Today we are all getting used to interactive dashboards and plots in self-service business intelligence (BI) solutions to drill down and slice our facts and figures. The market for BI tools has seen an increased competition recently with Microsoft Power BI challenging proven solutions such as Tableau, Qlik, IBM Cognos, SAP Lumira and others. At the same time, it is hard to benchmark tools against each other as they all come with very similar features. Has the BI development saturated?
Compared to how we are used to consume graphics and information, the BI approach to interactive analysis is somewhat different. For instance: a dashboard or report is typically presented in a printer-oriented flat layout on white background, weeks of user training is typically needed before “self-service” can be reached, and interactions are heavily click-oriented – you could almost feel it in your mouse elbow when opening the BI frontend.
On the other hand, when surfing top internet sites and utilizing social media, our interactions are centred around the search box and the natural interface of typing or speaking. Furthermore, there is typically no training needed to make use of Google, Facebook, LinkedIn, Pinterest, Twitter, etc. Through an intuitive interface we learn along the way. And looking at graphics and visualization, we can learn a lot from the gaming industry where players are presented with well-designed artwork – including statistics presented in an intuitive way to maximize the graphical impression.
Rethink your business analytics
It appears as if BI tools are sub optimized for a limited scope and use case. To really drive digitalization and make use of our full information potential, we need a new way of thinking for business analytics. Not just continuous development, rather a revolution to the business intelligence approach. Remember: e-mail was not a consequence of the continuous development of post offices and mail handling. We need to rethink business analytics.
At Findwise, we see that the future for business analytics involves:
- added value by enriching information with new unstructured sources,
- utilizing the full potential of visualization and graphics to explore our information,
- using natural language to empower colleagues to draw their own conclusions intuitively and secure
There is a lot of talk about data science today; how we can draw conclusions from our data and make predictions about the future. This power largely depends on the value in the data we possess. Enriching data is all about adding new value. The enrichment may include a multitude of sources, internal and external, for instance:
- detailed customer transaction logs
- weather history and forecasts
- geospatial data (locations and maps)
- user tracking and streams
- social media and (fake) news
Comparing with existing data, a new data source could be orthogonal to the existing data and add a completely new understanding. Business solutions of today are often limited to highly structured information sources or information providers. There is a large power in unstructured, often untouched, information sources. However, it is not as straight forward as launching a data warehouse integration, since big data techniques are required to handle the volume, velocity and variety.
At Findwise, utilizing the unstructured data has always been the key in developing unique solutions for search and analytics. The power of our solutions lies in incorporating multiple sources online and continuously enrich with new aspects. For this we even developed our own framework, i3, with over hundred connectors for unstructured data sources. A modern search engine (or insight engine) scales horizontally for big data applications and easily consumes billions of texts, logs, geospatial and other unstructured – as well as structured – data. This is where search meets analytics, and where all the enrichment takes place to add unique information value.
As human beings we have very strong visual and cognitive abilities, developed over millions of years to distinguish complex patterns and scenarios. Visualization of data is all about packaging information in such a way that we can utilize our cognitive skills to make sense out of the noise. Great visualization and interaction unleash the human power of perception and derivation. It allows us make sense out of the complex world around us.
When it comes to computer visualization, we have seen strong development in the use of graphical processors (GPUs) for games but recently also for analytics – not the least in deep learning where powerful GPUs solve heavy computations. For visualisation however, typical business intelligence tools today only use a minimal fraction of the total power of our modern devices. As a comparison: a typical computer game renders millions of pixels in 3D several times per second (even via the web browser). In a modern BI tool however, we may struggle to display 20 000 distinct points in a plot.
Example from one of our prototypes: analysing the housing market – plotting 500 000 points interactively utilizing OpenGL.
Current analysis solutions and application built with advanced graphical analysis are typically custom made for a specific purpose and topic, as in the example above. This is very similar to how BI solutions were built before self-service BI came in to play – specific solutions hand crafted for a few use cases. In contrast to this, Open graphical libraries, incorporated as the core of visualizations, with inspiration from gaming art work, can spark a revolution to how we visually consume and utilize information.
Natural language empowers
The process of interpreting and working with speech and text is referred to as Natural Language Processing (NLP). NLP interfaces are moving towards the default interface to interaction. For instance Google’s search engine can give you instant replies on questions such as “weather London tomorrow” and with Google Duplex (under development) NLP is used to automate phone calls making appointments for you. Other examples include the search box popping up as a central feature on many larger web sites and voice services such as Amazon Alexa, Microsoft Cortana, Apple Siri, etc.
When it comes to analysis tools we have seen some movements in this direction lately. In Power BI Service (web) Cortana can be activated to allow for simple Q&A on your prepared reports. Tableau has started talking about NLP for data exploration with “research prototypes you might see in the not too distant future”. The clearest example in this direction is probably ThoughtSpot built with a search-driven analytics interface. Although for most of the business analytics carried out today, clicking is still in focus and clicking is what is being taught on trainings. How can this be, when our other interactions with information move towards natural language interfaces? The key to move forward is to give NLP and advanced visualization a vital role in our solutions, allowing for an entirely natural interface.
Initially it may appear hard to know exactly what to type to get the data right. Isn’t training needed also with an NLP interface? This is where AI comes in to help us interpret our requests and provide us with smart feedback. Having a look at Google again, we continuously get recommendations, automatic spelling correction and lookup of synonyms to optimize our search and hits. With a modern NLP interface, we learn along the way as we utilize it. Frankly speaking though, a natural language interface is best suited for common queries that aren’t too advanced. For more advanced data munging and customized analysis, a data scientist skillset and environment may well be needed. However, the power of e.g. Scientific Python or the R language could easily be incorporated into an NLP interface, where query suggestions turn into code completion. Scripting is a core part of the data science workflow.
An analytical interface built around natural language helps direct focus and fine-tunes your analysis to arrive at intuitive facts and figures, explaining relevant business questions. This is all about empowering all users, friends and colleagues to draw their own conclusions and spread a data-driven mentality. Data science and machine learning techniques fit well into this concept to leverage deeper insights.
Conclusion – Business data at everyone’s fingertips
We have highlighted the importance of enriching data with concern taken to unstructured data sources, demonstrated the importance of visual exploration to enable our cognitive abilities, and finally empowering colleagues to draw conclusions through a natural language interface.
Compared with the current state of the art for analysis and business intelligence tools, we stand before a paradigm shift. Standardized self-service tools built on clicking, basic graphics and the focus on structured data will be overrun by a new way of thinking analysis. We all want to create intuitive insights without the need of thorough training on how to use a tool. And we all want our insights and findings to be visually appealing. Seeing is believing. To communicate our findings, conclusions and decisions we need to show the why. Convincing. This is where advanced graphics and art will help us. Natural language is the interface we use for more and more services. It can easily be powered by voice as well. With a natural interface, anyone will learn to utilize the analytical power in the information and draw conclusions. Business data at everyone’s fingertips!
To experience our latest prototype where we demonstrate the concept of data enrichment, advanced visualization and natural language interfaces, take a look at this live presentation.
Author: Fredrik Moeschlin, senior Data Scientist at Findwise