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Are you ready for embedded and contextual analytics?

If you are considering embedded or white-labelled analytics solutions, this post will help to explain why it’s paramount for you to first understand the analytic capability of your software before taking action.  

As a product manager, you will need to determine what constitutes a Minimum Viable Product (MVP) before launching it. It is critical to first examine the existing software’s analytical capability and address areas for improvement. 

The ‘Embedded Analytics Maturity Curve’ is a useful strategic assessment framework that product managers and software owners can use to help plan out their implementation. It outlines defined phases of the overall product journey, and can be used as a roadmap to formulate analytical development, adoption and long-term strategy. 

This visual framework focusses on the usability of business intelligence analytics, and maps out the development effort required to reach the desired target.

The curve indicates the ideal trajectory of an analytics solution. As the product matures, it’s analytical capability and data value increase, ultimately becoming a more sophisticated piece of software.

Are you ready for embedded and contextual analytics? RhinoIT

By following this structured progress model, you can better determine and learn:

  1. The evolution of your analytics product
  2. Where improvements are required to move it to the next stage of maturity
  3. How the final product will deliver value through automated in-context workflows, reducing effort for both the developer and end user.


If you are at the beginning of building a Minimum Viable Product (MVP) and getting it ready for market, then you are at stage 1 of the analytics maturity curve. At this stage your software is likely to be purely transactional, without methods to analyse data such a dashboards and reports.

You may have decided to ship your product, perhaps as a proof of concept, with the intention of including analytic functionality in the future. However, what’s important to consider here is the user’s requirements, because these analytic constraints may pose severe problems when trying to introduce sophisticated features later on.

Key reasons to evolve:

  • clients are demanding more access to their data
  • you’re losing to competitors with reporting and data access API capabilities
  • lack of access to data and insights is the reason for lost deals

Signs that you’re ready for the next stage:

  • you have a good grasp of client’s information needs
  • you have the required data platform expertise within your organisation
  • your data structure is stable


At this stage you are providing data export tools such as CSV downloads or API access. This is to cater for clients that now recognise the need for report building and data consumption to guide their decision-making.

If your users can only access their data using an external solution then this presents limitations. It means they need to build their analysis from scratch and manage the data pipeline outside of your software. The disparate nature of the analytic experience becomes burdensome and time-consuming. Plus, the data is in its’ raw format, which may not represent an accurate picture for meaningful insights.

Exporting a CSV from your software and uploading it to a third-party BI tool for analysis, requires a user to keep switching back and forth between the two for data context. This creates a disjointed experience overall and without guidance on how and where to start, they could become easily frustrated.

Key reasons to evolve:

  • clients are integrating data into their own reporting solutions but struggling to build meaningful reports
  • you want to charge for data access but data exports provide little value to justify this

Signs that you’re ready for the next stage:

  • you have access to resources who understand your data model and can define and build basic reporting
  • you have a clear set of basic reporting requirements from your user base that is common across many clients
  • you have an underlying data structure that can accommodate reporting workloads without impacting performance


This stage is typically marked by the introduction of an in-house developed analytical solution or basic operational reporting capabilities, where users can build basic parameter-driven reports within an application. However, the set of dashboards and reports options are usually limited, and users cannot create their own custom analysis.

The user’s need to make quick decisions, based on reliable insights that are immediately available creates a new challenge. Requests for new reports mean that developers can struggle to keep up with demand. This can potentially slow down development of the core product.

Key reasons to evolve:

  • your clients are requesting more sophisticated insights
  • your clients want to give access to senior management and tabular reports don’t cut it
  • competitors are innovating with data and have a more targeted sales and marketing approach

Signs that you’re ready for the next stage:

  • you are able to define and measure KPIs in your data that are common across clients
  • you can define views of your data that can be combined into executive or operational dashboards
  • you understand what your competitors are offering and how you can match their offering or create a new unique selling point

ANALYTICS MATURITY STAGE 4 – Standalone Dashboard and Reporting Module

And now for the embedding of real-time reports, dashboards and data visualisations into your software!

This is where you can offer a true self-service reporting experience that enable users to create their own bespoke analytic content, using pre-defined, secure data sets.

Clients will have better access to data via standalone modules (dashboards/reports) with the ability to create bespoke reports, which frees up the developer’s time. Business Intelligence analytics become more feature-rich and user-friendly, providing higher value for users and reduced workload for your development team.

