Tag Archive for: Business Intelligence

Rapid Insights with Amazon Q in QuickSight

/by

Now more than ever, having the capacity to quickly access and evaluate data is essential for maintaining a competitive advantage. With reliable data gathering and robust infrastructure being the standard across industries, business leaders must take the next step: maximizing the valuable insights gleaned from this abundance of data. Business intelligence (BI) tools are typically the access point for stakeholders to get their reports and dashboards; however building out and maintaining a robust reporting ecosystem can require significant time and effort from analysts, engineers and BI teams.

Decision-makers have constantly evolving needs but the data required to make informed choices isn’t always readily accessible. Compounding the issue is that building ad-hoc reports demands considerable time and involves numerous back-and-forth with the BI team. This presents significant bottlenecks in time-to-insight. But what if business users could directly access and analyze their data without relying on technical expertise?

What Amazon Q in QuickSight Is

Amazon Q in QuickSight is a generative AI assistant embedded within QuickSight, the Amazon Web Services browser-based cloud Business Intelligence tool. The tool allows users to ask natural language inquiries about data and receive prompt, accurate answers accompanied by instant visualizations. Developers can also leverage natural language questions to create visuals, executive summaries, forecasts and anomaly reports. 

Amazon Q in QuickSight has the potential to significantly reduce time to insight and lighten the load on dashboarding teams. While BI analysts focus on building data sources and critical reports, business users can generate ad-hoc reports in seconds rather than hours. QuickSight operates securely within your Amazon QuickSight account, requiring only clean data sources, well-structured metadata, and well-formulated questions.

What it Is Not

Amazon Q in QuickSight is not a replacement for QuickSight or any other BI tool, nor is it a general-purpose AI virtual assistant. Unlike general-purpose AI assistants, such as Amazon Q Business, which use data from across the organization to answer broad business questions, Amazon Q in QuickSight is designed for narrow, data-specific inquiries. It works best with business-specific questions related to datasets that have been prepared for natural language queries, providing focused, data-driven answers tailored to the user’s needs

Leveraging Amazon Q in QuickSight

For Chief Data Officers

Empower your teams to explore data independently with Amazon Q in QuickSight. This tool allows everyone in the organization to ask questions and gain insights without relying on data analysts for every query. By democratizing data access, you can foster a culture where business decisions are quicker and more informed. This increased autonomy also frees up your data teams to focus on complex analyses and strategic initiatives, driving greater value across the organization.

For Business Intelligence Managers

Streamline your team’s workload by routing frequently asked data questions through Amazon Q in QuickSight. This approach significantly reduces the time spent on repetitive data requests and ad-hoc reports, allowing your team to focus on higher-value tasks. For example, you could create a Q Topic dedicated to quality assurance on dimensional data or for validating database metrics without writing SQL. By implementing Amazon Q in QuickSight, you not only boost your team’s productivity but enhance the efficiency of teams you work with as well.

For Analysts

Use Amazon Q in QuickSight to validate hypotheses and gather quick insights during exploratory analysis. Save time on running queries by asking data questions right in the Amazon Q in QuickSight interface. The tool also facilitates quickly switching between datasets, as all topics are available and can be accessed in any part of QuickSight. Amazon Q in QuickSight also supports building dashboard visuals and writing calculations directly in the Q interface, further accelerating report development.

Creating a Topic

Using Amazon Q in QuickSight is simple by design. Once enabled, the tool appears as an icon within QuickSight, which the user can click, ask a question about data and receive an answer.

To successfully implement Amazon Q in QuickSight, data topics must first be set up, which requires thoughtful planning.

Start Asking the Right Questions

Before even choosing the right dataset to answer questions, it’s important to establish a clear business case and determine what questions stakeholders need answered. This process is iterative and adaptable.

Start by engaging with your key stakeholders to understand their pain points and goals. What insights are driving business decisions? Which reports are they using frequently? Which are they rarely using? How much time is being spent on ad-hoc reporting? The insights gained from this process is invaluable, and ensures the stage is set for Amazon Q in QuickSight implementation.

Building Topics

Topics for Amazon Q in QuickSight are organized sets of data designed to give context for the AI, allowing users to ask questions and receive answers in seconds. When an end user interacts with Q, they will do so by selecting a topic within the QuickSight interface and asking questions related to that topic.

To create a topic, you must first select the datasets the topic will use, and manipulate the metadata to fit the chosen business case to make it user-friendly.  

