Whitepaper Sisense vs. Traditional Enterprise BI Tools
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Enterprise BI Tools at a Glance
The Sisense Advantage
The unique technology that powers Sisense is what sets it apart from traditional enterprise BI software and gives it a distinctive advantage in almost every point of comparison. It makes Sisense more flexible, more powerful and more user- friendly than traditional enterprise tools, at a fraction of the cost.
After you’ve decided to go ahead and purc
Traditional enterprise Business Intelligence tools are common in larger organizations that handle vast amounts of data. Typically they rely on Online Analytical Processing (OLAP) to join different sources into a single source of truth called a Data Warehouse. While these solutions are designed for scalability, they require a significant investment of time, effort and money as well as a staff with the technical know-how to operate them. Sisense aims to achieve the same or better levels of functionality and scalability, but replaces OLAP and data warehouses with its own, in-house developed technology - ElastiCube, i.e. the Agile Data Warehouse: a columnar database that can also handle terabytes of data coming from disparate sources - but requires minimal technical knowledge, works on commodity hardware and is much more flexible in rapidly changing business requirements.
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Contents
An in-depth comparison of Sisense and traditional enterprise Business Intelligence software Click to skip to the relevant comparison factor:
Data analytics capabilities
Total cost of ownership
Long-term scalability
Complexity and ease of use
Summary features
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Data Analytics Capabilities
Full-stack BI software is meant to perform several different functions, core competencies being:
Data preparation: Joining data from multiple sources and creating a
centralized repository (single source of truth) while maintaining the
integrity of the data
Analysis: Querying the data to receive answers to predefined and ad-
hoc questions in order to understand and improve business processes
Visualizations and dashboard reporting: Displaying data and query
results in pivot tables, charts and other graphical representations
Enterprise BI: Inflexible Analysis
OLAP technology relies on pre-aggregating results to pre-defined queries. Queries are complex and lengthy to set up and require professional knowledge (usually from IT). Typically they are calculated when the system is not being utilized by end-users, resulting in fast answers to pre-defined queries, but very limited support for ad-hoc queries. New questions coming from the business side, or ones that involve taking new data into account, could take weeks or more to answer, depending on the company’s IT and hardware resources.
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Sisense: Agile Business Intelligence
The ElastiCube can easily be augmented to include new data sources, with new data imports generated in hours rather than days and with minimal IT assistance. New data is added, and ETL functions performed, incrementally -- thus resolving the need to restart every data import from scratch, reducing time-to-production and enabling an agile environment that responds to change.
The In-Chip Analytics engine enables Sisense to provide instant answers to ad-hoc queries. Furthermore, because it’s a visual environment using excel like functions for ETL, with no coding or scripting required -- new queries can be made directly by non- technical (‘business’) users rather than having to go through IT. Comparison by Feature
Feature Sisense Traditional Enterprise BI
Data preparation Strong and fast; minimal IT Strong, but slow; creating new support required to add new data data imports is lengthy and
sources, prepare or clean data requires extensive IT support
Analysis In-Chip analytics engine provides Fast answers to pre-defined near-immediate answers to both queries; very limited support predefined and ad-hoc queries for ad-hoc queries
Reporting and Comes out of the box with Requires add-ons and third
visualizations advanced dashboard reporting party software and a rich library of visualizations
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Total Cost of Ownership
Understanding the full costs involved with a Business Intelligence project requires more than just checking the ‘sticker price’. Beyond the initial software costs, other factors that affect pricing include:
Hardware -- upgrades might be required at the time of
implementation or when scaling to larger datasets in the future
Implementation costs -- resources that will need to be invested
before the platform can actually be put to use
Operational costs --- typically manifested in man-hours devoted
by the IT department or external consultants to maintain and
operate the BI platform.
Enterprise BI: Expensive Implementation and Maintenance
Enterprise BI solutions, relying as they do on OLAP cubes, are expensive to implement and operate as using these tools requires proprietary technical knowledge. Additionally OLAP cubes rely on aggregations of data and constantly duplicating the existing database, which requires spending increasingly more on storage as the data grows larger, as well as technical skills needed to constantly perform the aggregations and modelling.
