Domo – The Hot New Visualization Stealth Startup

Domo seems to be hottest emerging company in the visualization, BI and so-called “Business Management Platform” area.  I have been seeing it at several clients recently.

According to their pitch.. “Domo is the future of business management.  For all the trillions spent on technology solutions, the way we do business still feels pretty painful. Data lives everywhere. Insights arrive too slowly. Collaboration is still wildly inefficient. Until now. Domo puts the right information, at the right time, into the hands of the people that actually use it to collaborate and make decisions. And it’s transforming the way people manage business.”

Domo grabs live business data from some 300 different sources — Salesforce, NetSuite, Twitter and Facebook included — and presents the data in a live interactive dashboard that can be customized and mixed and matched in all kinds of ways.

A screenshot of is shown below (courtesy of Recode).

 

Given the 2 bln valuation and growing hype, I have been struggling to figure out what Domo does that Tableau Software can’t do.  Domo claims to bring the business and all its data together in one intuitive platform. Doesn’t every BI and Data Visualization platform do this? Domo claims to be developed hundreds of proprietary connectors (traditional data sources and cloud based) that connect directly to any source of data across your entire organization, and bring it into one intuitive platform.  Again so does everyone else.

Recode explains this in the following manner…”Domo is not just an application but a platform, which means that if there’s some specialized business app that Domo hasn’t connected to yet, you can now start building your own connections to it and bring that data in. Those individual sections of data in the shot above are called “cards,” and you can rearrange them and add new ones to your view all the time. But if you need a special card that combines a few different bits of data and which hasn’t already been built, Domo today announced a feature it calls Card Builder that lets you create your own.”

You can be the judge and jury on how novel all this is.

Notes and References

1. Domo have been valued at 2 Bln and raised a round of 200M.

2. List of Domo Connectors

New Tools for New Times – Primer on Big Data, Hadoop and “In-memory” Data Clouds

Data growth curve:  Terabytes -> Petabytes -> Exabytes -> Zettabytes -> Yottabytes -> Brontobytes -> Geopbytes.  It is getting more interesting.

Analytical Infrastructure curve: Databases -> Datamarts -> Operational Data Stores (ODS) -> Enterprise Data Warehouses -> Data Appliances -> In-Memory Appliances -> NoSQL Databases -> Hadoop Clusters

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In most enterprises, whether it’s a public or private enterprise, there is typically a mountain of data, structured and unstructured data, that contains potential insights about how to serve their customers better, how to engage with customers better and make the processes run more efficiently.  Consider this:

• Online firms–including Facebook, Visa, Zynga–use Big Data technologies like Hadoop to analyze massive amounts of business transactions, machine generated and application data.
• Wall street investment banks, hedge funds, algorithmic and low latency traders are leveraging data appliances such as EMC Greenplum hardware with Hadoop software to do advanced analytics in a “massively scalable” architecture
• Retailers use HP Vertica  or Cloudera analyze massive amounts of data simply, quickly and reliably, resulting in “just-in-time” business intelligence.
• New public and private “data cloud” software startups capable of handling petascale problems are emerging to create a new category – Cloudera, Hortonworks, Northscale, Splunk, Palantir, Factual, Datameer, Aster Data, TellApart.

Data is seen as a resource that can be extracted and refined and turned into something powerful. It takes a certain amount of computing power to analyze the data and pull out and use those insights. That where the new tools like Hadoop, NoSQL, In-memory analytics and other enablers come in.

What business problems are being targeted?

Why are some companies in retail, insurance, financial services and healthcare racing to position themselves in Big Data, in-memory data clouds while others don’t seem to care?

World-class companies are targeting a new set of business problems that were hard to solve before – Modeling true risk, customer churn analysis,  flexible supply chains, loyalty pricing, recommendation engines, ad targeting, precision targeting, PoS transaction analysis, threat analysis, trade surveillance, search quality fine tuning,  and mashups  such as location + ad targeting.

To address these petascale problems an elastic/adaptive infrastructure for data warehousing and analytics capable of three things is converging:

• ability to analyze transactional,  structured and unstructured data on a single platform
• low-latency in-memory or Solid State Devices (SSD) for super high volume web and real-time apps
• Scale out with low cost commodity hardware; distribute processing  and workloads

As a result,  a new BI and Analytics framework is emerging to support public and private cloud deployments.

Private Equity and BI/Analytics – Case Study of KKR & Co

“Do you think that, or do you know that?”

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Went to an interesting talk by Ed  Brandman, CIO of KKR & Co, the legendary Private Equity firm, hosted by CIO Perspectives in New York city.   KKR is using a custom Business Intelligence solution, called Portfolio Central, to track and manage their portfolio of 62 companies. This portfolio includes some well-known companies like First Data, Toys R Us, Sungard, Dollar General, HCA and others.