Data analytics tools can help deliver that value and bring that data to life. A lot of hard work goes into extracting and transforming data into a usable format, but once that’s done, data analytics can provide users with greater insights into their customers, business, and industry.
There are three broad categories of
data analytics that offer different levels of insight:
Intelligence (BI) provides traditional, recurring reports.
Self-Service Analytics enable end-users to
structure their own analyses within the context of IT-provided data and
Embedded Analytics provides business
intelligence within the confines of a traditional business application, like an
HR system, CRM, or ERP. These analytics provide context-sensitive decision
support within users’ normal workflow.
you’re not using an analytics tool, you should be. Gartner predicts
that by 2020, self-service analytics and BI users will actually produce more
analysis than data scientists. No matter what level of insight you need, here
are 15 of the best data analytics tools to get you started on your journey, in
no particular order.
features robust functionality with fast speed to insight. With connectivity to
many different local and cloud-based data sources, Tableau’s intuitive
interface combines data sourcing, preparation, exploration, analysis, and
presentation in a streamlined workflow.
flexibility makes it well-suited to the three types of analytics discussed
above. Tableau Server can easily house recurring reports. Power users will
appreciate the integrated statistical and geospatial functionality for advanced
self-service. And finally, Tableau uses application integration technologies
Tableau analytics into common business applications.
strives to provide a unified data environment and centralized data governance
with a heavy emphasis on reusable components for data-savvy users. Using an
extract/load/transform (ELT) approach, Looker gives users the ability to model
and transform data as they need it.
also features proprietary LookML language, which harnesses SQL in a visual and
reusable way. The reusability concept extends to Looker’s Blocks components,
which are reusable utilities for data connections, analysis, visualization, and
distribution. Finally, Looker is designed to easily integrate with popular
collaboration and workflow tools such as Jira, Slack, and Segment.
offers modern, dynamic reporting with out-of-the-box integrations to many of
the world’s most popular on-premise and cloud-based ERP systems. This
easy-to-use report writer offers Excel, web, and mobile interfaces, and
provides finance professionals with powerful financial and operational
reporting capabilities in a variety of layouts and presentation formats.
also offers integrated budgeting workflow and analytics, including
industry-specific templates. Once you connect data sources to the BI360 Suite,
use these templates to access data, collaboratively develop a budget, and
display results on predefined dashboards.
DSS combines much of the data analysis lifecycle into one tool. It enables
analysts to source and prep data, build predictive models, integrate with data
mining tools, develop visualizations for end-users and set up ongoing data
flows to keep visualizations fresh. DSS’s collaborative environment enables
different users to work together and share knowledge, all within the DSS
its focus on data science, DSS tends to serve deeply analytical use cases like
churn analytics, demand forecasting, fraud detection, spatial analytics, and
lifetime value optimization.
open-source, enterprise-class analytics platform, KNIME is designed with the
data scientist in mind. KNIME’s visual interface includes nodes for everything
from extracting to presenting data, with an emphasis on statistical models.
KNIME integrates with several other data science tools including R, Python,
Hadoop, and H2O, as well as many structured and unstructured data types.
supports leading edge, data science use cases such as social media sentiment
analysis, medical claim outline detection, market basket analysis, and text
emphasizes speed to insight for complex data science. Its visual interface
includes pre-built data connectivity, workflow, and machine learning
components. With R and Python integration, RapidMiner automates data prep,
model selection, predictive modeling, and what-if gaming. This platform also
accelerates “behind-the-scenes” work with a combined development and
collaboration environment and integration with Hadoop and Spark big data
RapidMiner’s unique approach to self-service utilizes machine learning to glean
insight from its 250,000-strong developer community for predictive analytics
development. Its context-sensitive recommendations, automated parameter
selection, and tuning accelerate predictive model deployment.
emphasizes IoT data collection and blending with other data sources like ERP
and CRM systems, as well as big data tools like Hadoop and NoSQL. Its built-in
integration with IoT endpoints and unique metadata injection functionality
speed data collection from multiple sources. Pentaho’s visualization
capabilities range from basic reports to complex predictive models.
proactively approaches embedded analytics. In addition to investing in
integration components like REST APIs, Pentaho’s thorough training and project
management methodology help ensure customer success with embedded analytics.
