Want from Data Analytics?


We’ve done a lot of research on this question, and we’ve compiled that research into a list of the most critical benefits organizations are looking for in terms of business intelligence (BI) systems that provide data analytics.


In general, our research indicates that businesses are looking for a business solution, not a data solution. Business leaders want the ability to do self-service data exploration and discovery. They want to look at advanced analytics, build their own models, and understand what the data is telling them about their business.

You will probably find one or more of the things on this wish list are on your list of things you want from your data analytics. If you haven’t had a chance to conduct a survey in your organization, you can use this list to understand what some or all your customers will want you to provide in a business intelligence system that is based on analytics. They may not understand the underlying issues, but they want the overall benefit.


  1. Self-Service

    Customer expectations are changing for all types of businesses, and self-service is at the top of many customers’ list. Your customers are no different. They don’t want to wait for an IT developer to create a report, they want to be power users and develop their own reports.

    Obviously, there are things you’ll need to put in place to govern that activity. But, we talk to a lot of businesses that have a main goal of determining how to let users do that. They’re looking for ways to add a semantic layer on top of the data to allow users to generate reports concerning financial data, operational data, or a combination of the two.


  3. A Single Source of Truth

    You’re going to need to have data integrated into a single trusted data source as the foundation of data analytics for your customers. You know that if the data isn’t good quality and if it isn’t the right information at the right detail level, you’re not going to be able to provide insightful information.

    And that’s true whether you’re working on-premises or in the cloud, whether you’re using an Extract, Transform, and Load (ETL) process or an Extract, Load, and Transform (ELT) process. You can’t underestimate the importance of data governance so that you know where the data comes from, and you can bring it all together in one data source with confidence. It doesn’t need to be a monolithic governance structure, but things need to be in place to ensure the quality of the data.


  5. Answers to Questions Across Business Processes

    Your customers want to be able to answer questions such as:

    • What operational aspects drive financial aspects?
    • If I reduce costs in one area, what will happen to other areas?
    • If I could increase or reduce my lead time from a logistics standpoint, what would be the impact on the business?
    • If I develop new products, or change the pricing of existing products, what would be the impact on profitability?
    • What can I do to drive my business forward?

  7. The Need to Evaluate Both Structured and Unstructured Data

    There is a wide variety of both structured and unstructured data in any business. But, take social media and web-based data for an example. Your customers may want to use social media data to understand how products are working in the real world. Whether the data on social media is positive or negative, they want to use that data to get greater insight as to whether customer feedback was related to:

    • Product quality
    • Product design
    • Flexibility of use
    • Pricing

  9. Insight into Anyone Who Touches the Business

    Your customers want to know who they are dealing with in all the various roles that impact your decisions. For example, they want to know who the customers are and who the suppliers are. They want hints on how to establish strong relationships with those people to drive results, and how to take advantage of those relationships.


  11. Flexible Systems

    Your customers will want systems that are easily scalable. They want fast response. They want to be able to change the business model and see the effect on the business. Acquiring another business is one example, and the pandemic is another.

    When the pandemic hit, your customers would want to project the business impact if everyone had to stay home, or started working from home, or if your customers’ buying patterns changed, or if the percent of online buying increased. They’d want reports that could help them determine what the impact would be and how to react to it.

If you are challenged by meeting some of these requirements, Datavail experts can help you determine how to proceed. Contact us to discuss how you can take your business intelligence to the next level.

Read This Next

A Panoramic View of Cloud Analytics

The driver for moving analytics to the cloud is the business imperative to stop using data as a way to gauge what is happening in your business, and start using analytics to answer questions that will grow your business.

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Why Companies Are Moving Their Analytics to AWS Cloud


Moving analytics to the cloud is now a best practice for companies of all sizes and industries. According to a 2020 survey by MicroStrategy, 47 percent of organizations have already moved their analytics platform into the cloud, while another 42 percent have a hybrid cloud/on-premises analytics solution.