The challenge at this stage is to ensure users make optimal use of your embedded software. You need to ensure that they remain focussed within your application, without having to switch to external sources for context. The easier it is for them to discover insights, the less likely they will be distracted from their workflows.

Key reasons to evolve:

  • having a competitive edge in your analytics offering is essential to your strategy
  • you may have churned customers to competitors looking for greater analytics sophistication
  • you see key advantages for your users in enabling analytics at the point of consumption

Signs that you’re ready for the next stage:

  • your data model is highly mature and performant
  • you have mature data and analytics capability or partners who provide that skillset
  • you have UX expertise that can help design and combine analytics into your core application workflows

ANALYTICS MATURITY STAGE 5 – Contextual Analytics

You’ve made it – embedded analytics takes a giant leap into contextual analytics!

Integrating components like, charts, tables, dashboards, alerts, and visualisations. Delivering them directly in the user’s interface and core transaction workflow.

Users have access to relevant data and insights in real-time, right at the point when they need to take timely action. They may not even realise they’re using analytics because much of the data will be pre-defined, automated and seamless.

Contextual analytics is really the best way to fully optimise the use of your core software and future-proof it. Providing a high quality experience to users significantly increases the business benefits for everyone.

Improving your chances for maturity 

Progressing through the five stages of the embedded analytics maturity curve is a journey not a race, and can be achieved by every team regardless of their data skills.

A critical assessment of your current analytics capabilities and areas for improvement is the critical first step. Being clear about how well your software meets the curve criteria, what value it offers business users and where it may fall short.

It may be useful to look at lessons learned from other mature organisations in similar industries. Their successful use cases can inspire your own product initiatives.

Achieving an exceptional analytics offering is reliant on aligning your product’s data maturity and embedded maturity. You can’t try to get to Stage 5 insights while your data is at Stage 2. Start to prepare your data ahead of migration. Don’t limit yourself to just one stage, look further still, right to your end goal.

Get ready for embedded and contextual analytics 

Ultimately, taking the time to examine the state of your data, people and technologies in-depth can provide valuable guidance in maturing your software’s analytical capabilities, and even be a much needed wake-up call.

With the availability of modern solutions like Yellowfin that make the adoption and implementation of embedded and contextual analytics as seamless and streamlined as possible, there is no better time to begin assessing your product’s current analytical maturity.

Talk to us to find out how you can start planning the introduction of new and innovative features that will transform the way your users engage with data, and make better informed decisions sooner.

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Let Natural Language Query be your guide

Natural Language Query (NLQ) allows a user to enter search terms or phrases as if they are speaking them naturally. This includes statements, questions or a simple list of keywords. 

NLQ is a self-service BI capability that allows non technical users to ask questions of their data and receive a chart or report that answers their query, providing a deeper level of understanding. NLQ tools come in different forms and levels of integration, varying between software vendors.

Some platforms incorporate voice interaction, or querying data using a virtual personal assistant. The most common approach in the market is currently search-based NLQ. This is where users enter a query in a search box located within the BI interface, the tool parses the keywords, matches them with elements in known and/or related databases and shows a result.

The latest approach is Guided NLQ – where the programmed analytics solution acts as a guide, offering users pre-defined sequences and suggested prompts to help structure their query.

Guided NLQ take users step-by-step, making it simple for anyone in the organisation to ask complex questions of their data by:

  • formulating the type of question
  • building it with field auto-complete and automated filter selections 
  • adding the answer to other analytic content in a seamless workflow

It’s easy to set up, ask questions and get instant results. Non-technical users can forge their own path through with any question they wish, choosing the suggested options that are offered to them.

A truly unique self-service experience

Yellowfin is a BI tool that offers Guided NLQ capability. When a user selects a data view (dataset) they wish to query, it provides a question bar they can type into with a preset list of possible questions to choose from. The type of questions offered will be basic or complex depending on the query. The user is automatically shown relevant options in a drop down menu and dynamically prompted with further suggestions as they type.

Rather than using technical jargon, generic business terms such as ‘compare’ or ‘list’ will be highlighted as these are familiar. These important elements will lead a user in a more logical way where the meaning of their language cannot be missed, unlike using a free text search. Once the query is built, Guided NLQ presents the ideal visualisation (chart) and tabular report based on data best practices.