Identify Jargon

During this process, note down any business-specific language used by stakeholders. When creating a topic, you can give data fields synonyms. This helps Amazon Q in QuickSight understand questions and correctly identify the relevant field to use in its analysis. If a stakeholder asks a question using business jargon known to them, they should still get an answer they expect. Organizations that maintain a glossary/dictionary of terms for their business can use this document to further speed up adoption of Amazon Q in QuickSight by their stakeholders.

Topics: Broad or Narrow?

Should your topic be about sales across the organization, or only one product? Will it contain sales data, inventory data, or both? These are critical decisions to make when setting up your topics in Amazon Q in QuickSight.

Topics encompassing a broad range of target data can answer diverse questions and provide a comprehensive view of the business. They are useful for high-level reporting and for users who require a general overview.

Topics that are narrower in focus might include sales data, customer accounts, key financials or other limited scope data. These focused topics offer more targeted insights, making it easier for end users to get relevant answers from Amazon Q in QuickSight quickly.

Your exact topic setup can vary widely pending the business need defined at the outset, data available and audience for the topic. One helpful feature offered by QuickSight that helps with this is Row Level Security available natively within topics. When there is Row Level Security enforced on a QuickSight dataset, and a topic is built using that dataset, the user will only receive insights about data they have access to, with no additional setup required.

Choosing the Right Data

Once you have a clear understanding of common stakeholder data questions, pain points, and how they are talking about their data, you will then naturally find the data sources containing the information best equipped to answer them. The data you choose will be the bedrock of your Amazon Q in QuickSight solution.

Setting Up Topics

QuickSight will perform an initial ‘cleaning’ of your data on its own, assigning common synonyms to field names (e.g., a field called ‘Cost of Goods Sold’ will be given the synonym ‘COGS’ automatically). You will want to double check this for accuracy, as it can make mistakes.

Once data is selected, QuickSight will prompt you to ‘Start Review’ of the topic data. This is almost always your immediate first step. Proper data cleaning and preparation are crucial steps to ensure that Q in QuickSight provides accurate, actionable insights that align with your business goals.

Field Selection

Review each field to ensure that only relevant and high-quality data is included. Consider which fields are adding value to the analysis, and which are adding noise or aren’t useful for the analysis. Include and exclude fields as needed.

Synonyms

Add synonyms to fields to accommodate different terminologies used by your business users. This will make the topic (and results) more user-friendly and intuitive.

Data Roles and Formatting

Define roles (such as dates, measures, dimensions, etc.) and apply consistent formatting. You can manually set fields to be formatted as Currency, which will maintain the clarity and overall quality of the insights.

Data Aggregations

Set up the data aggregations of measures to set up meaningful summarizations. You can set default aggregations (e.g., a ‘Sales’ field can be set to SUM or AVERAGE automatically) and disallow other aggregations that either don’t make sense or are potentially sensitive.

Testing, Deployment and Beyond

Amazon Q in QuickSight encourages continued evaluation and performance monitoring after a topic is deployed to end users. For each topic, you can see how many questions have been asked and what they are, and view user feedback of their responses. Each time a user asks a question, they have the option to give the response a thumbs up or thumbs down, and give long text feedback.

End users should be highly encouraged to interact with the results they get from Amazon Q. Their input is extremely valuable and can be used to determine what is working well with the topic and what could use improvement. Additionally, you can see how many questions have been asked in total, what portion of those questions were answerable, and what portion weren’t. Use these metrics to assess engagement with the solution and calibrate or update the topics accordingly.

Listening to Feedback

From the outset, deploying Amazon Q in QuickSight works best as a collaborative process. Integrating it into your BI ecosystem is best done by working closely with the target end users at all stages of development, from ideation to deployment. Their expectations and usage of the tool will be the biggest variable impacting the success of the project, and their continued engagement after deployment will ensure issues are identified, fixed and maintained.

Negative feedback from users should always be addressed and corrected quickly. If end users find many of their questions unanswerable or are consistently getting unexpected results from the topic, it can lead to frustration and abandonment of the tool. Remember, the value of Q in QuickSight is being able to get the information you need quickly and easily. If users are using synonyms that the development team had not considered, if their queries are too complex, or irrelevant to the topic, they will have trouble getting the results they are looking for.

A great way to start users out using Amazon Q in QuickSight is with Suggested Questions. These are questions shown to users by default when they open a topic in the Q in QuickSight interface within the QuickSight console. These can be validated by the developers of the solution. Questions can be manually validated by the developer of the topic and shown to end users by default. These can also be re-validated periodically as the data refreshes. End users are shown the last validation time.