As an AWS Advanced Consulting Partner, Datavail has helped countless companies move their analytics tools to Amazon Web Services. Below, we’ll go over the benefits of migrating to AWS cloud analytics, as well as some tips and tricks we can share from our AWS cloud migrations.

The Benefits of Analytics on AWS Cloud

More and more companies are running their business intelligence and analytics workloads in the cloud. The advantages of moving analytics to AWS Cloud include:

  • Lower IT costs: Cloud migrations save businesses the costs of on-premises hardware, software licenses, and ongoing support and maintenance.
  • Increased scalability and flexibility: As you accumulate more and more data, scalability becomes an increasingly important concern for analytics, especially to handle rapid usage spikes. The AWS Auto Scaling feature lets you define rules to automatically adjust your capacity, so the system never goes down due to heavy demand.
  • Data backup and business continuity: Tools like AWS Backup help you get your business back up and running more quickly in the wake of a disaster.

Tips and Tricks for Moving Analytics to AWS Cloud

The benefits of an AWS cloud migration are clear—so what are some tips, tricks, and best practices for moving your analytics to AWS Cloud?

  1. Don’t rush into things

    Before executing any cloud migration, organizations need to perform due diligence and develop a clear strategy.

    The due diligence phase incorporates multiple steps:

    • “Discovery” of existing data assets and technologies.
    • Evaluating the organization’s goals and requirements.
    • Understanding the relationships between data, applications, and workflows that will be affected by the migration.

    Next, organizations need to decide the appropriate strategy for migrating their on-premises analytics solution. Amazon outlines 6 cloud migration strategies (known as the “6 Rs”):

    • Rehosting legacy applications by moving them to the cloud largely unchanged.
    • Replatforming applications by making a few changes and optimizations.
    • Refactoring applications by changing their architecture for the cloud.
    • Repurchasing by shifting to a SaaS (“software as a service”) model.
    • Retiring applications that are no longer used or necessary.
    • Retaining on-premises applications that require extra work before a cloud migration, but that are too critical to abandon.

  3. Use AWS tools to help with the migration

    If you’re moving your analytics to AWS Cloud, why not leverage all that the cloud has to offer? The AWS cloud includes multiple tools to assist with a cloud migration, including:


  5. Take full advantage of the AWS ecosystem

    Just the simple fact of having migrated your analytics workloads to AWS Cloud doesn’t mean that you’re actually taking full advantage of the capabilities of the AWS ecosystem. One of Datavail’s clients, a media supply chain company in California, had already migrated its on-premises analytics to the cloud, but was struggling to adapt the platform to its new home. As a result, the client was experiencing common growing pains of cloud migrations, such as poor scalability, lack of licenses and servers, and various technical limitations.

    With the help of Datavail, the client developed its analytics platform into a robust, completely serverless AWS cloud solution leveraging the entire AWS ecosystem:

    • First, existing data sources are placed in Amazon S3 staging areas, and then migrated into Amazon RDS data marts.
    • The client uses the Amazon QuickSight BI tool to run queries and self-service analytics.
    • Datasets can be easily transferred and delivered using S3 buckets and the AWS Data Pipeline managed ETL service.
    • Key decision-makers receive timely, scheduled reports in their email inboxes.
    • The entire AWS environment is monitored using the Amazon CloudWatch

How Datavail Can Help

Datavail is an AWS Advanced Consulting Partner, with years of experience helping businesses migrate to the AWS cloud. We have the knowledge and skillset to make your next AWS cloud migration a success. Our cloud migration services include:

  • Selecting the right cloud technologies.
  • Completing a cloud readiness assessment of your organization.
  • Developing a cloud migration roadmap with an appropriate timeline.
  • Creating a total cost of ownership (TCO) estimate and deploying a proof of concept.
  • Uplifting analytics solutions from on-premises to the cloud.
  • Providing ongoing cloud support, maintenance, and management.


