Beyond the ‘Dashboards Are Dead’ Rhetoric: Part 6
Evolution
Sisense’s journey began in 2004 when five Israeli undergraduate students transformed their college project into a commercial BI product. After several years of investing in the product’s research and development, Sisense founders went to the market with a new BI tool armed with an innovative in-memory technology called in-chip technology.
Distinguished as one of the veterans in the BI space, Sisense has navigated through various technology cycles over the last two decades, including transitions from desktop to web applications with the advent of HTML5, from Windows to Linux due to the rise of cloud-native technologies, and from exclusively on-premises solutions to offering managed services.
The period between 2014 and 2019 marked significant growth for Sisense, culminating in the acquisition of Periscope Data in mid-2019 to address previously uncovered use cases. Its latest funding round in early 2020 raised $100 million, valuing the company at over $1 billion.
Sisense debuted in the Gartner Magic Quadrant as a niche player in 2016 and ascended to a visionary by the following year. It has maintained its position with minor fluctuations since then.
Technology and Architecture
Installation & applications
Sisense offers both managed service and on-premises options, encompassing a Server Application for administrative tasks and API integrations, and a Web Application for user interactions with data modeling and analytics. Transitioning from a traditional desktop application for local data modeling, Sisense has fully embraced the web interface, aligning with current technological preferences.
Querying Data
Sisense allows its users to either query their data live from its data sources or store the data in the in-memory columnar database called ElastiCube. One dashboard can consume data from both Elasticube and live models and become a hybrid dashboard.
Structure, Entities & Relationships
Sisense connects to its data sources and allows its users to create data models in its Elastic Data Hub. In the case of an ElastiCube, users can perform heavy manipulations on their data and continue its transformation.From there, a user can create a dashboard and design multiple widgets. Each widget is related to only one dashboard. However, different widgets can point to different data models, hence including live and stored data from multiple models.
Dashboards Tabs
Sisense dashboard is a single-page canvas of Widgets and tiles.
Containers & Organizations
Sisense allows its user to create folders of dashboards to group their views under a segmented topic. A dashboard can be associated with either 1 folder or none. Sisense also allows having subfolders to better manage and categorize a customer’s dashboard.
Diagram
Focus and Strengths
Early in their journey, Sisense had pivoted itself to a niche market, embedded analytics, which since then got more and more attention from the market and became a major use case for most of these industry providers. Sisense gained expertise, knowledge and a significant customer base which provides them a competitive edge in this field.
Its cloud service agnostic approach offers a versatile solution for clients seeking flexibility beyond single-provider dependencies.
In recent years, Sisense has intensified its focus on the developer community, incorporating features like Git integration and SDK packages. Pioneering in adopting new technologies, Sisense introduced capabilities around natural language generation (NLG), AI, bot integrations, and a notable integration with ChatGPT. The innovative BloX feature enables the creation of custom, actionable analytic applications within dashboards or as standalone apps through API integration.
The Future
Sisense was on its way to dominating the embedded analytics market and performed significant growth between 2014-2020, but since then, things have slowed down.
The shift left movement makes sense with Sisense technology and focus, but there is a great question of how this shift will be managed – how Sisense will retain their cash cow – their big enterprise customers while investing in a developer persona, which is oftentimes associated with smaller organization and with a land and expand approach. Another question that comes to mind is their lack of pricing transparency. For PLG/PLS offering (which seems to be their direction to penetrate this new market) one would expect to have an immediate platform to start working with the offered product, which is not the case yet on Sisense website.
It feels like this is a make-or-break moment for Sisense – whether they will be able to succeed in both worlds – enterprise-scale solution as well as the with the DevOps initiative – and will hop once again on the growth path – or lose in one or two battles and will need to find a new path for itself in the form of narrowed use case to capture, merge forces with other solutions, or explore new directions altogether.
Catch Up on the Other Blogs in This Series
- Read more about the objective of this series of articles: to compare business intelligence vendors’ existing offerings and their take on the future impact of AI/GenAI.
- Read my take on Tableau in part 2.
- Examine Microsoft Power BI in part 3.
- Take a look at Google Looker in part 4.
- Read my thoughts on ThoughtSpot in part 5.
- Learn about Sisense in part 6.
- See my take on Amazon QuickSight in part 7.
- Conclude the journey with insights about the Resilience and Revolution of Business Intelligence In the Age of GenAI.