Setting Expectations

Given the rise in popularity of other AI-powered tools, it’s important to set user expectations about what Amazon Q in QuickSight is and isn’t useful for. It is designed for quick, high-level insights while also giving a user the ability to dig deep to answer specific questions about their data. Questions posed to a Topic should always be pertinent to the data in the topic.

Amazon Q in QuickSight is not a replacement for mission-critical dashboards, nor is it a general purpose, world-wise AI virtual assistant. Situations involving intricate calculations or carefully formatted reports are better carried out in a QuickSight Analysis. Additionally, while Amazon Q in QuickSight streamlines the querying process, it still requires well-prepared and clean data to function effectively.

Final Thoughts

Amazon Q in QuickSight is an AI solution completely integrated into a full-fledged BI tool. It is secure, and enables end users to perform their own ad-hoc reporting done at their convenience. By empowering users to query data directly and intuitively, Amazon Q in QuickSight reduces the load placed on technical teams, accelerates decision-making processes, and fosters a more data-driven culture within the organization. These make Amazon Q in QuickSight a valuable asset for any organization looking to enhance its reporting capabilities and drive positive business outcomes.. 

Bridging the Gap Between Legacy and Modern BI Platforms – Amazon Quicksight

/by

Today, Amazon QuickSight Announced Paginated Reporting; Cited Ironside as a QuickSight Delivery Partner

Connecting the Past to the Future

The past decade has seen a tremendous shift in how we consume analytics – from enterprise, templated, and paginated reporting to interactive, embedded dashboards with ML-augmented capabilities. It’s no surprise organizations have been eager to put these new tools in the hands of their employees. Unfortunately, they quickly realized a lift-and-shift approach for BI platforms requires extensive planning and training because of the fundamental differences between how legacy and modern BI tools address reporting needs.

Enterprise reporting has been the standard for decades. It’s what many business leaders and users alike are used to – and for good reason. Consumers could receive reports tailored to their individual needs and in various formats (PDF, CSV) on a scheduled cadence that contained all of their KPIs and performance metrics. Within the legacy BI space, organizations have been able to scale this extremely custom and robust reporting solution to their hundreds of users with great success for many years.

But in the age of big data, enterprises needed to approach data discovery and analysis differently. Data analysts became a highly valued and growing community within organizations. Companies rightly prioritized empowering these analysts to better leverage their technical skills and business acumen to drive meaningful impact. This meant migrating to modern BI platforms that favored interactive dashboards over reports numbering in the tens-of-hundreds of pages.

Among the many challenges of migrating from legacy to modern platforms was the reality that legacy users could no longer access reports with the same look and feel they’d grown accustomed to for years. Companies found that even with robust migration strategies, careful execution, and exhaustive change-management programs, they were left with reporting needs that neither a legacy system or modern BI tool could meet on its own. Instead, they had to maintain multiple systems to meet analysts’ needs for powerful dashboards and legacy users’ needs for robust operational reports.

Bridging the Gap

So – how do organizations move to a platform that incorporates the modern analytics movement of cloud-based, self-service and augmented analytics, while also creating limited friction for users entrenched in legacy reporting models? Amazon QuickSight Paginated Reporting is beginning to bridge the gap.

This release is centered around paginated reporting, distribution and analysis – the core tenets of an enterprise reporting implementation. The disparity between platforms continues to shrink, allowing organizations to spend more time evolving their new ideas rather than reimagining existing ones. Lastly, this release addresses an important piece to a successful adoption – creating a smooth transition for the user community.

Enterprise Reporting in the Cloud

Enterprise reporting entails the delivery of insights in templated and tabular formats on a regular basis. Some users prefer fewer visualizations and more granular data, including pivot tables spanning multiple pages. Amazon QuickSight has new features that allow report authors to design, build and distribute presentation-ready formats from within the same platform.

  New report creation tools

  • Headers & Footers
    Gives the author the ability to add custom report information within dedicated sections to make reports easier to scan and absorb
  • Page Margins, Padding Controls & Guardrails
    New formatting tools allow authors more flexibility in customizing how reports appear
  • Repeating Content
    Allows authors to quickly build stories by taking different slices of a particular chart and recreate them within a report

New report distribution and analysis tools

  • Custom schedules with enhanced features
    Gives administrators the flexibility to address the wide variety of distribution requirements from the user community
  • Historical snapshots
    Allows administrators to audit report delivery and track usage to inform scheduling
  • PDF or CSV
    Provides two options so users can receive reports in the desired format for effective analysis

Amazon QuickSight customers can rely on ongoing innovations. Some AWS releases feature exciting new technology. Other releases are about incorporating existing legacy functionality to better meet user needs. The goal is to help companies envision a future within a modern BI platform – and make getting there easier. Paginated Reporting accomplishes both.