Want to see the benefits of moving analytics to AWS Cloud for yourself? Get in touch with Datavail’s team of cloud migration experts today for a chat about your business needs and objectives—or download our white paper “Across the Continent with Cloud Analytics” to see how 8 of our clients have leveraged cloud analytics to their advantage.

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Why Companies Are Moving Their Analytics to Azure Cloud


Whether you’re a tiny startup or a massive Fortune 500 firm, cloud analytics has become a business best practice. A 2018 survey by MicroStrategy found that 39 percent of organizations are now running their analytics in the cloud, while another 45 percent are using analytics both in the cloud and on-premises.


As a Microsoft Gold Partner, Datavail has the skills and experience that companies need to make their next Azure cloud analytics migration a success. Below, we’ll discuss both the benefits of Azure cloud analytics, as well as some tips and tricks for companies who are considering a move to the Azure cloud.

The Benefits of Analytics on Azure Cloud

Azure Cloud is the perfect site for many organizations to run their business intelligence and analytics workloads. The advantages of running analytics on Azure Cloud include:

  • Lower IT costs:Cloud migrations represent a shift from the capital expenses (CAPEX) to the operating expenses (OPEX) pricing model—that means manageable monthly fees rather than pricey purchases of on-premises hardware and software licenses.
  • Increased scalability and flexibility:Scalability is an essential cloud feature to handle the ever-growing amounts of enterprise data at your fingertips. Azure Autoscale helps you dynamically scale your applications to respond to changes in usages and demand.
  • Data backup and business continuity:Tools like Azure Backup are essential to protect the integrity and continuity of your business after data loss or disaster.

Tips and Tricks for Moving Analytics to Azure Cloud

While the advantages of analytics on the Azure Cloud are obvious, the roadmap to getting there is less clear. According to a 2017 report, 62 percent of companies with cloud migration projects said that it was “harder than expected,” while 55 percent went over their allotted budget.

Below, we’ll go over some tips, tricks, and best practices for Azure cloud analytics migrations to successfully execute your next project.

  1. Don’t be afraid to experiment

    Moving to cloud analytics can represent a new opportunity for your organization to realign and shake things up. One of Datavail’s clients, a Bay Area restaurant chain, was originally using the Infor Birst BI and analytics software. However, the client found that Infor Birst suffered from problems such as a high learning curve and lagging user adoption.

    The client needed a better analytics solution that would provide real-time data and insights across different locations, including a single source of truth. By joining forces with Datavail, the client decided to migrate to Microsoft’s Power BI cloud analytics tool. This switch to the Azure ecosystem—including Power BI, SQL Server, Excel, and Visual Studio—gave the client a common toolset to unify employees across the organization.


  3. Look for “quick wins”

    Another of Datavail’s clients, a retail merchandising partner, had been using an on-premises SQL Server OLTP database that had some serious flaws, from an outdated architecture and performance issues to security vulnerabilities and the lack of a centralized repository for reporting. Datavail suggested that migrating to the Azure cloud would be an obvious next step.

    During and after the migration, the client was able to realize substantial improvements thanks to a few easy enhancements, such as:

    • Partitioning data to improve scalability and performance.
    • Making changes to system design to eliminate deadlocks.
    • Evaluating ways to reduce the cost of software licenses.

    In addition, Datavail helped the client use services such as Azure Data Factory and Azure Data Lake Storage to plan and prepare for its Power BI cloud migration.

Looking for more tips and tricks? Check out Microsoft’s Azure cloud migration checklist.

How Datavail Can Help

Datavail is a Microsoft Gold Partner, with years of experience helping businesses migrate to the Azure cloud. We have the knowledge and skillset to make your next Azure cloud migration a success. Our suite of cloud migration services includes:

  • Selecting the right cloud technologies for your business.
  • Completing a cloud readiness assessment of your organization.
  • Developing a cloud migration roadmap with an appropriate timeline.
  • Creating a total cost of ownership (TCO) estimate and deploying a proof of concept.
  • Uplifting analytics solutions from on-premises to the cloud.
  • Providing ongoing cloud support, maintenance, and management.