Unlike traditional BI analysis, these generated answers will likely reveal deeper insights by uncovering hidden patterns, trends, outliers or shifts in behaviour. From here, users can:

  • go back and rearrange the question at any stage
  • change data views to explore more answers from other datasets
  • update existing content within Yellowfin dashboards, presentation and stories with the generated answers
  • save the question for later

There’s no need to worry about using the right terminology because this tool quickly generates the most popular search dimensions to help users get started. They can even click ‘show more’ to see all available fields within the data view. The reliance on experts can be dramatically reduced when everyone in the business can search for their own answers!

There’s no such thing as a daft question

With Yellowfin Guided NLQ, there’s no need to continuously train the solution to understand users, or keep feeding it synonyms and word dictionaries. Luckily the metadata layer bypasses this problem.

The metadata layer is called a View, which is virtualised because it sits between the data source and all the dependent analytic content. This layer defines all relationships between tables, accessible fields, field type and formatting. Meaning that users creating analytic content can use the relationships and fields defined in the View without having to understand the underlying logic.

Unlike traditional search-based tools, Guided NLQ ensures that each piece of query text is recognised and understood by the system. With guided options offered, ambiguity and misunderstandings become a thing of the past.

Feel free to ask

Guided NLQ implements thousands of comprehensively modelled question types and sequences for every conceivable question combination. Basically, anyone can ask anything! 

Yellowfin Guided NLQ can support complexities such as:

  • Tabular and cross-tab reports
  • Automatic highlighting of items on charts, such as outliers, values and trends
  • Complex filter construction
  • Set analysis comparison, ranking and calculations
  • SubQueries, including minus and intersect

So, whether it’s a basic question: “What is the comparison of annual business performance?” OR a more complex one: “Which accounts have increased revenue month over month for a specified SKU?”. The tool has you covered because it’s been specifically built to accommodate a multitude of queries. 

One integrated solution

A major benefit of using Yellowfin Guided NLQ is that it’s fully integrated with the Dashboard, Stories and Presentation functionality. Simplifying the generation and collaboration of new and existing analytic content. In addition, the feature supports multiple languages, leverages the same security model as the rest of the platform and enables multi-tenant to suit various deployments.

Users no longer need to swap in and out of different systems. The integrated nature of this tool makes for a more streamlined workflow:

  • Self-service ad-hoc reporting for non-technical users with helpful data discovery methods such as Assisted Insights and Signals, means less reliance on an analyst
  • Adding answers to analytic content, simplifying the creation of dashboards, data stories and reports
  • Faster ways to create and share complex reports for analysts and subject matter experts

Users who are creating content within Dashboards, Stories etc. can easily access Guided NLQ from those builders, dropping in generated answers seamlessly. Overall, a more powerful analytics experience, lending itself to all self-service BI preferences.

Guided NLQ is for everyone

Yellowfin Guided NLQ is designed to be easily embedded. Whether it’s a CRM, HR/Payroll or Finance system. It can be used independently or plugged into any apps and launched from anywhere.

As a stand-alone module, it’s not tied to a user interface or single data set. Just curate a view and drop in NLQ capability for a quick and easy self-service deployment. It’s API-enabled to provide fine tuning, this way user experience can be controlled based on scope and relevance.

Yellowfin Guided NLQ is useful for:

  • Independent software vendors, as a flexible, white label feature. Reducing support burden while enhancing product value.
  • Enterprises, give all business users (analysts and non-technical) self-service ability. Freeing up time and resource.

DIY Business Intelligence is vital

As analytics continue to permeate every aspect of business activity, self-service BI applications are becoming vitally important to a broader range of users. Currently very few people are trained in analytics and those who are, quickly become involved in large-scale projects.

Guided NLQ will change the way BI is distributed and used by everyone in fast moving enterprises. The ultimate goal is to achieve user self reliance. Providing them access to fast, accurate and easy to use analytics solutions. Freeing up the data experts to delve into more complex analysis and uncover further insights to improve business performance.

As leaders you may well ask the question: “How can we better understand our business and ensure its long-term growth?” The answer is: Guided Natural Language Query.

“AI is maturing quickly and starting to create opportunities that never existed before. Autonomous vehicles, for example, have the potential to transform societies and create entirely new kinds of businesses. But AI-powered business transformations can happen at a smaller scale, as well.”
– Maria Korolov, Contributing writer of CIO IDG Communications: The Voice of IT Leadership, March 2022

Request a demo: to see this innovative software product in action.