Questions?

If you have questions about migrating to Amazon QuickSight, and how Ironside can help, email us at AscentIQ@IronsideGroup.com

,

Top 5 BI Expert Sessions of 2020: Part 2 of Take30’s One-Year Anniversary

/by

Looking back now that we’ve reached the one year anniversary of the Take30, we appreciated the opportunity to share our perspective on a range of Business Intelligence (BI) topics with a wide and diverse audience.  

The topics we covered ranged from the strategic and thought provoking, to the deep technical and “how-to” with a consistent focus on how to improve the analytics experience for you and your user community.

Over the course of the year we hosted 28 sessions focused on BI, most were either focused on a specific technology (Amazon QuickSight, IBM Cognos, Microsoft Power BI or Tableau) or a comparison on how these technologies addressed a capability such as Natural Language Query (NLQ), Embedded Analytics or Cloud BI.

Many of our sessions focused on our heritage as the go-to Cognos experts including deep dives into the modern BI features in Cognos Analytics including Data Modules, Data Sets and Explorations.  In one of our most highly anticipated (and highly attended) sessions, Rachel Su from IBM Offering Management joined Ironside to lead an overview of Cognos Analytics 11.1.7 – a role she reprised last month for Cognos Analytics 11.2).

In a number of other sessions, we explored Tableau new features, touched on many of the enterprise capabilities of Power BI and introduced Amazon QuickSight to our audience. 

Creating a Centralized Metadata Model in Power BI (4/16/20)

In our first session of the Take30 series, we explored the concept of shared datasets in Power BI and offered our point of view that, for many organizations who are maturing their Power BI capabilities, shared datasets mapped well to the “traditional” approaches of centralized (and governed) metadata, yet offered a degree of flexibility for decentralized teams to move at their own pace.  (Checkout this Playlist for Power BI)

Cloud BI: A Comparison of Leading Technologies (6/25/20)

As a majority of organizations see Cloud-based analytics as critical to their current and future analytics strategies, we thought it an opportune moment to take our audience through a review of the leading BI tools we work with on a daily basis.  

We reviewed the benefits of Cloud BI, including serverless and subscription-based licensing, then provided a comparison of vendors including Microsoft Power BI, Tableau, IBM Cognos and Amazon QuickSight.  

Amazon QuickSight – New Features (10/1/20)

While relatively new to the BI marketplace, we were excited to continue our focus on Amazon QuickSight and the significant progress the AWS team is making toward a solid enterprise featureset.  

Since that time, the roadmap and feature releases have become even more aligned to the enterprise reporting use case, especially in consideration of the compelling licensing story and scalable serverless architecture on which it is based.  (you may also want to check out this intro to QuickSight session  Introduction to Amazon QuickSight (5/7/20))

Enterprise Reporting: Assessment, Simplification and Migration (2/18/21)

Lastly, we wanted to address a topic that is of increasing prominence in our day to day conversations with clients – that of enterprise reporting migrations.  

In this session, we provided our point of view on the reasons why organizations migrate from their legacy tools, offered perspectives on approaches to migrations and the important pitfalls and lessons learned when considering such an initiative. 

We touched on tooling and accelerators we’ve developed to help those who have embarked on this journey reach their destination more quickly.

____

Throughout 2020, the Take30 webinar series gave our BI Experts a new way to connect with our clients and prospects in what was otherwise a challenging year. We confirmed that our participants are not only interested in diving deep into tool functionality, they are looking for guidance in managing multiple BI tools at enterprise scale, and understanding how cloud BI can enhance their analytics capabilities without breaking enterprise reports and functionality which are critical to their business operation.

Looking forward, we are going to explore those and other questions with you as we continue to share our knowledge and provide a mix of content; from the tactical to the strategic, across all the tools we help our clients with on a daily basis.

A message from Ironside’s CEO: Stronger Together

/by

As we enter into the Independence Holiday weekend, I wanted to drop a quick note. It’s hard to believe three months have passed since my last letter. The world is evolving daily and technology continues to play a critical role in how we all connect, track information and communicate worldwide. Despite the shift toward working remotely, the executive team and I continue to be impressed with the level of productivity, cohesiveness, employee engagement and strength as an organization that we have seen demonstrated by our team. 