Want to see the benefits of moving your analytics to Azure Cloud for yourself? Get in touch with Datavail’s team of cloud migration experts today for a chat about your business needs and objectives—or download our white paper “Across the Continent with Cloud Analytics” to see how 8 of our clients have leveraged cloud analytics to their advantage.

The post Why Companies Are Moving Their Analytics to Azure Cloud appeared first on Datavail.

Cloud Analytics: Everything You Need to Know

Business Intelligence is a key component to staying competitive in today’s market. With 89 percent of companies leveraging cloud-based platforms for some or all of their BI workloads, you may be looking into migrating from your on-premises systems.

Making the decision to move to the cloud from on-premises analytics solutions can be challenging. Let Datavail put your mind at ease by answering some of the most common questions about cloud analytics and addressing any concerns.

Understanding Cloud Analytics

What is cloud analytics?

At the most basic level, cloud analytics is using cloud computing technology for analytics functions. These solutions may be complete BI platforms or have components that are essential to gaining insights from your organization’s data. Many on-premises analytics solutions also have a cloud version, so it’s entirely possible that you could use the software you’re already familiar with when you migrate to the cloud.

How is cloud analytics different from on-premises?

With cloud analytics, the vendor handles everything from software updates to maintaining the hardware needed to run it. Rather than adding servers on-site when you need more resources, you can quickly scale up capacity based on changing requirements. You access cloud analytics software by logging into the solution on the vendor’s portal or website, or you may be using it as embedded analytics that’s pulled into another application.

What are the types of cloud analytics solutions?

You have several options for deploying cloud analytics in your organization:

  • Fully in the cloud: Your analytics solution is completely in the cloud, as is the data that it’s processing.
  • Hybrid cloud: You have a mix of on-premises and cloud-based software for your business intelligence needs. Organizations that have heavy investments in their on-premises solutions may want to leverage their available resources with this combination. Other companies may keep sensitive data stored on on-premises databases rather than keeping it in the cloud along with the analytics tool.
  • Public cloud: These cloud providers are widely available to users, and have multi-tenant configurations.
  • Private cloud: A private cloud is developed specifically for your organization’s requirements and usage, so you don’t have the same data privacy concerns that you would in a multi-tenant environment.
  • Multi-cloud: Vendor lock-in can be problematic in some situations. Using a multi-cloud analytics deployment means that you leverage more than one public cloud provider.
  • All-in-one cloud analytics platform: You get all analytics functionality in a single platform. These comprehensive solutions reduce the complexity of your business intelligence stack and allow you to leverage economy of scale for contracts.
  • Best-in-class cloud analytics applications: All-in-one platforms do a lot, but they might not be able to cover very specific use cases or could fall short in key areas. Best-in-class solutions are the best in a particular area of cloud analytics. They may be focused on a particular industry or functionality. You don’t get the same comprehensive set of tools that you would with an all-in-one, but you’ll get the ones that matter most to your organization.
  • Integrated cloud analytics components: You can create your own purpose-built cloud analytics solutions through cloud vendors offering the different components required for this functionality. For example, you can use an Extract, Transform, Load (ETL) tool for getting data into a cloud-based data warehouse or data lake, then connecting that to a data visualization and dashboard creation tool.
  • Custom-developed, cloud-native analytics tool: You can also build a cloud analytics solution completely from scratch, although this approach requires substantially more resources than other options on this list.


Why Migrate to the Cloud

What are the benefits of cloud analytics?

