Big Data Gets Big Money for Big Reasons

Big Data Gets Big Money for Big Reasons

8 minute read
Hyoun Park avatar

Companies have been throwing money hand over fist into the predictive analytics, data management and business intelligence world over the last few weeks. And while it would be easy to toss all of these under the "Big Data" umbrella, it's more interesting to look at these deals in light of the challenges that each will solve.

Let's take a quick look at five transactions: $225 million total to Birst, Health Catalyst, Localytics and Ayasdi as well as Apple's recent acquisition of FoundationDB.

Looking at these deals helps show existing analytic and data challenges and where new competitors are coming into the market to partner with and challenge traditional players like Oracle, Teradata, IBM, SAP and Microsoft.

Birst: The Need for Two-Tier Business Intelligence

Birst raised $65 million to support the concept of two-tier business intelligence (BI) on March 17. Business intelligence has been around for decades and smart companies have already made significant investments in BI tools such as Hyperion or Cognos.

However, despite these big investments and the concerted efforts of data analysts, IT managers and executives, companies still struggle to grow enterprise BI adoption past the first 20 percent of their employee base.

Birst seeks to change this by creating an infrastructure that supports individual business users. It does this through a combination of a cloud-based platform that supports governed and shared data and a self-service experience for actual users.

This creates a cloud-based "fabric" that brings in legacy data that may have to stay in its existing environment for political reasons or because the company has been trained to use legacy tools. By combining this older data with newer user tools and greater flexibility, Birst seeks to open up business intelligence to the rest of the enterprise in a cost-effective and IT-trusted manner.

This is a more realistic approach than expecting companies to toss out a Microsoft or Oracle in a wholesale manner. While businesses may still view large vendors as mission-critical for specific tasks, this still leaves a lot of business intelligence gaps in the environment.

The $65 million will help Birst educate the enterprise world on how to fill these considerable gaps and bring the traditional MISO (Microsoft, IBM, SAP, and Oracle) world with the new demands of data-driven business decisions.

Health Catalyst: Data-Driven Healthcare Optimization

On March 17, Health Catalyst raised $70 million to support its goal of optimizing healthcare environments.Health Catalyst started as a healthcare data warehousing organization focused on gaining financial and clinical benefits through solutions that consolidate data across healthcare organizations.

Health Catalyst has also developed an analytics application platform that provides apps to improve health care outcomes. This is an interesting evolution of the "appified" world often associated with Apple, Google and the world of mobile apps.

By bringing this flexibility to the healthcare world and potentially allowing these organizations to quickly implement valuable applications into their data environment, Health Catalyst has an opportunity to drive new innovations and visibility in healthcare.

This funding round is expected to push Health Catalyst towards a future IPO as a publicly traded organization.

Localytics: Predictive Analytics More Personal

Localytics raised $35 million on March 24 to support web and mobile application marketing analytics.

The key here is for Localytics to develop predictive analytics that help mobile application developers maintain user loyalty and deliver personalized experiences. Mobile experience today depends on creating apps that are flexible enough to support users on an individualized basis and provide enhancements, recommendations or corrections in real time based on the user's preferences.

This real time and predictive use of analytics combines mobile, analytic and cloud technologies to focus directly on helping one person at a specific time to complete the most important priority at that moment.

Companies continue to improve user interfaces, enhance the usefulness of their own offerings, upsell new services, and reduce churn to create better experiences. To deliver these experiences, companies need to embrace predictive and big data capabilities to help build extremely personalized profiles for each user and each transaction.

Localytics' latest $35 million dollar round suggests the immediate opportunity of making better mobile experiences.

Ayasdi: Calculates Interaction Analytics and Topological Data for the Masses

On March 25, Ayasdi raised $55 million for topological data analysis.

For those not mathematically inclined, what this means is that Ayasdi has strong capabilities to support networked sets of data with multiple internal relationships and also find patterns in what can look like a jumbled visualization. Healthcare providers have used Ayasdi to find correlations in breast cancer tumors and understand potential tracks for healthcare delivery.

But the potential goes far beyond healthcare.