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Sisense: Minimal Overhead
Traditional enterprise software might appear only slightly more costly than Sisense when examining only the initial price of the software. However, Sisense works on commodity hardware, has a typical implementation cycles of a few weeks rather than months and requires little to no IT support for day to day operations as it can be fully deployed without coding or scripting.
Comparison by Cost Factor
Cost Factor Sisense Traditional Enterprise BI
Software Low Moderate
Hardware Low; designed for commodity Vary according to data size; can hardware become very high with larger datasets
Implementation Low; requires minimal High; requires specialized technical knowledge and IT knowledge and can take months support,
Operational Low; once the database is set High; maintenance and ongoing up operations require continuous IT work
Total Cost of Low High Ownership
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Long-term Scalability
As businesses grow, so does the amount of data that needs to be processed by their BI software. There are three factors the software needs to scale to:
Bigger datasets -- the organization has more historical data and
technological developments allow it to collect more real-time
data
More data sources -- new business venues and software tools
create entirely new datasets, which were not in place when the BI
software was initially implemented
More users -- more people work in the company, and more
people need access to the BI platform.
Traditional Enterprise BI: Difficult Scaling
Enterprise BI tools are meant to handle huge databases, but their technological limitations once again put them in a place of disadvantage. An OLAP-based database becomes very difficult to maintain and control when data gets big, requiring more storage space and computational resources. Additionally, and as mentioned above, adding new data sources is a complex affair that must be left to IT.
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Sisense: Easily Augmentable and Scalable
The ElastiCube makes it easy to add new data sources as it requires little to no coding and new builds are generated incrementally and quickly -- in minutes rather than days. The platform scales to terabytes without losing performance, and Crowd Speed means the system actually performs faster when the number of concurrent users grows.
Comparison by Scalability Factor
Scalability Feature Sisense Enterprise BI
Dataset size Terabyte-scale Terabyte-scale, but becomes difficult to maintain
New data sources Add easily and quickly Lengthy and complicated
More concurrent Works faster with more Entails significant hardware upgrades, users users and more unique particularly if new queries are being queries being made made
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Complexity and Ease of Use
The complexity of the chosen BI solution affects every aspect of its use in the organization - from operational costs to company-wide adoption to plain usability. Complexity and ease of use can be measured in 3 aspects:
Implementation -- getting the BI solution up and running
Maintenance -- adding new data sources, ensuring the integrity
of the contained data
Operations -- getting answers to queries quickly, even as data
and users increase
Traditional Enterprise BI: Technical and IT-Centric
Setting up and maintaining an OLAP cube requires advanced technical and coding skills and is inevitably a project that belongs to the IT department. This applies both to creating the initial data warehouse, updating it to include additional data sources, and running new queries when new business questions arise: As OLAP necessitates well defined requirements it is a slow process to define, consolidate and then structure the data. This makes it time consuming to initially create a cube but also to take into account changing requirements.
Sisense: User Friendly and Built for Simplicity
The majority of data preparation in the ElastiCube can be done using an intuitive drag and drop interface and without any coding or scripting. The automated nature of the software allows it to take care
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of the bulk of integrating different sources with minimal manual work. More advanced datasets might require some IT support, but incomparably less than with enterprise BI.
Once the build is generated and a dashboard created, users with no technical expertise can run any query they can think of with no IT assistance.
Comparison by Complicating Factor
Feature Sisense Traditional Enterprise BI
Implementation Mostly automated, possibly requiring Handled entirely by IT, minimal IT support; initial data import is typically takes months rapidly developed and new sources quickly added
Maintenance Minimal IT intervention IT is the project owner and is responsible for maintenance
Operations Simple; handled entirely by non- Every operation runs technical end-users, through IT
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Summary Sisense vs. Traditional Enterprise BI Software
Sisense Traditional Enterprise BI
Standalone solution Yes No
Scalability (1 server) Terabytes Terabytes
Time to production Days/Weeks Months/Years
Advanced end-user Yes Yes analytics
Agility and scalability High Low
Dashboard reporting Yes Requires add-ons
Software costs Low Medium
Hardware costs Low High
Professional services Low High costs
Req. technical expertise Low High
Dependency on IT Low High
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