There are a number of benefits companies are taking advantage of by moving their analytics environments to one central location in the cloud. Companies who have their analytics on the cloud can work more effectively and efficiently. Here is a list of additional benefits:

  • Flexible: You can pick and choose from a wide range of cloud analytics solutions, whether you need an application that handles your entire BI workload or one that fills in an important gap.
  • Scalable to massive data volumes: New data sources continually add to data sets, and having a system that can scale to accommodate the increased load is essential. These agile systems seamlessly scale, with many of them offering automated scaling so you can focus on the data analysis rather than the underlying hardware.
  • Easier to integrate than legacy tools: Many cloud analytics solutions have APIs and native integration support, allowing you to connect them to other software in your organization without extensive development.
  • May enable access to more modern technology: If you have legacy hardware and software on-site, you may have fallen behind on modern technology. Beyond the improvements in analytics solutions, such as AI-powered predictive analysis, you can also gain access to better security, more compute power, and other improvements.
  • Cost-effective: Your CAPEX costs shift to OPEX through the cloud’s subscription payment or a pay for what you use model. You don’t have to price according to your maximum required capacity but can scale your resources based on your workloads.
  • Access anytime, anywhere: Cloud analytics tools are accessible remotely, which is an important capability for remote-heavy workforces. Even if you don’t have a large number of employees working from home, having this level of system access means that everyone from executives to the sales team can get insights from any location.
  • Reduces load on in-house IT department: The software and hardware maintenance is handled by the cloud provider, so your in-house IT department has more time for their other important duties.

What pain points does the cloud solve for?

Moving to the cloud solves many pain points that organizations have with their analytics solutions.

  • Data silos: You can centralize your data in a cloud analytics tool, which allows you to have full visibility into your organization’s insights. Important metrics won’t be locked away. You can make data-driven decisions based on all the information available.
  • Unexpected expenses: You can better predict the total cost of ownership with the cloud solution, based on your current usage and future estimates.
  • Difficulty scaling analytics tools: Scaling on-premises solutions requires significant work and downtime. You avoid all that with a cloud analytics platform.
  • Problems with integrating data sources: Legacy software may face issues working with newly emerging data sources. The cloud’s flexibility can accommodate these groundbreaking needs without requiring significant custom development.
  • Obsolete solutions: Sometimes your hardware or software powering your BI solution is just obsolete. You can get a big upgrade by moving your operations to the cloud.
  • Limited IT resources: If your IT team can’t keep up with their daily workload, especially if it’s because they are constantly trying to prop up legacy solutions, moving to the cloud frees up large amounts of their time.
  • Lack of mobility: Most cloud-based solutions can be accessed through any Internet-enabled device, which opens up mobility for your organization.


The Logistics of a Cloud Migration

Where do I start once my company has decided to migrate to the cloud?

The first thing you should do is to take stock of your current analytics solution. Does your existing solution serve your needs? If not, where are the gaps? How much capacity do you need now and five years from now?

Go through a thorough audit and get feedback from all stakeholders, especially the end users. You can evaluate cloud analytics solutions based on this data to get a system that meets your current and future requirements.

What are some common issues to be aware of with cloud migrations?

If you’re in a regulated industry, check to see whether your chosen cloud solution meets requirements before you migrate. You may need a plan to store sensitive data on-site in a hybrid cloud infrastructure or a way to scrub this data before it’s analyzed. A comprehensive change management plan will help you transition staff from legacy systems to modern solutions.

How much will it cost to migrate?

The migration cost varies significantly depending on the type of cloud analytics solution you select, where you deploy it, and the scale of this system. Budget estimates during the planning process help you avoid unexpected expenses as you shift from CAPEX to OPEX.

What type of support services are available?

Managed service providers, such as Datavail, offer many types of support services for cloud analytics solutions and migrations. You can get help from end-to-end if necessary, along with ongoing maintenance and support.

Let Datavail Experts Guide You Through a Cloud Migration

Datavail has led hundreds of customers through successfully migrating products of all kinds to the cloud. We can guide you from start to finish with our team of experts so your cloud analytics deployment meets your expectations. We’re a Microsoft Gold Partner, Oracle Platinum Partner and AWS Advanced Tier Consulting Partner ready to tailor our services to your cloud migration project.

Reach out to Datavail to begin your cloud migration project today.

Learn more about Datavail Cloud Analytics services and also check out our extensive library of resources.