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Prepare data that’s fit for consumption

When creating a meal the Chef carefully considers the correct mix of essential ingredients. Ensuring the meal not only looks and taste great but also provides the optimum health and nutrition.

This is also true for business intelligence data preparation. Organisations need to consolidate essential information from disparate sources, in a way that provides satisfying insights without bloating their database systems.

Data comes in all shapes and sizes – images, spreadsheets and other real-time sensor systems. It therefore requires intense attention to get them to work collectively.

What is Data Preparation?

It’s the act of manipulating (pre-processing) raw data (from disparate data sources) into a form that can readily and accurately be analysed. It is the first step in any analytic project and includes many discrete tasks such as; data loading, ingestion, cleansing, fusion and augmentation.

The aim of which is to produce accurate, consistent and comprehensive data for the organisation to base business decisions on.

A logical approach to this process will likely include the following steps:
Prepare data that's fit for consumption RhinoIT

Data Strategy – identifying the scope of the project and creating a workflow of requirements. This is like the Chef obtaining the ingredients for the recipe and understanding the cooking method

Data Collection – defining required data and gathering it from the various sources. ETL (Extract, Transform and Load) plays a key role in data integration, making it possible for different data types to work together.

Data Preprocessing – formatting and cleansing raw data by adding missing values, reducing duplicates, labelling metadata with categories and sampling into smaller memory sizes. Generally reworking real world data into an understandable format.

Data Transformation – reorganising data in such a way that users can use the database properly for further queries and analysis – usually known as ‘normalising’. Breaking complex data into smaller and more manageable parts for easier examination and design.

The good news is that RhinoIT can cook your dinner for you!

Data preparation may sound time consuming, however the production of enriched, accurate data is crucial for the success of your Business Intelligence projects.

Our data team can provide your users with powerful analytics by automating this lengthy and manual process, saving the organisation time and money. 

To find out how we can transform your data into stunning visualisations, please contact

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With mobility less is more

When making a fast food order do we then expect to receive a 3 course gourmet meal?  Of course not, even if this may occasionally be a fleeting wish! So why do we get exasperated with the lack of detailed features on our mobile devices?

In the pursuit of instant access, product managers and developers are now required to keep remote workers quickly up to speed and agile. User experience for analytics on mobile and tablet devices is therefore a top priority. Their challenge is to somehow replicate the same relevant information that’s usually found on desktops.

Best practice for a ‘high level’ dashboard view requires a balance of context and relevance. The ability to slice and dice data, and clearly display charts on tablets is tricky, but even less amenable to mobile phones. As with all mobile apps, there is less screen ‘real estate’ available with only specific functions on offer. When users are on the move, mobile dashboards can only provide a ‘current status’ overview. Charts are minimal and the lack of ‘hover’ function allows for limited interactions.

In our recent blog post about data storytelling we explain how Yellowfin Stories instantly creates more relevant, interesting and better understood analytics. Another advantage to this tool is how it easily lends itself to mobile devices. Notice how social media apps successfully present stories in an easily digestible way, while still preserving the wider context. Yellowin Stories can mimic this with data mobility. Developers just need to consider size restrictions and be mindful of which data to display and how it can be navigated.

Better yet, why not have your BI platform automate data discovery by trawling your business data for statistically significant changes, notifying users of those relevant to their role. This type of ‘news flash’ data can be presented without the need for specific dashboard or ‘card’ design, meaning that trend changes, period comparisons, spikes, dips and more can be accessed from mobile devices.

Find out how RhinoIT can help you unlock meaningful insights from your data.

With mobility less is more RhinoIT
Yellowfin Mobile App

Next, we discuss Preparation.

In the meantime, please leave us a comment or question

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Once upon a time series

‘Tell me a fact and I’ll learn. Tell me a truth and I’ll believe. But tell me a story and it will live in my heart forever.’ 
– Native American Proverb.

Since childhood stories have shaped our view of the world. Expanding our imagination and introducing us to new ideas to deal with real life situations. By triggering our emotions they provide meaning and purpose, linking us all to universal truths that transcend generations. People are motivated to engage with and share a good story, if it authentically connects to the core of an experience.

When presenting data why not create intrigue and, dare we say, excitement by adopting a ‘storytelling’ approach to your dashboards. In this way you can slowly build up critical information, helping users to understand the wider business purpose and overall picture.