Any graph dataset or socially-enabled data could potentially be more useful if analyzed by Ayasdi. For instance, think of the tangled webs that are often shown as social networks. It is quite difficult to find insights out of many of these visualizations.

Learning Opportunities

Ayasdi's differentiation is to look into these webs of social interaction and detect the patterns associated with better results. This complex and topological approach to analysis is still just starting to be fully understood, but should be part of the toolkit for any enterprise data analyst working with datasets that have a large degree of interaction.

Apple: Purchases FoundationDB, Expect Faster Apple Services

Apple agreed to purchase FoundationDB on March 24. It provided its typical guidance with a statement that,

Apple buys smaller technology companies from time to time, and we generally do not discuss our purpose or plans."

FoundationDB is an Open Source database known forACID (Atomicity, Consistency, Isolation, Durability) key value store and SQL access in NoSQL. This differentiates it from hot NoSQL technologies like Cassandra and MongoDB.FoundationDB's data model has also been compared to Google, so expect to see Apple work on better search, storage and synchronization updates throughout the Apple iCloud and iTunes.

Although no visibility is available into the purchase price, FoundationDB had raised $22.7 million over the past few years including a $17 million Series A in December 2013. Based on how these rounds tend to work, we at Blue Hill would assume that the Series A buyers bought into a valuation of around $75 to $150 million and Apple offered enough to make it worth the investors' interest to sell (which is not hard when you have $178 billion in cash on hand).

FoundationDB will be useful both for the technology and talent that Apple acquires, but also takes one of the top NoSQL technologies off the Open Source market.

Lessons Learned

The most obvious lesson is that big data is basically a money magnet. But more importantly, the use cases in data and analytics are quickly transforming. It's not enough to just think about "business intelligence" or "data storage" any more. That was good enough 10 years ago, but doesn't work for today.

So, the real lessons are:

There are now multiple levels of Business Intelligence. From the Birst funding we can see that just thinking about the core reporting technologies in your company isn't good enough. Beyond the cliche of being a "data-driven" and "Moneyball" company, employees need relevant data that actually helps them to do their jobs. Companies can choose to get tools for the other 80 percent of their employees or expect these people to find their own tools and sources outside of the corporate environment.

The shape and nature of data continues to evolve. Although analysts have used social and topological data for decades, they have typically been hidden in very specific use cases such as the analysis of sociological relationships or computational chemistry. Ayasdi's funding shows that there are now tools for standard data analysts to use topology-based analysis tools.

Companies need to realize that personalization equals datafication. Any company trying to provide a personalized experience needs to invest deeply into big data and advanced analytics or risk falling behind. From Localytics, we can see that one upside of predictive analytics is in personalizing mobile and app-based experiences.

At the end of the day, big data and advanced analytics are just technical accomplishments. The real goal is to make our lives easier by providing more personalized experiences, developing better and value-added services and providing these functionalities on-demand.

Analytics provides the opportunity to transform complex industries that affect our lives and our cultures. Health Catalyst's goal of improving healthcare through better analytics is a strong reminder that even life-preserving industries still have room for improvement (as anyone who has ever had a health insurance dispute can quickly corroborate!).

And, finally, expect even the Apples and Googles of the world to continue investing in analytic technologies to support their core functionalities. Behind the buzzwords and technologies, FoundationDB is interesting because it makes semi-structured data (like documents and files) much faster to organize. As content continues to be a big differentiator between Apple, Google, Amazon, Netflix and other cloud providers, expect the competition for content management and metadata management to accelerate.

The world of Big Data is changing quickly and hundreds of millions of dollars are being spent each month to accelerate that change. Be ready for the constant changes and they will serve you well.

Creative Commons Creative Commons Attribution 2.0 Generic LicenseTitle image by  ToGa Wanderings 


About the author

Hyoun Park

Hyoun Park is the founder and chief analyst at Amalgam Insights, an analyst firm focused on managing the data and finances of enterprise technology. Our key focus areas are on bridging the value gaps between analytics and AI, improving business planning, and cutting out the 30% excess cost in poorly managed IT environments.