toolset is meant to accelerate data integration projects and speed time to
value. An open-source tool, Talend comes with wizards to connect to big data
platforms like Hadoop and Spark. Its integrated toolset and unique data fabric
functionality enable self-service data preparation by business users. By making
data prep easier for users who understand the business context for the data,
Talend removes the IT bottleneck on clean and usable data, which reduces the
time to merge data sources.
focuses on speed to insight for less technical users. It features 500+ built-in
data connectors and a visual data prep interface to accelerate data sourcing
and transformation. Its robust business intelligence capabilities enable
visualization and social commenting to facilitate collaboration. Domo also
boasts native mobile device support with the same analysis, annotation, and
collaboration experience as desktop.
simplifies remotely embedding analytics using “Cards,” or deployable,
interactive visualization portlets. These components integrate with web
the unique endpoint.
offers an end-to-end analytics platform with a strong governance component. It
offers a visual data sourcing and preparation environment, plus alerts that
notify users when a given metric falls outside a configurable threshold.
Sisense deploys to on-premises, private-cloud, or Sisense-managed environments,
and enables governance at the user role, object, and data levels.
comprehensive approach to embedded analytics includes integration components
embedded visualizations, adding a dimension of self-service to embedded
emphasizes speed to insight by automating data discovery and relationships
between multiple data sources during data acquisition and preparation. Instead
of the traditional query-based approach to acquiring data, Qlik’s Associative
Engine automatically profiles data from all inbound sources, identifies
linkages, and presents this combined data set to the user. Multiple, concurrent
users can quickly explore large and diverse data sets because of Qlik’s
in-memory processing architecture, which includes compressed binary indexing,
logical inference, and dynamic calculation.
web, business application, and mobile platform integration for enterprise-wide
in 1989, Microstrategy is one of the older data analytics platforms and has the
robustness that one would expect from such a mature toolset. Microstrategy
connects to numerous enterprise assets like ERPs and cloud data vendors and
integrates with multiple common user clients like Android, iOS, and Windows. It
also provides a variety of common services such as alerts, distribution, and
security, and enables many BI functions like data enrichment, visualization,
and user administration.
enhances data governance by using end-point telemetry to manage user access. By
gathering location, access, authentication, timestamp, and authorization data,
this functionality can help analyze utilization and strengthen security
features a search engine-like interface and AI to enable users to take a
conversational approach to data exploration and analytics. Its SpotIQ engine
parses search requests such as “revenue by the country for 2014,” and produces
a compelling visualization showing a bar chart ordered least to greatest.
Thoughtspot platform helps companies quickly deploy this unique approach to
analytics with a visual data sourcing and preparation pane, extensive in-memory
processing, back-end cluster management for big data environments, centralized
row-level security, and built-in embeddable components.
focuses on solving one of the most vexing challenges in data analytics:
establishing trust in data from many different sources within the enterprise.
Birst’s user data tier automatically sources, maps, and integrates data sources
and provides a unified view of the data to users.
using Birst’s Adaptive User Experience, which breaks down the silo between data
discovery and dashboarding, users can access the unified data sources to
develop analytics with no coding or IT intervention. Finally, Birst enables
distribution to multiple platforms and other analytics tools like R and
Server Reporting Services (SSRS) is a business intelligence and reporting tool
that tightly integrates with the Microsoft data management stack, SQL Server
Management Services, and SQL Server Integration Services. This toolset enables
a smooth transition from the database to the business intelligence environment.
SSRS, in particular, offers a visual authoring environment, basic self-service
analytics, and the ability to output spreadsheet versions of reports and
and the Microsoft data management stack are the workhorses of traditional BI.
They are a mature toolset that performs very well with recurring reports and
well-known vendors above support multiple use cases across many industries.
However, the volume of data generated by traditional business activity, social
media, and IoT technology continues to explode every year, so data analytics
options continue to evolve. With so many options, choosing a vendor can be
key to making an informed choice is to understand the unique analytics needs of
your organization and industry. Knowing where your needs fall on the analytics
spectrum will help you productively engage with vendors – and make the most of
the analytics you produce.
Hir Infotech provides web data for
analysis using any of these data analytics tools. Contact us to find out
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