Here are a few highlights:

Teamwork. My team has pointed out how their interaction with each other has expanded — collaboratively tackling projects, sharing knowledge to prepare for webinars, and helping clients deal with COVID-19’s impact on their data and business analytics. We’ve hosted weekly Town Hall forums and internal Step competitions that have promoted teamwork company-wide.Under our Strategies for Success free content offerings to our clients, we’ve rallied around our Take30 Series. 

These sessions hosted by Ironside’s Data Science, Data Advisor and Business Intelligence Leads, have made it important for Senior Consultants, Partners and Clients to come together to offer the best of our thinking. The planning and delivery has been mutually beneficial for our team and the ever-growing number of participants who we have shared 30 minutes together, multiple times per week since the start of the pandemic.

Education. This unprecedented time when we are not traveling to clients has offered a time for our consultants to learn additional skill sets and to expand their certifications. One of the greatest values we offer to our clients is understanding best practices related to integration aspects between our key partners: IBM, AWS, Precisely, Microsoft, Trifacta, Tableau, DataRobot, Snowflake, Alteryx, Matillion and Alation. For us, to continue to excel with these partners — cross-training between our Business Intelligence, Information Management and Data Science practices on our most utilized tools — has created many “a-ha” moments toward streamlining our delivery services.

Client Engagements. Despite the sunsetting of “business as usual” for now, Ironside’s business is strong. COVID-19 has impacted businesses in various ways, whether they are operating and accessing data differently or needing to measure the impact of the global environment on their businesses’ analytics. Perhaps now, more than ever, the demand for information and analytics is a “must-have” versus a “nice-to-have.” Some of our clients have found themselves busier than ever and racing to keep up with the demand for new analytics and reports. Other clients are compelled to be more hands-on with analytics that used to be automated by machine learning models that have been rendered invalid. In these cases and beyond, Ironside’s Analytics Assurance Service is here to help. Our team’s expertise is being leveraged for immediate, short term assistance to support organizations running as efficiently as possible, allowing clients to use their own skills for other tasks to avoid stifling tradeoffs. 

Thank you for your continued relationship with our team. From myself and my team to you and yours, we wish you a wonderful Independence Day. Stay safe and remain strong, both in business and in health.

Best,
Tim

, ,

Analytics Anarchy: How To Manage a Multi-Vendor Analytics Ecosystem

/by

When you think about the different ways that data gets used in your company, what comes to mind?

You surely have some executive dashboards, and some quarterly reports. There might be a reporting portal containing everything that IT created for anyone within the past decade.

Read more
,

From BI to Data Science: An Interview with Tom Clancy

/by

At Ironside, we believe that data science is a team sport, and should be accessible to and enable as many players as possible. We work with clients on a regular basis to make data science accessible within their organization. But we also do this within our own company. Meet Tom Clancy – hear about his journey and what he has learned along the way.

Read more
,

New Features of Cognos Analytics v11.1: Data Module Enhancements

/by

In previous releases of Cognos Analytics, we have seen a trend of integrating many of the features of metadata modeling in Framework Manager into the Cognos Analytics interface. This trend is continuing with new or improved modeling capabilities being incorporated into Cognos Analytics 11.1 Data Modules.

Read more
,

Challenges of Customer Segmentation

/by

Customer segmentation is defined as “the process of dividing customers into groups based on common characteristics so companies can market to each group effectively and appropriately.” By using the correct attributes to define the customer segment, it allows companies to identify the right customers for targeted and relevant offers. Those who successfully define and maintain customer segmentation can derive a competitive advantage from the implementation by improving customer experience.

Read more
,

Metadata Comparison – Microsoft vs. Cognos

/by

Metadata has long been an essential part of every successful organization’s analytics strategy. In this article, I sat down with two of Ironside’s foremost experts on the topic to discuss its origin and value and how two of the industry’s leading vendors in the analytics space address this core capability in a bimodal analytics world.

Susan Ma and Suresh Edara are both long-time Ironside consultants with deep expertise in top BI toolsets: Microsoft and Cognos.

Read more
,

What to Consider When Building Your Data Democratization Strategy

/by

Data democratization is the ability of an organization to provide information to end users in an easy and effective way. The goal is to provide self-service of information to end users with minimal IT support. There are many things that can go wrong when rolling out data democratization projects. The purpose of this article is to identify potential issues and provide guidance on how to avoid them in the democratization process.

Read more