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Transforming Information into Insights: Visual Analytics


Visual analytics tools are how businesses turn cold, hard data into clear, beautiful visualizations. The right choice of visual analytics tool will dramatically simplify your data visualization workflows, offering pre-built templates to convert datasets into visual representations (e.g. bars, tables, charts, graphics and even geospatial representations).


In this article, we’ll go over some of the most popular visual analytics tools for building data visualizations, as well as some tips for how to choose the best visual analytics tool for your needs.

5 Visual Analytics Tools for Data Visualization

  1. Tableau

    Tableau is an interactive visual analytics platform that is widely used for BI and analytics workloads. The Tableau product suite consists of the desktop and cloud software versions (Tableau Desktop and Tableau Online, respectively), as well as the Tableau Server tool for hosting and sharing your visualizations with other users. Tableau starts at $70 per month for a single “creator” user, with higher costs for more users and add-ons.


  3. Microsoft Power BI

    Microsoft Power BI is Microsoft’s visual analytics solution: it easily integrates with and pulls data from the rest of the Microsoft ecosystem (Excel, OneDrive, SharePoint, Dynamics 365, etc.), as well as many other data sources. Power BI Premium costs $20 per user per month, or a flat $4,995 monthly rate for the entire organization, offering features such as artificial intelligence and self-service data preparation.


  5. Oracle Analytics Cloud

    Oracle Analytics Cloud (OAC) is cloud-based visual analytics software for Oracle customers. It includes a robust set of features for data preparation and cleansing, data visualization, collaboration and more. OAC lets users make use of machine learning and statistical modeling for data discovery, revealing hidden patterns and insights in their enterprise data.


  7. Qlik Sense

    Qlik Sense is a BI and data visualization platform that offers user-friendly, self-service analytics and reporting capabilities. Whereas Power BI and Oracle Analytics Cloud are primarily intended for large enterprises already using the Microsoft and Oracle ecosystems, Qlik Sense is ideal for smaller organizations with less in-house analytics talent available. Qlik pricing starts at $30 per user per month, with custom pricing available for large enterprises.


  9. SAS Visual Analytics

    SAS Visual Analytics is a data visualization and analytics offering from SAS, another enterprise software giant. Users of SAS Visual Analytics can quickly get the big picture from their data while uncovering hidden connections, clusters, outliers, and ideas. According to Capterra, SAS Visual Analytics pricing starts at $8,000 per year.

How to Choose the Right Visual Analytics Tool

The “right visual analytics tool” will depend on your precise business requirements: the data you have on hand, the ways in which you want to represent it, and the skill level of your data analysts. When choosing a visual analytics tool, consider the following factors:

  • Compatibility: Selecting a visual analytics tool that works well with your existing data and software is a must. Your choice of tool should either be compatible out of the box, or there should be an easy way to convert your data so that it becomes compatible.
  • Integrations: In addition to technical compatibility, also consider how well the tool integrates with your existing IT ecosystem. For example, if you heavily use Microsoft software, picking Microsoft’s own Power BI tool is a natural choice.
  • Pricing: The price of enterprise software is always a concern for everyone except the most cash-flush organizations. Decide your budget and your preferred payment model—would you rather purchase software licenses or pay a monthly fee?
  • Scalability: For organizations with gigabytes and terabytes of data to analyze, scalability is an essential concern. Choose a tool that’s suitable not only for your current level of usage, but also one that can grow alongside your business. Scalability is a major reason why many businesses use visual analytics tools in the cloud, which can expand your usage of storage and processing power on the fly.
  • Ease of use: Some businesses will prefer a visual analytics solution that’s easy to use for even the most non-technical employees, letting them easily spin up visualizations. Others with more in-house expertise will prefer a powerful, highly customizable visual analytics solution that lets users dig deep into the code base.


Visual analytics tools are the most obvious way for you to start bringing data visualizations into your business—but they’re far from the only factor you should think about. Discover how to build a robust, production-ready analytics and visualization pipeline by reading Datavail’s white paper: From Raw Data to Insightful Stories: Transform Analytics into Innovation.

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