We don’t suggest penning a novel, bear in mind that the message needs to be clear, simple and focussed. However, a little artistic flair can go a long way in capturing the attention of your audience by depicting the whole narrative of key performance indicators.

That’s why we advocate the use of Yellowfin Stories.

By combining real-time accessible data with insight, context and explanation, this Business Intelligence tool makes analytics instantly more relevant, interesting and better understood. Whether you are giving a presentation or people are reading a data story, it really is the best way to share and collaborate on a single source of accurate, credible and secure information.

Plus anyone with a meaningful message can easily compose and share a Yellowfin Story. The simple interface springs data into life with images, video and embedded reports from other dashboard vendors.

Once upon a time series RhinoIT
Created in Yellowfin Stories

Next, we discuss Mobility.

In the meantime, we would love to hear from you. Please leave us a comment or question

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Prepare to visually impress your audience

It’s a tough break for modern software applications. To be considered a catch they need to have it all: good looks, brains and wealth!

That’s why great dashboards with visually stunning insights, actionable information and rich analytics are a key requirement.

The question is: “How do you transform puzzling data into transparent and meaningful business decisions. Actions that resonate with users?” 

Too much swagger with limited practicality is the classic ‘style over function’ trap.

Effective dashboards require a careful selection of useful data. Striking a balance between being sufficiently actionable and overly cumbersome to view.

RhinoIT are keeping a close eye on the emerging trends for dashboard designs. This is the first in a series of posts where we share our thoughts on the most popular ones. First under the microscope is Asymmetrical Design.

Asymmetrical Design

As the name suggests, this visual design departs from the usual little blocks of content with organised rows and columns. The clever use of inequality produces an ‘infographic’ style. Considered to be more engaging than traditional dashboard design.

A delightful but sparing use of colour with illustrations, produce intelligent dashboards where pertinent facts pop. The main elements really stand out, which is useful for focussing the eye of users in a hurry.

In terms of colour, be mindful of all users and ensure that your product is accessible for the visually impaired. Avoid gradient colours on important information: bar charts, line graphs and KPIs.

Visual cues in the form of icons can help to logically guide users through the dataset.

Analog gauges and 3D visuals are now out of style. Instead, try material and flat designs. Material adds gradient and shadows to create a sense of depth, whereas flat doesn’t use any.

Prepare to visually impress your audience RhinoIT
Created using Yellowfin 9.2.2 canvas dashboard and code mode

Next, we discuss Data Storytelling.

In the meantime, we would love to hear from you. Please leave us a comment or question

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Can’t see the view for the fields?

Can you ever imagine a time without spreadsheets? They don’t appear to be going away anytime soon, and for good reason. Most businesses hold historical data within them, and let’s face it, they are a snap to create. However, in today’s fast-paced world of data consumption via dazzling pretty pics our trusted friend ‘the spreadsheet’ may start to leave us a little flat!

“So how do we evolve past spreadsheets, leverage their content, and start to gain real insight into our data?”

At RhinoIT, we were recently asked this question by one of our customers.

Yes, spreadsheets are convenient and easy. Almost everyone has a spreadsheet system installed on their computer ready to use. Unfortunately, this blessing can also be a curse. If every member of your team has access to the same spreadsheet, it’s likely to result in more than one version of your data existing. Unless you have a ‘Mary Poppins’ stance to document housekeeping, this poses an issue with continuity.

One version may have today’s data, while another version holds all the aesthetically pleasing column and cell updates made yesterday. How do you decide which version to use? Something as simple as a misplaced decimal point, deletion of cell, column or row can have an adverse impact on the integrity of your spreadsheet. Changing a positive value to a negative value or adding a new worksheet can also wreak havoc with your best-laid plans.

“If spreadsheets are here to stay, then how do we work around these issues for the foreseeable future?”
The answer is: Integration.

In the case of our customer, we weren’t suggesting a complex solution, quite the opposite. By merely ingesting the data directly from their spreadsheets into a Business Intelligence tool they were able to satisfy their end user requirements.

The result was an effective dashboard with all the requested data views and visualisations. Data could still be collected within spreadsheet fields but with the added benefit of security, governance and one true source of historic reports.

Have you recently struggled getting to grips with business data? What has your experience been with gaining more insight? We would love to hear from you. Please leave us a comment or question

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